CN114126159A - Extreme weather-oriented intelligent street lamp dimming method and system and storage medium - Google Patents

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

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CN114126159A
CN114126159A CN202111341764.3A CN202111341764A CN114126159A CN 114126159 A CN114126159 A CN 114126159A CN 202111341764 A CN202111341764 A CN 202111341764A CN 114126159 A CN114126159 A CN 114126159A
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street lamp
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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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Abstract

The invention discloses an extreme weather-oriented intelligent street lamp dimming method, system and storage medium, wherein the intelligent street lamp dimming system and method which have cloud edge cooperativity and can cope with extreme weather changes are designed according to a sensor on the basis of conventional configuration of an intelligent street lamp, a specific cooperation strategy is formed on the basis of a risk item and a decision item of cloud end control of a dimming model by unfolding and specifically setting, a cloud edge cooperativity dimming decision model is provided, the intelligent street lamp dimming system and system can be used for a city undifferentiated weather tracking dimming task, and the defects that manual hysteresis intervention is needed under extreme conditions, continuous adjustment cannot be tracked, and the control scale is not fine in the existing street lamp control are overcome.

Description

Extreme weather-oriented intelligent street lamp dimming method and system and storage medium
Technical Field
The invention relates to the technical field of computers, in particular to an extreme weather-oriented intelligent street lamp dimming method, an extreme weather-oriented intelligent street lamp dimming system and a storage medium.
Background
In recent years, along 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. The weather such as urban rainstorm can enable the natural illumination of the city from the sun to change by about 90% at most within 1-2 min, and the natural illumination fluctuates for many times in one day. The method has serious influence on urban high-frequency and high-density traffic. The existing municipal lighting system mainly adopts a dispersed timing control mode, and the preset control requirements are realized 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 informatization high-speed development of urban road hardware, and has the defects of manual hysteresis intervention, incapability of tracking continuous adjustment and non-fine control scale under extreme conditions.
Although numerical forecasting (NWP) has been commonly used and gradually improved in forecasting operations in countries around the world since the 70 th century. However, the non-linear interaction of variables in atmospheric motion makes it difficult for a pure physical model to be completely accurate, and the resolution accuracy of urban street level cannot be achieved at present based on the existing radar-based tracking correction. In recent years, artificial intelligence methods based on deep neural networks are increasingly used for forecasting tasks of extreme weather, and the methods can be used for short-term forecasting with 1km resolution at intervals of minutes under the conditions of ideal terrain factors and sufficient monitoring conditions. However, the pure data-driven method is difficult to interpret on a model and depends greatly on the data quality of the input sample. The weather monitoring stations are freely arranged in cities, so that a plurality of limiting factors exist, and the activities such as the layout of buildings, the shielding of green trees, city construction and the like can generate external interference, so that the forecasting performance of the fitting model is influenced. The method removes the problem of data quality, is based on an artificial intelligence method, has the fusion difficulty of multi-source heterogeneous data, needs huge calculation force as support, is more suitable for the refined forecast of special important targets at present, and is difficult to serve the undifferentiated weather tracking dimming task in cities.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an extreme weather-oriented intelligent street lamp dimming system, which comprises: the physical acquisition module is used for acquiring the illumination 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 intensity of the street lamp through the performance parameters, the installation position and the converted electric energy of a photovoltaic cell panel erected at the top of a lamp post of the corresponding street lamp; the edge processing module is used for acquiring a dimming matrix I of the street lamp according to the acquired solar radiation light intensity at each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv(ii) a A platform processing module for receiving the dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvThe matrix is a dimming matrix, W is a main risk matrix based on meteorological observation, C is a transfer matrix formed by splitting and adjusting city blocks according to forecast of disaster types and grades of meteorology, D is a decision matrix used for bearing constraints from the aspect of an electric power system, and M is a mode matrix for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
Preferably, the main risk matrix W is:
Figure BDA0003352383670000021
wherein the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation, wherein BiiA submatrix with the size of qxq representing a block, wherein q is the projection of the smallest scale which can be resolved by the existing forecasting system on W; the transition matrix C is formed by correlating historical data with geographic dataAccording to pair BiiObtained by performing fine resolution and adjustment, wherein
Figure BDA0003352383670000022
Adjustment factor cii∈R+Wherein R is+Is a positive real number set.
Preferably, the edge processing module illuminates the road surface below the edge processing module through any street lamp i
Figure BDA0003352383670000023
The dimming amount corresponding to the street lamp i
Figure BDA0003352383670000024
To know about
Figure BDA0003352383670000025
Wherein
Figure BDA0003352383670000026
Wherein
Figure BDA0003352383670000027
Is the estimated value of the light intensity from the solar radiation on the street lamp I, and the value range is [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Ev1Uncertainty parameter of real-time state, its magnitude and
Figure BDA0003352383670000031
the correlation is positive and the correlation is negative,
Figure BDA0003352383670000032
is 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 an estimated value of light intensity from solar radiation on the street lamp i
Figure BDA0003352383670000033
Wherein
Figure BDA0003352383670000034
PsolarFor the output power of the photovoltaic cell panel erected on the top of the lamp post of the street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0.
Preferably, the edge processing module further estimates the τ by observing the adjacent street lamp through a video monitoring system carried under the street lamp, and acquires an observed value of the road illumination
Figure BDA0003352383670000035
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 iiComprises the following steps:
Figure BDA0003352383670000036
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, Ii is the light intensity value received by the sensor,
Figure BDA0003352383670000037
the initial value of the light intensity collected for the ith street lamp.
