CN109902402B - Intelligent lighting dimming control method based on multi-environment parameters - Google Patents
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Abstract
The invention relates to an intelligent lighting dimming control method based on multi-environment parameters, and belongs to the field of intelligent lighting control. The method constructs a relation model of 5 environmental parameters of illumination brightness, traffic flow, pedestrian flow, humidity, PM2.5 and environmental illumination. Firstly, acquiring n groups of environment parameter values, and constructing an environment parameter matrix; then normalizing the environment parameter matrix to obtain a normalized matrix, and obtaining an entropy weight of each environment parameter by using an entropy weight method according to the normalized matrix; secondly, obtaining a subjective weight of each environmental parameter by using an analytic hierarchy process; combining and weighting the entropy weight and the subjective weight of each environment parameter to obtain a comprehensive weight of each environment parameter; and finally, weighting the normalized environmental parameter values and the comprehensive weight of the environmental parameters to obtain an illumination brightness judgment value, and further carrying out illumination brightness regulation and control. The invention establishes the logical relation between the multi-environment parameter and the illumination brightness control, and achieves the purpose of adaptive dimming control.
Description
Technical Field
The invention belongs to the field of intelligent lighting control, and relates to an intelligent lighting dimming control method based on multiple environment parameters.
Background
In the research and construction of smart cities, smart lighting is an important component of urban road construction in smart cities, and the increase and decrease of the problems of lighting energy consumption, traffic jam, environmental emergencies and the like are directly influenced by the intelligent control and management mode efficiency.
At present, although experts at home and abroad develop corresponding products aiming at the field of intelligent street lamps, for example: in 7 months in 2017, 26 intelligent street lamps of a new generation of an internet of things communication technology NB-IOT adopted by Pudong in Shanghai perform trial operation; in 2015, 7 months, information applications such as illumination energy conservation, video monitoring, wireless WIFI, charging piles and information pushing are integrated in the Hangzhou city to perform test run; in 2016, 12 months, a batch of intelligent street lamps integrating seven functions of illumination, air monitoring, micro base stations, wiFi equipment, video monitoring, information release, electric vehicle charging and the like are lighted on the Beijing street; in 2016, the U.S. los angeles government planned to intelligently network street lamps all together and exchange all street high-voltage lamps for LED lamps; nearly 1000 street lamps are added into an intelligent charging pile for use in Berlin Germany; the uk MK stadium uses the schrader intelligent integrated light to provide multi-functionality services to nearby citizens. However, the research of experts and scholars at home and abroad in the field of intelligent lighting dimming is relatively less, and meanwhile, with the increasing complexity of road environment conditions, the street lamp lighting control mode with the same brightness is difficult to deal with various emergency situations. Therefore, there is a need to research intelligent lighting dimming strategies to help management and service of intelligent lighting by related management personnel.
Disclosure of Invention
In view of the above, the present invention provides an intelligent lighting dimming control method based on multiple environmental parameters, which constructs a model of the environmental parameters and the lighting brightness to achieve the purpose of adaptive brightness adjustment and improve the lighting management efficiency.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent lighting dimming control method based on multiple environmental parameters constructs a relational model of lighting brightness and 5 environmental parameters of traffic flow, pedestrian flow, humidity, PM2.5 and environmental illumination, and specifically comprises the following steps:
s1: analyzing the relation between the environmental parameters and the road illumination brightness, and determining the environmental parameters related to the illumination brightness; the environmental parameters related to the illumination brightness comprise vehicle flow, people flow, humidity, PM2.5 and environmental illumination;
s2: acquiring n groups of environment parameter values, constructing an environment parameter matrix X', and normalizing the environment parameter matrix to obtain a normalized matrix X;
s3: calculating entropy weight alpha of each environment parameter according to the normalized matrix X j ；
S4: obtaining a judgment matrix Y according to the importance degree of different environment parameters, and determining the subjective weight theta of each environment parameter by using an analytic hierarchy process j ；
S5: the subjective weight theta of the jth environmental parameter is calculated j And entropy weight alpha j The combined weighting is carried out to obtain the comprehensive weight omega of the jth environmental parameter j ；
S6: weighting the normalized environmental parameter values and the comprehensive weight value of each environmental parameter to obtain the illumination brightness judgment value G of the group of environmental parameters i ；
S7: the lighting equipment judges the value G according to the lighting brightness i And adjusting the illumination brightness to a corresponding brightness value L in the interval.
