Automatic sunlight sensing sunshade blind control method and system

Abstract

The present invention relates to a method and system for controlling intelligent sunshade blinds based on environmental data and target parameters, and belongs to the field of information perception of the industrial Internet of Things. By obtaining ambient light intensity, solar altitude angle and azimuth, ambient temperature and / or humidity data, and target indoor light intensity and target temperature and humidity data input by the user, the present invention calculates the real-time baseline blind inclination angle, and combines light intensity regulation particles and somatosensory regulation particles for dynamic adjustment, and finally obtains the optimal blind inclination angle to achieve intelligent control of the sunshade blinds. The present invention can respond to changes in the external environment in real time, dynamically adjust the blind inclination angle, effectively block direct light and optimize the indoor light and thermal environment. At the same time, the present invention supports intelligent adjustment according to weather conditions and multi-regional needs, thereby enhancing the adaptability and accuracy of the system.

Description

Technical Field

[0001] The present invention belongs to the field of information perception and automatic optimization control of the industrial Internet of Things, and specifically relates to a method and system for automatically sensing daylight shading blinds. Background Art

[0002] With the development of green and intelligent buildings, shading systems, as a crucial component of building energy conservation, have played a significant role in improving indoor thermal environments, reducing the need for artificial lighting, and enhancing living and working comfort. Sunshade blinds, due to their adjustability, energy efficiency, and aesthetic appeal, are widely used in residential, office, and public buildings. By adjusting the tilt angle of the blades, sunshade blinds achieve a dynamic balance between blocking and diffusing sunlight.

[0003] Most existing sunshade louver systems rely solely on a single parameter: ambient light intensity. For example, the external sunshade louver device described in patent publication number CN102606054B lacks comprehensive sensing and control capabilities for the sun's position (altitude and azimuth), as well as ambient temperature and humidity. This makes it impossible to dynamically adjust the louver's tilt to accommodate changing time and environmental conditions, making it difficult to simultaneously ensure shading effectiveness and indoor comfort.

[0004] Traditional systems often use fixed blind angles or control logic based on simple rules, such as timers controlling the opening and closing of blinds, or switching actions based on light intensity thresholds. For example, patent document CN222162438U provides a blind with timed sunshade adjustment. This static adjustment method is difficult to adapt to dynamic changes in sunlight angles and cannot respond to users' real-time needs, limiting the system's efficiency and comfort.

[0005] Previous technologies have primarily focused on shading functions, failing to incorporate human comfort indicators such as indoor temperature and humidity into their control. For example, the intelligent control system for external sunshade venetian blinds described in Patent Publication No. CN110306918A may, while achieving effective sunshade, result in excessive light blocking or additional heat load in the room, increasing energy consumption for air conditioning and artificial lighting, making it difficult to achieve a balance between energy conservation and comfort. Furthermore, the automatic blind control system based on light regulation described in Patent Publication No. CN116464363A can determine the blind adjustment angle based on the reflection of sunlight from different directions. However, the automatic adjustment of the blinds requires obtaining the blind angle data from the previous moment and the current blind angle. This is neither instantaneous nor real-time, making it impossible to respond to changes in the external environment in real time, such as when strong wind conditions are detected. Existing technologies often use fixed step sizes or simple incremental adjustments to adjust the blind angle, lacking a sophisticated dynamic adjustment strategy. When environmental parameters change rapidly, over-response (overly frequent adjustments) or under-response (untimely adjustments) are likely to occur, affecting system stability and user experience.

[0006] In recent years, although some systems have attempted to introduce intelligent control technology, problems such as the weight distribution of regulating particles and the response to nonlinear environmental data have not been effectively solved, making it difficult to achieve truly intelligent and personalized regulation. Summary of the Invention

[0007] The purpose of the present invention is to propose an automatic sensing daylight shading blind control method and system to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0008] The present invention relates to a method and system for intelligent sunshade blind control based on environmental data and target parameters. By acquiring ambient light intensity, solar altitude and azimuth, ambient temperature, and / or humidity data, as well as user-entered target indoor light intensity, temperature, and humidity data, the method calculates a real-time baseline blind tilt angle. This is then dynamically adjusted using light intensity modulation particles and somatosensory modulation particles, ultimately determining the optimal blind tilt angle for intelligent sunshade blind control.

[0009] This system can respond to changes in the external environment in real time, dynamically adjusting the blinds' tilt angle to effectively block direct sunlight and optimize the indoor light and thermal environment. Furthermore, it supports intelligent adjustments based on weather conditions, historical data, and multi-regional needs, enhancing the system's adaptability and accuracy.

[0010] In order to achieve the above object, according to one aspect of the present invention, a method for automatically sensing sunlight shading blinds is provided, the method comprising the following steps:

[0011] Acquire data on ambient light intensity, solar altitude and solar azimuth, as well as ambient temperature and / or humidity. The client inputs data on target indoor light intensity and target indoor temperature and / or humidity. Based on the data on solar altitude and solar azimuth, the real-time baseline blind tilt angle is calculated.

[0012] The optimal blind angle is then calculated by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity.