The invention also discloses an extreme weather-oriented intelligent street lamp dimming method, 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 intensity of the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected on the top of the lamp post of the corresponding street lamp;
s2, obtaining the dimming matrix I of the street lamp according to the obtained solar radiation intensity of each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv；
S3, receiving a dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvThe matrix is a dimming matrix, W is a main risk matrix based on meteorological observation, C is a transfer matrix formed by splitting and adjusting city blocks according to forecast of disaster types and grades of meteorology, D is a decision matrix used for bearing constraints from the aspect of an electric power system, and M is a mode matrix for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
Preferably, the main risk matrix W is:
Figure BDA0003352383670000041
wherein the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation, wherein BiiA submatrix with the size of qxq representing a block, wherein q is the projection of the smallest scale which can be resolved by the existing forecasting system on W; the transfer matrix C is a pair B of historical data and geographic data by correlationiiObtained by performing fine resolution and adjustment, wherein
Figure BDA0003352383670000042
Adjustment factor cii∈R+Wherein R is+Is a positive real number set.
Preferably, the step S2 includes:
the illumination of the road surface below the street lamp is realized through any street lamp i
Figure BDA0003352383670000043
The dimming amount corresponding to the street lamp i
Figure BDA0003352383670000044
To know about
Figure BDA0003352383670000045
Wherein
Figure BDA0003352383670000046
Figure BDA0003352383670000047
Estimation of light intensity from solar radiation on street light iA value in the range of [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Ev1Uncertainty parameter of real-time state, its magnitude and
Figure BDA0003352383670000048
the correlation is positive and the correlation is negative,
Figure BDA0003352383670000049
is the illuminance of the street lamp i to the road surface below the street lamp i.
Preferably, the step S2 further includes: obtaining an estimate of the intensity of light from solar radiation on street light i
Figure BDA00033523836700000410
Wherein
Figure BDA00033523836700000411
PsolarFor the output power of the photovoltaic cell panel erected on the top of the lamp post of the street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0;
the estimation of tau is realized by observing the adjacent street lamp through the video monitoring system carried under the street lamp, and the observed value of the road illumination is obtained
Figure BDA00033523836700000412
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 iiComprises the following steps:
Figure BDA00033523836700000413
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, Ii is the light intensity value received by the sensor,
Figure BDA00033523836700000414
the initial value of the light intensity collected for the ith street lamp.
The invention also discloses a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as set forth in any one of the above.
The invention provides a cloud-edge cooperative dimming decision model T, which is designed on the basis of conventional configuration of a smart street lamp according to a sensor, has cloud-edge cooperative light dimming system and method for dealing with extreme weather change in the market direction, analyzes the influence of the extreme weather on the road surface illumination, combines the research on the basis of a hardware system of the smart street lamp, develops and specifically sets a risk item and a decision item for cloud control of the light dimming model, forms a specific cooperative strategy on the basis of the risk item and the decision item, provides the cloud-edge cooperative light dimming decision model T, and aims at a light dimming matrix I with real-time dynamic change at the edge side in the TvA 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 adjacent street lamps is provided, and the problem of directly calculating the extinction coefficient is solved. For IsolarIlluminance varying with tau over time
Figure BDA0003352383670000051
And (3) dynamically adjusting the problem, providing a dynamic system model for illumination discretization under a Kalman filtering theory framework, and stripping the uncertainty and nonlinearity of the system from a state vector to ensure the iteration speed of the main iteration process and the edge calculation force adaptation.
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.
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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 embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent street lamp for dimming according to an embodiment of the present invention.
Fig. 2 is a schematic step diagram of an extreme weather-oriented intelligent street lamp dimming method according to an embodiment of the present invention.
FIG. 3 shows an example of an STF-STAKF combination-based I disclosed in an embodiment of the present inventionvAnd (4) an algorithm flow schematic diagram.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, "above" or "below" a first feature means that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact with each other via another feature therebetween. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly under and obliquely below the second feature, or simply meaning that the first feature is at a lesser elevation than the second feature.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and similar terms in the description and claims of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Also, the use of 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, along 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 natural illumination of the city from the sun can be changed by about 90% at most in the weather of urban rainstorm and the like within 1-2 min, and the urban natural illumination fluctuates for many times in one day, so that the urban high-frequency and high-density traffic is seriously influenced. The existing municipal lighting system mainly adopts a dispersed timing control mode, and the preset control requirements are realized through various controllers arranged in a distribution box. The simple and low-cost control method is not suitable for and fully utilizes the objective condition of informatization high-speed development of urban road hardware; the method has the defects of manual hysteresis intervention, incapability of tracking continuous adjustment and non-fine control scale under extreme conditions.
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 an urban area. Wherein this wisdom street lamp 1 can include lighting module 11, video monitoring module 12, power module 13, environment monitoring module 14, communication module 15 and information issuing module 16, lighting module 11 optional configuration has light sensor or photovoltaic board, realize the luminance regulation to the light source through lighting controller, the control of single lamp mainly is directly realized by DC intelligent control power at present, the centralized control in a street then is accomplished by the centralized control ware in the power cabinet, this edge controller has stronger operational capability at present, can handle the video stream smoothly, the picture stream, be favorable to synthesizing various analysis functions of multisource data extension. The video monitoring module 12 and the environment monitoring module 14 are an intelligent information basis of lamp poles and are provided with various sensor devices, and the camera is mainly used for identifying and tracking urban security special targets and monitoring the states of traffic flow and people flow in traffic 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 such as weather, traffic and the like sensed or received by the modules can be transmitted to the lighting system big data cloud platform by the communication module.
