Automatic dimming control system of smart home lighting system

Abstract

The invention relates to the field of home lighting, and discloses an automatic dimming control system of an intelligent home lighting system, which comprises an ambient light detection module, a human body induction module, a central control module and a lighting lamp module. And the brightness and the color temperature of the LED lamp group are automatically adjusted in combination with a preset dimming strategy. A dynamic illumination compensation mechanism is introduced into the system, an environment illumination intensity prediction model is established through historical data, and illumination parameters are adjusted in advance to realize smooth transition; the wireless communication module is supported to receive an external control instruction, and remote regulation and control of the intelligent terminal are realized; a plurality of LED lamp groups which can be independently controlled work cooperatively through the central control module, so that personalized illumination requirements in different scenes are met; the brightness and color temperature of the LED lamp group are adjusted in advance through an environment illumination intensity prediction model in combination with historical data and real-time detection signals, so that the illumination variation amplitude is controlled within a range acceptable to human eyes.

Description

Technical Field

[0001] The present invention relates to the field of home lighting, and particularly to an automatic dimming control system for a smart home lighting system. Background Art

[0002] Traditional lighting systems generally adopt a dimming strategy with a fixed threshold, and can only perform switching or brightness adjustment based on a simple interval division of the ambient light intensity, such as "strong light - weak light", unable to capture the dynamic characteristics of light changes, resulting in lag or sudden changes in light adjustment, which is likely to cause visual fatigue of the human eye. In addition, most existing systems lack a perception linkage mechanism for the human activity state: high - brightness lighting is still maintained in unoccupied areas, causing energy waste; while the design of completely turning off the lights may pose safety hazards at night.

[0003] At the same time, traditional dimming systems lack the ability of "prediction - pre - adjustment" for ambient light, and can only passively respond based on the "current state", unable to predict the light trend in advance through historical data modeling and actively optimize lighting parameters. This results in the out - of - sync of light adjustment with environmental changes, making it difficult to achieve a smooth transition effect of "completing parameter adjustment before light changes", limiting the improvement space of smart home lighting in terms of comfort, energy efficiency, and health. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic dimming control system for a smart home lighting system to solve the above - mentioned technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An automatic dimming control system for a smart home lighting system, comprising:

[0007] An ambient light detection module, which real - time detects the ambient light intensity based on a photoresistor or an ambient light sensor, and outputs a corresponding light intensity signal;

[0008] A human body sensing module, which detects whether there is human activity based on an infrared human body sensor or a microwave human body sensor, and outputs a human presence signal;

[0009] A central control module, which is used to receive the light intensity signal and the human presence signal, and generate a dimming control signal according to a preset dimming strategy;

[0010] A lighting fixture module, which is used to receive the dimming control signal and adjust the brightness and / or color temperature of the corresponding LED lamp group according to the dimming control signal;

[0011] Wherein, the lighting fixture module includes a plurality of independently controllable LED lamp groups, and the central control module controls the brightness and / or color temperature of each LED lamp group respectively.

[0012] As a further technical solution, it further includes:

[0013] A wireless communication module, configured to receive a dimming control instruction sent by an external intelligent terminal and transmit the dimming control instruction to the central control module, and the central control module generates a corresponding dimming control signal according to the dimming control instruction.

[0014] As a further technical solution, the preset dimming strategy includes a dynamic light compensation mechanism, and the central control module establishes a prediction model of ambient light intensity change by analyzing historical light intensity data and the dimming control signal in the corresponding time period;

[0015] After detecting the current light intensity signal, the brightness and color temperature of the LED lamp group are adjusted in advance in combination with the prediction model of ambient light intensity change.

[0016] As a further technical solution, the expression of the prediction model of ambient light intensity change is:

[0017]

[0018] Wherein, L pred (t + Δt) is the predicted light intensity at a future moment, and L curr (t) is the measured light intensity at the current moment t, is the prediction sensitivity coefficient, with a value range of 0.5 - 1.5, T is the historical data backtracking window, ω(r) is the time window weight function, r is the historical moment, t is the current moment, JKF is the relative change rate, and DG is the attenuation coefficient.

[0019] As a further technical solution, the calculation formula of the relative change rate KF is:

[0020] Wherein, L(r - Δr) is, and L(r) is the measured value of the light intensity at the historical moment r, and the measured value of the light intensity one time step Δr earlier than the historical moment r.

