CN114126159B - Extreme weather-oriented intelligent street lamp dimming method, system and storage medium - Google Patents

Extreme weather-oriented intelligent street lamp dimming method, system and storage medium Download PDF

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CN114126159B
CN114126159B CN202111341764.3A CN202111341764A CN114126159B CN 114126159 B CN114126159 B CN 114126159B CN 202111341764 A CN202111341764 A CN 202111341764A CN 114126159 B CN114126159 B CN 114126159B
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street lamp
matrix
dimming
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姜淏予
徐今强
葛泉波
赵小龙
刘洺辛
罗朋
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Guangdong Ocean University
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Abstract

The invention discloses a smart street lamp dimming method, a smart street lamp dimming system and a smart street lamp dimming storage medium for extreme weather, which are designed according to a sensor on the basis of conventional configuration of smart street lamps, wherein the smart street lamp dimming system and the smart street lamp dimming method capable of coping with extreme weather changes are provided with cloud edge cooperativity.

Description

Extreme weather-oriented intelligent street lamp dimming method, system and storage medium
Technical Field
The invention relates to the technical field of computers, in particular to a smart street lamp dimming method, a smart street lamp dimming system and a smart street lamp dimming storage medium for extreme weather.
Background
In recent years, with global climate change, the occurrence probability of extreme weather is gradually rising, and economic damage and casualties are caused to a plurality of cities in the world. Weather, such as city storm, can vary the city's natural illuminance from the sun up to about 90% at maximum within 1-2 minutes, and fluctuate many times a day. And the urban high-frequency and high-density traffic is seriously affected. The existing municipal lighting system mainly adopts a scattered timing control mode, and preset control requirements are achieved through various controllers arranged in a distribution box. The simple low-cost control method is not suitable for and fully utilizes the objective condition of the high-speed development of urban road hardware informatization, and has the defects that manual hysteresis intervention is needed under extreme conditions, continuous adjustment cannot be tracked, and the control scale is not fine.
Although numerical forecasting (NWP, numerical weather prediction) has been commonly used and gradually perfected in forecasting business in countries around the world since the 70 s of the 20 th century. However, the nonlinear interaction of all variables in the atmospheric motion makes a pure physical model difficult to achieve complete accuracy, and the resolution accuracy of urban street level cannot be achieved at present based on the existing tracking correction based on radar. In recent years, artificial intelligence methods based on deep neural networks are increasingly used for forecasting tasks of extreme weather, and it should be noted that the methods can make short-term forecasting with 1km resolution at minute intervals under the conditions of ideal topography factors and sufficient monitoring conditions. However, the purely data-driven approach is difficult to interpret on a model, and is highly dependent on the data quality of the input samples. And a plurality of limiting factors exist in the urban free arrangement of weather monitoring stations, and external interference can be generated in the activities of building layout, shielding of greening trees, urban construction and the like, so that the forecasting performance of the fitting model is affected. The problem of data quality is removed, the fusion difficulty of multi-source heterogeneous data exists based on an artificial intelligence method, huge calculation force is needed to serve as support, the method is more suitable for the fine forecast of special important targets at present, and the urban indiscriminate weather tracking dimming task is difficult to serve.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent street lamp adjuster for extreme weatherAn optical system, comprising: the physical acquisition module is used for acquiring the illuminance value and the ground reflection coefficient of the road surface below each street lamp in the urban area needing dimming, and acquiring the solar radiation light intensity at the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected at the top of the lamp post of the corresponding street lamp; the edge processing module is used for obtaining a dimming matrix I of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the area v The method comprises the steps of carrying out a first treatment on the surface of the A platform processing module for receiving the dimming matrix I v Updating and issuing dimming strategies T, T=I aiming at all street lamps in a control area v * M is D is C is W, wherein I v The system comprises a dimming matrix, a model matrix, a light intensity adjustment matrix and a control matrix, wherein the dimming matrix is mainly based on meteorological observation, the model matrix is formed by dividing and adjusting urban street blocks according to disaster types and grade forecast of meteorological, the decision matrix is used for bearing constraints from the aspect of an electric power system, and the model matrix is used for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
Preferably, the main risk matrix W is: Wherein the Q x Q matrix simulates a matrix projected by a affine transformation of street lamps spread in a desired dimmed urban area, wherein B ii For representing a sub-matrix of a block with a size of q×q, q is the projection of the minimum scale that can be resolved by the existing prediction system on W; the transfer matrix C is a data pair B of historical data and geographic data through correlation ii Fine resolution and adjustment, wherein ∈>Adjustment factor c ii ∈R + Wherein R is + Is a positive real set.
Preferably, the edge processing module uses any one of the lamps i to illuminate the road surface below the edge processing moduleDimming amount corresponding to the street lamp i>Is informed of the relation of->Wherein->Wherein->Is the estimated value of the light intensity from the solar radiation on the street lamp i, and the range of the estimated value is [0,I (L m )]，I(L m ) For the upper boundary of the starting of intermediate vision, L m For presetting the intermediate vision brightness, l is the height of a lamp post of the street lamp i, tau is the air turbidity, and w is E v1 Uncertainty parameter of real-time state, its size and +.>Positive correlation, ->The illuminance of the street lamp i to the road surface below the street lamp i.
Preferably, the physical acquisition module is used for acquiring the estimated value of the light intensity of the solar radiation on the street lamp iWherein->P solar For the output power of a photovoltaic cell panel arranged on the top of a lamp post of a street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, A pv K is the spectral luminous efficacy coefficient, I r As the ground reflection coefficient, when the photovoltaic cell panel is erected parallel to the ground, I r Is 0.