Specifically, the intelligent street lamp dimming system facing extreme weather comprises a physical acquisition module, an edge processing module and a platform processing module, wherein the physical acquisition module is used for acquiring illumination values and ground reflection coefficients of road surfaces below street lamps in an urban area needing dimming, and acquiring solar radiation light intensity of the street lamps through performance parameters, installation positions and converted electric energy of photovoltaic cell panels erected at the tops of lamp poles of the corresponding street lamps. The edge processing module is used for acquiring a dimming matrix I of the street lamp according to the acquired solar radiation light intensity at each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv. A platform processing module for receiving the dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvThe matrix is a dimming matrix, W is a main risk matrix based on meteorological observation, C is a transfer matrix formed by splitting and adjusting city blocks according to forecast of disaster types and grades of meteorology, D is a decision matrix used for bearing constraints from the aspect of an electric power system, and M is a mode matrix for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
In this implementation, the master risk matrix W is:
Figure BDA0003352383670000081
the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation; wherein B isiiTo represent the size of a blockThe sub-matrix is q multiplied by q, and the physical meaning is close to the block of a city; q is the projection of the smallest scale on W that existing forecasting systems can resolve, where existing forecasting systems are mainly based on meteorological satellite and radar decisions. Due to differences in planning and infrastructure of city blocks, the degree of response to extreme weather risks may vary. Therefore, under weather-based disaster type and grade forecast, the cloud platform can associate historical data with the GIS + BIM system pair B of the smart cityiiAnd (3) performing more fine splitting and adjusting to form a transfer matrix C: the transfer matrix C is a pair B of historical data and geographic data by correlationiiObtained by performing fine resolution and adjustment, wherein
Figure BDA0003352383670000082
Adjustment factor cii∈R+Wherein R is+Is a positive real number set. C and W together form a risk item in the collaborative dimming T model, and the effect is to give differentiated dimming degrees based on the evaluation of the individual extreme weather influence of the urban street lamps.
The decision matrix D is mainly used to bear constraints from power systems and other aspects, including external considerations such as hardware controllability, grid scheduling, and economy, and can be set as a 1-0 matrix. If the D matrix needs to be optimized in the use process of the subsequent model, the matrix Sigmoid may be transformed:
Figure BDA0003352383670000083
in this embodiment, the influence of extreme weather types can be considered as required, the color temperature of the existing LED lighting mode can be adjusted according to the weather and environmental changes, and different color temperatures have different S/P values and thus different intermediate visual brightness Lm. Due to Iv＝∫LvdA cos θ, and therefore the difference in brightness adjustment targets can affect the decision of light intensity adjustment. To make the light modulation matrix I in the modelvIs uniform, it is therefore necessary to normalize the effect in this respect to a mode matrix M, the matrix elements Mii∈(0，1]. W, C, D, M cloud existing information and instruction setting based on intelligent street lamp management and control system, IvDynamic changes of extreme weather at the edge side need to be reflected in a centralized mode, and therefore regulation and control are completed by adopting a strong tracking algorithm in combination with related sensing data. And finally, a risk item and a decision item controlled by the cloud of the dimming model are expanded and specifically set, and a cooperation strategy specifically aiming at the control of the luminous intensity of the intelligent street lamp is formed on the basis that the cloud has a preset decision judgment.
In the present embodiment, the evaluation target E of the entire controlTIota capable of being written into target matrix T2-ι1Norm regularization form:
Figure BDA0003352383670000091
wherein t isiThe elements in T are continuously arranged according to a certain geographical rule; defining a calibration matrix T*As the evaluation standard value of T,
Figure BDA0003352383670000092
is T*Element (2) and tiAnd (7) corresponding. T is*The measurement is completed by the engineering vehicle of road operation and maintenance work under corresponding working conditions with professional instruments, and the measurement is related to the road type, the vehicle flow and the road shape and the arrangement of the lamps according to the standards. Ideally, the dimming target T should be compared with the standard and measured values T*As close as possible, thus evaluating object ETTwo norm containing both
Figure BDA0003352383670000093
Form (a). But due to uncertainties in the model, calibration and measurement processes, including inaccuracies and uncertainties, and there are inherent deviations in the control system transfer functions. Thus according to the substantial characteristic quantities T and T*Overfitting tendency can occur, and sparse norm | | · | | calcualtion needs to be introduced into the regularization term1To limit its local flatness, i.e. the brightness of adjacent street lamps does not tend to change abruptly, it needs to be as close as possible. χ is the tunable hyper-parameter of the evaluation model, used to control the tendency of this proximity, and can be based on preferenceThe arrangement is free. N (i) is a local neighborhood of i, which is determined by the field of view of the smart street light video surveillance module camera, tjThe light intensity of the street lamp in the field of view. KappaijFor the affinity coefficient, the affinity coefficient calculation method can be written as a dual-core function:
Figure BDA0003352383670000094
wherein sigma1And σ2The constant is set for controlling the attention of the model to the light intensity difference and the structure. ST (ST)iAnd STjEach representing a multi-scale structure based on a priori weighting strategies at points i and j, in STiFor example, the following steps are carried out:
Figure BDA0003352383670000095
wherein
Figure BDA0003352383670000096
Is a two-dimensional gaussian kernel with a multi-scale parameter σ, σ ∈ {1, 2, 3 }.
