CN108990233A - A kind of intelligent light control system and method - Google Patents
A kind of intelligent light control system and method Download PDFInfo
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- CN108990233A CN108990233A CN201810904633.3A CN201810904633A CN108990233A CN 108990233 A CN108990233 A CN 108990233A CN 201810904633 A CN201810904633 A CN 201810904633A CN 108990233 A CN108990233 A CN 108990233A
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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
- H05B47/105—Controlling the light source in response to determined parameters
-
- 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
It includes: lighting control module, infrared detection module, vehicle flowrate detecting module, illumination test module, cloud server module, data processing module and lamp terminal module that the present invention, which provides a kind of intelligent light control system and method, the system,.This method detects that flow of the people and vehicle flowrate data in different time periods are transferred to the storage of cloud server module by infrared detection module and vehicle flowrate detecting module, data processing module reads data from cloud server module, it places data into the BP neural network based on Genetic Algorithm for Correction and is trained, predict people/vehicle flowrate of following one day day part, and obtained people/vehicle flowrate prediction result is uploaded in cloud server module and is stored, the intensity of illumination that lighting control module is detected according to data prediction result and illumination test module determines the adjusting of lamplight brightness jointly, then control lamp terminal module realizes the illuminating effect of different mode.
Description
Technical field
The present invention relates to lamps intelligent control fields, more particularly, to a kind of intelligent light control system and method.
Background technique
In traditional lamp light control system, in the period entirely illuminated, the lamp of street lamp, corridor lamp and public place
Light is all to maintain the state all lighted, there is no in view of specific time, place and people/vehicle flowrate come to signal light control into
Row is adjusted, while not adjusting the brightness of light at times, thus traditional lamp light control system there are serious power consumptions
Big problem.
Current most of lighting system, often without realization simultaneously according to the self-starting of natural light intensity, according to the period not
It is same that lamplight brightness is adjusted and this three aspect is realized to the brightness of light and switch real-time control according to people/vehicle flowrate
Function, there is very serious electric power resource wastes.
Summary of the invention
The present invention is directed to solve above-mentioned technical problem at least to a certain extent.
Primary and foremost purpose of the invention is to provide a kind of intelligent light control system,
The further object of the present invention is to provide a kind of lamps intelligent control method,
The intelligent light control system include: lighting control module, infrared detection module, vehicle flowrate detecting module,
Illumination test module, cloud server module, data processing module and lamp terminal module；
Wherein lamp terminal module is connected with lighting control module respectively with illumination test module, lighting control module, red
Outer detecting module is connected by network with cloud server module with vehicle flowrate detecting module, at cloud server module and data
Module is managed to be connected；
The infrared detection module includes power module, infrared detector and Pedestrian flow detection module, power module and red
External detector is electrically connected with Pedestrian flow detection module respectively；
The vehicle Flow Detection module includes: W loop coil, LC oscillating circuit, shaping circuit, main control module, power supply mould
Block, display module, wherein W loop coil is connected with LC oscillating circuit, and oscillating circuit is connected with shaping circuit, shaping circuit, electricity
Source module and display module are electrically connected with main control module respectively；
The illumination detection module includes: power module, illumination sensing module and processor module, power module and light
It is electrically connected respectively with processor module according to sensor module.
The lamps intelligent control method is based on intelligent light control system, method includes the following steps:
S1: flow of the people and vehicle flowrate number in different time periods is detected by infrared detection module and vehicle flowrate detecting module
It is stored according to being transferred in cloud server module；
S2: data processing module is read from cloud server module by infrared detection module and the detection of wagon flow detecting module
These data are then put into the BP neural network based on Genetic Algorithm for Correction by the data of the flow of the people and vehicle flowrate that get
It is trained, predicts people/vehicle flowrate of following one day day part, and obtained people/vehicle flowrate prediction result is uploaded into cloud
It is stored in the server module of end；
S3: lighting control module reads the people/vehicle flowrate prediction result being stored in remote server, root by network
The intensity of illumination detected according to people/vehicle flowrate prediction result and illumination test module determines the adjusting of lamplight brightness jointly,
Then control lamp terminal module realizes the illuminating effect of different mode.
Preferably, in the step S1 flow of the people detecting step are as follows:
S1.1.1 infrared detector using detect detection zone nobody when temperature as ambient temperature, when human body enters detecting area
When interior, pyroelectric infrared sensor detects the difference signal of human body temperature and ambient temperature, and the signal is then passed to the stream of people
It measures in detection module；
After S1.1.2 signal is passed to Pedestrian flow detection module, the method for Pedestrian flow detection module inquiry sentences signal
Disconnected, when signal is effective, stream of people's quantity adds one.
