CN114599129B - Campus vision environment control method and system based on Internet of things - Google Patents
Campus vision environment control method and system based on Internet of things Download PDFInfo
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- CN114599129B CN114599129B CN202210175534.2A CN202210175534A CN114599129B CN 114599129 B CN114599129 B CN 114599129B CN 202210175534 A CN202210175534 A CN 202210175534A CN 114599129 B CN114599129 B CN 114599129B
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B45/00—Circuit arrangements for operating light-emitting diodes [LED]
- H05B45/10—Controlling the intensity of the light
- H05B45/12—Controlling the intensity of the light using optical feedback
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
- H05B47/11—Controlling the light source in response to determined parameters by determining the brightness or colour temperature of ambient light
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/175—Controlling the light source by remote control
- H05B47/19—Controlling the light source by remote control via wireless transmission
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
Abstract
The invention provides a campus vision environment control method and device based on the internet of things, wherein a plurality of LED illumination devices capable of adjusting brightness and a plurality of light sensors are arranged in a classroom, and an influence function of each illumination device on each light sensor is obtained through ICA analysis, so that guidance is provided for illumination in the classroom, illumination conditions in the classroom can be adjusted more uniformly and automatically, and brightness of each place in the classroom is within standard brightness.
Description
Technical Field
The invention belongs to the technical field of the Internet of things, and particularly relates to a campus vision environment control method and system based on the Internet of things.
Background
The classroom is one of the places where students are located for the longest time, especially pupil, is in a development stage, the visual environment in the classroom has great influence on the vision of the pupil, the illumination in the classroom is not beneficial to the vision development of the pupil, and the national standardization management committee and education departments dispute out corresponding campus visual environment related standards, such as the general classroom illumination design and installation sanitation requirement of primary and secondary schools (GB/T36876-2018), the classroom illumination environment evaluation and rating specification (T/CIC 203-2021), and the primary and secondary schools classroom illumination technical specification (T/JYBZ 005-2018). So more and more illumination intensity adjustable illumination devices, such as multi-stage adjustable LED illumination devices, are currently presented so as to enable teachers to adjust visual environments of campuses in real time.
However, if the unified setting of the light intensity in the classroom is simply performed according to the brightness level of the bright device, the light conditions in the classroom are uneven due to the influence of the ambient light, such as sunlight, the bright light of the multimedia device in the classroom, and the like, and some places are too dark and some places are too bright, which is not beneficial to the eyesight of students, and meanwhile, the problem of inaccurate adjustment can also occur due to manual adjustment.
Disclosure of Invention
In order to solve the technical problem that indoor illumination is difficult to uniformly adjust due to ambient light, the invention provides a campus vision environment control method and system based on the Internet of things.
On the one hand, the application provides a campus vision environment control method based on the Internet of things, which is characterized in that: the visual environment control method is applied to a classroom lighting system, and the classroom lighting system comprises: dividing a classroom ceiling into a plurality of grids, wherein each grid is provided with an LED lighting device of the Internet of things, N lighting devices are all arranged, and the luminosity adjustable range of each LED lighting device of the Internet of things is 1-D level; the LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and regulated by the central server; k optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level of the optical sensor to the central server; the visual environment control method comprises the following steps: data analysis stage: step S110, turning off the ambient light, sequentially changing the brightness levels of the N LED lighting devices through the central server, and transmitting the received corresponding brightness level data to the central server by the optical sensor; step S120, after enough data are collected, the independent component analysis ICA method is adopted to obtain the brightness level function of each light sensor; brightness adjustment stage: step S210, the central server acquires the brightness level data of each sensor in real time, and obtains the maximum value that the brightness level of each sensor does not reach the standard; step S220, selecting a sensor with a maximum value of which the brightness level does not reach the standard, inquiring a corresponding brightness level function, selecting an LED lighting device with the maximum weight coefficient, adding one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too weak, and subtracting one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too strong; step S230, repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and jumping to step S220 if the brightness level of the sensor still does not reach the standard; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
Further, the brightness level function is
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN And (5) representing the solved weight coefficient.
Further, fastICA is used to solve the luminance level function of each photosensor.
Further, the light sensor is arranged on a wall surface of a classroom.