In summary, the main purpose of evaluating and optimizing T is to adjust the decision term and risk term of the whole model. The parameters or the formulated strategy of the part are relatively fixed, offline delay completion can be allowed, and the intelligent street lamp operation and maintenance and management cloud platform based on the big data of the whole model can be executed.
In this embodiment, the key to the entire T model is to process IvReal-time dynamic changes. Based on the data driving idea, the main task of the part is to establish the acquisition quantity, the ground illumination and the adjustment quantity I of the intelligent street lamp sunlight amplitude sensorvA sample set of (a); wherein the real-time adjustment amount IvCan be directly obtained by the electrical system of the intelligent street lamp and comes from the light intensity I of the solar radiationsolarOn one hand, the intelligent street lamp can be directly acquired or simply calculated by an environment-weather sensing system of the intelligent street lamp. On the other hand, it is also more general, IsolarOr can be made ofPhotovoltaic panel output P carried on topsolarEstimating:
Figure BDA0003352383670000101
Psolaris the output power of the photovoltaic array, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic panel. Wherein P issolarIs determined by the intensity of the total solar radiation received on the photovoltaic array, including the ground reflection IreThe conversion into photometric problems needs to be multiplied by the spectral luminous efficiency coefficient K. Because wisdom street lamp photovoltaic board generally erects at the lamp pole top and approaches to the level, consequently Ir0. Remainder IsolarThe intensity of direct solar radiation IdAnd intensity of sky scattered radiation IsConsists of the following components:
Figure BDA0003352383670000102
wherein IbaThe amount of solar radiation that is local is mainly dependent on factors such as solar declination angle, and is a known function of the date. h is the altitude angle of the sun,
Figure BDA0003352383670000103
is the included angle between the solar azimuth angle and the photovoltaic array angle, and theta is the inclination angle of the photovoltaic array. Under non-extreme weather, the size degree alpha of the atmospheric particles is considered to be less than 1, mainly reflected by Rayleigh scattering effect, and the light intensity I of the received solar radiation at the momentsolarIntensity of direct radiation mainly from the sun IdForming; and the inclination angle theta → 0 of the photovoltaic panel at the top of the intelligent street lamp is considered, so that the intelligent street lamp is ideal
Figure BDA0003352383670000104
It can also be written as a representation mainly by the local solar altitude:
Figure BDA0003352383670000105
in the preferred embodiment, it is apparent 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 any street lamp i illuminates the road surface below itDegree of rotation
Figure BDA0003352383670000106
Figure BDA0003352383670000107
In this embodiment, the edge processing module illuminates the road surface below the edge processing module through any street lamp i
Figure BDA0003352383670000108
The dimming amount corresponding to the street lamp i
Figure BDA0003352383670000109
To know about
Figure BDA00033523836700001010
Wherein
Figure BDA00033523836700001011
Wherein
Figure BDA0003352383670000111
Is the estimated value of the light intensity from the solar radiation on the street lamp I, and the value range is [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Ev1Uncertainty parameter of real-time state, its magnitude is mainly equal to
Figure BDA0003352383670000112
And (4) positively correlating. Specifically, w is an uncertain quantity or noise of parameter estimation, and is generally unknown, so that it cannot be directly expressed quantitatively, and if operation is required, it is assumed to be a normal (gaussian) distribution.
Figure BDA0003352383670000113
Is the illuminance of the street lamp i to the road surface below the street lamp i. Due to the fact that
Figure BDA0003352383670000114
For generating the dimming matrix I, due to the determined reference standardvCalculating
Figure BDA0003352383670000115
The problem is to obtain a value for exp (- τ l), l being the lamp stem height, i.e. estimate the turbidity τ.
Wherein the edge processing module is further used for obtaining the light intensity estimated value from the solar radiation on the street lamp i
Figure BDA0003352383670000116
Wherein
Figure BDA0003352383670000117
PsolarFor the output power of the photovoltaic cell panel erected on the top of the lamp post of the street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0.