Preferably, which is characterized in that steps are as follows for vehicle Flow Detection in step S1:
The frequency of oscillation that S1.2.1LC oscillating circuit is generated when no vehicle passes through is benchmark frequency of oscillation, W ring-shaped inductors
Coil is acquired the information of wagon flow；When vehicle driving into cross W toroidal inductor when, LC oscillating circuit generate vibration
Frequency is swung to change；
The signal is input to main control modules to calculate relevant vehicle after shaping circuit by the sine wave of oscillation by S1.2.2
Information, and it is transmitted to cloud service wherein.
Preferably, steps are as follows for the calculating of the BP neural network based on genetic algorithm in step S2:
S2.1.1 determines the topological structure of BP neural network, encodes to the weight and threshold value of network, obtains initial kind
Group；
S2.1.2 decodes to obtain the weight of neural network and threshold value, and assigns newly-built BP neural network；
S2.1.3 is trained BP neural network using training dataset, using test data set to BP neural network into
Row test, obtains the error of test result and actual value, using this error as fitness function individual in genetic algorithm:
F=E=Y-Yt
Wherein, E is the output error of neural network, and Y is the reality output of test set, YtFor after training neural network it is defeated
Out；
S2.1.4 carries out genetic manipulation to test result and obtains new weight and threshold value；
S2.1.5 is compared by fitness function, if genetic algebra is less than setting value, return step S2.1.2；If hereditary
Algebra is greater than the set value, then is decoded to weight and threshold value, and optimal weight and threshold value are exported.
Preferably, the step S2.1.4 carries out the specific steps of genetic manipulation are as follows:
S2.1.4.1 selects test result using random ergodic sampling back-and-forth method；
Intersected after S2.1.4.2 selection using single point crossing method；
S2.1.4.3 is made a variation to obtain new weight and threshold value after intersecting using discrete variation method.
Preferably, as follows the step of people/vehicle flowrate prediction of result in step S2:
S2.2.1 is using the obtained best weight value of S2.1.5 and threshold value as the weight of BP neural network and threshold value；
S2.2.2 is input in the BP neural network having had corrected that and carries out using the stream of people/vehicle flowrate data as input
Training；
The output data that S2.2.2 obtains S2.2.2 training is as flow of the people/vehicle flowrate of prediction as a result, and will prediction
As a result it uploads in cloud server module and stores.
Preferably, people/vehicle flowrate and light intensity are divided into tetra- grades of A, B, C, D in step S3, this four ranking scores
Not Dui Ying 4 people/vehicle flowrates or light intensity range, while it is 25% that grade A, which corresponds to minimum luminous power, and grade B is corresponding
Minimum luminous power 50%, grade C correspond to minimum luminous power 75%, and grade D corresponds to minimum luminous power 100%, each
Under grade, make lighting control module that light is adjusted to corresponding brightness.
When concrete application, when people/vehicle flowrate is greater than threshold value set by respective level or naturally luminous intensity is low
When threshold value set by respective level, light is adjusted to corresponding brightness by lighting control module.
Compared with prior art, the beneficial effect of technical solution of the present invention is: with existing technology, this lamps intelligent control
System can adjust the brightness value of light according to people/vehicle flowrate predicted value, on the one hand can reduce the wasting of resources of altar lamp,
On the other hand loss to lamps and lanterns can be reduced to adjust intensity of illumination by adjusting lamplight brightness value, extends its service life.Simultaneously
This lamp light control system also has nature light compensation function, i.e., actual bright come appropriate adjustment light according to the brightness value of natural light
Degree, further saves energy resources, more intelligent humanized.
Detailed description of the invention
Fig. 1 is intelligent light control system structural schematic diagram.
Fig. 2 is vehicle Flow Detection modular system schematic diagram.
Fig. 3 is the BP neural network flow chart based on genetic algorithm.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
The present embodiment provides a kind of intelligent light control systems for this, as shown in Figure 1, the intelligent light control system packet
It includes: lighting control module, infrared detection module, vehicle flowrate detecting module, illumination test module, cloud server module, data
Processing module and lamp terminal module；
Wherein lamp terminal module is connected with lighting control module respectively with illumination test module, lighting control module, red
Outer detecting module is connected by network with cloud server module with vehicle flowrate detecting module, at cloud server module and data
Module is managed to be connected；
The infrared detection module includes power module, infrared detector and Pedestrian flow detection module, power module and red
External detector is electrically connected with Pedestrian flow detection module respectively；
Infrared sensor is pyroelectric infrared sensor D203S, the passive infrared skill of pyroelectric infrared sensor D203S
It is electric signal that wavelength can be the infrared signal change transitions between 8~12 μm by art, while it can believe the white light in nature
Number inhibiting effect is played, so in the investigative range of passive infrared detector, and the infrared waves of human body radiation a length of 8~14
μm, when no human motion, the only ambient temperature that pyroelectric infrared sensor senses, when human body enters in detecting area,
By Fresnel Lenses, pyroelectric infrared sensor detects the difference signal of human body temperature and ambient temperature, this faint
Zoom comparison of the difference signal by human detection module multistage after generate status signal, high level is exported, when output is height
When level, stream of people's quantity then adds one.