On the other hand, the application provides a campus vision environment control system based on thing networking, its characterized in that: the campus vision environment control system based on the Internet of things comprises an in-classroom system: dividing a classroom ceiling into a plurality of grids, wherein each grid is provided with an LED lighting device of the Internet of things, N lighting devices are all arranged, and the luminosity adjustable range of each LED lighting device of the Internet of things is 1-D level; the LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and regulated by the central server; k optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level of the optical sensor to the central server; the campus vision environment control system based on the Internet of things comprises a data analysis system, wherein the analysis system specifically comprises: the data collection device is used for closing the ambient light, sequentially changing the brightness levels of the N LED lighting devices through the central server, and the light sensor transmits the received corresponding brightness level data to the central server; the data analysis device is used for obtaining the brightness level function of each light sensor by adopting an independent component analysis ICA method after collecting enough data; the campus vision environment control system based on the Internet of things comprises a brightness adjusting system, wherein the brightness adjusting system specifically comprises: the brightness level substandard calculating device is used for acquiring the brightness level data of each sensor in real time by the central server and solving the maximum value of the substandard brightness level of each sensor; the brightness adjustment computing device is used for selecting a sensor with the maximum value of the brightness level which does not reach the standard, inquiring a corresponding brightness level function, selecting an LED lighting device with the maximum weight coefficient, adding one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too weak, and subtracting one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too strong; the brightness adjusting device is used for repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and if the brightness level of the sensor still does not reach the standard, the brightness adjusting device is jumped to; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
Further, the brightness level function is
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN And (5) representing the solved weight coefficient.
Further, fastICA is used to solve the luminance level function of each photosensor.
Further, the light sensor is arranged on a wall surface of a classroom.
According to the invention, the plurality of brightness-adjustable LED illumination devices of the Internet of things and the plurality of light sensors are arranged in the classroom, and the influence function of each illumination device on each light sensor is obtained through ICA analysis, so that guidance is provided for illumination in the classroom, the illumination conditions in the classroom can be more uniformly and automatically adjusted, and the brightness of each place in the classroom is within the standard brightness.
Drawings
Fig. 1 is a schematic view of an illumination device and sensor arrangement of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, based on the examples provided herein, which are within the scope of the present application, will be within the purview of one of ordinary skill in the art to which the present application pertains without the inventive effort
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein refers to two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The first embodiment discloses a campus vision environment control method based on the internet of things, which is characterized in that: the visual environment control method is applied to a classroom lighting system, and the classroom lighting system comprises:
dividing a classroom ceiling into a plurality of grids, installing an LED lighting device of the Internet of things in each grid, and setting the luminosity adjustable range of each LED lighting device of the Internet of things as 1-D level.
Preferably, the classroom ceiling is divided into a grid of a x B, where A, B is a positive integer, with one LED lighting device mounted per grid. Illustratively, taking fig. 1 as an example, the ceiling is divided into 2*8 cells, one LED luminaire per cell, for a total of 16 LED luminaires, each luminaire adjustable by a level of 1-10.
The LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and adjusted by the central server.
The LED lighting device is connected to the central server by wired or wireless means, and the central server may be any conventional computer in the field, and the model and the specific configuration are not limited as long as the relevant data of the present invention can be processed. The central server can send out brightness adjustment instructions to the LED lighting devices through the network so as to control the brightness of the LED lighting devices, and the central server can also inquire or receive the current brightness level of any LED device through the network.
K optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level to the central server.
The light sensor belongs to a photosensitive device, the light sensor converts the received light intensity into grade data, and the specific grade number can be set according to the parameters of the light sensor; preferably, the light sensor should have a light sensing intensity greater than 5 levels.
Preferably, the light sensor is mounted on a classroom wall. In theory, the optical sensor is accurately installed near the desk, but because students move in the classroom and easily block the sensor, the sensor cannot work normally, so that the sensor is installed on the wall of the classroom according to the preferred scheme of the application, as shown in fig. 1.