In this embodiment, the edge processing module further estimates the turbidity τ by observing the adjacent street lamp through the video monitoring system mounted under the street lamp, and obtains the observed value of the road illumination
Figure BDA0003352383670000118
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 iiComprises the following steps:
Figure BDA0003352383670000119
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, Ii is the light intensity value received by the sensor,
Figure BDA00033523836700001110
the initial value of the light intensity collected for the ith street lamp. Specifically, the air turbidity estimated value of the ith street lamp
Figure BDA00033523836700001111
The solution vector ω can be adjusted by a shallow neural network, such as ELM adjustment, with its dynamic weights when data is accumulated. Or, considering that the light of the street lamps in the near future is stronger, i.e. the signal-to-noise ratio is higher, an exponentially weighted average may be used to give higher weight to the street lamps in the near future. In fact, different road surfaces (cement concrete and asphalt) have different brightness coefficients without considering the influence of the road surface material, and the road surface is considered to have full diffuse reflection for the sake of convenience in discussion. The road surface brightness of the nearest neighbor street lamp in the vertical direction shot by the camera is IK(ii) a To prevent outlier interference, the actual image processing can be replaced by the average of the pixel regions, noted
Figure BDA00033523836700001112
The corresponding direction angle is γ. Then at camera height hcUnder the following conditions:
Figure BDA00033523836700001113
where Θ is a transfer function, related to the particular parameter settings of the camera, and hence
Figure BDA00033523836700001114
And
Figure BDA00033523836700001115
a constant proportionality is present. The overall optimization strategy is limited by a norm to locally present smoothness, so that the illumination of the nearest neighbor road surface acquired 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 IvWith extreme weather changes, Isolarτ, etc. as a function of time, the estimate of which
Figure BDA0003352383670000121
Figure BDA0003352383670000122
Etc. are time series with certain intervals (step sizes) Δ t. According to the aforementioned pair EvDiscretizing and expanding the vector E to the illumination vectors corresponding to the q street lamps:
v1：
Figure BDA0003352383670000123
v2：
Figure BDA0003352383670000124
wherein the content of the first and second substances,
Figure BDA0003352383670000125
in the form of a state vector, the state vector,
Figure BDA0003352383670000126
to control vector, process noise
Figure BDA0003352383670000127
Satisfy the Gaussian distribution
Figure BDA0003352383670000128
Figure BDA0003352383670000129
In order to observe the vector, the vector is,
Figure BDA00033523836700001210
for observing the matrix, observing the noise
Figure BDA00033523836700001211
Satisfy the Gaussian distribution
Figure BDA00033523836700001212
Defining:
Figure BDA00033523836700001213
Figure BDA00033523836700001214
the recursive calculation formula can be listed by reference to the standard linear Kalman filtering theory:
Figure BDA00033523836700001215
Pk|k-1＝(1+Δtk)2Pk-1+ΔtkQk
Kk＝Pk|k-1HT(HPk|k-1HT+Rk/Δtk)-1
Figure BDA00033523836700001216
Figure BDA00033523836700001217
wherein
Figure BDA00033523836700001218
Is composed of
Figure BDA00033523836700001219
Is estimated a priori of the time-of-flight,
Figure BDA00033523836700001220
in order to be the prior error covariance,
Figure BDA00033523836700001221
is a matrix of the units,
Figure BDA00033523836700001222
in order to obtain the Kalman gain, the method,
Figure BDA00033523836700001223
is composed of
Figure BDA00033523836700001224
Is estimated by the a posteriori of (c),
Figure BDA00033523836700001225
to update the error covariance. Considering no adaptive step length adjustment, the standard Kalman filtering is used as a non-closed loop filtering, KkIt is difficult to adapt to sudden changes caused by extreme weather and accumulated errors caused by modeling accuracy limitations, so that the performance in tracking and responding of actual dimming is to be improved.
In this embodiment, preferably, aiming at the problem that the algorithm needs to introduce strong tracking filtering to solve the inaccuracy of the model and the sudden change of the environmental state, the core idea is to introduce an evanescent factor that changes in real time to adjust the covariance matrix of the prediction error, and the approximate suboptimal calculation method is as follows:
Figure BDA0003352383670000131
wherein: xi0＝tr[Nk]/tr[Ak]；
Wherein: n is a radical ofk＝Vk-HΔtkQkHT-βRk/Δtk，Ak＝(1+Δtk)2HPk-1HT；
Where β ∈ [1, ∞) ] is the attenuation factor to be set, and the effect is to adjust the degree of smoothing of the state estimation value. VkAs an innovation covariance matrix:
Figure BDA0003352383670000132
where rho epsilon (0, 1)]Is forgetting factor, upsilonkAs an innovation sequence:
Figure BDA0003352383670000133
if selected, to subtract the factor xikActing on the error covariance matrix, there is an STF method:
Figure BDA0003352383670000134
Figure BDA0003352383670000135
under the constraint of the orthogonality principle, the adjustment of the error covariance matrix is equivalent to an adjustment of the process noise without difference, while if the subtraction factor is directly applied to the process noise, the STAKF method with multiple subtraction factors:
Figure BDA0003352383670000136
wherein:
Figure BDA0003352383670000137
to ensure Pk|k-1When gamma iskSymmetry when the diagonal elements are not equal, the above can be written as:
Figure BDA0003352383670000138
wherein
Figure BDA0003352383670000139
Is gammakObtained by Cholesky triangulation decomposition technique
Figure BDA00033523836700001310
Figure BDA00033523836700001311
By using
Figure BDA00033523836700001312
Is represented by FkThe elements of i rows and i columns on the diagonal,
Figure BDA00033523836700001313
in the same way, then:
Figure BDA00033523836700001314
thereby obtaining multiple fading factor matrix gammakOr
Figure BDA00033523836700001315
The difference in this approach is reflected in the tracking effect on the amount of mutations that STF tends to consider the system model as feasibleIt is believed that what needs to be changed is the estimation error at the last time; STAKF tends to consider mutations caused by system model inaccuracies, and there are differences in processing ideas between the two. In the research problem, a photoelectric measurement means is adopted, so that the device is easily interfered by the environment; and the subject of the study is extreme weather, there is a possibility that the relevant parameters change abruptly within a processing interval, so an effective combination of the two tracking filters is required.