As shown in Fig. 2, the vehicle Flow Detection module includes: W loop coil, LC oscillating circuit, shaping circuit, master control mould
Block, power module, display module, wherein W loop coil is connected with LC oscillating circuit, and oscillating circuit is connected with shaping circuit, whole
Shape circuit, power module and display module are electrically connected with main control module respectively；
W loop coil is collecting vehicle information end, and the traveling of vehicle causes the inductance of coil to change；LC oscillating circuit is in nothing
Reference frequency oscillator when vehicle driving, when there is vehicle by coil, frequency of oscillation changes, and the sine wave of oscillation passes through
Relevant information of vehicles is calculated the signal is input to main control module after shaping circuit and is delivered to display module Real-time Feedback, together
When by information of vehicles be transferred to Cloud Server module carry out calculation process.
The illumination detection module includes: power module, illumination sensing module and processor module, power module and light
It is electrically connected respectively with processor module according to sensor module.
Embodiment 2
Based on the intelligent light control system, the present embodiment provides a kind of lamps intelligent control method, including it is following
Step:
S1: flow of the people and vehicle flowrate number in different time periods is detected by infrared detection module and vehicle flowrate detecting module
It is stored according to being transferred in cloud server module；
S2: data processing module is read from cloud server module by infrared detection module and the detection of wagon flow detecting module
These data are then put into the BP neural network based on Genetic Algorithm for Correction by the data of the flow of the people and vehicle flowrate that get
It is trained, predicts people/vehicle flowrate of following one day day part, and obtained people/vehicle flowrate prediction result is uploaded into cloud
It is stored in the server module of end；
S3: lighting control module reads the people/vehicle flowrate prediction result being stored in remote server, root by network
The intensity of illumination detected according to people/vehicle flowrate prediction result and illumination test module determines the adjusting of lamplight brightness jointly,
Then control lamp terminal module realizes the illuminating effect of different mode.
More specifically, in the step S1 flow of the people detecting step are as follows:
S1.1.1 infrared detector using detect detection zone nobody when temperature as ambient temperature, when human body enters detecting area
When interior, pyroelectric infrared sensor detects the difference signal of human body temperature and ambient temperature, and the signal is then passed to the stream of people
It measures in detection module；
After S1.1.2 signal is passed to Pedestrian flow detection module, the method for Pedestrian flow detection module inquiry sentences signal
Disconnected, when signal is effective, stream of people's quantity adds one.
More specifically, steps are as follows for vehicle Flow Detection in step S1:
The frequency of oscillation that S1.2.1LC oscillating circuit is generated when no vehicle passes through is benchmark frequency of oscillation, W ring-shaped inductors
Coil is acquired the information of wagon flow；When vehicle driving into cross W toroidal inductor when, LC oscillating circuit generate vibration
Frequency is swung to change；
The signal is input to main control modules to calculate relevant vehicle after shaping circuit by the sine wave of oscillation by S1.2.2
Information, and it is transmitted to cloud service wherein.
More specifically, as shown in figure 3, the calculating of the BP neural network in step S2 based on genetic algorithm steps are as follows:
S2.1.1 determines the topological structure of BP neural network, encodes to the weight and threshold value of network, obtains initial kind
Group；
S2.1.2 decodes to obtain the weight of neural network and threshold value, and assigns newly-built BP neural network；
S2.1.3 is trained BP neural network using training dataset, using test data set to BP neural network into
Row test, obtains the error of test result and actual value, using this error as fitness function individual in genetic algorithm:
F=E=Y-Yt
Wherein, E is the output error of neural network, and Y is the reality output of test set, YtFor after training neural network it is defeated
Out；
S2.1.4 carries out genetic manipulation to test result and obtains new weight and threshold value；
S2.1.5 is compared by fitness function, if genetic algebra is less than setting value, return step S2.1.2；If hereditary
Algebra is greater than the set value, then is decoded to weight and threshold value, and optimal weight and threshold value are exported.