The optical sensor is connected to the central server in a wired or wireless mode and periodically transmits the identifier and the sensitization intensity value of the optical sensor to the central server; illustratively, if a light sensor is numbered "one-grade ten-shift No. 3 sensor" and the light sensing level is level 4, the light sensor (the "one-grade ten-shift No. 3 sensor, level 4") is transmitted as a set of data to the central server.
The visual environment control method comprises the following steps:
data analysis stage:
in step S110, the ambient light is turned off, and the central server sequentially changes the brightness levels of the N LED lighting devices, and the light sensor transmits the received corresponding brightness level data to the central server.
The data collection stage is mainly to test the influence of each LED lighting device on each light sensor, so that the influence of ambient light is eliminated, and the test can be performed at night without the ambient light for measuring accurate data.
When a specific data test is performed, for example, the brightness levels of the 16 LED lighting devices are (1, 2,3 … …,4, 5) in sequence, the light intensities received by the 24 ambient light sensors are (2, 4,2, … …,2, 3) in sequence, and the data are a set of corresponding data, and the corresponding data are sent to a server and then stored for subsequent steps. In some situations, if the number of the LED lighting devices is large, or the adjustable grades are large, if all lighting devices of all grades are to be completely traversed, the total number of data will be massive, in this case, sparse test can be performed, for example, only test 1,3,5 grades for lighting device one, only test 2,4,6 grades for lighting device two, etc., and specific test data can be set according to practical situations.
In step S120, the luminance ranking function of each light sensor is obtained by using an independent component analysis (Independent Component Analysis, ICA) method after collecting enough data.
For each light sensor, the perceived light intensity is a superposition of ambient light and the light emitted by each LED lighting device, and since the light intensity is a linear superposition, it is similar to an observer for each light sensor, and each LED lighting device is a superposition component, i.e. a signal source, so that the ICA characteristics can be just satisfied.
Component solutions can be performed using ICA after sufficient data is collected, which is a conventional technical means in the art, preferably, in order to accelerate the solution, the application uses fastca algorithm for the solution, concretely, reference is made to "The FastICA Algorithm Revisited: convergence Analysis", doi:10.1109/tnn.2006.880980. Thus, the brightness level of each sensor can be obtained as a function of the brightness level of each light source, which is expressed by the following formula
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN And (5) representing the solved weight coefficient.
Illustratively, s1=0.6l1+0.4l2+ … … 0.1LN, meaning that the intensity of light processed by sensor S1 is superimposed by 0.6 parts of the 1 st LED light plus +0.4 parts of the 2 nd LED light intensity … … plus 0.1 parts of the NLED light intensity.
When the ambient light exists, assuming that the ambient light at each sensor is v1, v2 and … … vk respectively, the light intensity at k sensors can be expressed as the following formula according to the linear superposition principle of light
If at present s 1 ，s 2 ，……s k If the lighting requirement is not met, the requirement is to adjust it, since the ambient light is unchanged, assuming that the adjusted equation is expressed as follows, where s 1 ’，s 2 ’，……s k ' light intensity for sensor chip after adjustment, L 1’ ，L 2’ ，……L N’ Indicating the light intensity of the adjusted LED lighting device
Subtracting the two types
It can be seen that the influence of ambient light is eliminated, s 1 -s 1′ ，s 2 -s 2′ ，……s 2 -s k′ The amount of adjustment is required for each sensor and therefore for those places where the illumination is unacceptableThe adjustment is only required according to the relation between each sensor and each light source.
Brightness adjustment stage:
step S210, the central server acquires the brightness level data of each sensor in real time, and obtains the maximum value that the brightness level of each sensor does not reach the standard.
The maximum value of the non-standard is the maximum difference between the light sensing grade of the current sensor and the standard value, and the minimum standard brightness grade is (3, … …, 3) the maximum (6, … …, 6), the light sensing grade of the current sensor is (3, 1,2, … … 4), the brightness grade of the 2 nd sensor is 1, which is smaller than the standard brightness, the brightness grade of the 3 rd sensor is 2, which is smaller than the standard brightness by 1, and if the other sensors all reach the standard (i.e. 3 or more and 6 or less), the 2 nd sensor is the sensor with the maximum difference, and the non-standard maximum value is 2; in another example, the current sensor has a light sensing grade (9,1,2, … …), the 1 st sensor has a brightness grade of 9, which is 3 greater than the standard brightness, the 2 nd sensor has a brightness grade of 1, which is 2 less than the standard brightness, and if the other sensors all meet the standard (i.e., 3 or more and 6 or less), the 1 st sensor is the sensor with the largest difference, and the maximum value of not meeting the standard is 3.