Preferably, the edge processing module also realizes estimation of tau through observation of adjacent street lamps by a video monitoring system carried under the street lampsGauge and obtain the observed value of road surface illuminanceSelecting Z adjacent street lamps in the visual field of the video monitoring equipment to estimate the air turbidity tau, and aiming at the air turbidity tau of the street lamp i i The method comprises the following steps: />Wherein Ω i The direction angle observed by the sensor mounted on the street lamp is d is the distance from the sensor to the ground, ii is the light intensity value received by the sensor, and +.>And the initial value of the light intensity collected by the ith street lamp.
The invention also discloses a smart street lamp dimming method facing extreme weather, which comprises the following steps:
s1, obtaining the illuminance value and the ground reflection coefficient of the road surface below each street lamp in the urban area needing dimming, and obtaining the solar radiation light intensity at the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected at the top of the lamp post of the corresponding street lamp;
s2, obtaining a dimming matrix I of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the area v ；
S3, receiving a dimming matrix I v Updating and issuing dimming strategies T, T=I aiming at all street lamps in a control area v * M is D is C is W, wherein I v The system comprises a dimming matrix, a model matrix, a light intensity adjustment matrix and a control matrix, wherein the dimming matrix is mainly based on meteorological observation, the model matrix is formed by dividing and adjusting urban street blocks according to disaster types and grade forecast of meteorological, the decision matrix is used for bearing constraints from the aspect of an electric power system, and the model matrix is used for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
Preferably, the main risk matrix W is:wherein the Q x Q matrix simulates a matrix projected by a affine transformation of street lamps spread in a desired dimmed urban area, wherein B ii For representing a sub-matrix of a block with a size of q×q, q is the projection of the minimum scale that can be resolved by the existing prediction system on W; the transfer matrix C is a data pair B of historical data and geographic data through correlation ii Fine resolution and adjustment, wherein ∈>Adjustment factor c ii ∈R + Wherein R is + Is a positive real set.
Preferably, the step S2 includes:
illuminance to road surface below by any one of the lamps iDimming amount corresponding to the street lamp i>Is known by the relation of (a)Wherein-> Is the estimated value of the light intensity from the solar radiation on the street lamp i, and the range of the estimated value is [0,I (L m )]，I(L m ) For the upper boundary of the starting of intermediate vision, L m For presetting the intermediate vision brightness, l is the height of a lamp post of the street lamp i, tau is the air turbidity, and w is E v1 Uncertainty parameter of real-time state, its size and +.>Positive correlation, ->The illuminance of the street lamp i to the road surface below the street lamp i.
Preferably, the step S2 further includes: obtaining an estimated value of the intensity of light from solar radiation on street lamp iWherein the method comprises the steps ofP solar For the output power of a photovoltaic cell panel arranged on the top of a lamp post of a street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, A pv K is the spectral luminous efficacy coefficient, I r As the ground reflection coefficient, when the photovoltaic cell panel is erected parallel to the ground, I r Is 0;
estimation of tau is achieved through observation of adjacent street lamps by a video monitoring system carried under the street lamps, and observation values of road surface illuminance are obtainedSelecting Z adjacent street lamps in the visual field of the video monitoring equipment to estimate the air turbidity tau, and aiming at the air turbidity tau of the street lamp i i The method comprises the following steps: />Wherein Ω i The direction angle observed by the sensor mounted on the street lamp is d is the distance from the sensor to the ground, ii is the light intensity value received by the sensor, and +.>And the initial value of the light intensity collected by the ith street lamp.
The invention also discloses a computer readable storage medium storing a computer program which when executed by a processor implements the steps of any of the methods described above.
The invention designs a cloud-edge cooperative energy-saving intelligent street lamp based on the conventional configuration of intelligent street lamps according to the sensorAiming at the influence analysis of extreme weather on road illumination, combining with the research on the basis of a hardware system of the intelligent street lamp, developing and specifically setting a risk item and a decision item of cloud control of a dimming model to form a specific collaborative strategy on the basis, providing a cloud-edge collaborative dimming decision model T and aiming at a dimming matrix I with real-time dynamic change of the edge side in the T v A state estimation and observation method under static conditions is provided. Particularly, for the air turbidity tau, a calculation method based on video monitoring equipment and a neighbor street lamp is provided, and the problem of directly calculating an extinction coefficient is avoided. For I solar Illuminance as a function of timeThe dynamic adjustment problem is that a dynamic system model with discrete illumination is given under a Kalman filtering theory framework, uncertainty and nonlinearity of the system are stripped from a state vector, and the iteration speed of the main iteration process and the edge calculation force adaptation is ensured.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
fig. 1 is a schematic structural diagram of a smart street lamp for dimming according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of steps of a smart street lamp dimming method for extreme weather according to an embodiment of the present invention.
FIG. 3 shows an embodiment of the invention of STF-STAKF combination based I v Algorithm flow schematic.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
In the present invention, unless explicitly specified and limited 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; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
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 invention belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. Likewise, the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
In recent years, with global climate change, the occurrence probability of extreme weather is gradually rising, and economic damage and casualties are caused to a plurality of cities in the world. For example, the weather such as urban storm can lead the maximum change of natural illuminance of the city from the sun to be about 90% within 1-2 min, and the natural illuminance fluctuates for many times in one day, thereby seriously affecting urban high-frequency and high-density traffic. The existing municipal lighting system mainly adopts a scattered timing control mode, and preset control requirements are achieved through various controllers arranged in a distribution box. The simple low-cost control method is not suitable for and fully utilizes the objective conditions of the high-speed development of urban road hardware informatization; the defects that manual hysteresis intervention is needed under extreme conditions, continuous adjustment cannot be tracked and the control scale is not fine exist.
