CN113505346B - Urban street lamp data processing and combined regulation and control system based on artificial intelligence - Google Patents
Urban street lamp data processing and combined regulation and control system based on artificial intelligence Download PDFInfo
- Publication number
- CN113505346B CN113505346B CN202111058581.0A CN202111058581A CN113505346B CN 113505346 B CN113505346 B CN 113505346B CN 202111058581 A CN202111058581 A CN 202111058581A CN 113505346 B CN113505346 B CN 113505346B
- Authority
- CN
- China
- Prior art keywords
- illumination area
- sequence
- street lamp
- current
- color temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
- H05B47/115—Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/165—Controlling the light source following a pre-assigned programmed sequence; Logic control [LC]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- Biomedical Technology (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Probability & Statistics with Applications (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Circuit Arrangement For Electric Light Sources In General (AREA)
Abstract
The invention relates to an artificial intelligence-based urban street lamp data processing and joint regulation and control system, and belongs to the technical field of street lamp regulation and control. The system includes a memory and a processor that executes a computer program stored by the memory to implement the steps of: acquiring a target environment characteristic and a first traffic flow of a current illumination area; inputting the target environment characteristics into a preset first regulation and control model to obtain a first street lamp illumination degree index and a first color temperature index of a predicted illumination area; inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area; and integrating the first street lamp illumination degree index and the second street lamp illumination degree index to obtain a final street lamp illumination degree index, and integrating the first color temperature index and the second color temperature index to obtain a final color temperature index. The method provided by the invention can improve the accuracy of system regulation and control, and enables the overall management to be more flexible.
Description
Technical Field
The invention relates to the field of street lamp regulation, in particular to an artificial intelligence-based urban street lamp data processing and combined regulation system.
Background
The street lamps are visible facilities in cities, the distribution range of the street lamps and the number of the street lamps are continuously enlarged and increased along with the continuous development of the society, but the regulation and control requirements on the street lamps are also continuously improved.
At present, the current situation of street lamp regulation and control management in China mostly stays at a lower level, most of street lamp regulation and control management mainly depends on a manual mode, and most of street lamps are controlled singly; the method for regulating and controlling the operation by the manual mode has the advantages of large labor amount, low working efficiency, no real-time property, and easy occurrence of untimely and poor regulation and control.
Disclosure of Invention
The invention provides an artificial intelligence-based urban street lamp data processing and combined regulation and control system, which is used for solving the technical problems that the existing street lamp regulation and control mode cannot regulate and control street lamps in real time, and the situations of untimely regulation and control and poor regulation and control are easy to occur, and adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides an artificial intelligence-based urban street lamp data processing and joint regulation system, which includes a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the following steps:
acquiring a target environmental characteristic and a first traffic flow of a current illumination area, and sequentially adjacent target environmental characteristics and first traffic flow before the current illumination area
A second vehicle flow of the first illumination area,
；
inputting the target environment characteristics into a preset first regulation and control model to obtain a first street lamp illumination degree index and a first color temperature index of a predicted illumination area, wherein the predicted illumination area is a next illumination area adjacent to the current illumination area;
inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area; wherein the second regulatory model comprises: the second road lamp illumination degree index and the second color temperature index are equal to the first vehicle flow and each second vehicle flow in a weighted sum, and the closer the distance between the second road lamp illumination degree index and the second color temperature index and the predicted illumination area is, the larger the illumination degree weight value and the color temperature weight value of the vehicle flow corresponding to the illumination area are;
integrating the first street lamp illumination degree index and the second street lamp illumination degree index to obtain a final street lamp illumination degree index, and integrating the first color temperature index and the second color temperature index to obtain a final color temperature index;
and regulating and controlling the street lamps in the predicted illumination area according to the final street lamp illumination degree index and the final color temperature index.