Step S220, selecting a sensor with a maximum value of which the brightness level does not reach the standard, inquiring a corresponding brightness level function, selecting an LED lighting device with the maximum weight coefficient, if the brightness at the sensor is too weak, adding one to the brightness of the LED lighting device with the maximum weight coefficient, and if the brightness at the sensor is too strong, subtracting one to the brightness of the LED lighting device with the maximum weight coefficient.
Exemplary, the sensor having the maximum value of the luminance level falling short is the 2 nd sensor, and the corresponding light intensity expression is
s 2 ＝a 21 L 1 +a 22 L 2 +…+a 2N L N
The weight coefficients a21, a22 … … a2N are compared, the maximum value of the weight coefficients, namely, the LED lighting device with the greatest influence on the 2 nd sensor is selected, and the brightness of the lighting equipment is adjusted, so that the aim of adjusting the brightness of the 2 nd sensor is fulfilled.
Once the brightness of one lamp is changed, the brightness at all the sensors will change, so that the brightness calculation is re-performed according to the light intensity expression at each sensor.
Step S230, repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and jumping to step S220 if the brightness level of the sensor still does not reach the standard; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
Further, since the brightness level is increased or decreased in step S220, a problem of dead-cycling may occur (adding and subtracting operations are repeatedly performed on one LED lighting device), in order to prevent the dead-cycling from occurring, the LED lighting device to be adjusted is recorded, and if there is a possibility of dead-cycling (i.e., the previous addition operation is followed by the subtraction operation or the previous subtraction operation is followed by the double addition operation), the LED lighting device with the next weight coefficient is adjusted (i.e., the previous adjustment is first, the previous adjustment is second, and the next adjustment is third)
In another embodiment, the invention also discloses a campus vision environment control system based on the Internet of things, which is characterized in that: the campus vision environment control system based on the Internet of things comprises an in-classroom system:
dividing a classroom ceiling into a plurality of grids, installing an LED lighting device of the Internet of things in each grid, and setting the luminosity adjustable range of each LED lighting device of the Internet of things as 1-D level.
Preferably, the classroom ceiling is divided into a grid of a x B, where A, B is a positive integer, with one LED lighting device mounted per grid. Illustratively, taking fig. 1 as an example, the ceiling is divided into 2*8 cells, one LED luminaire per cell, for a total of 16 LED luminaires, each luminaire adjustable by a level of 1-10.
The LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and adjusted by the central server.
The LED lighting device is connected to the central server by wired or wireless means, and the central server may be any conventional computer in the field, and the model and the specific configuration are not limited as long as the relevant data of the present invention can be processed. The central server can send out brightness adjustment instructions to the LED lighting devices through the network so as to control the brightness of the LED lighting devices, and the central server can also inquire or receive the current brightness level of any LED device through the network.
K optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level to the central server.
The light sensor belongs to a photosensitive device, the light sensor converts the received light intensity into grade data, and the specific grade number can be set according to the parameters of the light sensor; preferably, the light sensor should have a light sensing intensity greater than 5 levels.
Preferably, the light sensor is mounted on a classroom wall. In theory, the optical sensor is accurately installed near the desk, but because students move in the classroom and easily block the sensor, the sensor cannot work normally, so that the sensor is installed on the wall of the classroom according to the preferred scheme of the application, as shown in fig. 1.
The optical sensor is connected to the central server in a wired or wireless mode and periodically transmits the identifier and the sensitization intensity value of the optical sensor to the central server; illustratively, if a light sensor is numbered "one-grade ten-shift No. 3 sensor" and the light sensing level is level 4, the light sensor (the "one-grade ten-shift No. 3 sensor, level 4") is transmitted as a set of data to the central server.
The campus vision environment control system based on the Internet of things comprises a data analysis system, wherein the analysis system specifically comprises:
the data collection device is used for turning off the ambient light, sequentially changing the brightness levels of the N LED lighting devices through the central server, and the light sensor transmits the received corresponding brightness level data to the central server.