Further, the step S2 specifically includes the following steps:
s21: acquiring n groups of environment parameter values, and constructing an environment parameter matrix X' as follows:
wherein, x' ij J, i =1,2,3, …, n, j =1,2,3,4,5, which represents the j-th environmental parameter value of the i-th group, the environmental parameter value refers to the acquired values of the traffic flow, the human flow, the humidity, the PM2.5, and the ambient illuminance;
s22: because the environmental parameter value units in the environmental parameter matrix X 'are not uniform, and the data cannot be directly compared, the matrix X is obtained after normalization processing is carried out on the environmental parameter matrix X':
the normalization formula is as follows:
positive parameters are as follows: x is the number of ij ＝(x′ ij -min(x′ j ))/(max(x′ j )-min(x′ j ))；
Inverse parameters: x is the number of ij ＝(max(x′ j )-x′ ij )/(max(x′ j )-min(x′ j ))；
Wherein, max (x' j ) Represents the element with the maximum value in the j-th column element in the environment parameter matrix X ', min (X' j ) The element with the smallest value in the jth column element in the environment parameter matrix X' is shown. The positive parameter represents that the illumination brightness value is correspondingly increased along with the increase of the environmental parameter value, and the inverse parameter represents that the illumination brightness value is correspondingly reduced along with the increase of the environmental parameter value; in all the above environmental parameters, only the environmental illumination is an inverse parameter, that is, the environmental illumination value is reduced, the brightness of the street lamp is increased, and other environmental parameters are positive parameters.
Further, the step S3 specifically includes the following steps:
s31: determining entropy e of jth parameter j Entropy value e j The calculation formula of (a) is as follows:
wherein the content of the first and second substances,
k is related to the number of sets n of environmental parameter values, and k is calculated as follows:
k＝1/lnn
s32: entropy value e of j parameter to be obtained j Calculation formula with entropy weight is substituted to obtain entropy weight alpha of j item index j Entropy weight α j The calculation formula of (a) is as follows:
further, the step S4 specifically includes the following steps:
s41: the environmental parameters of illumination, traffic flow, pedestrian flow, PM2.5 and humidity are respectively numbered as A 1 、A 2 、A 3 、A 4 、A 5 And each environment parameter has an importance degree value relative to another environment parameter, and a judgment matrix Y is constructed according to the importance degree values between the parameters:
wherein y is ij Representing an environmental parameter A i For the environmental parameter A j The importance level values of (1), slightly important (3), obviously important (5), strongly important (7) and extremely important (9,2, 4, 6 and 8) are importance level values between the importance levels, and y ji Is y ij The reciprocal of (i.e. y) ji ＝1/y ij ；
S42: according to the judgment matrix Y, an Analytic Hierarchy Process (AHP) is utilized to obtain the subjective weight of each environment parameter as follows:
θ＝{θ 1 ,θ 2 ,…,θ 5 }
further, the step S5 specifically includes: according to the jth environmentEntropy weight of parameter alpha j And the subjective weight theta j Obtaining the comprehensive weight omega of the jth environmental parameter j ，ω j The calculation formula of (a) is as follows:
further, the step S6 specifically includes: the comprehensive weight value omega j Weighting the normalized matrix X to obtain the illumination brightness judgment value G of the ith group of environmental parameters i ，G i The calculation formula of (a) is as follows:
further, the step S7 specifically includes: obtaining the illumination brightness judgment value G by the relation model of brightness i ，G i Will fall in 4 illumination zones 0,G 1 ]、[G 1 ，G 2 ]、[G 2 ，G 3 ]、[G 3 ，G 4 ]In the lighting interval, 4 lighting brightness values L are respectively corresponding to 4 lighting intervals 1 、L 2 、L 3 、L 4 The lighting device will judge the value G according to the lighting brightness i The brightness of the street lamp is adaptively adjusted to a corresponding brightness value in the illumination interval where the value is located.