[0021] As a further technical solution, the calculation formula of the attenuation coefficient DG is:

[0022]

[0023] Wherein, is the attenuation rate coefficient, with a value range of 0.01 - 1, k is the attenuation exponent, with a value range of 1 - 3, and t - r is the time interval between the current moment and the historical moment.

[0024] As a further technical solution, the process of adjusting the brightness of the LED lamp group in advance by combining the prediction model of the ambient light intensity change after detecting the current light intensity signal is as follows:

[0025] Calculate through the formula: to obtain the brightness adjustment value;

[0026] where L tor is the target light intensity, γ is the correction factor, and its value range is 0.4 - 0.6, which is used to compensate for the non-linear perception of the human eye to the brightness change;

[0027] When L pred (t + Δt) < L tor , increase the brightness of the LED lamp group; otherwise, decrease the brightness.

[0028] As a further technical solution, the process of adjusting the color temperature of the LED lamp group in advance by combining the prediction model of the ambient light intensity change after detecting the current light intensity signal is as follows:

[0029] Calculate through the formula:

[0030]

[0031] to obtain the color temperature adjustment value;

[0032] where ΔC is the color temperature adjustment range, and L max , L min are respectively the preset maximum and minimum light intensities, which are used to control the amplitude of the color temperature change.

[0033] Advantages of the present invention:

[0034] 1. Through the ambient light intensity prediction model of the present invention, which includes a time weight function, a logarithmic change rate, and a power function attenuation coefficient, and based on historical data, the light change trend is modeled and analyzed, and the light intensity change can be predicted Δt time in advance; compared with the passive dimming triggered by the traditional threshold, the system can actively adjust the brightness and color temperature of the LED lamp group before the actual change of the ambient light, so that the light intensity smoothly transitions with an exponential curve;

[0035] 2. Through the coordinated operation of the ambient light detection module and the human body sensing module in the present invention, high-precision monitoring of the light intensity and human activities is realized: the human body sensing module selects an infrared or microwave sensor. The former realizes the accurate identification of moving human bodies by setting the detection distance and delay time, and the latter uses radar technology to penetrate obstacles to detect static or moving human bodies. The human body presence signal response delay is short, effectively avoiding missed detections, and accurately perceiving the environment and human body states in multiple dimensions.

[0036] 3. Traditional systems rely on real-time detection to trigger dimming, which is prone to frequent adjustment due to environmental fluctuations. In contrast, the present invention anticipates the lighting trend in advance, reducing the number of lamp switches and the frequency of brightness adjustment. For example, in an unoccupied scenario, after the system detects no human activity within a preset time, it automatically reduces the brightness to the energy-saving mode, while maintaining the necessary lighting by combining the predicted light intensity to avoid the energy waste of "lights still on after people leave". BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings.

[0038] Figure 1 It is a system structure diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Please refer to Figure 1 As shown, the present invention is an automatic dimming control system for a smart home lighting system, including:

[0041] An ambient light detection module that real-time detects the ambient light intensity based on a photoresistor or an ambient light sensor and outputs a corresponding light intensity signal; Example: When using a photoresistor, configure a voltage-dividing circuit in the prior art to convert the light intensity into a voltage signal, such as connecting the photoresistor in series with a fixed resistor and taking the voltage-dividing value at the output end; When using a digital ambient light sensor, directly output the light intensity digital signal through the I2C interface, with the unit lux.

[0042] A human body sensing module that detects whether there is human activity based on an infrared human body sensor or a microwave human body sensor and outputs a human presence signal; Example: The infrared human body sensor is set with a detection distance of 2 - 7 meters and a delay time of 5 seconds - 30 minutes; The microwave sensor, such as a radar module, can penetrate non-metallic obstacles and is suitable for complex occlusion environments.

[0043] A central control module for receiving the light intensity signal and the human presence signal and generating a dimming control signal according to a preset dimming strategy;

[0044] A lighting fixture module for receiving the dimming control signal and adjusting the brightness and / or color temperature of the corresponding LED lamp group according to the dimming control signal;

[0045] Among them, the lighting fixture module includes a plurality of independently controllable LED lamp groups, and the central control module controls the brightness and / or color temperature of each LED lamp group respectively.