The data collection stage is mainly to test the influence of each LED lighting device on each light sensor, so that the influence of ambient light is eliminated, and the test can be performed at night without the ambient light for measuring accurate data.
When a specific data test is performed, for example, the brightness levels of the 16 LED lighting devices are (1, 2,3 … …,4, 5) in sequence, the light intensities received by the 24 ambient light sensors are (2, 4,2, … …,2, 3) in sequence, and the data are a set of corresponding data, and the corresponding data are sent to a server and then stored for subsequent steps. In some situations, if the number of the LED lighting devices is large, or the adjustable grades are large, if all lighting devices of all grades are to be completely traversed, the total number of data will be massive, in this case, sparse test can be performed, for example, only test 1,3,5 grades for lighting device one, only test 2,4,6 grades for lighting device two, etc., and specific test data can be set according to practical situations.
And the data analysis device is used for obtaining the brightness grade function of each light sensor by adopting an independent component analysis (Independent Component Analysis, ICA) method after collecting enough data.
For each light sensor, the perceived light intensity is a superposition of ambient light and the light emitted by each LED lighting device, and since the light intensity is a linear superposition, it is similar to an observer for each light sensor, and each LED lighting device is a superposition component, i.e. a signal source, so that the ICA characteristics can be just satisfied.
Component solutions can be performed using ICA after sufficient data is collected, which is a conventional technical means in the art, preferably, in order to accelerate the solution, the application uses fastca algorithm for the solution, concretely, reference is made to "The FastICA Algorithm Revisited: convergence Analysis", doi:10.1109/tnn.2006.880980. Thus, the brightness level of each sensor can be obtained as a function of the brightness level of each light source, which is expressed by the following formula
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN And (5) representing the solved weight coefficient.
Illustratively, s1=0.6l1+0.4l2+ … … 0.1LN, meaning that the intensity of light processed by sensor S1 is superimposed by 0.6 parts of the 1 st LED light plus +0.4 parts of the 2 nd LED light intensity … … plus 0.1 parts of the NLED light intensity.
When the ambient light exists, assuming that the ambient light at each sensor is v1, v2 and … … vk respectively, the light intensity at k sensors can be expressed as the following formula according to the linear superposition principle of light
If at present s 1 ，s 2 ，……s k If the lighting requirement is not met, the requirement is to adjust it, since the ambient light is unchanged, assuming that the adjusted equation is expressed as follows, where s 1’ ，s 2’ ，……s k’ L for the light intensity of the sensor chip after adjustment 1’ ，L 2’ ，……L N’ Indicating the light intensity of the adjusted LED lighting device
Subtracting the two types
Can be used forIt is seen that the influence of ambient light is eliminated s 1 -s 1′ ，s 2 -s 2′ ，……s 2 -s k′ The amount of adjustment is required for each sensor, and therefore, only adjustment is required for a place where the illumination is defective according to the relational expression between each sensor and each light source.
The campus vision environment control system based on the Internet of things comprises a brightness adjusting system, wherein the brightness adjusting system specifically comprises:
and the brightness level substandard calculating device is used for acquiring the brightness level data of each sensor in real time by the central server and solving the maximum value of the substandard brightness level of each sensor.
The maximum value of the non-standard is the maximum difference between the light sensing grade of the current sensor and the standard value, and the minimum standard brightness grade is (3, … …, 3) the maximum (6, … …, 6), the light sensing grade of the current sensor is (3, 1,2, … … 4), the brightness grade of the 2 nd sensor is 1, which is smaller than the standard brightness, the brightness grade of the 3 rd sensor is 2, which is smaller than the standard brightness by 1, and if the other sensors all reach the standard (i.e. 3 or more and 6 or less), the 2 nd sensor is the sensor with the maximum difference, and the non-standard maximum value is 2; in another example, the current sensor has a light sensing grade (9,1,2, … …), the 1 st sensor has a brightness grade of 9, which is 3 greater than the standard brightness, the 2 nd sensor has a brightness grade of 1, which is 2 less than the standard brightness, and if the other sensors all meet the standard (i.e., 3 or more and 6 or less), the 1 st sensor is the sensor with the largest difference, and the maximum value of not meeting the standard is 3.