[0013] Furthermore, the method further includes the step of adjusting the optimal louver inclination angle according to real-time weather conditions, specifically:

[0014] Automatically close the shutters when strong wind conditions are detected, or when rainfall conditions are detected;

[0015] When it is detected that the sky is clear and the wind speed is lower than the preset threshold, the optimal louver tilt angle is determined according to the calculation method described above.

[0016] Furthermore, the target indoor light intensity is input by independent clients in multiple areas.

[0017] In some embodiments, target lighting values ​​and current environmental data for multiple zones can be obtained. Based on each zone's solar altitude, azimuth, and real-time environmental data, the optimal blind angle for each zone is calculated. Furthermore, the optimal blind angles for multiple zones can be combined and weighted to uniformly adjust the overall blind angle, ensuring comprehensive optimization of the lighting needs of multiple zones. By supporting independent demand input for multiple zones, the system can meet the lighting requirements of multiple zones in complex architectural environments, making it suitable for multi-zone light environment management in large buildings.

[0018] Furthermore, the real-time baseline louver tilt angle is calculated based on the data of the solar altitude angle and the solar azimuth angle, specifically: the sine value of the solar altitude angle is calculated, the arccosine value of the sine value of the solar altitude angle is calculated, and the cosine value of the solar azimuth angle is calculated, and then the product of the arccosine value of the sine value of the solar altitude angle and the cosine value of the solar azimuth angle is calculated, and the value of the obtained product is the value of the real-time baseline louver tilt angle.

[0019] In some embodiments, the louver tilt angles α and φ are the sun's altitude (in degrees) and φ is the sun's azimuth (in degrees). The real-time baseline louver tilt angle value, fshade(α, φ) = arccos(sin(α)) * cos(φ), represents the louver tilt angle calculated based on the sun's position to block direct sunlight.

[0020] Furthermore, the real-time baseline louver tilt angle is processed by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity, specifically:

[0021] The value obtained by subtracting the target indoor light intensity from the ambient light intensity is used as the light intensity adjustment distance, and the ratio of the light intensity adjustment distance to the target indoor light intensity is used as the light intensity adjustment particle;

[0022] The somatosensory adjustment distance is calculated by subtracting the target indoor temperature and / or humidity from the ambient temperature and / or humidity, and the somatosensory adjustment particles are calculated. If only the ambient temperature and the target indoor temperature are compared, the somatosensory adjustment distance is calculated by subtracting the target indoor temperature from the ambient temperature, and the ratio of the somatosensory adjustment distance to the target indoor temperature is used as the somatosensory adjustment particle.

[0023] If there is only a comparison between the ambient humidity and the target indoor humidity, the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatosensory adjustment distance, and the ratio of the somatosensory adjustment distance to the target indoor humidity is used as the somatosensory adjustment particle;

[0024] If there is a comparison between the ambient temperature and the target indoor temperature and the ambient humidity and the target indoor humidity, the value obtained by subtracting the target indoor temperature from the ambient temperature is used as the somatic temperature adjustment distance, and the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatic humidity adjustment distance. Then, the ratio of the somatic temperature adjustment distance to the target indoor temperature is used as the somatic temperature adjustment particle, and the ratio of the somatic humidity adjustment distance to the target indoor humidity is used as the somatic humidity adjustment particle. The somatic temperature adjustment particle and the somatic temperature adjustment particle are combined to obtain the somatic temperature adjustment particle. Among them, the average value of the somatic temperature adjustment particle and the somatic temperature adjustment particle can be used as the somatic temperature adjustment particle.

[0025] Furthermore, the light intensity adjustment particles and the body sensory adjustment particles are used to adjust the value of the real-time baseline louver tilt angle. The method for obtaining the optimal louver tilt angle is as follows:

[0026] The optimal louver tilt angle value is obtained by adjusting the value of the real-time baseline louver tilt angle using the value of the light intensity adjustment particles and the body sensation adjustment particles as an amplification factor.

[0027] Preferably, the average value of the light intensity regulating particles and the body sensation regulating particles is used as the increase, the value of the real-time baseline louver inclination angle is adjusted with the said increase, and the adjusted value is used as the value of the optimal louver inclination angle.

[0028] Preferably, the values ​​of the light intensity regulating particles and the body sensation regulating particles are used as amplifications respectively, and the values ​​of the real-time baseline louver inclination angle are adjusted successively, and the adjusted values ​​are used as the values ​​of the optimal louver inclination angle.

[0029] Among them, the weights of the light intensity regulating particles and the somatosensory regulating particles can be obtained through statistical calculations, and the values ​​of the light intensity regulating particles and the somatosensory regulating particles can be weighted according to the weights of the light intensity regulating particles and the somatosensory regulating particles, respectively. The values ​​of the processed light intensity regulating particles and the somatosensory regulating particles can be used as the amplification to adjust the values ​​of the real-time baseline louver inclination angle, and the adjusted values ​​can be used as the values ​​of the optimal louver inclination angle.