[0046] A wireless communication module, which is used to receive the dimming control instruction sent by an external intelligent terminal and transmit the dimming control instruction to the central control module. The central control module generates a corresponding dimming control signal according to the dimming control instruction. Communication protocol: MQTT or WiFi direct connection is adopted. The intelligent terminal (mobile phone / tablet) sends instructions in JSON format through the APP. The central control module integrates ESP8266 / ESP32 chips, and after parsing the instructions, forwards them to the LED driver module through the GPIO port or serial port; the wireless communication module supports remote adjustment of lighting parameters by the intelligent terminal. Users can monitor the lighting status in real time or preset automated scenarios (such as turning off all lamps with one key in the "leave home mode" and turning on the welcome lights in advance in the "return home mode") through the mobile phone APP, which improves the control convenience while breaking through the space limitation and meets the remote management requirements of modern smart homes.

[0047] In this embodiment, through the collaborative work of the ambient light detection module and the human body sensing module, high-precision monitoring of the light intensity and human activities is realized: the human body sensing module selects an infrared or microwave sensor. The former realizes the accurate identification of moving human bodies by setting the detection distance and delay time. The latter uses radar technology to penetrate obstacles to detect static or moving human bodies. The human presence signal response delay is short, effectively avoiding missed detection, and accurately perceiving the environment and human body state in multiple dimensions;

[0048] The combination of multiple independently controllable LED lamp groups of the lighting fixture module and the central control module realizes the refined management of lighting scenarios: it supports independent adjustment of the lights in different functional areas (such as the living room, bedroom, and study). For example, different brightness differences can be set between the main light and the background light in the living room to create a layered lighting effect, which not only meets the high-brightness requirements of the main activity area but also sets off the atmosphere through the background light, improving the space comfort. Flexible zoning control meets diverse lighting needs; at the same time, as the core of the system, the central control module realizes the rapid generation and execution of dynamic dimming strategies by real-time fusing the light intensity signal and the human presence signal, and the intelligent linkage improves the system response speed and reliability.

[0049] The preset dimming strategy includes a dynamic light compensation mechanism. The central control module analyzes the historical light intensity data and the dimming control signals in the corresponding time period to establish a prediction model for the change of ambient light intensity;

[0050] After detecting the current light intensity signal, the brightness and color temperature of the LED lamp group are adjusted in advance in combination with the prediction model of the change of ambient light intensity.

[0051] Example: Historical data storage: Store at least 7 days of light intensity data in the EEPROM or external Flash of the central control module, with a sampling interval Δr = 5 minutes, and the format is timestamp - light value; Advance adjustment duration Δt: Dynamically set according to the environmental change frequency. For example, in an outdoor scenario, Δt = 30 minutes, and in an indoor scenario, Δt = 5 minutes. The parameter update is triggered by a software timer.

[0052] In this embodiment, through the prediction model of environmental light intensity change, the traditional passive dimming mode of "threshold triggering" is changed. Based on the prediction result, the lighting parameters are adjusted in advance, making the light transition smooth. For example, when predicting overcast weather, the light is brightened in advance to reduce visual discomfort.

[0053] The expression of the prediction model for environmental light intensity change is:

[0054]

[0055] Where L pred (t + Δt) is the predicted light intensity at a future time, and L curr (t) is the measured light intensity at the current time t, which is used as the prediction benchmark. is the prediction sensitivity coefficient, and its value range is 0.5 - 1.5. It is used to amplify or reduce the influence weight of historical data. The larger the value, the more sensitive the prediction is to historical changes. T is the historical data backtracking window, which is used to limit the historical duration involved in modeling. For example, T = 12 hours, filtering out invalid and long - ago data. ω(r) is the time - window weight function. r is the historical time, t is the current time. The weight at the central time (r = t) is 1, and the weight at the edge times (r = t ± T) is 0, ensuring that recent data dominates the prediction. KF is the relative change rate, which is used to capture the logarithmic change trend of light intensity. DG is the attenuation coefficient, which is used to attenuate the weight of historical data according to the time interval.

[0056] In this embodiment, the cumulative effect of light change is modeled through an exponential function and integral operation to quantify the influence of historical data on the future. Specifically,

[0057] The predicted light intensity at a future time is calculated; Combining the light change rate, time weight, and attenuation characteristics, a non - linear prediction of the light trend is realized, and an adjustment signal is output Δt time in advance.

[0058] By introducing the time - window weight function ω(r), relative change rate KF, and attenuation coefficient DG, a change trend model can be established based on historical light data such as the past 12 hours, and the light intensity can be predicted 5 - 30 minutes in advance by Δt time; The model accumulates the historical change rate through integral operation and combines the exponential function to output the predicted value, making the light intensity change present a continuous exponential curve.