More specifically, the step S2.1.4 carries out the specific steps of genetic manipulation are as follows:
S2.1.4.1 selects test result using random ergodic sampling back-and-forth method；
Intersected after S2.1.4.2 selection using single point crossing method；
S2.1.4.3 is made a variation to obtain new weight and threshold value after intersecting using discrete variation method.
More specifically, as follows the step of people/vehicle flowrate prediction of result in step S2:
S2.2.1 is using the obtained best weight value of S2.1.5 and threshold value as the weight of BP neural network and threshold value；
S2.2.2 is input in the BP neural network having had corrected that and carries out using the stream of people/vehicle flowrate data as input
Training；
The output data that S2.2.2 obtains S2.2.2 training is as flow of the people/vehicle flowrate of prediction as a result, and will prediction
As a result it uploads in cloud server module and stores.
More specifically, people/vehicle flowrate and light intensity are divided into tetra- grades of A, B, C, D in step S3, this four grades
Respectively correspond the range of 4 people/vehicle flowrates or light intensity, while it is 25% that grade A, which corresponds to minimum luminous power, B pairs of grade
Minimum luminous power 50% is answered, grade C corresponds to minimum luminous power 75%, and grade D corresponds to minimum luminous power 100%, each
Under a grade, make lighting control module that light is adjusted to corresponding brightness.
When concrete application, when people/vehicle flowrate is greater than threshold value set by respective level or naturally luminous intensity is low
When threshold value set by respective level, light is adjusted to corresponding brightness by lighting control module.
The intensity of prevailing circumstances natural light is detected according to illumination detection module, only when the brightness value of natural light is lower than setting
When threshold value, lighting control module just opens lamp.
In view of greasy weather and sleet sky can reduce the brightness value of light sending, if illumination detection module detects at that time bright
Angle value reaches requirement lower than when light should issue the brightness value of light at that time, the lamplight brightness of lighting control module control at this time is lightened
Value guarantees the traffic safety of pedestrian or vehicle at that time.
The same or similar label correspond to the same or similar components；
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (8)
1. a kind of intelligent light control system, which is characterized in that the intelligent light control system includes: signal light control mould
Block, infrared detection module, vehicle flowrate detecting module, illumination test module, cloud server module, data processing module and photograph
Bright terminal module；
Wherein lamp terminal module and illumination test module are electrically connected with lighting control module respectively, lighting control module, infrared
Detecting module is connected by network with cloud server module telecommunication with vehicle flowrate detecting module, cloud server module with
Data processing module electrical connection；
The infrared detection module includes power module, infrared detector and Pedestrian flow detection module, power module and infrared spy
Device is surveyed to be electrically connected with Pedestrian flow detection module respectively；
The vehicle Flow Detection module includes: W loop coil, LC oscillating circuit, shaping circuit, main control module, power module, shows
Show module, wherein W loop coil, LC oscillating circuit, shaping circuit are sequentially connected electrically, shaping circuit, power module and display mould
Block is electrically connected with main control module respectively；
The illumination detection module includes: power module, illumination sensing module and processor module, and power module and illumination pass
Sensor module is electrically connected with processor module respectively.
2. a kind of lamps intelligent control method, the method is based on intelligent light control system described in claim 1, feature
Be, the lamps intelligent control method the following steps are included:
S1: detect that flow of the people and vehicle flowrate data in different time periods pass by infrared detection module and vehicle flowrate detecting module
It is defeated to be stored into cloud server module；
S2: data processing module is read from cloud server module to be got by infrared detection module and the detection of wagon flow detecting module
Flow of the people and vehicle flowrate data, then these data are put into the BP neural network based on Genetic Algorithm for Correction and are instructed
Practice, predict people/vehicle flowrate of following one day day part, and obtained people/vehicle flowrate prediction result is uploaded into cloud service
It is stored in device module；
S3: lighting control module reads the people/vehicle flowrate prediction result being stored in remote server by network, according to
The intensity of illumination that people/vehicle flowrate prediction result and illumination test module detect determines the adjusting of lamplight brightness jointly, so
Control lamp terminal module realizes the illuminating effect of different mode afterwards.
3. lamps intelligent control method according to claim 2, which is characterized in that the detection of flow of the people in the step S1
Step are as follows:
S1.1.1 infrared detector using detect detection zone nobody when temperature as ambient temperature, when human body enters in detecting area
When, pyroelectric infrared sensor detects the difference signal of human body temperature and ambient temperature, and the signal is then passed to flow of the people
In detection module；
After S1.1.2 signal is passed to Pedestrian flow detection module, the method for Pedestrian flow detection module inquiry judges signal,
When signal is effective, stream of people's quantity adds one.