[0030] Furthermore, the light intensity adjustment particles and the body sensory adjustment particles are used to adjust the value of the real-time baseline louver tilt angle to obtain the optimal louver tilt angle. Another method is:

[0031] The exponential value of the light intensity modulation particle is divided by the exponential value of the somatosensory modulation particle to obtain a light intensity modulation weight, and the value of the light intensity modulation particle is weighted by the light intensity modulation weight to obtain a light intensity modulation weight factor;

[0032] The exponential value of the somatosensory adjustment particle is divided by the exponential value of the light intensity adjustment particle to obtain a somatosensory adjustment weight, and the value of the somatosensory adjustment particle is weighted by the somatosensory adjustment weight to obtain a somatosensory adjustment weight factor;

[0033] The value of the optimal louver tilt angle is obtained by adjusting the value of the real-time baseline louver tilt angle using the values ​​of the light intensity adjustment weight factor and the body sensation adjustment weight factor as an increment.

[0034] The present invention also provides an automatic sensing daylight shading blind control system, the automatic sensing daylight shading blind control system comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the automatic control method for sensing daylight shading blinds when executing the computer program. The automatic sensing daylight shading blind control system can be run on computing devices such as desktop computers, laptop computers, mobile phones, PDAs, and cloud data centers. The executable systems may include, but are not limited to, processors, memories, and server clusters. The processor executes the computer program in the following system units:

[0035] A data acquisition unit is used to acquire data on ambient light intensity, solar altitude angle, solar azimuth angle, and ambient temperature and / or humidity. The client inputs data on target indoor light intensity and target indoor temperature and / or humidity.

[0036] An angle calculation unit, used to calculate the real-time baseline louver inclination angle based on the data of the solar altitude angle and the solar azimuth angle;

[0037] The optimization control unit is used to calculate the optimal blind inclination angle by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity.

[0038] The beneficial effects of the present invention are as follows: the present invention relates to an intelligent sunshade louver control method and system based on environmental data and target parameters. By obtaining ambient light intensity, solar altitude angle and azimuth angle, ambient temperature and / or humidity data, as well as target indoor light intensity and target temperature and humidity data input by the user, the present invention calculates the real-time baseline louver tilt angle, and combines light intensity adjustment particles and somatosensory adjustment particles for dynamic adjustment, and finally obtains the optimal louver tilt angle to achieve intelligent control of sunshade louvers. The present invention can respond to changes in the external environment in real time, dynamically adjust the louver tilt angle, effectively block direct light and optimize the indoor light and thermal environment. At the same time, the present invention supports intelligent adjustment according to weather conditions, historical data and multi-regional needs, enhancing the adaptability and accuracy of the system. Through multi-dimensional environmental data fusion and intelligent algorithm optimization, the shading effect is significantly improved to meet human comfort needs; through precise control, the energy consumption of air conditioning and artificial lighting is reduced, and the building operation cost is reduced; through multi-mode adjustment strategies, it adapts to different scenarios and needs, and has a wide range of building system application value and market potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features of the present invention will become more apparent through a detailed description of the embodiments shown in conjunction with the accompanying drawings. In the drawings of the present invention, the same reference numerals represent the same or similar elements. Obviously, the drawings described below are only some embodiments of the present invention. It is possible for a person skilled in the art to derive other drawings based on these drawings without inventive effort. In the drawings:

[0040] Figure 1 Shown is a flow chart of a method for automatically sensing sunlight shading blinds control;

[0041] Figure 2 Shown is a system structure diagram of an automatic sensing daylight shading shutter control system. DETAILED DESCRIPTION

[0042] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict.

[0043] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0044] like Figure 1 The figure shows a flow chart of an automatic sensing daylight shading shutter control method according to the present invention. Figure 1 To illustrate an automatic sensing daylight shading blind control method and system according to an embodiment of the present invention.

[0045] The present invention provides a method for automatically sensing sunlight-shading blinds, the method specifically comprising the following steps:

[0046] Acquire data on ambient light intensity, solar altitude angle, solar azimuth angle, and ambient temperature and / or humidity, and the client inputs data on target indoor light intensity and target indoor temperature and / or humidity;

[0047] Calculate the real-time baseline shutter inclination angle based on the data of solar altitude angle and solar azimuth angle;

[0048] The optimal blind angle is then calculated by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity.

[0049] Different building orientations have different lighting and shading requirements. Buildings can be divided into multiple orientation zones (such as east, south, west, and north), and the louver angles for each zone can be calculated and controlled separately. Buildings can be divided into multiple control zones based on their orientation. The optimal louver tilt angle is calculated for each zone, and the real-time shading requirements of the zone are determined by combining the sun's altitude and azimuth angles. Each zone's louver group is independently controlled to ensure precise shielding of direct sunlight from all directions while ensuring adequate indoor lighting and comfort. This system is suitable for large buildings, especially high-rise buildings, office buildings, and other scenarios requiring multi-faceted control.

[0050] The sun's position changes over time, necessitating adjustments to the blinds on different sides of a building at different times of the day for optimal shading. Based on the building's location, the daily sunlight exposure for each orientation is calculated in advance. For example, sunlight is concentrated primarily in the east in the morning and in the west in the afternoon. The system dynamically adjusts the blind angles for the corresponding orientations based on the temporal changes in the sun's altitude and azimuth. The system automatically switches blind control strategies for each zone throughout the day to avoid redundant calculations. This system is suitable for buildings in sunny areas and can optimize shading at different times of the day.