As shown in fig. 1, the invention discloses an extreme weather oriented intelligent street lamp dimming system, which is used for finely adjusting the brightness of each street lamp in a city area. The intelligent street lamp 1 can comprise a lighting module 11, a video monitoring module 12, a power module 13, an environment monitoring module 14, a communication module 15 and an information release module 16, wherein the lighting module 11 is optionally provided with a light sensor or a photovoltaic panel, the brightness of a light source is adjusted through a lighting controller, the control of a single lamp is mainly and directly realized by a DC intelligent control power supply, the centralized control of a street is realized by a centralized controller in a power cabinet, and the edge controller has stronger operation capability, can smoothly process video streams and picture streams, and is beneficial to expanding various analysis functions by comprehensive multi-source data. The video monitoring module 12 and the environment monitoring module 14 are intelligent information bases of lamp posts, are provided with various sensor devices, and are mainly used for identifying and tracking urban security special targets and monitoring states of traffic flow and people flow in real time; environmental sensors are of many types and can measure temperature, humidity, particle concentration, wind speed, wind direction, air pressure, noise, etc. The information of weather, traffic and the like sensed or received by the modules can be transmitted to the big data cloud platform of the lighting system by the communication module.
Specifically, this smart street lamp dimming system towards extreme weather includes physical collection module, edge processing module and platform processing module, and wherein physical collection module is used for obtaining the illuminance value and the ground reflection coefficient of each street lamp below road surface in the urban area that need adjust luminance to through setting up the performance parameter, the mounted position and the conversion electric energy of photovoltaic cell board at the lamp pole top of corresponding street lamp obtain the solar radiation light intensity of this street lamp department. The edge processing module is used for obtaining a dimming matrix I of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the area v . A platform processing module for receiving the dimming matrix I v Updating and issuing dimming strategies T, T=I aiming at all street lamps in a control area v * M is D is C is W, wherein I v The system comprises a dimming matrix, a model matrix, a light intensity adjustment matrix and a control matrix, wherein the dimming matrix is mainly based on meteorological observation, the model matrix is formed by dividing and adjusting urban street blocks according to disaster types and grade forecast of meteorological, the decision matrix is used for bearing constraints from the aspect of an electric power system, and the model matrix is used for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
In this embodiment, the main risk matrix W is:the Q multiplied by Q square matrix simulates a square matrix formed by projecting street lamps which are distributed and arranged in the urban area required to be dimmed according to affine transformation; wherein B is ii To represent a sub-matrix of size q×q for a block, the physical meaning is close to that of a city; q is the projection of the smallest scale that can be resolved by the existing forecasting system on W, where the existing forecasting system is based mainly on the decisions of weather satellites and radars. The extent of response of a city block under extreme weather risk may vary due to its planning and infrastructure differences. Therefore, under the disaster type and grade forecast based on weather, the cloud platform can correlate the historical data with the GIS+BIM system pair B of the smart city ii Make finer disassemblyAnd (3) forming a transfer matrix C by the sum adjustment: the transfer matrix C is a data pair B of historical data and geographic data through correlation ii Fine resolution and adjustment, wherein ∈>Adjustment factor c ii ∈R + Wherein R is + Is a positive real set. C and W together form a risk item in a cooperative dimming T model, and the function is to endow differentiated dimming degrees based on the evaluation of the extreme weather influence of the individual urban street lamps.
The decision matrix D is mainly used to take on constraints from the power system etc., including some external considerations in terms of hardware controllability, grid scheduling and economy, and may be set to a 1-0 matrix. If the optimization operation needs to be performed on the D matrix in the use process of the subsequent model, the matrix Sigmoid can be:
In this embodiment, the influence of extreme weather types can be considered as required, and the existing LED illumination mode can adjust the color temperature according to weather and environmental changes, where different color temperatures have different S/P values and further have different mesopic brightness L m . Due to I v ＝∫L v dA cos θ, the difference in brightness adjustment targets affects the decision of light intensity adjustment. In order to make the dimming matrix I in the model v The behavior scale of (a) is uniform, so that the influence of the aspect needs to be normalized to a mode matrix M, matrix element M ii ∈(0，1]. W, C, D, M can be set based on cloud existing information and instructions of the intelligent light tube control system, I v Dynamic changes of extreme weather at the edge side need to be reflected in a centralized way, so that the regulation and control are completed by adopting a strong tracking algorithm in combination with related sensing data. And finally, developing and specifically setting risk items and decision items controlled by the cloud of the dimming model, and forming a cooperative strategy specific to the control of the luminous intensity of the intelligent street lamp on the basis of the pre-decision judgment of the cloud.