Preferably, the acquiring the target environmental characteristics of the current illumination area includes:
acquiring a first environment characteristic sequence of each street lamp in a current illumination area according to a preset sampling period, wherein the first environment characteristic sequence comprises four first characteristic parameter sequences which are respectively a first temperature mean value sequence, a first haze concentration sequence, a first environment humidity sequence and a first noise intensity sequence;
carrying out error parameter filtering on the first environment characteristic sequence of each street lamp in the current illumination area according to the following error parameter filtering model, and reserving parameters which accord with the error parameter filtering model in the first environment characteristic sequence to obtain a second environment characteristic sequence of each street lamp in the current illumination area; the second environment characteristic sequence comprises four second characteristic parameter sequences, namely a second junction temperature mean value sequence, a second haze concentration sequence, a second environment humidity sequence and a second noise intensity sequence:
| F i,j,v _F i,j med) (|< Qj
wherein the content of the first and second substances,F i,j med) (is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The median of each element in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element; wherein the content of the first and second substances,
，
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The number of elements in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element and a
A gradient between the individual elements;
calculated according to the following formula
：
Wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The number of elements in the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The number of the elements is one,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The weight corresponding to each element; for the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of each second characteristic parameter sequence is subjected to reliability judgment according to the following reliability judgment model:
wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
Of a second sequence of characteristic parametersThe value of the one or more of the one,
is a decision factor;
when the current illumination area is within
Second environment characteristic sequence of individual street lamp
When the second characteristic parameter sequence does not meet the reliability judgment model, the first characteristic parameter sequence is used for judging the first characteristic parameter sequence in the current illumination area
Deleting the second environment characteristic sequence of each street lamp;
respectively summing the values of the second environment characteristic sequences of all the street lamps in the current illumination area which are left in the current illumination area after deletion, and then calculating the average value to obtain the target environment characteristic of the current illumination area; the target environment characteristics comprise four target environment characteristic parameters, namely a target junction temperature mean value, a target haze concentration, a target environment humidity and a target noise intensity.
Preferably, the step of inputting the target environment characteristics into a preset first regulation and control model to obtain a first street lamp illumination degree index and a first color temperature index of a predicted illumination area includes:
calculating a first street lamp illumination degree index of a predicted illumination area according to the following model:
wherein the content of the first and second substances,
to predict a first street light illumination level indicator for an illumination area,
as the first of the target environmental features of the currently illuminated area
The characteristic parameter of the target environment is measured,
is the current illumination area
The weight corresponding to each target environment characteristic parameter;
calculating a first color temperature index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
is the first color temperature index of the current illumination area,
is the target environment characteristic of the current illumination area
The characteristic parameter of the target environment is measured,
is the target environment characteristic of the current illumination area
And the weight corresponding to each target environment characteristic parameter.
Preferably, the step of inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area includes:
and calculating a second road lamp illumination degree index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
to predict the second street light illumination level indicator for the illumination area,
is the first traffic volume of the currently illuminated area,
the illumination degree weight corresponding to the first traffic flow of the current illumination area,
is the sequentially adjacent first before the current lighting area
A second volume of vehicle in the first illumination zone,
is the sequentially adjacent first before the current lighting area
The illumination degree weight corresponding to the first illumination area, and
；
a second color temperature indicator of the predicted illumination area is calculated according to the following model:
wherein the content of the first and second substances,
to predict the second color temperature indicator for the illuminated area,
is the first traffic volume of the currently illuminated area,
the color temperature weight corresponding to the first vehicle flow of the current illumination area,
is the first one adjacent to the current lighting area in sequence
Color temperature weights corresponding to second vehicle flows in the first illumination areas; and is
。
Preferably, integrating the first street lamp illumination degree index and the second street lamp illumination degree index to obtain a final street lamp illumination degree index, and integrating the first color temperature index and the second color temperature index to obtain a final color temperature index, includes:
calculating the final street lamp illumination degree index according to the following formula:
wherein the content of the first and second substances,
to predict the final street light lighting level indicator for the lighting area,
to predict a first street light illumination level indicator for an illumination area,
the second street lamp illumination degree index of the illumination area is predicted;
calculating a final color temperature index according to the following formula:
wherein the content of the first and second substances,
to predict the final color temperature index of the illumination area,
to predict a first color temperature indicator within an illumination area,
to predict a second color temperature indicator within the illumination area.
Preferably, after the street lamps in the predicted lighting area are regulated and controlled according to the final street lamp lighting degree index and the final color temperature index, the step of the artificial intelligence-based urban street lamp data processing and joint regulation and control system further comprises:
obtaining the average vehicle speed and the lane occupancy of the vehicle flow in the current illumination area according to the first vehicle flow in the current illumination area;
calculating the lane occupancy of the current illumination area according to the following formula:
wherein the content of the first and second substances,
as the lane occupancy of the current illumination area,
for the length of the road segment of the current illumination area,
the total length of the vehicles running on the road section with the unit length of the current illumination area is obtained;
and inputting the average vehicle speed of the vehicle flow and the lane occupancy into a prediction network model to obtain the street lamp regulation and control time and the street lamp maintenance time of the prediction illumination area.