4. lamps intelligent control method according to claim 2, which is characterized in that vehicle Flow Detection step is such as in step S1
Under:
The frequency of oscillation that S1.2.1LC oscillating circuit is generated when no vehicle passes through is benchmark frequency of oscillation, W toroidal inductor
The information of wagon flow is acquired；When vehicle driving into cross W toroidal inductor when, LC oscillating circuit generate frequency of oscillation
It changes；
The sine wave of oscillation is calculated relevant vehicle the signal is input to main control module after shaping circuit by S1.2.2 to be believed
Breath, and it is transmitted to cloud service wherein.
5. lamps intelligent control method according to claim 2, which is characterized in that based on the BP of genetic algorithm in step S2
Steps are as follows for the calculating of neural network:
S2.1.1 determines the topological structure of BP neural network, encodes to the weight and threshold value of network, obtains initial population；
S2.1.2 decodes to obtain the weight of neural network and threshold value, and assigns newly-built BP neural network；
S2.1.3 is trained BP neural network using training dataset, is surveyed using test data set to BP neural network
Examination, obtains the error of test result and actual value, using this error as fitness function individual in genetic algorithm:
F=E=Y-Yt
Wherein, E is the output error of neural network, and Y is the reality output of test set, YtFor the output of neural network after training；
S2.1.4 carries out genetic manipulation to test result and obtains new weight and threshold value；
S2.1.5 is compared by fitness function, if genetic algebra is less than setting value, return step S2.1.2；If genetic algebra
It is greater than the set value, then weight and threshold value is decoded, export optimal weight and threshold value.
6. lamps intelligent control method according to claim 5, which is characterized in that the step S2.1.4 carries out heredity
The specific steps of operation are as follows:
S2.1.4.1 selects test result using random ergodic sampling back-and-forth method；
Intersected after S2.1.4.2 selection using single point crossing method；
S2.1.4.3 is made a variation to obtain new weight and threshold value after intersecting using discrete variation method.
7. lamps intelligent control method according to claim 5, which is characterized in that people/vehicle flowrate result is pre- in step S2
The step of survey, is as follows:
S2.2.1 is using the obtained best weight value of S2.1.5 and threshold value as the weight of BP neural network and threshold value；
S2.2.2 is input in the BP neural network having had corrected that and is trained using the stream of people/vehicle flowrate data as input；
S2.2.2 is using the S2.2.2 obtained output data of training as the flow of the people/vehicle flowrate predicted as a result, and by prediction result
It uploads in cloud server module and stores.
8. lamps intelligent control method according to claim 2, which is characterized in that by people/vehicle flowrate and light in step S3
Line intensity is divided into tetra- grades of A, B, C, D, this four grades respectively correspond the range of 4 people/vehicle flowrates or light intensity, simultaneously
It is 25% that grade A, which corresponds to minimum luminous power, and grade B corresponds to minimum luminous power 50%, and grade C corresponds to minimum luminous power
75%, grade D correspond to minimum luminous power 100%, under each grade, make lighting control module light is adjusted to correspond to it is bright
Degree.
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| CN109766794A (en) * | 2018-12-25 | 2019-05-17 | 河海大学 | A kind of automation Real-time Road detection method and its system |
| CN110097973A (en) * | 2019-05-10 | 2019-08-06 | 重庆邮电大学 | The prediction algorithm of human health index based on genetic algorithm and BP neural network |
| CN110291995A (en) * | 2019-05-24 | 2019-10-01 | 丁韩 | Lamp light control method and device applied to pet supplies |
| CN110570610A (en) * | 2019-08-21 | 2019-12-13 | 安徽正风建设工程有限公司 | intelligent light guiding system of science and technology center |
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| CN113079613A (en) * | 2021-04-09 | 2021-07-06 | 江苏安岚特智能科技有限公司 | Intelligent lamp pole centralized dispatching method |
| CN113386705A (en) * | 2021-07-20 | 2021-09-14 | 中国重汽集团济南动力有限公司 | Intelligent control system and control method for snow wax vehicle |
| CN113597062A (en) * | 2021-08-06 | 2021-11-02 | 江苏艾通信息科技有限公司 | Intelligent auxiliary lighting control system for building |
| CN116234111A (en) * | 2023-02-08 | 2023-06-06 | 北京富润成照明系统工程有限公司 | Street lamp brightness control system and control method |
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| CN113079613A (en) * | 2021-04-09 | 2021-07-06 | 江苏安岚特智能科技有限公司 | Intelligent lamp pole centralized dispatching method |
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