[0051] For large buildings or building complexes, coordinated optimization of sunshade louvers can be implemented based on regional needs. Buildings are divided into functional areas (such as office, public, and rest areas), and target values ​​for light, temperature, and humidity are determined for each area. The optimal louver angle is calculated for each area. Based on the needs of each area, a weighted approach is used to coordinate and control the overall louver angle, ensuring an optimal global balance of light and thermal conditions. This system is suitable for complex building layouts such as shopping malls, exhibition halls, and office parks.

[0052] Existing louver systems typically control the opening and closing of louvers based solely on light intensity, lacking comprehensive consideration of multi-dimensional environmental parameters such as sun position, temperature, and humidity. Many systems only offer fixed-time angle adjustment and are unable to dynamically adapt to changes in sunlight angle and environmental conditions. Existing systems also fail to make fine adjustments based on target indoor conditions (such as user-defined light or temperature requirements). The present invention incorporates multi-dimensional environmental data such as light intensity, sun position (altitude and azimuth), temperature, and humidity, along with user-entered target values, enabling the system to comprehensively consider multiple influencing factors. The system calculates the optimal louver angle in real time, ensuring dynamic adjustment of the louver based on the environment and user needs, improving comfort and energy efficiency. By comparing target and environmental data, the system supports personalized indoor light and temperature control. By collecting and analyzing multi-dimensional parameters, a data-driven control strategy is constructed, enabling refined adjustments. By combining environmental parameters (such as light intensity) with building heat transfer, adjusting the louver angle effectively reduces heat load and the impact of glare on indoor spaces.

[0053] Furthermore, the method further includes the step of adjusting the optimal louver inclination angle according to real-time weather conditions, specifically:

[0054] Automatically close the shutters when strong wind conditions are detected, or when rainfall conditions are detected;

[0055] When it is detected that the sky is clear and the wind speed is lower than the preset threshold, the optimal louver tilt angle is determined according to the above calculation method.

[0056] During exceptional weather conditions, such as strong winds and heavy rain, ensuring system safety and protecting the building are paramount. Install wind speed and rainfall sensors to monitor environmental conditions in real time. When wind speeds exceed a set threshold, adjust all blinds to the lowest wind resistance angle (e.g., horizontal). During rainfall, close blinds in areas susceptible to rainwater to protect indoor facilities.

[0057] Furthermore, the target indoor light intensity is input by independent clients in multiple areas.

[0058] In some embodiments, target lighting values ​​and current environmental data for multiple zones can be obtained. Based on each zone's solar altitude, azimuth, and real-time environmental data, the optimal blind angle for each zone is calculated. Furthermore, the optimal blind angles for multiple zones can be combined and weighted to uniformly adjust the overall blind angle, ensuring comprehensive optimization of the lighting needs of multiple zones. By supporting independent demand input for multiple zones, the system can meet the lighting requirements of multiple zones in complex architectural environments, making it suitable for multi-zone light environment management in large buildings.

[0059] Furthermore, the real-time baseline louver tilt angle is calculated based on the data of the solar altitude angle and the solar azimuth angle, specifically: the sine value of the solar altitude angle is calculated, the arccosine value of the sine value of the solar altitude angle is calculated, and the cosine value of the solar azimuth angle is calculated, and then the product of the arccosine value of the sine value of the solar altitude angle and the cosine value of the solar azimuth angle is calculated, and the value of the obtained product is the value of the real-time baseline louver tilt angle.

[0060] In some embodiments, the calculation of the blinds tilt angle can be: α represents the solar altitude angle (unit: degree), φ represents the solar azimuth angle (unit: degree), and the real-time baseline blinds tilt angle value fshade(α,φ)=arccos(sin(α))×cos(φ) is used to indicate that the blinds tilt angle is calculated according to the sun position to block direct light.

[0061] Most existing technologies use simple fixed angles or empirical values ​​to calculate the inclination of blinds, which cannot accurately block direct light. Some systems do not take into account the dynamic changes of the sun's altitude angle and azimuth angle, resulting in poor shading effect. The present invention provides a calculation formula based on the sun's altitude angle and azimuth angle, and accurately calculates the baseline blinds' inclination angle through geometric and trigonometric functions. Real-time adjustment of the blinds' inclination angle can accurately block direct light, while optimizing the diffuse entry of light and improving the quality of the indoor light environment. Through precise control, the demand for indoor artificial lighting and the increase in heat load caused by excessive shading can be reduced. By calculating the shading angle through trigonometric functions, precise control of the light path can be achieved. Dynamic adjustment of the shading angle minimizes direct light, while controlling the amount of diffuse light entering and optimizing the balance between daylighting and shading.

[0062] Furthermore, the real-time baseline louver tilt angle is processed by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity, specifically:

[0063] The value obtained by subtracting the target indoor light intensity from the ambient light intensity is used as the light intensity adjustment distance, and the ratio of the light intensity adjustment distance to the target indoor light intensity is used as the light intensity adjustment particle.