[0059] In the present invention, the central control module establishes a prediction model based on historical light data, adjusts the brightness and color temperature of the LED lamp group in advance, and avoids the sudden change of light caused by threshold triggering in the traditional system. For example, when it is predicted that the weather is turning cloudy, the system increases the brightness in advance and switches to cold white light, so that the light intensity changes gradually along a natural curve, and the change rate of the brightness perceived by the human eye is controlled within a comfortable range, effectively reducing visual fatigue and discomfort caused by frequent scaling of the pupil.

[0060] As a further technical solution, the calculation formula of the KF of the relative change rate is: where L(r - Δr) is, L(r) is the measured value of the light intensity at historical time r, and the measured value of the light intensity at a time step Δr earlier than historical time r.

[0061] In this embodiment, through the formula it is realized to convert the absolute change of light intensity into a relative change rate based on logarithmic operation, adapting to the non-linear perception of light by the human eye; achieving the elimination of the influence of the absolute value of light intensity on the adjustment sensitivity, such as the KF values of 100lux → 200lux and 1000lux → 2000lux are both ln2 / Δr, ensuring that the adjustment strength is consistent in different light intervals and avoiding excessive adjustment under strong light.

[0062] The calculation formula of the attenuation coefficient DG is: where is the attenuation rate coefficient, and its value range is 0.01 - 1, which is used to control the attenuation speed. The larger the value, the faster the weight of the long-term data decreases. k is the attenuation exponent, and its value range is 1 - 3, which is used to control the steepness of the attenuation curve. k = 1 is linear attenuation, and k ≥ 2 is non-linear rapid attenuation. t - r is the time interval between the current time and the historical time.

[0063] In this embodiment, through the formula it is realized to attenuate the weight of historical data through a power function, simulate the "recency effect", thereby suppressing the interference of outdated data and highlighting the leading role of recent light changes. For example, the weight of the data one hour ago drops to <10%, improving the response speed of the prediction to real-time changes.

[0064] The process of adjusting the brightness of the LED lamp group in advance by combining the prediction model of the ambient light intensity change after detecting the current light intensity signal is as follows:

[0065] Through the formula: calculate to obtain the brightness adjustment value;

[0066] where L tor is the target light intensity, such as 500lux for the reading scene, γ is the correction factor, and its value range is 0.4 - 0.6, which is used to compensate for the non-linear perception of brightness change by the human eye; when Lpred (t+Δt) <L tor When the light is on, increase the brightness of the LED light group; otherwise, reduce the brightness.

[0067] In this embodiment, by formula: Based on the ratio of predicted illumination to target illumination, nonlinear correction is used to achieve smooth brightness adjustment. When the predicted illumination is lower than the target, the system automatically brightens the image, and vice versa. The adjustment accuracy is 1%, and gamma correction is used to avoid sudden brightness changes. For example, when the actual brightness changes by 10%, the human eye perceives about 5%-7%.

[0068] After detecting the current light intensity signal, the process of adjusting the color temperature of the LED light group in advance in combination with the prediction model of the ambient light intensity change is as follows:

[0069] By formula:

[0070] Perform calculation to obtain the color temperature adjustment value;

[0071] Among them, ΔC is the color temperature adjustment range, such as ±2000K, which controls the switching range of cold and warm light, L max , L min They are respectively the preset maximum and minimum values of light intensity, which are used to control the amplitude of color temperature change.

[0072] In this embodiment, by formula:

[0073] By utilizing the smoothness of the sine function, the color temperature change is linked to the light intensity trend to simulate the natural spectrum change; the color temperature automatically increases when the light increases, such as from warm light to cold light, and decreases when the light decreases. The color temperature change rate is ≤100K / second, which is close to the color temperature gradient rhythm of natural light and improves the adaptability to the circadian rhythm.

[0074] In this example, the prediction model achieves the following effects:

[0075] First, accurate prediction and advance response of lighting trends;

[0076] Multi-factor modeling improves prediction accuracy. The time window weight function highlights the dominance of recent data, combines the relative change rate to capture the relative change trend of light, and then uses the attenuation coefficient to filter out the interference of long-term data. The light prediction error is significantly reduced, and the adjustment signal is output in advance, which greatly improves the response time in advance compared with the traditional system. Dynamic parameters adapt to environmental characteristics, the prediction sensitivity coefficient is automatically adjusted with the light intensity, and the lookback window is switched according to the season to capture different lighting patterns; the fluctuation of prediction errors in different seasons is reduced, and the system's ability to adapt to environmental changes is significantly improved.

