Intelligent home environment perception and energy-saving control system based on Internet of Things

Abstract

The invention relates to the field of smart home, and discloses a smart home environment sensing and energy-saving control system based on the Internet of Things, and the system comprises a sensing module which comprises an environment sensor unit and a human body sensor unit, and the environment sensor unit is used for collecting the indoor temperature, humidity, illumination intensity and air quality in real time; the human body sensor unit is used for detecting existence and activity conditions of indoor personnel; the Internet of Things communication module transmits data collected by the sensing module to the control module through Internet of Things communication; the energy-saving analysis module receives the data acquired by the sensing module, analyzes and processes the data, and judges whether to adjust the energy-saving strategy of each smart home according to the analysis result; the control module controls the operation state of each smart home according to the energy-saving strategy; the energy-saving strategy comprises the steps of automatically switching on and off light and air conditioning equipment according to the personnel activity condition, automatically adjusting the indoor illumination brightness according to the illumination intensity and adjusting the air conditioner operation parameters according to the indoor and outdoor temperature difference.

Description

Technical Field

[0001] The present invention relates to the field of smart home, and particularly to a smart home environment perception and energy-saving control system based on the Internet of Things. Background Art

[0002] With the continuous progress of technology, smart home systems have gradually entered people's lives. However, existing smart home systems have obvious deficiencies in environmental perception and energy-saving control; on the one hand, they cannot perceive environmental changes in real time and accurately, such as indoor temperature, humidity, light intensity, and human activity conditions, etc.; on the other hand, they lack intelligence and timeliness in energy-saving control, and often cannot dynamically adjust the operating status of household appliances according to real-time environmental changes, resulting in relatively serious energy waste.

[0003] For example, when there is no one in the room, devices such as lights and air conditioners may still be running; or when the light is sufficient, the indoor lighting system does not automatically adjust the brightness, etc.; one of the key reasons is that traditional systems have not fully utilized Internet of Things technology to achieve efficient and stable interconnection and data interaction between devices, resulting in a significant reduction in the real-time performance and accuracy of environmental perception and device control.

[0004] Therefore, developing a smart home system based on the Internet of Things to achieve real-time and accurate environmental perception and energy-saving control has important practical significance. Summary of the Invention

[0005] The purpose of the present invention is to provide a smart home environment perception and energy-saving control system based on the Internet of Things to solve the technical problem that smart home cannot perform energy-saving control according to the environment in real time and accurately.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A smart home environment perception and energy-saving control system based on the Internet of Things, comprising:

[0008] A perception module, including an environmental sensor unit and a human body sensor unit, the environmental sensor unit is used to collect indoor temperature, humidity, light intensity and air quality in real time, and the human body sensor unit is used to detect the presence and activity of indoor people;

[0009] An Internet of Things communication module, which transmits the data collected by the perception module to the control module through Internet of Things communication;

[0010] An energy-saving analysis module, which is used to receive the data collected by the perception module, analyze and process it, and then judge whether to adjust the energy-saving strategy for each smart home according to the analysis result;

[0011] A control module, which controls the operating status of each smart home according to the energy-saving strategy;

[0012] The energy-saving strategy includes automatically turning on and off lights and air-conditioning equipment according to the personnel activity situation, automatically adjusting the indoor lighting brightness according to the light intensity, and adjusting the air-conditioning operation parameters according to the temperature difference between indoors and outdoors.

[0013] Furthermore, the energy-saving analysis module calculates the personnel activity prediction index based on the following method:

[0014] Substitute the time weight factor ξ of each time interval t t and the personnel activity situation A in the past n time periods t-i (i = 0, 1, 2,..., n - 1) into the following formula:

[0015]

[0016] In the formula, represents the personnel activity prediction index at time t, R represents the comprehensive environmental index, α and β are weight coefficients, and α + β = 1, ρ i represents the influence factor of each time period;

[0017] The expression of the comprehensive environmental index R is: T x represents the indoor temperature, Γ x represents the light intensity, Z x represents the air quality, H x represents the indoor humidity, T y 、Γ x 、Z y 、H y respectively represent the reference values of indoor temperature, light intensity, air quality, and indoor humidity, and λ1, λ2, λ3, and λ4 are set coefficients, and λ1 + λ2 + λ3 + λ4 = 1.

[0018] Furthermore, the strategy of automatically turning on and off lights according to the personnel activity situation is:

[0019] When the personnel activity prediction index , the control module turns off all the indoor lights in advance; when , the lights in the preset area are turned on in advance.