The urban street lamp data processing and combined regulation and control system based on artificial intelligence provided by the invention has the technical effects that: compared with the traditional mode depending on manual regulation or a single control mode, the urban street lamp data processing and combined regulation and control system based on artificial intelligence provided by the invention avoids the subjectivity of manual regulation and control, also avoids the problems of low working efficiency, no real-time property, untimely regulation and control and poor regulation effect, improves the working efficiency, also improves the precision of system regulation and control, realizes the purpose of energy conservation, and enables the overall management to be more flexible; furthermore, the parameters used for the regulation were: the environmental characteristic and the traffic flow of the current illumination area and the traffic flow of at least two adjacent illumination areas in proper order before the current illumination area, respectively with the environmental characteristic of the current illumination area, and the traffic flow of the current illumination area and a plurality of illumination areas before it is input respectively into the corresponding regulation and control model, just can obtain two street lamp illumination degree indexes and colour temperature index, obtain final street lamp illumination degree index and final colour temperature index at last, in order to carry out street lamp regulation and control, promote regulation and control accuracy and reliability, can avoid appearing adjusting the condition of undulant too big in the accommodation process, improve the adaptability of system to the vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an artificial intelligence-based city street lamp data processing and joint regulation system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a road area of an artificial intelligence-based urban street lamp data processing and joint regulation system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention belong to the protection scope of the embodiments of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment provides an urban street lamp data processing and joint regulation and control system based on artificial intelligence, which is described in detail as follows:
as shown in fig. 1, the city street lamp data processing and joint regulation and control system based on artificial intelligence comprises the following steps:
step S001, obtaining the target environment characteristic and the first traffic flow of the current illumination area, and the adjacent one in sequence before the current illumination area
A second vehicle flow of the first illumination area,
。
in this embodiment, all control devices of the street lamps need to be counted, each control device may control a plurality of street lamps, and each street lamp is divided into regions according to each obtained control device of the street lamp, so as to obtain a region set:
wherein the content of the first and second substances,
in order to divide the regions of the street lamps according to the obtained control devices of the street lamps to obtain a region set,
in the first area, the first area is,
in order to be the last one of the regions,
to control the number of devices; wherein each region is an illumination region; as another embodiment, the illumination areas may be divided by another area division mechanism.
In this embodiment, carry out information acquisition to each region through equipment such as road camera and sensor, moreover, can keep the start-up control mechanism of street lamp in this embodiment, specifically do: when the sensors in the areas detect that the collected environmental illumination value is lower than the threshold value, the control equipment of each street lamp starts the street lamp, and simultaneously, the street lamps in the areas are automatically adjusted to be in a low power consumption state.
In this embodiment, the ambient illuminance threshold is set as
(ii) a In other embodiments, different threshold values may be set for the ambient illuminance according to actual conditions.
It should be understood that the applicable scenes of the smart city street lamp joint regulation and control method provided by the embodiment are as follows: the road is a one-way road or a two-way lane is a one-way road formed by isolating a middle green belt, namely, vehicles on the road can only run along one direction, and street lamps on the road can be arranged on one side of the road or on two sides of the road; for the current illumination area, the positional relationship of the current illumination area, the predicted illumination area, and the first illumination area can be represented by fig. 2.
In this embodiment, the target environmental characteristics of the current illumination area, the first traffic flow of the current illumination area, and the sequentially adjacent traffic flows before the current illumination area need to be acquired
A second vehicle flow of the first illumination area,
(ii) a In the present embodiment, the first and second electrodes are,
a value of 4; as another embodiment, the method may be performed according to the requirements
Setting different values, e.g.
May be 6.
As a specific embodiment, acquiring the target environment characteristics of the current illumination area includes:
acquiring first environmental characteristics of each street lamp in the current illumination area through a sensor according to a preset sampling period to obtain a first environmental characteristic sequence of each street lamp in the current illumination area, wherein the first environmental characteristic sequence comprises four first characteristic parameter sequences which are respectively a first temperature mean value sequence, a first haze concentration sequence, a first environmental humidity sequence and a first noise intensity sequence; the method specifically comprises the following steps: for example, to the currently illuminated area
Junction temperature mean value of each street lamp
Secondary collection, wherein the collection period is once every 30 minutes, and the collected results are sorted from large to small to obtain the first illumination area
First junction temperature mean sequence of individual street lamps:
wherein the content of the first and second substances,
is the current illumination area
A first sequence of mean values of the junction temperatures of the individual street lamps,
is composed of
The minimum value in the secondary acquisition is,
is composed of
Only values in the sub-acquisition that are greater than the minimum value,
is composed of
Maximum in secondary acquisition; the first lighting area can be obtained in turn by the above mode
First haze concentration sequence and first ring of individual street lampThe environment humidity sequence and the first noise intensity sequence further obtain a first environment characteristic sequence of each street lamp in the current illumination area.