[0064] The value obtained by subtracting the target indoor temperature and / or humidity from the ambient temperature and / or humidity is used as the somatosensory adjustment distance, and the somatosensory adjustment particles are calculated, specifically:

[0065] If there is only a comparison between the ambient temperature and the target indoor temperature, the value obtained by subtracting the target indoor temperature from the ambient temperature is used as the somatosensory adjustment distance, and the ratio of the somatosensory adjustment distance to the target indoor temperature is used as the somatosensory adjustment particle;

[0066] If there is only a comparison between the ambient humidity and the target indoor humidity, the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatosensory adjustment distance, and the ratio of the somatosensory adjustment distance to the target indoor humidity is used as the somatosensory adjustment particle;

[0067] If there is a comparison between the ambient temperature and the target indoor temperature and the ambient humidity and the target indoor humidity, the value obtained by subtracting the target indoor temperature from the ambient temperature is used as the somatic temperature adjustment distance, and the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatic humidity adjustment distance. Then, the ratio of the somatic temperature adjustment distance to the target indoor temperature is used as the somatic temperature adjustment particle, and the ratio of the somatic humidity adjustment distance to the target indoor humidity is used as the somatic humidity adjustment particle. The somatic temperature adjustment particle and the somatic temperature adjustment particle are combined to obtain the somatic temperature adjustment particle. In one embodiment, the average of the somatic temperature adjustment particle and the somatic temperature adjustment particle can be used as the somatic temperature adjustment particle.

[0068] The existing technology only considers the change of light intensity and ignores the comprehensive impact of temperature and humidity on user comfort. The failure to introduce difference and ratio calculations has resulted in the system's response to different environmental conditions being too simple or rigid. The present invention adjusts particles through the difference and ratio calculations of light intensity, temperature and humidity to achieve multi-dimensional dynamic response and a more intelligent system. By also calculating the difference between the target and the environment, the system can respond more accurately to the comfort conditions set by the user. Multi-dimensional factor adjustment enables sunshade blinds to adapt to complex environmental changes and enhances the applicability of the system. By comprehensively considering factors such as light, temperature and humidity, the adjustment plan is closer to the comfort needs of the human body. In addition, the difference and ratio can be combined to construct a multi-factor weight model, so that multi-objective optimization can be achieved.

[0069] Furthermore, the light intensity adjustment particles and the body sensory adjustment particles are used to adjust the value of the real-time baseline louver tilt angle. The method for obtaining the optimal louver tilt angle is as follows:

[0070] The optimal louver tilt angle value is obtained by adjusting the value of the real-time baseline louver tilt angle using the value of the light intensity adjustment particles and the body sensation adjustment particles as an amplification factor.

[0071] In Example 4-1, the average value of the light intensity regulating particles and the body sensation regulating particles is used as the amplification, the value of the real-time baseline louver inclination angle is adjusted with the amplification, and the adjusted value is used as the value of the optimal louver inclination angle.

[0072] In Example 4-2, the values ​​of the light intensity regulating particles and the body sensation regulating particles are used as amplifications, and the values ​​of the real-time baseline louver tilt angle are adjusted successively, and the adjusted values ​​are used as the values ​​of the optimal louver tilt angle.

[0073] In one of the embodiments 4-3, the weights of the light intensity regulating particles and the somatosensory regulating particles can be obtained through statistical calculations, and the values ​​of the light intensity regulating particles and the somatosensory regulating particles can be weighted according to the weights of the light intensity regulating particles and the somatosensory regulating particles, respectively. The value of the real-time baseline louver inclination angle can be adjusted using the values ​​of the processed light intensity regulating particles and the somatosensory regulating particles as an increment, and the adjusted value can be used as the value of the optimal louver inclination angle.

[0074] Traditional systems lack flexible adjustment methods and often use fixed values ​​or simple incremental adjustments, which makes it difficult to meet a variety of usage scenarios. In addition, their adjustment step size is too large or too small. This adjustment mechanism cannot dynamically adjust the increase according to different environmental conditions, which may lead to excessive or insufficient tilt adjustment. The present invention provides a variety of adjustment methods based on average value, gradual adjustment and weighted processing to enhance system flexibility. In this way, by processing the average value or weight of the adjustment particles, the problem of excessive or insufficient adjustment amplitude is avoided. Moreover, different adjustment methods can adapt to different environmental conditions and user needs, improving the universality of the system. By dynamically adjusting the tilt value, the adjustment process is made smoother and more stable. The weighted average can assign weights to the multi-dimensional adjustment particles for post-processing, which can optimize the decision-making process and improve the adjustment effect.

[0075] Furthermore, the light intensity adjustment particles and the body sensory adjustment particles are used to adjust the value of the real-time baseline louver tilt angle to obtain the optimal louver tilt angle. Another method is:

[0076] The exponential value of the light intensity modulation particle is divided by the exponential value of the somatosensory modulation particle to obtain a light intensity modulation weight, and the value of the light intensity modulation particle is weighted by the light intensity modulation weight to obtain a light intensity modulation weight factor;

[0077] The exponential value of the somatosensory adjustment particle is divided by the exponential value of the light intensity adjustment particle to obtain a somatosensory adjustment weight, and the value of the somatosensory adjustment particle is weighted by the somatosensory adjustment weight to obtain a somatosensory adjustment weight factor;

[0078] The value of the optimal louver tilt angle is obtained by adjusting the value of the real-time baseline louver tilt angle using the values ​​of the light intensity adjustment weight factor and the body sensation adjustment weight factor as an increment.