The invention has the beneficial effects that: the invention establishes the logical relation between the environmental parameter information acquisition and the illumination brightness adjustment, provides a self-adaptive brightness adjustment strategy, and can effectively improve the road illumination management efficiency and the automation management degree.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a flowchart of the illumination brightness determination value acquisition;
fig. 2 is a diagram of a hierarchy of environmental parameters and illumination levels.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, considering that some environmental parameters have a certain relationship with The adjustment of The illumination brightness of The street lamp, a model of relationship between The illumination brightness of The street lamp and 5 environmental parameters of traffic flow, pedestrian flow, PM2.5, humidity, and environmental illumination is constructed based on The relationship, and an adaptive brightness adjustment strategy is proposed. The flow of determining the illumination brightness determination value is shown in fig. 1.
In the analytic hierarchy process, the illumination brightness and 5 environmental parameters construct a three-layer hierarchy, which is shown in fig. 2.
The embodiment provides an intelligent lighting dimming control method based on multi-environment parameters, which specifically comprises the following steps:
s1: analyzing the relation between the environment parameters and the road illumination brightness, and determining the environment parameters related to the illumination brightness; the environment parameters related to the illumination brightness are vehicle flow, people flow, humidity, PM2.5 and environment illumination;
s2: acquiring n groups of environment parameter values, constructing an environment parameter matrix X', and normalizing the environment parameter matrix to obtain a normalized matrix X, wherein the method comprises the following steps:
s21: the environment parameter matrix X' constructed by the n groups of environment parameter values is as follows:
wherein, x' ij J, i =1,2,3, …, n, j =1,2,3,4,5, which represents the j-th environmental parameter value of the i-th group, the environmental parameter value refers to the acquired values of the traffic flow, the human flow, the humidity, the PM2.5, and the ambient illuminance;
the value of n is required to be greater than or equal to 2, that is, n is greater than or equal to 2, and the larger the value of n is, the closer the entropy weight value obtained finally is to the reality, in the following description, by way of example, the selection of n sets of environment parameter matrices X' is illustrated, assuming that two sets of environment parameter values are obtained, and the values of the first set of traffic flow, pedestrian flow, humidity, PM2.5, and environment illumination are respectively 4 (vehicle/minute), 6 (human/minute), 50%, and 30 (ug/m) 3 ) 20 (lx), the values of the traffic flow, the passenger flow, the humidity, the PM2.5 and the ambient illumination of the first group of the second group are respectively 5 (vehicle/min), 8 (human/min), 76% and 16 (ug/m) 3 ) 14 (lx), the environment parameter matrix X' is constructed as follows:
s22: because the environmental parameter value units in the environmental parameter matrix X 'are not uniform, and the data cannot be directly compared, the matrix X is obtained after normalization processing is carried out on the environmental parameter matrix X':
the normalization formula is as follows:
positive parameters are as follows: x is a radical of a fluorine atom ij ＝(x′ ij -min(x′ j ))/(max(x′ j )-min(x′ j ))；
Inverse parameters: x is the number of ij ＝(max(x′ j )-x′ ij )/(max(x′ j )-min(x′ j ))；
Wherein, max (x' j ) Represents the element with the maximum value in the j-th column element in the environment parameter matrix X ', min (X' j ) The element with the smallest value in the jth column element in the environment parameter matrix X' is shown. The positive parameter means that the illumination brightness value is correspondingly increased along with the increase of the environmental parameter value, and the inverse parameter means that the illumination brightness value is correspondingly reduced along with the increase of the environmental parameter value; of all the above environmental parameters, only the ambient illumination is the inverse parameter, i.e. the ambient illumination value decreases, the street lamp brightness increases, and the other environmental parameters areA positive parameter.