In the present embodiment, the evaluation target E of the entire control T Iota writable as target matrix T 2 -ι 1 Norm regularization form:wherein t is i The elements in the T are continuously arranged according to a certain geographic rule; defining a calibration matrix T * As an evaluation criterion value of T, < >>Is T * Element and t in (2) i Corresponding to the above. T (T) * The measurement of the road operation and maintenance operation is completed by an engineering vehicle carrying a professional instrument under corresponding working conditions, and the special instrument is related to the road type, the traffic flow, the road shape and the lamp arrangement according to the standard. In an ideal case, the dimming target T should be matched with the standard and measured value T * As close as possible, thus evaluating target E T Two norms including both->Form of the invention. But due to uncertainties in the model, standard and measurement processes, including inaccuracy and inaccuracy, and natural deviations in the control system transfer function exist. Thus according to the substantial characterization quantities T and T * Overfitting tendencies occur and a sparse one-norm |·| needs to be introduced into the regularization term 1 To limit its local flatness, i.e. the brightness of adjacent street lamps does not tend to change steeply, and needs to be as close as possible. χ is an adjustable hyper-parameter of the evaluation model to control the tendency of such proximity, which can be freely set according to preference. N (i) is a local neighborhood of i, and the neighborhood is determined by the field of view of a camera of the intelligent street lamp video monitoring module, and t j Is the light intensity of the street lamp in the field of view. Kappa (kappa) ij For affinity coefficients, the affinity coefficient calculation method can be written as a binuclear function:
Wherein sigma 1 And sigma (sigma) 2 To set a constant, the model is used to control the attention of the model to the light intensity differences and structures. ST (ST) i And ST (ST) j Each representing a multi-scale structure based on a priori weighting strategy at points i and j, in ST i The following are examples:
wherein the method comprises the steps ofIs a two-dimensional gaussian kernel with a multiscale parameter σ, σ e {1,2,3}.
In summary, the main purpose of evaluating and optimizing T is to adjust the decision terms and risk terms of the whole model. The parameters or the formulation strategies of the part are relatively fixed, offline delay can be allowed to be completed, and the intelligent street lamp operation and maintenance and management cloud platform based on big data of the whole model can be seen to be executable.
In this embodiment, the key to the overall T model is to handle I v Is a real-time dynamic change of (c). Based on the thought of data driving, the main task of the intelligent street lamp sun-shine amplitude sensor is to establish the acquisition quantity, the ground illuminance and the adjustment quantity I v Is a sample set of (1); wherein the adjustment amount I in real time v Can be directly obtained by an electrical system of the intelligent street lamp, and the light intensity I from solar radiation solar On the one hand, the intelligent street lamp can be directly acquired by an environment-weather sensing system of the intelligent street lamp or simply calculated. On the other hand, is more general, I solar The photovoltaic panel mounted on the top can also exert force P solar Estimating:P solar is the output power of the photovoltaic array, eta is the photoelectric conversion efficiency of the photovoltaic cell, A pv Is the total area of the photovoltaic cell panel. Wherein P is solar Is determined by the total solar radiation intensity received by the photovoltaic array, including ground reflection I re Converted into lightThe problem of the degree is to multiply the spectral luminous efficacy coefficient K. Because the intelligent street lamp photovoltaic panel is generally erected on the top of the lamp post and approaches to the horizontal, I is that r =0. Remainder I solar K is directly radiated by the sun with intensity I d And sky scattered radiation intensity I s The composition is as follows:
wherein I is ba The total amount of solar radiation that is local is dependent primarily on factors such as the declination angle of the sun, and is a known function of date. h is the solar altitude, < >>The angle θ is the inclination angle of the photovoltaic array. The atmospheric particle size alpha < 1 under non-extreme weather conditions is considered to be mainly represented by Rayleigh scattering effect, and the intensity I of the solar radiation received at this time solar Mainly by the intensity I of direct solar radiation d Constructing; and consider the intelligent street lamp top photovoltaic panel tilt angle θ→0, therefore ideal>And can be written as a representation of the local solar altitude angle: />In the preferred embodiment, it is evident that the height of the lamp post is negligible compared to the atmospheric height and the differentiated unit area is approximately equal to the sphericity, so that the illuminance of any street lamp i on the road surface below it is +. >
In this embodiment, the edge processing module uses any one of the lamps i to illuminate the road surface below the edge processing moduleDimming amount corresponding to the street lamp i>Is informed of the relation of->Wherein->Wherein->Is the estimated value of the light intensity from the solar radiation on the street lamp i, and the range of the estimated value is [0,I (L m )]，I(L m ) For the upper boundary of the starting of intermediate vision, L m For presetting the intermediate vision brightness, l is the height of a lamp post of the street lamp i, tau is the air turbidity, and w is E v1 Uncertainty parameters of real-time state, the size of which is mainly equal to +.>Positive correlation. Specifically, w is an uncertainty or noise of the parameter estimation, and is generally unknown, and therefore cannot be directly quantified, if an operation is required, it is assumed to be a normal (gaussian) distribution. />The illuminance of the street lamp i to the road surface below the street lamp i. Due to->Due to the defined reference standard, in order to generate the dimming matrix I v Calculate->The value of exp (- τl), i being the lamp pole height, needs to be obtained, the problem is to estimate the turbidity τ.
Wherein the edge processing module is also used for acquiring solar radiation from the street lamp iIs an estimated value of the light intensity of (2)Wherein the method comprises the steps ofP solar For the output power of a photovoltaic cell panel arranged on the top of a lamp post of a street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, A pv K is the spectral luminous efficacy coefficient, I r As the ground reflection coefficient, when the photovoltaic cell panel is erected parallel to the ground, I r Is 0.