Current illumination area of the present embodiment
The number of elements in the first junction temperature mean value sequence of each street lamp is related to the collection times, and the collection times in the embodiment are
The sampling period is 30 minutes, then the current illumination area is the second
The number of elements in the first temperature mean value sequence of each street lamp is
(ii) a As another embodiment, the sampling period may be set according to actual conditions, and may be, for example, one hour.
It should be noted that the sensor in this embodiment is mounted on each street lamp, and as another embodiment, the sensor may be mounted on the control device; in addition, in the embodiment, junction temperature is acquired through a temperature sensor, the temperature sensor is arranged at a semiconductor in the street lamp and used for detecting the actual working temperature of the semiconductor, then the junction temperature average value is obtained through average value operation, haze concentration is acquired through a haze sensor, environment humidity is acquired through a humidity sensor, and noise intensity is acquired through a noise sensor.
As another embodiment, the elements in the acquired first environment feature sequence may also be sorted in different manners, for example, the elements may be sorted from large to small, or sorted according to the order of acquisition.
Then, carrying out error parameter filtering on the first environment characteristic sequence of each street lamp in the current illumination area according to the following error parameter filtering model, and reserving parameters which accord with the error parameter filtering model in the first environment characteristic sequence to obtain a second environment characteristic sequence of each street lamp in the current illumination area; the second environment characteristic sequence comprises four second characteristic parameter sequences, namely a second junction temperature mean value sequence, a second haze concentration sequence, a second environment humidity sequence and a second noise intensity sequence:
| F i,j,v _F i,j med) (|< Q j
wherein the content of the first and second substances,F i,j med) (is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The median of each element in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element; wherein the content of the first and second substances,
，
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The number of elements in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element and a
The gradient between the individual elements.
It should be noted that when the current illumination area is within the first
In the first environment characteristic sequence of the individual street lamp
When the number of the elements in the first characteristic parameter sequence is odd, the median is intermediate data in the first characteristic parameter sequence; when the current illumination area is within
In the first environment characteristic sequence of the individual street lamp
When the number of elements in the first characteristic parameter sequence is even, the median is the average of two intermediate data in the first characteristic parameter sequence.
As a specific embodiment, for example, the second to the current illumination area
The first junction temperature mean value sequence of each street lamp is used for filtering error parameters, and specifically comprises the following steps: first, the current illumination area is calculated
The median of the first mean sequence of the temperatures of the individual street lamps is recordedF i,1 med) (Then to the second of the region according to the following formula
And (3) carrying out error parameter filtering on the first temperature mean value sequence of each street lamp:
| F i, ,v1 _F i,1 med) (|< Q 1
wherein the content of the first and second substances,F i,1 med) (is the first in the current illumination area
The median of each element in the first temperature mean sequence in the first environment characteristic sequence of each street lamp,
is the first in the current illumination area
The first in the first junction temperature mean value sequence in the first environment characteristic sequence of each street lamp
An element; wherein the content of the first and second substances,
，
is the first in the current illumination area
The number of elements in the first junction temperature mean sequence in the first environmental characteristic sequence of the individual street lamps,
is the first in the current illumination area
The first in the first junction temperature mean value sequence in the first environment characteristic sequence of each street lamp
An element and a
The gradient between the elements, therefore
Is the first in the current illumination area
The mean value of the gradient among all elements in a first temperature mean value sequence in a first environment characteristic sequence of each street lamp; when elements in the first junction temperature mean value sequence do not satisfy the formula, the elements are considered to be error data, and the error data are deleted, so that the first junction temperature mean value sequence after filtering can be obtained and is marked as a second junction temperature mean value sequence:
wherein the content of the first and second substances,
is the first in the current illumination area
A second sequence of junction temperature means in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
A minimum value in a second sequence of junction temperature mean values in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
Only values of the second junction temperature mean value sequence in the second environment characteristic sequence of the individual street lamps that are greater than the minimum value,
is the first in the current illumination area
A maximum value in a second sequence of junction temperature mean values in a second sequence of environmental characteristics of the individual street lamps.