[0079] In Example 5, it is preferred that the light intensity regulation weight is obtained by dividing the exponential value of the light intensity regulation particle by the exponential value of the somatosensory regulation particle, as the weight for the light intensity regulation particle. The somatosensory regulation weight is obtained by dividing the exponential value of the somatosensory regulation particle by the exponential value of the light intensity regulation particle, as the weight for the somatosensory regulation particle. And weighted processing is performed in this way, and the values ​​of the processed light intensity regulation particles and somatosensory regulation particles are used as the increase to adjust the value of the real-time baseline louver inclination angle, and the adjusted value is used as the value of the optimal louver inclination angle. In some embodiments, the value of the increase can be positive or negative, so that the value of the real-time baseline louver inclination angle can be increased or decreased.

[0080] The existing technology usually does not process the weights of the adjustment particles, which may cause the influence of certain factors on the adjustment results to be ignored or over-amplified. The nonlinear influence of the adjustment particles on the adjustment results is not taken into account, such as the lack of exponential response (including but not limited to the exp function and other representations related to the constant e), resulting in the adjustment effect being not accurate enough. The present invention ensures that the proportion of the influence of light intensity adjustment and somatosensory adjustment particles on tilt adjustment is scientific and reasonable through exponential weight calculation. The exponential processing of nonlinear optimization can more sensitively reflect the changes in environmental parameters and improve the accuracy and response speed of adjustment. The dynamic weight distribution can dynamically adjust the weight distribution according to real-time environmental conditions, thereby improving the system's adaptability to complex scenarios. The exponential function used therein is more sensitive to input changes, so that the sensitivity of instantaneous automatic control is greatly improved, and the accuracy can keep up in the process of automatic real-time triggering response, making it suitable for the nonlinear adjustment requirements of environmental data.

[0081] In an implementation record provided by the present invention, the obtained solar altitude angle is 45 degrees and the solar azimuth angle is 90 degrees. First, the sine value of the solar altitude angle is calculated. It is known that the sine value of 45 degrees is approximately 0.707. Then the arccosine value of the sine value is calculated, and the result is 45 degrees. Then the cosine value of the solar azimuth angle is calculated. It is known that the cosine value of 90 degrees is 0. Finally, the arccosine value (45 degrees) is multiplied by the cosine value (0), and the result of the real-time baseline blind inclination angle is 0 degrees. The real-time baseline blind inclination angle is 0 degrees, indicating that the sunlight is parallel to the front of the building and the blinds do not need to adjust the inclination angle.

[0082] In another implementation record provided by the present invention, in the calculation steps of the light intensity regulating particles and the somatosensory regulating particles, the ambient light intensity is 600 lux and the target indoor light intensity is 400 lux. The ambient temperature is 30 degrees Celsius and the target indoor temperature is 25 degrees Celsius. The ambient humidity is 60% and the target indoor humidity is 50%. When calculating the light intensity regulating particles, the target light intensity is subtracted from the ambient light intensity to obtain a light intensity difference of 200 lux. Then, this light intensity difference is divided by the target light intensity of 400 lux, and the light intensity regulating particles are obtained to be 0.5. When calculating the temperature regulating particles, the target temperature is subtracted from the ambient temperature to obtain a temperature difference of 5 degrees Celsius. Dividing this temperature difference by the target temperature of 25 degrees Celsius, the temperature regulating particles are obtained to be 0.2. When calculating the humidity regulating particles, the target humidity is subtracted from the ambient humidity to obtain a humidity difference of 10%. Dividing this humidity difference by the target humidity of 50%, the humidity regulating particles are obtained to be 0.2. Combining the temperature and humidity adjustment particles, taking the average of the two, we get a value of 0.2 for the body sensation adjustment particle, 0.5 for the light intensity adjustment particle, and 0.2 for the body sensation adjustment particle.

[0083] In another embodiment of baseline tilt adjustment, the real-time baseline blind tilt is 10 degrees, the light intensity adjustment particle is 0.5, and the body sensation adjustment particle is 0.2.

[0084] Method 1: Add the values ​​of the light intensity adjustment particle and the somatosensory adjustment particle and take the average value, which is 0.35. Add this increase value to the real-time baseline blind tilt angle (10 degrees), and the adjusted optimal blind tilt angle is 10.35 degrees.

[0085] Method 2: Add the light intensity adjustment particles and the body sensory adjustment particles to the real-time baseline blind tilt angle. First, add 0.5 to 10 degrees, obtaining a midpoint of 10.5 degrees. Then, add 0.2 to the midpoint, obtaining an optimal blind tilt angle of 10.7 degrees.

[0086] The optimal louver inclination angles are 10.35 degrees and 10.7 degrees depending on the method.