In this embodiment, since IsolarThe fitting function of tau, etc. varying with time may have an unusual first or second order differential, and in order to fit the variation locus of extreme weather as much as possible, it is necessary to emphasize Δ t in the discrete modelkAnd considering the judgment and update problems. First, defining the normalized distance of the error covariance matrix
Figure BDA0003352383670000141
By passing
Figure BDA0003352383670000142
Lead-out DeltatkThe decision criterion is adjusted so that when Δ tkOn a time scale of → 0,
Figure BDA0003352383670000143
can get
Figure BDA0003352383670000144
Minimum value d of diagonal elementskAnd agreeing on the target threshold d*Then there is Δ tkThe adjustment rule of (2):
Figure BDA0003352383670000145
wherein s is 0.1-0.2, mainly for assisting convergence judgment. The fine adjustment amount epsilon is a set value, and the value range meets the constraint:
Figure BDA0003352383670000146
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 tracking real-time performance of various hyper-parameters in the model, and is not favorable forIn that the ability to a priori discriminate the cause of the mutation from the model itself is lost. The coping strategy is to set
Figure BDA0003352383670000147
Threshold value of
Figure BDA0003352383670000148
Figure BDA0003352383670000149
Is defined as a vector
Figure BDA00033523836700001410
Is then based on
Figure BDA00033523836700001411
Adopts a different method of switching, as described below, if
Figure BDA00033523836700001412
Adopting an STF strong tracking filtering strategy; if it is not
Figure BDA00033523836700001413
And is
Figure BDA00033523836700001414
Adopting an STF strong tracking filtering strategy; if it is not
Figure BDA00033523836700001415
And is
Figure BDA00033523836700001416
Then the STAKF strong tracking filtering strategy is used.
It can be seen that for states with undefined breakthrough thresholds or trends, this is equivalent to the tracking effect that requires the STAKF to limit the STF, achieving a conservative regulatory effect. In particular the outputs of the two filters in this case
Figure BDA00033523836700001417
Is marked as Y1，Y2(ii) a The final output result
Figure BDA00033523836700001418
Figure BDA00033523836700001419
Fusion coefficient matrix
Figure BDA00033523836700001420
Is a diagonal matrix, P in this examplekIs also equal to ηkDiagonal matrix of the same size, so that two filters PkDiagonal element of
Figure BDA00033523836700001421
And ηkDiagonal element η ofiThe three have corresponding relation, calculate etaiTo obtain etak：
Figure BDA00033523836700001422
In summary, a single filter can be regarded as ηiTaking O or 1 to
Figure BDA00033523836700001423
Target illumination of and system
Figure BDA00033523836700001424
Can be used to set Uk+1And obtaining the time k +1
Figure BDA00033523836700001425
Received by the whole cloud
Figure BDA00033523836700001426
Are arranged according to a convention rule to obtain a total dimming matrix IvThe whole algorithm flow is shown in fig. 3. .
Above-mentioned wisdom street lamp is transferred towards extreme weather that disclosesOn the basis of conventional configuration of intelligent street lamps specified by national standards, the light system designs a municipal lighting system tracking dimming method which has cloud edge cooperativity and can cope with extreme weather changes according to a sensor, and the following technical problems are specifically solved: the influence analysis to extreme weather to road surface illuminance combines the research to wisdom street lamp hardware system basis, provides a cloud limit collaborative model of adjusting luminance. And (3) expanding and specifically setting and describing risk items and decision items of cloud control of the light modulation model, and giving an optimization target of T of the target matrix. Dimming matrix I aiming at real-time dynamic change of edge side in TvA 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 adjacent street lamps is provided, and the problem of directly calculating the extinction coefficient is solved. For IsolarIlluminance varying with tau over time
Figure BDA0003352383670000151
And (3) a force state adjustment problem, namely providing a dynamic system model for illumination discretization under a Kalman filtering theory frame, and stripping the uncertainty and nonlinearity of the system from a state vector to ensure the iteration speed of the main iteration process and the edge calculation force adaptation. Based on the difficulty of state mutation prior judgment caused by the operation, the difference of two strong tracking filtering methods of STF and STAKF is considered, a combined strategy of the STF and the STAKF is provided, and step length optimization is performed on the tracking track.
Fig. 2 is a schematic diagram of another embodiment of a smart street lamp dimming method for extreme weather, the method including the following steps:
and 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 intensity of 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 the dimming matrix I of the street lamp according to the obtained solar radiation intensity of each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv。
Wherein the step S2 includes: the illumination of the road surface below the street lamp is realized through any street lamp i
Figure BDA0003352383670000152
The dimming amount corresponding to the street lamp i
Figure BDA0003352383670000153
To know about
Figure BDA0003352383670000154
Wherein
Figure BDA0003352383670000155
Figure BDA0003352383670000156
Is the estimated value of the light intensity from the solar radiation on the street lamp I, and the value range is [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Ev1Uncertainty parameter of real-time state, its magnitude is mainly equal to
Figure BDA0003352383670000157
And (4) positively correlating. Specifically, w is an uncertain quantity or noise of parameter estimation, and is generally unknown, so that it cannot be directly expressed quantitatively, and if operation is required, it is assumed to be a normal (gaussian) distribution.
Figure BDA0003352383670000158
Is the illuminance of the street lamp i to the road surface below the street lamp i.
This step S2 further includes obtaining an estimate of the intensity of light from the solar radiation on street light i
Figure BDA0003352383670000161
Wherein
Figure BDA0003352383670000162
PsolarFor erecting photovoltaic electricity on top of lamp post of street lamp iOutput power of the cell plate, eta is photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0; the estimation of tau is realized by observing the adjacent street lamp through the video monitoring system carried under the street lamp, and the observed value of the road illumination is obtained
Figure BDA0003352383670000163
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 iiComprises the following steps:
Figure BDA0003352383670000164
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, Ii is the light intensity value received by the sensor,
Figure BDA0003352383670000165
the initial value of the light intensity collected for the ith street lamp.