S3: calculating entropy weight alpha of each environment parameter according to the matrix X after normalization j The method comprises the following steps:
s31: determining the entropy e of the jth parameter j Entropy value e j The calculation formula of (a) is as follows:
wherein the content of the first and second substances,
k is related to the number of sets n of environmental parameter values, and k is calculated as follows:
k＝1/lnn
s32: entropy e of j parameter to be obtained j Calculating the entropy weight alpha of the j index by the calculation formula with the entropy weight j Entropy weight α j The calculation formula of (a) is as follows:
s4: obtaining a judgment matrix Y according to the importance degree of different environment parameters, and determining the subjective weight theta of each environment parameter by using an analytic hierarchy process j The method comprises the following steps:
s41: the environmental parameters of illumination, traffic flow, pedestrian flow, PM2.5 and humidity are respectively numbered as A 1 、A 2 、A 3 、A 4 、A 5 And each environment parameter has an importance degree value relative to another environment parameter, and a judgment matrix Y is constructed according to the importance degree values between the parameters:
wherein y is ij Representing an environmental parameter A i For the environmental parameter A j Importance degree value, importance degreeThe values are divided into importance degree values between 1 for equal importance, 3 for slight importance, 5 for obvious importance, 7 for strong importance and 9,2, 4, 6 and 8 for extreme importance, y ji Is y ij The reciprocal of (i.e. y) ji ＝1/y ij . An importance degree table is listed for the environment parameters related to the brightness, as shown in table 1;
table 1 degree of importance between environmental parameters regarding brightness
| Degree of importance | Illuminance of light | Flow rate of vehicle | Flow of people | PM2.5 | Humidity |
| Illuminance of light | Of equal importance (1) | Of slight importance (3) | Of obvious importance (5) | Of strong importance (7) | Of extreme importance (9) |
| Flow rate of vehicle | Of equal importance (1) | Of slight importance (3) | Of obvious importance (5) | Of great importance (7) | |
| Flow of people | Of equal importance (1) | Of slight importance (3) | Of obvious importance (5) | ||
| PM2.5 | Of equal importance (1) | Of slight importance (3) | |||
| Humidity | Of equal importance (1) |
The white portion in table 1 is the reciprocal of the corresponding degree of importance.
S42: and according to the judgment matrix Y, solving the subjective weight of each environmental parameter by using an analytic hierarchy process as follows:
θ＝{θ 1 ,θ 2 ,…,θ 5 }
s5: entropy weight alpha according to environmental parameters j And the subjective weight theta j Obtaining the comprehensive weight omega of the environmental parameter j ，ω j The calculation formula of (a) is as follows:
wherein ω is j Is an environmental parameter A j Integrated weight relative to illumination brightness.
S6: the comprehensive weight value omega j Weighting the matrix X after normalization to obtain a judgment value G of illumination brightness i ，G i The calculation formula of (a) is as follows:
wherein G is i Is the illumination brightness judgment value of the ith set of environmental parameters.
S7: obtaining the illumination brightness judgment value G by the relation model of brightness i ，G i Will fall in 4 illumination zones 0,G 1 ]、[G 1 ，G 2 ]、[G 2 ，G 3 ]、[G 3 ，G 4 ]In the lighting interval, 4 lighting brightness values L are respectively corresponding to 4 lighting intervals 1 、L 2 、L 3 、L 4 The lighting device will judge the value G according to the lighting brightness i The brightness of the street lamp is adaptively adjusted to a corresponding brightness value in the illumination interval where the value is located.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (7)
1. An intelligent lighting dimming control method based on multi-environment parameters is characterized by comprising the following steps:
s1: analyzing the relation between the environment parameters and the road illumination brightness, and determining the environment parameters related to the illumination brightness; the environment parameters related to the illumination brightness are vehicle flow, people flow, humidity, PM2.5 and environment illumination;
s2: acquiring n groups of environment parameter values, constructing an environment parameter matrix X', and normalizing the environment parameter matrix to obtain a normalized matrix X;
s3: calculating entropy weight alpha of each environment parameter according to the normalized matrix X j ；
S4: obtaining a judgment matrix Y according to the importance degree of different environment parameters, and determining the subjective weight theta of each environment parameter by using an analytic hierarchy process j ；
S5: the subjective weight theta of the jth environmental parameter is calculated j And entropy weight alpha j The combined weighting is carried out to obtain the comprehensive weight omega of the jth environmental parameter j ；
S6: weighting the normalized environmental parameter values and the comprehensive weight value of each environmental parameter to obtain the illumination brightness judgment value G of the group of environmental parameters i ；
S7: the lighting equipment judges the value G according to the lighting brightness i And adjusting the illumination brightness to a corresponding brightness value L in the interval.