[0087] In another embodiment of indexed weight calculation, the light intensity adjustment particle is 0.5 and the somatosensory adjustment particle is 0.2. During the calculation process, first, the light intensity adjustment particle and the somatosensory adjustment particle are indexed separately. The indexed light intensity adjustment particle is approximately 1.65, and the somatosensory adjustment particle is approximately 1.22. The indexed value of the light intensity adjustment particle is divided by the indexed value of the somatosensory adjustment particle, resulting in a light intensity adjustment weight of 1.65. The indexed value of the somatosensory adjustment particle is divided by the indexed value of the light intensity adjustment particle, resulting in a somatosensory adjustment weight of 0.61. The corresponding adjustment particles are weighted using the weights, and the weighted result of the light intensity adjustment particle is 0.83, and the weighted result of the somatosensory adjustment particle is 0.12. The two weighted factor values ​​are added together as the adjustment increase. The increase is added to the real-time baseline louver tilt angle (10 degrees), and the optimal louver tilt angle is 10.95 degrees. The optimal louver tilt angle is 10.95 degrees.

[0088] The present invention comprehensively considers the ambient light intensity, solar altitude and azimuth, ambient temperature and humidity, and the target indoor parameters set by the user, and constructs a multi-dimensional data-driven sunshade blinds intelligent control system. Compared with the traditional single-dimensional lighting control method, the present invention can dynamically adapt to complex environmental changes and accurately meet user needs. By calculating the baseline blinds inclination angle in real time, combined with the regulation of light intensity regulating particles and somatosensory regulating particles, the present invention can intelligently adjust the blinds angle according to the real-time position of sunlight and the indoor lighting and temperature and humidity targets set by the user. This not only effectively blocks direct light, reduces glare and heat load, but also optimizes indoor lighting conditions and improves energy efficiency.

[0089] The present invention provides a variety of tilt adjustment strategies, including factor-based average value adjustment, gradual adjustment and exponential weight processing, so that the system can adapt to different environmental conditions and usage scenarios. In particular, the exponential weight method improves the response capability to changes in nonlinear environmental parameters. The present invention achieves precise control of tilt adjustment through a calculation method for adjustment particles that combines differences and ratios, as well as a weight distribution optimization mechanism. At the same time, through dynamic gain control and logical anti-shake design, excessive or frequent adjustments caused by rapid changes in environmental parameters are avoided, thereby improving the stability of the system and user experience. By introducing somatosensory adjustment particles, the present invention not only focuses on the shading effect, but also incorporates the impact of indoor temperature and humidity on human comfort into the control logic, providing an intelligent solution that is closer to user needs. The present invention reduces the energy consumption of artificial lighting and air-conditioning systems through precise shading and intelligent adjustment, meets the demand of modern buildings for green energy-saving technologies, and has significant social and economic benefits.

[0090] The automatic sensing daylight shading blind control system runs on any computing device such as a desktop computer, a laptop computer, a mobile phone, a PDA or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the automatic sensing daylight shading blind control method are implemented. The executable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0091] An embodiment of the present invention provides an automatic sensing daylight shading shutter control system, such as Figure 2 As shown, an automatic sensing daylight shading blind control system of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned automatic sensing daylight shading blind control method embodiment are implemented. The processor executes the computer program to run in the following system units:

[0092] A data acquisition unit is used to acquire data on ambient light intensity, solar altitude angle, solar azimuth angle, and ambient temperature and / or humidity. The client inputs data on target indoor light intensity and target indoor temperature and / or humidity.

[0093] An angle calculation unit, used to calculate the real-time baseline louver inclination angle based on the data of the solar altitude angle and the solar azimuth angle;

[0094] The optimization control unit is used to calculate the optimal blind inclination angle by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity.

[0095] Among them, preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0096] Among them, dimensionless numerical calculations are used between physical quantities of different units.

[0097] The automatic sensing daylight shading blind control system can be run on computing devices such as desktop computers, laptops, mobile phones, PDAs, and cloud data centers. The automatic sensing daylight shading blind control system includes, but is not limited to, a processor and a memory. Those skilled in the art will understand that the example is merely an example of an automatic sensing daylight shading blind control method and system, and does not constitute a limitation of an automatic sensing daylight shading blind control method and system. The system may include more or fewer components than the example, or a combination of certain components, or different components. For example, the automatic sensing daylight shading blind control system may also include input and output devices, network access devices, buses, etc.

[0098] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete component gate circuits or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the automatic sensing daylight shading blind control system, and utilizes various interfaces and lines to connect the various sub-regions of the entire automatic sensing daylight shading blind control system.

[0099] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the automatic daylight-sensing sunshade blind control method and system by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0100] The present invention relates to an intelligent sunshade blind control method and system based on environmental data and target parameters. By obtaining ambient light intensity, solar altitude angle and azimuth, ambient temperature and / or humidity data, as well as target indoor light intensity and target temperature and humidity data input by the user, the present invention calculates the real-time baseline blind inclination angle, and combines light intensity adjustment particles and somatosensory adjustment particles for dynamic adjustment, and finally obtains the optimal blind inclination angle to achieve intelligent control of the sunshade blinds. The present invention can respond to changes in the external environment in real time, dynamically adjust the blind inclination angle, effectively block direct light and optimize the indoor light and thermal environment. At the same time, the present invention supports intelligent adjustment according to weather conditions, historical data and multi-regional needs, thereby enhancing the adaptability and accuracy of the system.

[0101] Although the present invention has been described in considerable detail and with particularity with respect to several embodiments, it is not intended to limit the present invention to any of these details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the present invention. In addition, the present invention has been described above with respect to embodiments foreseen by the inventors for the purpose of providing a useful description, and those insubstantial modifications of the present invention that are not currently foreseen may still represent equivalent modifications of the present invention.