Step S3, receiving a dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvThe matrix is a dimming matrix, W is a main risk matrix based on meteorological observation, C is a transfer matrix formed by splitting and adjusting city blocks according to forecast of disaster types and grades of meteorology, D is a decision matrix used for bearing constraints from the aspect of an electric power system, and M is a mode matrix for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
Wherein the main risk matrix W is:
Figure BDA0003352383670000166
the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation; wherein B isiiA sub-matrix of size qxq representing a block, the physical meaning of which is close to that of a block of a city; q is the minimum ruler that the existing forecasting system can distinguishThe projection of the degree onto W, where existing forecasting systems are mainly based on meteorological satellite and radar decisions. Due to differences in planning and infrastructure of city blocks, the degree of response to extreme weather risks may vary. Therefore, under weather-based disaster type and grade forecast, the cloud platform can associate historical data with the GIS + BIM system pair B of the smart cityiiAnd (3) performing more fine splitting and adjusting to form a transfer matrix C: the transfer matrix C is a pair B of historical data and geographic data by correlationiiObtained by performing fine resolution and adjustment, wherein
Figure BDA0003352383670000171
Adjustment factor cii∈R+Wherein R is+Is a positive real number set. C and W together form a risk item in the collaborative dimming T model, and the effect is to give differentiated dimming degrees based on the evaluation of the individual extreme weather influence of the urban street lamps.
The decision matrix D is mainly used to bear constraints from power systems and other aspects, including external considerations such as hardware controllability, grid scheduling, and economy, and can be set as a 1-0 matrix. If the D matrix needs to be optimized in the use process of the subsequent model, the matrix Sigmoid may be transformed:
Figure BDA0003352383670000172
in this embodiment, the influence of extreme weather types can be considered as required, the color temperature of the existing LED lighting mode can be adjusted according to the weather and environmental changes, and different color temperatures have different S/P values and thus different intermediate visual brightness Lm. Due to Iv＝∫LvdA cos θ, and therefore the difference in brightness adjustment targets can affect the decision of light intensity adjustment. To make the light modulation matrix I in the modelvIs uniform, it is therefore necessary to normalize the effect in this respect to a mode matrix M, the matrix elements Mii∈(0，1]. W, C, D, M cloud existing information and instruction setting based on intelligent street lamp management and control system, IvRequiring concentrated presentation of extreme weather on the edge sideDynamic, therefore, strong tracking algorithm is needed to complete the regulation and control in combination with the related sensing data. And finally, a risk item and a decision item controlled by the cloud of the dimming model are expanded and specifically set, and a cooperation strategy specifically aiming at the control of the luminous intensity of the intelligent street lamp is formed on the basis that the cloud has a preset decision judgment.
It should be noted that, in the present specification, the foregoing embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and like parts between the embodiments may be referred to each other. For the intelligent street lamp dimming method for extreme weather disclosed in the embodiment, since it corresponds to the intelligent street lamp dimming system for extreme weather disclosed in the previous embodiment, the description is simple, and the relevant points can be referred to 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 executes the computer program to realize the steps of the extreme weather-oriented intelligent street lamp dimming method described in the embodiments.
The extreme weather-oriented intelligent street lamp dimming device can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the schematic diagram is merely an example of the extreme weather-oriented intelligent street lamp dimming device, and does not constitute a limitation of the extreme weather-oriented intelligent street lamp dimming device, and may include more or less components than those shown, or combine some components, or different components, for example, the extreme weather-oriented intelligent street lamp dimming device may further include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the extreme weather-oriented intelligent street lamp dimming device equipment, and various interfaces and lines are utilized to connect all parts of the whole extreme weather-oriented intelligent street lamp dimming device equipment.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the extreme weather-oriented intelligent street lamp dimming device equipment by running or executing the computer program and/or the module stored in the memory and calling 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 non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic 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 for 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 is sold or used as an independent product. Based on such understanding, all or part of the processes in the method of the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned embodiments of the intelligent street lamp dimming method for extreme weather may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
In summary, the above-mentioned embodiments are only preferred embodiments of the present invention, and all equivalent changes and modifications made in the claims of the present invention should be covered by the claims of the present invention.

Claims (10)

1. The utility model provides a towards extreme weather's wisdom street lamp dimming system which characterized in that includes:
the physical acquisition module is used for acquiring the illumination 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 intensity of the street lamp through the performance parameters, the installation position and the converted electric energy of a photovoltaic cell panel erected at the top of a lamp post of the corresponding street lamp;
the edge processing module is used for acquiring a dimming matrix I of the street lamp according to the acquired solar radiation light intensity at each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv；
A platform processing module for receiving the dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvThe matrix is a dimming matrix, W is a main risk matrix based on meteorological observation, C is a transfer matrix formed by splitting and adjusting city blocks according to forecast of disaster types and grades of meteorology, D is a decision matrix used for bearing constraints from the aspect of an electric power system, and M is a mode matrix for normalizing decision influences of differences of brightness adjustment targets on light intensity adjustment.
2. The extreme weather-oriented intelligent street lamp dimming system as claimed in claim 1, wherein:
the main risk matrix W is:
Figure FDA0003352383660000011
wherein the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation, wherein BiiA submatrix with the size of qxq representing a block, wherein q is the projection of the smallest scale which can be resolved by the existing forecasting system on W;
the transfer matrix C is a pair B of historical data and geographic data by correlationiiObtained by performing fine resolution and adjustment, wherein
Figure FDA0003352383660000012
Adjustment factor cii∈R+Wherein R is+Is a positive real number set.