In this embodiment, the edge processing module further realizes the estimation of the turbidity τ through the observation of the adjacent street lamp by the video monitoring system mounted under the street lamp, and obtains the observation value of the road illuminanceSelecting Z adjacent street lamps in the visual field of the video monitoring equipment to estimate the air turbidity tau, and aiming at the air turbidity tau of the street lamp i i The method comprises the following steps: />Wherein Ω i The direction angle observed by the sensor mounted on the street lamp is d is the distance from the sensor to the ground, ii is the light intensity value received by the sensor, and +.>And the initial value of the light intensity collected by the ith street lamp. Specifically, the air turbidity estimation value +.>The solution vector ω can be adjusted with a shallow neural network, such as ELM adjustment, to adjust its dynamic weights if data is accumulated. Or an exponentially weighted average may be used to give higher weight to the nearest street lamp, considering that the more recent street lamps are more intense, i.e. have higher signal to noise ratios. Irrespective of the influence of the road surface material, practically different road surfaces (cement concrete, asphalt) have no differenceWith the same brightness coefficient, the road surface is considered to have total diffuse reflection for convenience of discussion. The brightness of the road surface in the vertical direction of the nearest neighbor road lamp shot by the camera is I K The method comprises the steps of carrying out a first treatment on the surface of the To prevent outlier interference, the actual image processing can be replaced by the average value of the pixel area, noted +.>The corresponding direction angle is gamma. Then at camera height h c The conditions are as follows: />Where Θ is the transfer function, which is related to the specific parameter settings of the camera, thus +.>And->A constant proportional relationship is presented. The whole optimization strategy is limited by a norm and is smooth in local, so that the illumination of the nearest road surface collected by the camera can be used as the observation of the vertical illumination of the ith street lamp, and v is the uncertainty of the observation.
Consider I v As the extreme weather changes, I solar τ, etc. is a function of time, an estimate of which And is equal to a time series with a certain interval (step size) Δt. According to the aforementioned pair E v Discretization and expansion to the corresponding illuminance vector E of q street lamps:
v1：
v2：
wherein,is a state vector +.>For controlling the vector, process noise->Satisfy Gaussian distribution-> For observing vector, +.>For observing matrix, noise is observedSatisfy Gaussian distribution->Definition:
the recursive calculation formula can be listed with reference to the standard linear Kalman filter theory:
P k|k-1 ＝(1+Δt k ) 2 P k-1 +Δt k Q k
K k ＝P k|k-1 H T (HP k|k-1 H T +R k /Δt k ) -1
wherein the method comprises the steps ofIs->A priori estimates of +.>For a priori error covariance ++>Is a unitary matrix->For Kalman gain, +.>Is- >Posterior estimate of->To update the error covariance. The adaptive adjustment of step length is not considered, the standard Kalman filtering is used as a non-closed loop filtering, K k It is difficult to accommodate abrupt changes caused by extreme weather and to refine modelingThe cumulative error caused by the degree limitation makes the performance in tracking and response of actual dimming to be improved.
In this embodiment, preferably, for the above algorithm, strong tracking filtering needs to be introduced to solve the problems of inaccuracy of the model and abrupt change of environmental state, and the core idea is to introduce a real-time varying fading factor to adjust the covariance matrix of the prediction error, and the approximate suboptimal calculation mode is as follows:
wherein: zeta type toy 0 ＝tr[N k ]/tr[A k ]；
Wherein: n (N) k ＝V k -HΔt k Q k H T -βR k /Δt k ，A k ＝(1+Δt k ) 2 HP k-1 H T ；
Wherein, beta epsilon [1, ] is a weakening factor to be set, and is used for adjusting the smoothness of the state estimation value. V (V) k For the innovation covariance matrix:wherein ρ ε (0, 1)]Gamma is amnesia factor k The sequence of the innovation is as follows: />If it is selected to subtract factor xi k Acting on the error covariance matrix, there is the STF method: /> Under the constraint of orthogonality principle, the adjustment of the error covariance matrix is equivalent to an adjustment of no difference to the process noise, while if the cancellation factor is directly applied to the process noise, there is a multiple cancellation factor's STAKF method: / >Wherein:
to ensure P k|k-1 When Γ is k Symmetry when the diagonal elements are not equal, which can be written as: />Wherein->Is Γ k Obtaining +.A. by Cholesky trigonometric decomposition technique> By usingRepresents F k Elements of row i column on diagonal, +.>Similarly, the following steps are: />Thereby obtaining multiple fading factor matrix gamma k Or->The difference of the processing modes is reflected in the tracking effect on the mutation quantity, the STF tends to consider the system model to be credible, and the estimation error at the last moment needs to be changed; the STAKF tends to consider mutations caused by system model inaccuracy, both of which differ in processing ideas. In the research problem, photoelectric measurement means are mostly adopted, so that the method is easy to be interfered by the environment; and the study object is extremeWeather, the relevant parameters may change steeply within a processing interval, so that an efficient combination of the two tracking filters is required.
In the present embodiment, due to I solar The time-varying fitting functions such as τ and the like may have unsteady first and second order differentials, and in order to be able to follow the extreme weather variation trajectories as much as possible, it is necessary to emphasize Δt in the discrete model k And consider the judgment and update problems. First, the normalized distance of the error covariance matrix is defined By->Extraction of Deltat k The decision criteria are adjusted such that when Δt k At 0 → 0>Can take->Minimum d of diagonal element k And appoints a target threshold d * Then there is Deltat k Is set according to the adjustment rules of: />S is 0.1-0.2, mainly to assist convergence determination. The fine adjustment quantity epsilon is a set value, and the value range meets the constraint: />In order to meet the deployment requirement of the edge, the processing method of the dynamic system model intentionally avoids the nonlinearity of a state transition matrix, is beneficial to the self-adaptive adjustment and the real-time tracking of various super parameters in the model, and is disadvantageous in that the capability of distinguishing mutation causes from the model itself in a priori is lost. The coping strategy is set +.>Threshold of (2)/> The convention is vector +.>The maximum value of (2) then being in accordance with +.>The convexity of (a) adopts a different method of switching, the strategy is as follows, if +.>Then an STF strong tracking filtering strategy is adopted; if it is
And->Then an STF strong tracking filtering strategy is adopted; if it isAnd->Then a strong tracking filtering strategy of the star is employed.