The brightness adjustment computing device is used for selecting the sensor with the maximum value of the brightness level which does not reach the standard, inquiring the corresponding brightness level function, selecting the LED lighting device with the maximum weight coefficient, adding one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too weak, and subtracting one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too strong.
Exemplary, the sensor having the maximum value of the luminance level falling short is the 2 nd sensor, and the corresponding light intensity expression is
s 2 ＝a 21 L 1 +a 22 L 2 +…+a 2N L N
The weight coefficients a21, a22 … … a2N are compared, the maximum value of the weight coefficients, namely, the LED lighting device with the greatest influence on the 2 nd sensor is selected, and the brightness of the lighting equipment is adjusted, so that the aim of adjusting the brightness of the 2 nd sensor is fulfilled.
Once the brightness of one lamp is changed, the brightness at all the sensors will change, so that the brightness calculation is re-performed according to the light intensity expression at each sensor.
The brightness adjusting device is used for repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and if the brightness level of the sensor still does not reach the standard, the brightness adjusting device jumps to the brightness adjusting device for processing; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
Further, since the brightness level is increased or decreased in the brightness adjustment calculation device, a problem of dead-loop may occur (adding or subtracting operation is repeatedly performed on one LED lighting device), in order to prevent the dead-loop from being performed, the LED lighting device to be adjusted is recorded, and if there is a possibility of dead-loop (i.e., adding operation is performed before followed by subtracting operation or subtracting operation is performed before followed by adding operation), the LED lighting device with the weight coefficient being the second (i.e., the last adjustment is first, the last adjustment is second, and the next adjustment is third)
In the present application, the term "plurality" means two or more, unless explicitly defined otherwise. The terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; "coupled" may be directly coupled or indirectly coupled through intermediaries. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
In the description of the present specification, the terms "one embodiment," "some embodiments," "particular embodiments," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (6)
1. A campus vision environment control method based on the Internet of things is characterized by comprising the following steps: the visual environment control method is applied to a classroom lighting system, and the classroom lighting system comprises:
dividing a classroom ceiling into a plurality of grids, wherein each grid is provided with an LED lighting device of the Internet of things, N lighting devices are all arranged, and the luminosity adjustable range of each LED lighting device of the Internet of things is 1-D level; the LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and regulated by the central server;
k optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level of the optical sensor to the central server;
the visual environment control method comprises the following steps:
data analysis stage:
step S110, turning off the ambient light, sequentially changing the brightness levels of the N LED lighting devices through the central server, and transmitting the received corresponding brightness level data to the central server by the optical sensor;
step S120, after enough data are collected, the independent component analysis ICA method is adopted to obtain the brightness level function of each light sensor;
the brightness level function is
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN Representing the solved weight coefficient;
brightness adjustment stage:
step S210, the central server acquires the brightness level data of each sensor in real time, and obtains the maximum value that the brightness level of each sensor does not reach the standard;
step S220, selecting a sensor with a maximum value of which the brightness level does not reach the standard, inquiring a corresponding brightness level function, selecting an LED lighting device with the maximum weight coefficient, adding one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too weak, and subtracting one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too strong;
step S230, repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and jumping to step S220 if the brightness level of the sensor still does not reach the standard; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
2. The campus vision environment control method based on the internet of things as set forth in claim 1, wherein: the luminance level function of each photosensor is solved using fastca.
3. The campus vision environment control method based on the internet of things as set forth in claim 1, wherein: the light sensor is arranged on the wall surface of a classroom.