3. The extreme weather-oriented intelligent street lamp dimming system as claimed in claim 2, wherein:
the edge processing module illuminates the road surface below the edge processing module through any street lamp i
Figure FDA0003352383660000013
The dimming amount corresponding to the street lamp i
Figure FDA0003352383660000014
To know about
Figure FDA0003352383660000015
Wherein
Figure FDA0003352383660000016
Wherein
Figure FDA0003352383660000017
Is the estimated value of the light intensity from the solar radiation on the street lamp I, and the value range is [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Eυ1Uncertainty parameter of real-time state, its magnitude and
Figure FDA0003352383660000021
the correlation is positive and the correlation is negative,
Figure FDA0003352383660000022
is the illuminance of the street lamp i to the road surface below the street lamp i.
4. The extreme weather-oriented intelligent street lamp dimming system as claimed in claim 3, wherein: the physical acquisition module is used for acquiring the light intensity estimation value from solar radiation on the street lamp i
Figure FDA0003352383660000023
Wherein
Figure FDA0003352383660000024
PsolarFor the output power of the photovoltaic cell panel erected on the top of the lamp post of the street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0.
5. The extreme weather-oriented intelligent street lamp dimming system as claimed in claim 4, wherein:
the edge processing module also realizes the estimation of tau through the observation of the video monitoring system carried under the street lamp to the adjacent street lamp and obtains the observed value of the road illumination
Figure FDA0003352383660000025
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 iiComprises the following steps:
Figure FDA0003352383660000026
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, IiIs the value of the light intensity received by the sensor,
Figure FDA0003352383660000027
the initial value of the light intensity collected for the ith street lamp.
6. An extreme weather-oriented intelligent street lamp dimming method is characterized by comprising 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 intensity of the street lamp through the performance parameters, the installation position and the converted electric energy of the photovoltaic cell panel erected on the top of the lamp post of the corresponding street lamp;
s2, obtaining the dimming matrix I of the street lamp according to the obtained solar radiation intensity of each street lamp, the illuminance of the road surface below each street lamp and the regional air turbidityv；
S3, receiving a dimming matrix IvUpdating and issuing a dimming strategy T aiming at each street lamp in the control area, wherein T is IvM, D, C, W, wherein IvIs a light modulation matrix, W is a main risk matrix based on meteorological observation, C is a forecast of city according to the type and grade of meteorological disastersThe method comprises the steps of splitting and adjusting a block to form a transfer matrix, D is a decision matrix for bearing the constraint from the aspect of a power system, and M is a mode matrix for normalizing the decision influence of the difference of brightness adjustment targets on light intensity adjustment.
7. The extreme weather-oriented intelligent street lamp dimming method according to claim 6, wherein:
the main risk matrix W is:
Figure FDA0003352383660000031
wherein the QxQ square matrix simulates a square matrix formed by projecting street lamps scattered in an urban area needing dimming according to affine transformation, wherein BiiA submatrix with the size of qxq representing a block, wherein q is the projection of the smallest scale which can be resolved by the existing forecasting system on W;
the transfer matrix C is a pair B of historical data and geographic data by correlationiiObtained by performing fine resolution and adjustment, wherein
Figure FDA0003352383660000032
Adjustment factor cii∈R+Wherein R is+Is a positive real number set.
8. The extreme weather-oriented intelligent street lamp dimming method according to claim 7, wherein the step S2 comprises:
the illumination of the road surface below the street lamp is realized through any street lamp i
Figure FDA0003352383660000033
The dimming amount corresponding to the street lamp i
Figure FDA0003352383660000034
To know about
Figure FDA0003352383660000035
Wherein
Figure FDA0003352383660000036
Figure FDA0003352383660000037
Is the estimated value of the light intensity from the solar radiation on the street lamp I, and the value range is [0, I (L)m)]，I(Lm) Starting the upper bound for mesopic vision, LmIn order to preset mesopic vision brightness, l is the height of a lamp post of a street lamp i, tau is air turbidity, and w is Fυ1Uncertainty parameter of real-time state, its magnitude and
Figure FDA0003352383660000038
the correlation is positive and the correlation is negative,
Figure FDA0003352383660000039
is the illuminance of the street lamp i to the road surface below the street lamp i.
9. The extreme weather-oriented intelligent street lamp dimming method according to claim 8, wherein the step S2 further comprises:
obtaining an estimate of the intensity of light from solar radiation on street light i
Figure FDA00033523836600000310
Wherein
Figure FDA00033523836600000311
PsolarFor the output power of the photovoltaic cell panel erected on the top of the lamp post of the street lamp i, eta is the photoelectric conversion efficiency of the photovoltaic cell, ApvIs the total area of the photovoltaic cell panel, K is the spectral luminous efficiency coefficient, IrFor ground reflection coefficient, when the photovoltaic panels are erected parallel to the ground IrIs 0;
the estimation of tau is realized by observing the adjacent street lamp through the video monitoring system carried under the street lamp, and the observed value of the road illumination is obtained
Figure FDA00033523836600000312
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 iiComprises the following steps:
Figure FDA00033523836600000313
wherein omegaiThe direction angle observed by the sensor carried by the street lamp, d is the distance from the sensor to the ground, IiIs the value of the light intensity received by the sensor,
Figure FDA0003352383660000041
the initial value of the light intensity collected for the ith street lamp.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program when being executed by a processor realizes the steps of the method as claimed in any one of the claims 6-9.
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