It can be seen that for states where the breakthrough threshold or trend is ambiguous, this is equivalent to requiring a steady f to limit the tracking effect of the STF, achieving a conservative adjustment effect. In particular the outputs of the two filters in this caseDenoted as Y 1 ，Y 2 The method comprises the steps of carrying out a first treatment on the surface of the The final output result-> Fusion coefficient matrix->As a diagonal array, P in this example k Also is equal to eta k Diagonal arrays of the same size, so two filters P k Diagonal element->And eta k Diagonal element η i The three have corresponding relation and calculate eta i Obtaining eta k ：
In summary, a single filter can be regarded as η i Taking O or 1 toTarget illuminance with maintenance->Can be used to set U k+1 And get +.1 at time k+1>The whole cloud receives->Is arranged according to a convention rule to obtain a total dimming matrix I v The whole algorithm flow is shown in figure 3. .
The intelligent street lamp dimming system for extreme weather disclosed by the above designs a municipal lighting system tracking dimming system capable of coping with extreme weather changes with cloud edge cooperativity according to the sensor on the basis of the conventional configuration of intelligent street lamps specified by national standardsThe method specifically solves the following technical problems: aiming at the influence analysis of extreme weather on the road illumination, a cloud-edge cooperative dimming model is provided in combination with the research on the basis of an intelligent street lamp hardware system. And developing and specifically setting description is carried out on risk items and decision items of the cloud control of the dimming model, and an optimization target of T of the target matrix is given. Dimming matrix I for real-time dynamic change of edge side in T v A state estimation and observation method under static conditions is provided. Particularly, for the air turbidity tau, a calculation method based on video monitoring equipment and a neighbor street lamp is provided, and the problem of directly calculating an extinction coefficient is avoided. For I solar Illuminance as a function of timeThe dynamic system model with discrete illumination is given under the Kalman filtering theory framework, uncertainty and nonlinearity of the system are stripped from the state vector, and the iteration speed of the main iteration process and the edge calculation force adaptation is ensured. Based on the difficulty of state mutation priori judgment caused by the operation, the difference of the STF and STAKF two strong tracking filtering methods is considered, a strategy combining the STF and STAKF is provided, and the tracking track is optimized in step size.
Fig. 2 is a schematic diagram of another embodiment of a smart street lamp dimming method for extreme weather, which includes the following steps:
step S1, obtaining the illuminance value and the ground reflection coefficient of the road surface below each street lamp in the urban area needing dimming, and obtaining the solar radiation light intensity at the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected at the top of the lamp post of the corresponding street lamp.
Step S2, obtaining a dimming matrix I of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the area v 。
Wherein the step S2 includes: illuminance to road surface below by any one of the lamps iDimming amount corresponding to the street lamp i>Is informed of the relation of->Wherein-> Is the estimated value of the light intensity from the solar radiation on the street lamp i, and the range of the estimated value is [0,I (L m )]，I(L m ) For the upper boundary of the starting of intermediate vision, L m For presetting the intermediate vision brightness, l is the height of a lamp post of the street lamp i, tau is the air turbidity, and w is E v1 Uncertainty parameters of real-time state, the size of which is mainly equal to +.>Positive correlation. Specifically, w is an uncertainty or noise of the parameter estimation, and is generally unknown, and therefore cannot be directly quantified, if an operation is required, it is assumed to be a normal (gaussian) distribution.The illuminance of the street lamp i to the road surface below the street lamp i.
The step S2 further comprises obtaining an estimated value of the intensity of the light from the solar radiation on the street lamp iWherein the method comprises the steps ofP solar For the output power of a photovoltaic cell panel arranged on the top of a lamp post of a street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, A pv K is the spectral luminous efficacy coefficient, I r Is the groundSurface reflectance, I when the photovoltaic panel is erected parallel to the ground r Is 0; estimation of tau is achieved through observation of adjacent street lamps by a video monitoring system mounted under the street lamps, and observation values of road surface illuminance are obtained>Selecting Z adjacent street lamps in the visual field of the video monitoring equipment to estimate the air turbidity tau, and aiming at the air turbidity tau of the street lamp i i The method comprises the following steps: />Wherein Ω i The direction angle observed by the sensor mounted on the street lamp is d is the distance from the sensor to the ground, ii is the light intensity value received by the sensor,and the initial value of the light intensity collected by the ith street lamp. />
Step S3, receiving a dimming matrix I v Updating and issuing dimming strategies T, T=I aiming at all street lamps in a control area v * M is D is C is W, wherein I v The system comprises a dimming matrix, a model matrix, a light intensity adjustment matrix and a control matrix, wherein the dimming matrix is mainly based on meteorological observation, the model matrix is formed by dividing and adjusting urban street blocks according to disaster types and grade forecast of meteorological, the decision matrix is used for bearing constraints from the aspect of an electric power system, and the model matrix is used for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
The main risk matrix W is:the Q multiplied by Q square matrix simulates a square matrix projected by street lamps which are distributed and arranged in the urban area required to be dimmed according to affine transformation; wherein B is ii To represent a sub-matrix of size q×q for a block, the physical meaning is close to that of a city; q is the projection of the smallest scale that can be resolved by the existing forecasting system on W, where the existing forecasting system is based mainly on the decisions of weather satellites and radars. Urban blocks due to differences in planning and infrastructureAlso, the degree of response to extreme weather risks may vary. Therefore, under the disaster type and grade forecast based on weather, the cloud platform can correlate the historical data with the GIS+BIM system pair B of the smart city ii Finer resolution and adjustment are performed to form a transfer matrix C: the transfer matrix C is a data pair B of historical data and geographic data through correlation ii Fine resolution and adjustment, wherein ∈>Adjustment factor c ii ∈R + Wherein R is + Is a positive real set. C and W together form a risk item in a cooperative dimming T model, and the function is to endow differentiated dimming degrees based on the evaluation of the extreme weather influence of the individual urban street lamps.