2. The method as claimed in claim 1, wherein the step S2 specifically comprises the steps of:
s21: acquiring n groups of environment parameter values, and constructing an environment parameter matrix X' as follows:
wherein, x' ij The j-th environmental parameter value representing the i-th group, i =1,2,3, …, n, j =1,2,3,4,5, refers to the acquired traffic flow, pedestrian flow, wet trafficThe values of degree, PM2.5, and ambient illuminance;
s22: because the units of all the environmental parameter values in the environmental parameter matrix X 'are not uniform, and the data cannot be directly compared, the environmental parameter matrix X' is normalized to obtain the matrix X:
the normalization formula is as follows:
positive parameters are as follows: x is a radical of a fluorine atom ij ＝(x′ ij -min(x′ j ))/(max(x′ j )-min(x′ j ))；
Inverse parameters: x is the number of ij ＝(max(x′ j )-x′ ij )/(max(x′ j )-min(x′ j ))；
Wherein, max (x' j ) Represents the element with the maximum value in the j-th column element in the environment parameter matrix X ', min (X' j ) Representing the element with the smallest value in the jth column element in the environment parameter matrix X'; the positive parameter indicates that the illumination brightness value is correspondingly increased along with the increase of the environmental parameter value, and the inverse parameter indicates that the illumination brightness value is correspondingly reduced along with the increase of the environmental parameter value; in all the above environmental parameters, only the environmental illumination is an inverse parameter, that is, the environmental illumination value is reduced, the brightness of the street lamp is increased, and other environmental parameters are positive parameters.
3. The method as claimed in claim 2, wherein the step S3 comprises the following steps:
s31: determining the entropy e of the jth parameter j Entropy value e j The calculation formula of (a) is as follows:
wherein the content of the first and second substances,
k is related to the number of sets n of environmental parameter values, and k is calculated as follows:
k＝1/lnn
s32: entropy value e of j parameter to be obtained j Calculating the entropy weight alpha of the j index by the calculation formula with the entropy weight j Entropy weight α j The calculation formula of (c) is as follows:
4. the method as claimed in claim 3, wherein the step S4 comprises the following steps:
s41: the environmental parameters of illumination, traffic flow, pedestrian flow, PM2.5 and humidity are respectively numbered as A 1 、A 2 、A 3 、A 4 、A 5 Wherein each environmental parameter has an importance degree value relative to another environmental parameter, and a judgment matrix Y is constructed according to the importance degree values between the parameters:
wherein y is ij Representing an environmental parameter A i For the environmental parameter A j The importance level values of (1), slightly important (3), obviously important (5), strongly important (7) and extremely important (9,2, 4, 6 and 8) are importance level values between the importance levels, and y ji Is y ij The reciprocal of (i.e. y) ji ＝1/y ij ；
S42: according to the judgment matrix Y, an Analytic Hierarchy Process (AHP) is utilized to obtain the subjective weight of each environment parameter as follows:
θ＝{θ 1 ,θ 2 ,…,θ 5 }。
5. the method as claimed in claim 4, wherein the step S5 is specifically as follows: entropy weight alpha according to jth environmental parameter j And the subjective weight theta j Obtaining the comprehensive weight omega of the jth environmental parameter j ，ω j The calculation formula of (a) is as follows:
6. the method as claimed in claim 5, wherein the step S6 is specifically as follows: the comprehensive weight value omega j Weighting the normalized matrix X to obtain the illumination brightness judgment value G of the ith group of environmental parameters i ，G i The calculation formula of (a) is as follows:
7. the method as claimed in claim 6, wherein the step S7 is specifically as follows: obtaining the illumination brightness judgment value G by the relation model of brightness i ，G i Will fall in 4 illumination zones 0,G 1 ]、[G 1 ，G 2 ]、[G 2 ，G 3 ]、[G 3 ，G 4 ]In the lighting interval, 4 lighting brightness values L are respectively corresponding to 4 lighting intervals 1 、L 2 、L 3 、L 4 The lighting device will judge the value G according to the lighting brightness i The brightness of the street lamp is adaptively adjusted to a corresponding brightness value in the illumination interval where the value is located.
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| CN111785051B (en) * | 2020-06-29 | 2021-06-25 | 广东荣文科技集团有限公司 | Road condition indication control method, intelligent street lamp and related products |
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| CN114126159B (en) * | 2021-11-12 | 2024-02-23 | 广东海洋大学 | Extreme weather-oriented intelligent street lamp dimming method, system and storage medium |
| CN115802535B (en) * | 2023-01-30 | 2023-12-26 | 承德政通电子工程有限公司 | Environment self-adaptive LED illumination method based on Internet of things |
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