4. Campus vision environment control system based on thing networking, its characterized in that:
the campus vision environment control system based on the Internet of things comprises an in-classroom system:
dividing a classroom ceiling into a plurality of grids, wherein each grid is provided with an LED lighting device of the Internet of things, N lighting devices are all arranged, and the luminosity adjustable range of each LED lighting device of the Internet of things is 1-D level; the LED lighting device is connected to the central server, and the brightness level of the LED lighting device can be controlled and regulated by the central server;
k optical sensors of the Internet of things are installed in a classroom, each optical sensor can receive the brightness level of the position of the optical sensor, the K optical sensors are connected to a central server, and each optical sensor periodically transmits an identifier and the brightness level of the optical sensor to the central server;
the campus vision environment control system based on the Internet of things comprises a data analysis system, wherein the analysis system specifically comprises:
the data collection device is used for closing the ambient light, sequentially changing the brightness levels of the N LED lighting devices through the central server, and the light sensor transmits the received corresponding brightness level data to the central server;
the data analysis device is used for obtaining the brightness level function of each light sensor by adopting an independent component analysis ICA method after collecting enough data;
the brightness level function is
Wherein s is 1 ，s 2 ，……s k Representing the brightness level of k photosensors, L 1 ，L 2 ，……L N Representing the brightness level of N light sources, a 11 ,a 12 ……a 1N ,……a kN Representing the solved weight coefficient;
the campus vision environment control system based on the Internet of things comprises a brightness adjusting system, wherein the brightness adjusting system specifically comprises:
the brightness level substandard calculating device is used for acquiring the brightness level data of each sensor in real time by the central server and solving the maximum value of the substandard brightness level of each sensor;
the brightness adjustment computing device is used for selecting a sensor with the maximum value of the brightness level which does not reach the standard, inquiring a corresponding brightness level function, selecting an LED lighting device with the maximum weight coefficient, adding one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too weak, and subtracting one to the brightness of the LED lighting device with the maximum weight coefficient if the brightness at the sensor is too strong;
the brightness adjusting device is used for repeatedly calculating the brightness level of each sensor after adjustment by using the brightness level function, and if the brightness level of the sensor still does not reach the standard, the brightness adjusting device jumps to the brightness adjusting device for processing; otherwise, the brightness of all the sensors reaches the standard, the central server obtains the final adjustment level of each LED lighting device, and the corresponding adjustment is carried out on each LED lighting device according to the final adjustment level.
5. The campus vision environment control system based on the internet of things as set forth in claim 4, wherein: the luminance level function of each photosensor is solved using fastca.
6. The campus vision environment control system based on the internet of things as set forth in claim 4, wherein: the light sensor is arranged on the wall surface of a classroom.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101400207A (en) * | 2007-09-29 | 2009-04-01 | 财团法人工业技术研究院 | Automatic light modulation system and method for brightness control |
| WO2013111134A1 (en) * | 2012-01-24 | 2013-08-01 | Yechezkal Evan Spero | Detector controlled illuminating system |
| TWM519371U (en) * | 2015-02-26 | 2016-03-21 | 洪德儒 | Illumination control system and illumination control apparatus |
| TW201632031A (en) * | 2015-02-26 | 2016-09-01 | 洪德儒 | Illumination control system and illumination control method |
| CN106037651A (en) * | 2016-06-14 | 2016-10-26 | 北京极客天下科技发展有限公司 | Heart rate detection method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9955551B2 (en) * | 2002-07-12 | 2018-04-24 | Yechezkal Evan Spero | Detector controlled illuminating system |
| US10477641B2 (en) * | 2017-09-24 | 2019-11-12 | Massachusetts Institute Of Technology | Methods and apparatus for image analysis for lighting control |
-
2022
- 2022-02-25 CN CN202210175534.2A patent/CN114599129B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101400207A (en) * | 2007-09-29 | 2009-04-01 | 财团法人工业技术研究院 | Automatic light modulation system and method for brightness control |
| WO2013111134A1 (en) * | 2012-01-24 | 2013-08-01 | Yechezkal Evan Spero | Detector controlled illuminating system |
| TWM519371U (en) * | 2015-02-26 | 2016-03-21 | 洪德儒 | Illumination control system and illumination control apparatus |
| TW201632031A (en) * | 2015-02-26 | 2016-09-01 | 洪德儒 | Illumination control system and illumination control method |
| CN106037651A (en) * | 2016-06-14 | 2016-10-26 | 北京极客天下科技发展有限公司 | Heart rate detection method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于ZigBee通讯技术的智能防爆LED照明系统;杜江波,渠国庆,李军世,吕北轩;《工业控制计算机》;第29卷(第9期);全文 * |
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