[0020] Furthermore, the strategy of automatically turning on and off the air conditioner according to the personnel activity situation is:

[0021] When the personnel activity prediction index and this situation lasts for Y minutes, the control module turns off the air conditioner in advance;

[0022] When and the indoor temperature T x exceeds the preset comfortable temperature range [Tmin , T max , turn on the air conditioner in advance and adjust it to the corresponding operating mode.

[0023] Furthermore, the strategy for automatically adjusting the indoor lighting brightness according to the light intensity is as follows:

[0024] When , the control module automatically reduces the brightness B of the indoor lighting fixtures to M% of the original brightness, that is

[0025] When , the brightness B of the lighting fixtures is automatically increased to P% of the original brightness, that is

[0026] represents the predicted light intensity index, which is obtained through the light intensity analysis model.

[0027] Furthermore, the expression of the light intensity analysis model is:

[0028] In the formula, μ o , μ σ , are weight coefficients, t day is the time variable within a day, S is the weather condition score; L t-i is the historical light intensity in the past n time periods, v i represents the corresponding influence factor; different numerical values are assigned to the weather condition score according to different weather types, S = 1 for sunny days, S = 0.6 for cloudy days, and S = 0.2 for overcast days.

[0029] Furthermore, the strategy for adjusting the air conditioner operating parameters according to the indoor and outdoor temperature difference is as follows:

[0030] When , the control module increases the air conditioner set temperature T set by K1, that is T NEW = T set + K1, and reduces the wind speed by one gear;

[0031] When , the control module reduces the air conditioner set temperature T set by K2, that is T NEW = T set - K2, and increases the wind speed by one gear;

[0032] When , keep the current set temperature and wind speed of the air conditioner unchanged;

[0033] Indicates the predicted temperature difference index, obtained through the indoor-outdoor temperature difference prediction model. [Q, F] represents the preset temperature difference threshold range, and K1, K2 are temperature adjustment set values.

[0034] Further, the expression of the indoor-outdoor temperature difference prediction model is:

[0035]

[0036] In the formula, ψ o , ψ σ , are weight coefficients, T t-i is the historical indoor-outdoor temperature difference value in the past n time periods, represents the corresponding influence factor, and l is the influence coefficient of weather on the temperature difference.

[0037] Advantages of the present invention:

[0038] (1) At the environmental perception level, the environmental sensor unit and the human sensor unit in the perception module of the present invention work together to comprehensively and real-time collect environmental parameters such as indoor temperature, humidity, light intensity, air quality, etc., as well as the presence and activities of personnel, providing a rich and reliable data basis for the subsequent energy-saving control of the system, enabling the system to have a comprehensive and accurate understanding of the indoor environment; at the same time, relying on the high-speed data transmission and device interconnection characteristics of the Internet of Things, the indoor environmental parameters and personnel activities collected by multiple sensors can be transmitted to the control module in a timely and accurate manner, enabling the system to timely and accurately understand the indoor environmental state;

[0039] (2) The present invention realizes the rapid response between the control layer and the execution layer through the Internet of Things. The energy-saving analysis module analyzes and processes the data collected by the perception module, and judges whether to adjust the energy-saving strategy for each smart home according to the analysis results, reflecting the intelligence of the system. The intelligent analysis process can flexibly formulate and adjust the energy-saving strategy according to the actual environment and personnel situation, effectively avoiding unnecessary waste of energy and improving the energy utilization efficiency; at the same time, it can also automatically adjust the operating state of household appliances according to environmental changes and personnel activities to achieve high-efficiency energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 is the system structure block diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0043] Please refer to Figure 1 As shown, the present invention is a smart home environment perception and energy-saving control system based on the Internet of Things, including:

[0044] A perception module, which includes an environmental sensor unit and a human body sensor unit. The environmental sensor unit is used to collect indoor temperature, humidity, light intensity, and air quality in real time. The human body sensor unit is used to detect the presence and activities of indoor personnel. The environmental sensor unit realizes the collection of indoor environmental parameters based on a temperature sensor, a humidity sensor, a light sensor, and an air quality sensor. The human body sensor unit realizes the detection of whether there are people in the room and the activities of people based on an infrared human body sensor.

[0045] An Internet of Things communication module, which transmits the data collected by the perception module to the control module through Internet of Things communication.

[0046] An energy-saving analysis module, which is used to receive the data collected by the perception module, analyze and process it, and then judge whether to adjust the energy-saving strategies for each smart home according to the analysis results.

[0047] A control module, which controls the operating states of each smart home according to the energy-saving strategies.

[0048] The energy-saving strategies include automatically turning on and off lights and air-conditioning equipment according to the activities of people, automatically adjusting the indoor lighting brightness according to the light intensity, and adjusting the air-conditioning operation parameters according to the temperature difference between indoors and outdoors.