[0077] Second, the smoothness and comfort of light adjustment are optimized;

[0078] The nonlinear correction of brightness adjustment is based on the design of correction factors based on the human eye perception characteristics, which converts physical brightness changes into linear perception of the human eye to avoid visual impact caused by step-type adjustment; the brightness adjustment rate is controlled within the comfortable range of the human eye, and the subjective comfort score is significantly improved. The natural rhythm simulation of color temperature adjustment achieves smooth transition of color temperature through the sine function algorithm, and is linked to the trend of light intensity (increase color temperature when light is enhanced, and decrease vice versa); the color temperature change conforms to the law of natural light, and the user's circadian rhythm adaptability is significantly improved.

[0079] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0080] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An automatic dimming control system for a smart home lighting system, characterized in that, Including: An ambient light detection module that detects the ambient light intensity in real time based on a photoresistor or an ambient light sensor and outputs a corresponding light intensity signal; A human body sensing module that detects whether there is human activity based on an infrared human sensor or a microwave human sensor and outputs a human presence signal; A central control module that is used to receive the light intensity signal and the human presence signal and generate a dimming control signal according to a preset dimming strategy; A lighting fixture module that is used to receive the dimming control signal and adjust the brightness and / or color temperature of the corresponding LED lamp group according to the dimming control signal; Wherein, the lighting fixture module includes a plurality of independently controllable LED lamp groups, and the central control module controls the brightness and / or color temperature of each LED lamp group respectively.

2. The automatic dimming control system of the smart home lighting system according to claim 1, characterized in that, It further includes: A wireless communication module that is used to receive a dimming control instruction sent by an external intelligent terminal and transmit the dimming control instruction to the central control module, and the central control module generates a corresponding dimming control signal according to the dimming control instruction.

3. The automatic dimming control system of the smart home lighting system according to claim 1 or 2, characterized in that, The preset dimming strategy includes a dynamic light compensation mechanism. The central control module analyzes historical light intensity data and the dimming control signals in the corresponding time periods to establish a prediction model for the change of ambient light intensity; After detecting the current light intensity signal, the brightness and color temperature of the LED lamp group are adjusted in advance in combination with the prediction model of the change of ambient light intensity.

4. The automatic dimming control system of the smart home lighting system according to claim 3, characterized in that, The expression of the prediction model for the change of ambient light intensity is: Among them, L pred ((t + Δt) is the predicted light intensity at a future time, and L curr ((t) is the measured light intensity at the current time t, is the predicted sensitivity coefficient, with a value range of 0.5 - 1.5, T is the historical data backtracking window, ω(r) is the time window weight function, ω r is the historical time, t is the current time, KF is the relative change rate, and DG is the attenuation coefficient.

5. The automatic dimming control system of the smart home lighting system according to claim 4, characterized in that, The calculation formula of KF for the relative change rate is as follows: Among them, L(r - Δr) is the measured value of the light intensity at historical moment r, and L(r) is the measured value of the light intensity at historical moment r, which is one time step Δr earlier than historical moment r.

6. The automatic dimming control system of the smart home lighting system according to claim 4, characterized in that, The calculation formula of the attenuation coefficient DG is: Among them, θ is the attenuation rate coefficient, with a value range of 0.01 - 1, k is the attenuation exponent, with a value range of 1 - 3, and t - r is the time interval between the current moment and the historical moment.

7. The automatic dimming control system of the smart home lighting system according to claim 4, characterized in that, The process of adjusting the brightness of the LED lamp group in advance in combination with the prediction model of the change of ambient light intensity after detecting the current light intensity signal is: Through the formula: Perform the calculation to obtain the brightness adjustment value; Among them, L tor is the target light intensity, γ is the correction factor, and its value range is 0.4 - 0.6, which is used to compensate for the non-linear perception of the human eye to brightness changes; When L pred ((t + Δt) < L tor increase the brightness of the LED lamp group; otherwise, decrease the brightness.

8. The automatic dimming control system of the smart home lighting system according to claim 4, wherein, The process of adjusting the color temperature of the LED lamp group in advance in combination with the prediction model of the change of ambient light intensity after detecting the current light intensity signal is: Through the formula: Perform calculations to obtain the color temperature adjustment value; Among them, ΔC is the color temperature adjustment range, and L max , L min are respectively the preset maximum and minimum light intensities, which are used to control the color temperature change range.

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