The decision matrix D is mainly used to take on constraints from the power system etc., including some external considerations in terms of hardware controllability, grid scheduling and economy, and may be set to a 1-0 matrix. If the optimization operation needs to be performed on the D matrix in the use process of the subsequent model, the matrix Sigmoid can be:
In this embodiment, the influence of extreme weather types can be considered as required, and the existing LED illumination mode can adjust the color temperature according to weather and environmental changes, where different color temperatures have different S/P values and further have different mesopic brightness L m . Due to I v ＝∫L v dA cos θ, the difference in brightness adjustment targets affects the decision of light intensity adjustment. In order to make the dimming matrix I in the model v The behavior scale of (a) is uniform, so that the influence of the aspect needs to be normalized to a mode matrix M, matrix element M ii ∈(0，1]. W, C, D, M can be set based on cloud existing information and instructions of the intelligent light tube control system, I v Dynamic changes of extreme weather at the edge side need to be reflected in a centralized way, so that the regulation and control are completed by adopting a strong tracking algorithm in combination with related sensing data. Finally, risk through cloud control of dimming model is formedThe item and decision item are expanded and specifically arranged, and a cooperative strategy specifically aiming at the intelligent street lamp luminous intensity control is formed on the basis of the pre-decision judgment of the cloud.
It should be noted that, in the present specification, the foregoing embodiments are described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the smart street lamp dimming method facing extreme weather disclosed in the embodiment, the smart street lamp dimming system facing extreme weather disclosed in the previous embodiment corresponds to the smart street lamp dimming system facing extreme weather, so that the description is simpler, and relevant parts only need to be referred to in the description of the method section.
The invention also provides an extreme weather oriented intelligent street lamp dimming device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the extreme weather oriented intelligent street lamp dimming method described in the embodiments when executing the computer program.
The extreme weather oriented intelligent street lamp dimming device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of an extreme weather oriented intelligent street lamp dimmer apparatus and does not constitute a limitation of an extreme weather oriented intelligent street lamp dimmer apparatus device, which may include more or less components than illustrated, or may combine certain components, or different components, e.g., the extreme weather oriented intelligent street lamp dimmer apparatus device may further include an input output device, a network access device, a bus, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the extreme weather oriented intelligent streetlamp dimming appliance, and connects various parts of the whole extreme weather oriented intelligent streetlamp dimming appliance by using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the extreme weather oriented intelligent street lamp dimming device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the memory may include a high-speed random access memory, and may further include a nonvolatile memory such as a hard disk, a memory, a plug-in type hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other volatile solid-state storage device.
The data management method of the intelligent street lamp dimming device facing extreme weather can be stored in a computer readable storage medium if the data management method is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, the present invention may implement all or part of the above-described embodiment of the method, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above-described embodiments of the extreme weather oriented intelligent street lamp dimming method. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
In summary, the foregoing description is only of the preferred embodiments of the present invention, and all equivalent changes and modifications made in accordance with the claims should be construed to fall within the scope of the invention.

Claims (3)

1. An extreme weather oriented intelligent street lamp dimming system, comprising:
the physical acquisition module is used for acquiring the illuminance value and the ground reflection coefficient of the road surface below each street lamp in the urban area needing dimming, and acquiring the solar radiation light intensity at the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected at the top of the lamp post of the corresponding street lamp; acquisition street lampUpper light intensity estimate from solar radiation +. >Wherein，/>For being erected on street lamp->Output power of photovoltaic panel at the top of lamp pole, +.>For the photoelectric conversion efficiency of photovoltaic cells, +.>K is spectral luminous efficacy coefficient which is the total area of the photovoltaic cell panel>For the ground reflection coefficient +.>Is 0;
the edge processing module is used for obtaining a dimming matrix of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the areaThe method comprises the steps of carrying out a first treatment on the surface of the Through any one of the lamps->Illuminance on road surface below it +.>Is +.>Corresponding dimming amount->Is informed of the relation of->Wherein->Wherein->The estimated value of the light intensity from solar radiation on the street lamp i is in the range of +.>，/>For the upper boundary of the onset of mesopic vision, +.>For presetting the middle vision brightness->Is street lamp->Height of lamp pole of (2),>air turbidity>Is->Uncertainty parameter of real-time state, its size and +.>Positive correlation, ->Is street lamp->Illuminance to road surface below it; the observation of the adjacent street lamp by the video monitoring system carried under the street lamp is realized>And obtain the observed value of the road surface illuminanceSelecting a part of the video monitoring equipment in the field of view>Air turbidity of adjacent street lamps >Estimating, aiming at street lamp->Is the air turbidity of (2)The method comprises the following steps: />Wherein->The direction angle observed by the sensor mounted on the street lamp is d is the distance from the sensor to the ground,/for the street lamp>The intensity value received by the sensor, +.>Is->The initial value of the light intensity collected by each street lamp;
platform processing mouldA block for receiving the dimming matrixUpdating and issuing dimming strategies aiming at all street lamps in a control area>，/>Wherein->For dimming matrix +.>The method is based on weather observation, wherein C is a transfer matrix formed by splitting and adjusting city blocks according to disaster type and grade forecast of weather, and +.>For the decision matrix to be used to assume constraints from aspects of the power system +.>A mode matrix for normalizing the decision impact of the difference of the brightness adjustment targets on the light intensity adjustment; wherein the main risk matrix->The method comprises the following steps: />Wherein the->Matrix simulation street lamps distributed in urban area required to be dimmed are projected into matrix according to affine transformation, wherein +.>To represent a block of size +.>Is>The smallest dimension that can be resolved for the existing forecasting system is +.>Projection onto; the transfer matrix C is a data pair of +.>Fine resolution and adjustment, wherein Regulating factor->Wherein->Is a positive real set.