[0049] At the environmental perception level of the present invention, the environmental sensor unit and the human body sensor unit in the perception module work together to comprehensively and real-time collect environmental parameters such as indoor temperature, humidity, light intensity, air quality, etc., as well as the presence and activities of personnel, providing a rich and reliable data basis for the subsequent energy-saving control of the system, enabling the system to have a comprehensive and accurate understanding of the indoor environment; at the same time, with the high-speed data transmission and device interconnection characteristics of the Internet of Things, the indoor environmental parameters and personnel activities collected in real time by various sensors can be transmitted to the control module in a timely and accurate manner, enabling the system to timely and accurately understand the indoor environmental state; thus realizing the comprehensive perception and energy-saving control of the smart home environment. Through the collaborative work of each module, it can collect indoor environmental data and personnel activity information in real time, and adjust the energy-saving strategy based on these data, improve the energy utilization efficiency, avoid energy waste, and create a comfortable and energy-saving home environment for users.

[0050] The energy-saving analysis module calculates the personnel activity prediction index based on the following method:

[0051] Substitute the time weight factor ξ of each time interval t t and the personnel activity situation A in the past n time periods t-i (i = 0, 1, 2,..., n - 1) into the following formula:

[0052]

[0053] In the formula, represents the personnel activity prediction index at time t, R represents the comprehensive environmental index, α and β are weight coefficients, determined based on historical data and experimental data, and α + β = 1, ρ i represents the influence factor of each time period, determined based on historical data analysis; when there is personnel activity, A t-i = 1, otherwise, A t-i = 0; activities are usually more frequent during the day and relatively less at night, so a time weight factor ξ t is assigned to each time interval t, which can be determined by counting the personnel frequency in this time period in historical data;

[0054] The expression of the comprehensive environmental index R is: T x represents the indoor temperature, Γ x represents the light intensity, Z x represents the air quality, H x represents the indoor humidity, T y 、Γ x 、Z y 、H yrespectively represent the reference values of indoor temperature, light intensity, air quality, and indoor humidity. λ1, λ2, λ3, and λ4 are set coefficients determined based on historical data analysis, and λ1 + λ2 + λ3 + λ4 = 1.

[0055] In the present invention, by calculating the personnel activity prediction index and the environmental comprehensive index, the system can more accurately predict the trend of personnel activities, and comprehensively judge in combination with environmental factors, so as to provide a more scientific basis for automatically controlling devices such as lights and air conditioners, further optimizing the energy-saving strategy, and improving the energy-saving effect and the degree of home intelligence; among them, the formula for the personnel activity prediction index comprehensively considers the time weight factor, the past personnel activity situation, and the environmental comprehensive index, and comprehensively reflects the influence of various factors on personnel activity prediction; the formula for the environmental comprehensive index Calculates environmental parameters such as indoor temperature, light intensity, air quality, and indoor humidity with the reference values, quantifies the environmental conditions, and provides a reference basis for personnel activity prediction from the environmental aspect.

[0056] The strategy for automatically turning on and off lights according to the personnel activity situation is as follows:

[0057] When the personnel activity prediction index is satisfied, the control module turns off all indoor lights in advance; when is satisfied, the lights in the preset area are turned on in advance.

[0058] In the present invention, automatically controlling the turning on and off of lights according to the personnel activity prediction index can avoid unnecessary turning on of lights when there is no one or few people in the room, effectively reduce energy consumption, and at the same time ensure timely lighting when needed by personnel, improving the user experience; the personnel activity prediction index herein is used to compare with the set light threshold to judge whether to turn off or turn on the lights, realizing intelligent light control and making the light control more in line with the actual personnel activity situation.

[0059] The strategy for automatically turning on and off the air conditioner according to the personnel activity situation is as follows:

[0060] When the personnel activity prediction index and this situation lasts for Y minutes, the control module turns off the air conditioner in advance;

[0061] When and the indoor temperature T x exceeds the preset comfortable temperature range [T min , T max , the air conditioner is turned on in advance and adjusted to the corresponding operating mode.

[0062] In the present invention, the on / off and operation mode of the air conditioner are automatically controlled by combining the personnel activity prediction index and the indoor temperature condition, avoiding energy waste caused by the continuous operation of the air conditioner when there is no one in the room, and adjusting the air conditioner in time when people enter and the temperature is uncomfortable, ensuring indoor comfort while improving energy utilization efficiency; the personnel activity prediction index formula is used to judge whether to turn off the air conditioner, and the indoor temperature data is used to judge whether to turn on the air conditioner and adjust the operation mode. The combination of the two realizes the intelligent control of the air conditioner, improving the energy-saving performance of the system and the user comfort.