Claims

1. A method for automatically sensing sunlight shading blinds, characterized in that: The method comprises the following steps: Acquire data on ambient light intensity, solar altitude and solar azimuth, and ambient temperature and / or humidity; the client inputs data on target indoor light intensity and target indoor temperature and / or humidity; calculates a real-time baseline louver tilt angle based on the solar altitude and solar azimuth data; and then calculates light intensity regulating particles and somatosensory regulating particles by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity, respectively; uses the values ​​of the light intensity regulating particles and the somatosensory regulating particles as an increment, and adjusts the value of the real-time baseline louver tilt angle to obtain the value of the optimal louver tilt angle; Among them, by comparing the ambient light intensity with the target indoor light intensity, and the ambient temperature and / or humidity with the target indoor temperature and / or humidity, the light intensity adjustment particles and the body sensation adjustment particles are calculated, specifically: The value obtained by subtracting the target indoor light intensity from the ambient light intensity is used as the light intensity adjustment distance, and the ratio of the light intensity adjustment distance to the target indoor light intensity is used as the light intensity adjustment particle; The value obtained by subtracting the target indoor temperature and / or humidity from the ambient temperature and / or humidity is used as the somatosensory adjustment distance to calculate the somatosensory adjustment particles, where: If there is only a comparison between the ambient temperature and the target indoor temperature, the value obtained by subtracting the target indoor temperature from the ambient temperature is used as the somatosensory adjustment distance, and the ratio of the somatosensory adjustment distance to the target indoor temperature is used as the somatosensory adjustment particle; If there is only a comparison between the ambient humidity and the target indoor humidity, the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatosensory adjustment distance, and the ratio of the somatosensory adjustment distance to the target indoor humidity is used as the somatosensory adjustment particle; If there is a comparison between the ambient temperature and the target indoor temperature and a comparison between the ambient humidity and the target indoor humidity at the same time, the value obtained by subtracting the target indoor temperature from the ambient temperature is used as the somatic temperature adjustment distance, and the value obtained by subtracting the target indoor humidity from the ambient humidity is used as the somatic humidity adjustment distance. Then, the ratio of the somatic temperature adjustment distance to the target indoor temperature is used as the somatic temperature adjustment particle, and the ratio of the somatic humidity adjustment distance to the target indoor humidity is used as the somatic humidity adjustment particle. The somatic temperature adjustment particle and the somatic humidity adjustment particle are combined to obtain the somatic adjustment particle.

2. The automatic sensing sunlight shading blind control method according to claim 1, characterized in that: in, The method further includes the step of adjusting the optimal blinds inclination angle according to real-time weather conditions, specifically: Automatically close the shutters when strong wind conditions are detected, or when rainfall conditions are detected; When it is detected that the sky is clear and the wind speed is lower than a preset threshold, the optimal louver tilt angle is determined according to the method of claim 1.

3. The automatic sensing sunlight shading blind control method according to claim 1, characterized in that: in, The target indoor light intensity is input by independent clients in multiple zones.

4. The automatic sunlight sensing sunshade blind control method according to claim 1, characterized in that: in, According to the data of the solar altitude angle and the solar azimuth angle, the real-time baseline louver tilt angle is calculated, specifically: the sine value of the solar altitude angle is calculated, the arccosine value of the sine value of the solar altitude angle is calculated, and the cosine value of the solar azimuth angle is calculated. Then, the product of the arccosine value of the sine value of the solar altitude angle and the cosine value of the solar azimuth angle is calculated. The value of the obtained product is the value of the real-time baseline louver tilt angle.

5. The automatic sensing sunlight shading blind control method according to claim 1, characterized in that: The method for adjusting the real-time baseline blind angle using light intensity adjustment particles and body sensory adjustment particles to obtain the optimal blind angle is as follows: The optimal louver tilt angle value is obtained by adjusting the value of the real-time baseline louver tilt angle using the value of the light intensity adjustment particles and the body sensation adjustment particles as an amplification factor.

6. The automatic sensing sunlight shading blind control method according to claim 1, characterized in that: There are other methods to adjust the real-time baseline blind angle using light intensity adjustment particles and body sensory adjustment particles to obtain the optimal blind angle: The exponential value of the light intensity modulation particle is divided by the exponential value of the somatosensory modulation particle to obtain a light intensity modulation weight, and the value of the light intensity modulation particle is weighted by the light intensity modulation weight to obtain a light intensity modulation weight factor; The exponential value of the somatosensory adjustment particle is divided by the exponential value of the light intensity adjustment particle to obtain a somatosensory adjustment weight, and the value of the somatosensory adjustment particle is weighted by the somatosensory adjustment weight to obtain a somatosensory adjustment weight factor; The value of the optimal louver tilt angle is obtained by adjusting the value of the real-time baseline louver tilt angle using the values ​​of the light intensity adjustment weight factor and the body sensation adjustment weight factor as an increment.

7. An automatic sensing sunlight shading blinds control system, characterized in that: The automatic sensing daylight shading blind control system runs on any computing device such as a desktop computer, a laptop computer or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps in the automatic sensing daylight shading blind control method as described in any one of claims 1 to 6.

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