2. The intelligent street lamp dimming method facing extreme weather is characterized by comprising the following steps of:
s1, obtaining the illuminance value and the ground reflection coefficient of the road surface below each street lamp in the urban area needing dimming, and obtaining the solar radiation light intensity at the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected at the top of the lamp post of the corresponding street lamp; acquisition street lampUpper light intensity estimate from solar radiation +.>Wherein->，For being erected on street lamp->Output power of photovoltaic panel at the top of lamp pole, +.>For the photoelectric conversion efficiency of photovoltaic cells, +.>K is spectral luminous efficacy coefficient which is the total area of the photovoltaic cell panel>For the ground reflection coefficient +.>Is 0;
s2, obtaining a dimming matrix of the street lamps according to the obtained solar radiation light intensity of each street lamp, the illuminance of the road surface below each street lamp and the air turbidity of the areaThe method comprises the steps of carrying out a first treatment on the surface of the Through any one of the lamps->Illuminance on road surface below it +.>Is +.>Corresponding dimming amount->Is informed of the relation of->Wherein->Wherein->The estimated value of the light intensity from solar radiation on the street lamp i is in the range of +. >，/>For the upper boundary of the onset of mesopic vision, +.>For presetting the middle vision brightness->Is street lamp->Height of lamp pole of (2),>air turbidity>Is->Uncertainty parameter of real-time state, its size and +.>Positive correlation, ->Is street lamp->Illuminance to road surface below it; the observation of the adjacent street lamp by the video monitoring system carried under the street lamp is realized>And acquires the observation value +.>Selecting a part of the video monitoring equipment in the field of view>Air turbidity of adjacent street lamps>Estimating, aiming at street lamp->Air turbidity +.>The method comprises the following steps:wherein->The direction angle observed by the sensor carried by the street lamp is d is the distance from the sensor to the ground,the intensity value received by the sensor, +.>Is->The initial value of the light intensity collected by each street lamp;
s3, receiving a dimming matrixUpdating and issuing dimming strategies aiming at all street lamps in a control area>，Wherein->For dimming matrix +.>The method is based on weather observation, wherein C is a transfer matrix formed by splitting and adjusting city blocks according to disaster type and grade forecast of weather, and +.>For the decision matrix to be used to assume constraints from aspects of the power system +.>A mode matrix for normalizing the decision impact of the difference of the brightness adjustment targets on the light intensity adjustment; wherein the main risk matrix- >The method comprises the following steps: />Wherein the->Matrix simulation street lamps distributed in urban area required to be dimmed are projected into matrix according to affine transformation, wherein +.>To represent a block of size +.>Is>The smallest dimension that can be resolved for the existing forecasting system is +.>Projection onto; the transfer matrix C is a data pair of +.>Fine resolution and adjustment, wherein ∈>Regulating factor->Wherein->Is a positive real set.
3. A computer-readable storage medium storing a computer program, characterized in that: which computer program, when being executed by a processor, carries out the steps of the method according to claim 2.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101183359B1 (en) * 2012-04-13 2012-09-14 군산대학교산학협력단 Dimming control system for street lighting
CN104202881A (en) * 2014-09-10 2014-12-10 广州广日电气设备有限公司 Street lamp control system
KR101980049B1 (en) * 2018-08-20 2019-05-17 주식회사 아토텍 solar street lamp having color control facility
CN109902402A (en) * 2019-03-05 2019-06-18 重庆邮电大学 A kind of wisdom illumination dimming controlling method based on multi-environmental parameter
CN112085076A (en) * 2020-08-27 2020-12-15 河北智达光电科技股份有限公司 Decision-making method and device based on smart street lamp big data and terminal
CN112333870A (en) * 2020-09-24 2021-02-05 中国计量大学 Wisdom street lamp and control system thereof based on arduino
CN112540557A (en) * 2020-11-02 2021-03-23 杭州电子科技大学 5G application-oriented intelligent lamp pole integrated device and implementation method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101183359B1 (en) * 2012-04-13 2012-09-14 군산대학교산학협력단 Dimming control system for street lighting
CN104202881A (en) * 2014-09-10 2014-12-10 广州广日电气设备有限公司 Street lamp control system
KR101980049B1 (en) * 2018-08-20 2019-05-17 주식회사 아토텍 solar street lamp having color control facility
CN109902402A (en) * 2019-03-05 2019-06-18 重庆邮电大学 A kind of wisdom illumination dimming controlling method based on multi-environmental parameter
CN112085076A (en) * 2020-08-27 2020-12-15 河北智达光电科技股份有限公司 Decision-making method and device based on smart street lamp big data and terminal
CN112333870A (en) * 2020-09-24 2021-02-05 中国计量大学 Wisdom street lamp and control system thereof based on arduino
CN112540557A (en) * 2020-11-02 2021-03-23 杭州电子科技大学 5G application-oriented intelligent lamp pole integrated device and implementation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
一种基于无线传感器网络的城市灯光智能控制系统;周旋;吕红;黄国韬;;信息系统工程(第09期);全文 *
节能道路照明系统的无线智能控制设计;张志明;庄玮琳;余有灵;许维胜;王翠霞;陆继诚;谭学军;;照明工程学报(第02期);全文 *

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