[0063] The strategy for automatically adjusting the indoor lighting brightness according to the light intensity is as follows:

[0064] When the control module automatically reduces the brightness B of the indoor lighting fixtures to M% of the original brightness, that is

[0065] When the lighting fixture brightness B is automatically increased to P% of the original brightness, that is

[0066] represents the predicted light intensity index, which is obtained through the light intensity analysis model.

[0067] The expression of the light intensity analysis model is:

[0068] In the formula, μ o and μ σ and are weight coefficients, obtained based on historical data analysis, t day is the time variable within a day. For example, mapping 24 hours of a day to the 0-1 interval, different times have a significant impact on the light intensity. S is the weather condition score; L t-i is the historical light intensity in the past n time periods, and v i represents the corresponding influence factor, obtained based on historical data analysis; different numerical values are assigned to the weather condition score according to different weather types. For sunny days, S = 1, for cloudy days, S = 0.6, and for overcast days, S = 0.2; ln(1 + t day ) is used for non-linear mapping of the time factor, enabling the model to better capture the variation law of light intensity over time; the weight of the historical data closer to the current time is greater for v i to highlight the impact of recent light intensity changes on future predictions.

[0069] In the present invention, the indoor lighting brightness is automatically adjusted according to the predicted light intensity. When the light is sufficient, the lighting brightness is reduced, and when the light is insufficient, the brightness is increased. This can not only meet the indoor lighting requirements but also avoid energy waste caused by excessive lighting, further optimizing the energy-saving effect of indoor lighting; the predicted light intensity index formula The result calculated is compared with the set light intensity threshold to determine whether to adjust the brightness of the lighting fixture and the adjustment amplitude, realizing the intelligent adjustment of the lighting brightness and achieving the purpose of energy conservation; the light intensity analysis model comprehensively considers factors such as time, weather conditions, and historical light intensity to predict the light intensity, providing a more accurate basis for the automatic adjustment of indoor lighting brightness and improving the intelligence and energy-saving level of the lighting system; by assigning weights to different influencing factors and performing calculations, the light intensity index is accurately predicted, providing quantitative data support for adjusting the lighting brightness according to the light intensity subsequently.

[0070] The strategy for adjusting the air conditioner operation parameters according to the indoor-outdoor temperature difference is as follows:

[0071] When , the control module increases the air conditioner set temperature T set by K1, that is, T NEW = T set + K1, and reduces the wind speed by one gear;

[0072] When , the control module decreases the air conditioner set temperature T set by K2, that is, T NEW = T set - K2, and increases the wind speed by one gear;

[0073] When , keep the current set temperature and wind speed of the air conditioner unchanged;

[0074] represents the predicted temperature difference index, obtained through the indoor-outdoor temperature difference prediction model, [Q, F] represents the preset temperature difference threshold interval, and K1, K2 are the temperature adjustment set values.

[0075] In the present invention, the air conditioner operation parameters are adjusted according to the predicted indoor-outdoor temperature difference. When the temperature difference is small, the air conditioner set temperature is reasonably increased and the wind speed is reduced. When the temperature difference is large, the set temperature is decreased and the wind speed is increased. When in the appropriate temperature difference range, the current state is maintained, effectively reducing the air conditioner energy consumption while maintaining a comfortable indoor temperature; among them, the result calculated by the predicted temperature difference index formula is compared with the preset temperature difference threshold interval to determine the adjustment direction and amplitude of the air conditioner set temperature and wind speed, realizing the intelligent optimization of the air conditioner operation parameters and achieving the dual goals of energy conservation and comfort.

[0076] The expression of the indoor-outdoor temperature difference prediction model is:

[0077]

[0078] where ψ o , ψ σ , are weighting coefficients obtained based on historical data analysis, ψ o + T t-i is the historical indoor-outdoor temperature difference in the past n time periods, represents the corresponding influence factor obtained based on historical data analysis, and ι is the influence coefficient of weather on the temperature difference.

[0079] In the present invention, the indoor-outdoor temperature difference prediction model comprehensively considers factors such as time, weather conditions, and historical indoor-outdoor temperature differences, and can more accurately predict the temperature difference, providing a reliable basis for adjusting the operating parameters of the air conditioner, and improving the accuracy and effectiveness of the energy-saving control of the air-conditioning system; the formula obtains the predicted temperature difference index through weighted calculation of various influencing factors, which is used to compare with the preset temperature difference threshold subsequently to determine the adjustment strategy of the operating parameters of the air conditioner, making the air conditioner control more in line with the actual environmental temperature change.

[0080] It should be noted that: the calculation formula and each parameter participating in the operation in the present invention have been pre-dimensionless processed, and the process of dimensionless processing is well known in the industry and will not be described here.

[0081] The above has described a specific embodiment of the present invention in detail, but the described content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A smart home environment perception and energy-saving control system based on the Internet of Things, characterized in that: include: The sensing module includes an environmental sensor unit and a human sensor unit. The environmental sensor unit is used to collect indoor temperature, humidity, light intensity and air quality in real time, and the human sensor unit is used to detect the presence and activities of people indoors. An Internet of Things communication module transmits the data collected by the sensing module to the control module through Internet of Things communication; The energy-saving analysis module is used to receive and analyze the data collected by the perception module, and then determine whether to adjust the energy-saving strategy of each smart home based on the analysis results; The control module controls the operation status of each smart home according to the energy-saving strategy; The energy-saving strategy includes automatically turning on and off lights and air-conditioning equipment according to personnel activities, automatically adjusting indoor lighting brightness according to light intensity, and adjusting air-conditioning operating parameters according to the temperature difference between indoor and outdoor.

2. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 1 is characterized in that: The energy-saving analysis module calculates the personnel activity prediction index based on the following method: The time weight factor ξ of each time interval t is t And the personnel activities in the past n time periods A t-i (i=0, 1, 2, ..., n-1) Substitute into the following formula: In the formula, represents the personnel activity prediction index at time t, R represents the comprehensive environmental index, α and β are weight coefficients, and α+β=1, ρ i Indicates the impact factor for each time period; The expression of the comprehensive environmental index R is: T x represents the indoor temperature, Γ x Represents the light intensity, Z x Indicates air quality, H x Indicates indoor humidity, T y , Γ x , Z y , H y They represent the reference values ​​of indoor temperature, light intensity, air quality and indoor humidity respectively. λ1, λ2, λ3 and λ4 are setting coefficients, and λ1+λ2+λ3+λ4=1.

3. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 2 is characterized in that: The strategy for automatically switching lights on and off according to personnel activities is: When the personnel activity prediction indicator <The set light threshold θ light When the control module turns off all lights in the room in advance; when ≥ set light threshold θ light When the vehicle is in motion, turn on the lights in the preset area in advance.

4. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 1 or 3, characterized in that: The strategy for automatically turning on and off the air conditioner according to personnel activities is: When the personnel activity prediction indicator <Set air conditioning threshold value θ ac , and when this situation lasts for Y minutes, the control module turns off the air conditioner in advance; when ≥ set air conditioning threshold θ ac , and the indoor temperature T x Exceeding the preset comfortable temperature range [T min , T max ], turn on the air conditioner in advance and adjust it to the corresponding operating mode.

5. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 4 is characterized in that: The strategy for automatically adjusting the indoor lighting brightness according to the light intensity is: when > Set the light intensity threshold L ccc When the control module automatically reduces the brightness B of the indoor lighting fixture to M% of the original brightness, that is, when When the light intensity is less than the set light intensity threshold N, the lighting fixture brightness B is automatically increased to P% of the original brightness, that is, Represents the predicted light intensity index, which is obtained through the light intensity analysis model.

6. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 5 is characterized in that: The expression of the light intensity analysis model is: In the formula, μ o , μ σ , is the weight coefficient, t day is the time variable within a day, S is the weather condition score; L t-i is the historical light intensity of the past n time periods, v i Indicates the corresponding influencing factor; different numerical values ​​are assigned to weather conditions according to different weather types, sunny S=1, cloudy S=0.6, overcast S=0.

2.

7. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 6 is characterized in that: The strategy for adjusting the air conditioning operating parameters according to the indoor and outdoor temperature difference is: when When the control module sets the air conditioner to set the temperature T set Increase K1, that is, T NEW =T set +K1, and reduce the wind speed by one level; when When the control module sets the air conditioner to set the temperature T set Reduce K2, that is, T NEW =T set -K2, and increase the wind speed by one level; when When the air conditioner is turned on, keep the current set temperature and wind speed unchanged; It represents the predicted temperature difference index, which is obtained through the indoor and outdoor temperature difference prediction model. [Q, F] represents the preset temperature difference threshold interval. K1 and K2 are the temperature adjustment set values.

8. The smart home environment perception and energy-saving control system based on the Internet of Things according to claim 7 is characterized in that: The expression of the indoor and outdoor temperature difference prediction model is: In the formula, ψ o , σ , is the weight coefficient, T t-i is the historical indoor and outdoor temperature difference over the past n time periods, represents the corresponding influencing factor, and ι is the influence coefficient of weather on temperature difference.

Images