It should be noted that the number of elements in each sequence in the second environment feature sequence of each street lamp in the current illumination area is less than or equal to the number of elements in each sequence in the first environment feature sequence of each street lamp in the current illumination area.
Therefore, the current illumination area can be sequentially illuminated by the above processes
Carrying out error parameter filtering on a first temperature mean value sequence, a first haze concentration sequence, a first environment humidity sequence and a first noise intensity sequence in a first environment characteristic sequence of each street lamp to obtain a second temperature mean value sequence, a first haze concentration sequence, a first environment humidity sequence and a first noise intensity sequence in a current illumination area
A second environment characteristic sequence of each street lamp, wherein the second environment characteristic sequence comprises a second junction temperature mean value sequence, a second haze concentration sequence, a second environment humidity sequence and a second noise intensity sequence; and then obtaining a second environment characteristic sequence of each street lamp in the current illumination area.
In this example, the calculation is based on the following formula
：
Wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The number of elements in the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The number of the elements is one,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The weight corresponding to each element.
Wherein the content of the first and second substances,
the calculation method is as follows:
wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The weight corresponding to each of the elements is,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The variance of the sequence of second characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The element is in the current lighting area
Second environment characteristic sequence of individual street lamp
And the difference value of a middle element of the second characteristic parameter sequence, wherein the middle element is the median of the second junction temperature mean value sequence.
As another embodiment, the determination may be made in other ways
For example empirically or by how much the individual elements influence the control of the street lamp.
As a toolEmbodiments of the body, e.g. calculating the number one in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of individual street lamps
：
Wherein the content of the first and second substances,
is the first in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
A number of elements in a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
The number of the elements is one,
is the first in the current illumination area
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
The weight corresponding to each element.
Wherein, the present embodiment adopts Gaussian distribution to the second in the current illumination area
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
The weight distribution is carried out on each element, and the second element in the current illumination domain is calculated according to the following formula
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
Weight corresponding to each element:
wherein the content of the first and second substances,
is the first in the current illumination area
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
The weight corresponding to each of the elements is,
is the first in the current illumination area
A variance of a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
The second junction temperature mean value sequence in the second environment characteristic sequence of each street lamp
The element is in the current lighting area
And the difference value of the middle element of the second junction temperature mean value sequence in the second environment characteristic sequence of the street lamps, wherein the middle element is the median of the second junction temperature mean value sequence.
According to the above process, the second illumination area after processing can be obtained
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of individual street lamps
And in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of individual street lamps
。
For the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of each second characteristic parameter sequence is subjected to reliability judgment according to the following reliability judgment model:
wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of features,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
Of a second sequence of characteristic parametersThe value of the one or more of the one,
is a decision factor.
The method specifically comprises the following steps: judging the first illumination area of the model according to the following reliability
And (3) reliability judgment is carried out on the values of a second junction temperature mean value sequence in a second environment characteristic sequence of the street lamps:
wherein the content of the first and second substances,
is the first in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
is the first in the current illumination area
Values of a second junction temperature mean sequence in a second sequence of environmental characteristics of the individual street lamps,
to determine the factor, in this embodiment
The value of (d) is set to 5.
As another embodiment, the determination factor can be determined according to actual conditions
A different value is set, which may be 3, for example.
When the current illumination area is within
Second environment characteristic sequence of individual street lamp
When the second characteristic parameter sequence does not meet the reliability judgment model, the first characteristic parameter sequence is used for judging the first characteristic parameter sequence in the current illumination area
Deleting the second environment characteristic sequence of each street lamp; the method specifically comprises the following steps: when the current illumination area is within
When the value of the second junction temperature mean value sequence in the second environment characteristic sequence of the street lamp meets the formula for judging the reliability, judging the second junction temperature mean value sequence in the region
The reliability of a second junction temperature mean value sequence in a second environment characteristic sequence of each street lamp is high; when in the area
When the value of the second junction temperature mean value sequence in the second environment characteristic sequence of the street lamp does not meet the formula for judging the reliability, judging the second junction temperature mean value sequence in the area
The second junction temperature mean value sequence in the second environment characteristic sequence of the street lamps has low reliability, anddeleting the second environment characteristic sequence, and transmitting the second environment characteristic sequence in the area remotely
The sensor of each street lamp gives an early warning signal to prompt a manager to check and maintain the sensor as soon as possible.
Finally, summing the values of the second environment characteristic sequences of all the street lamps in the current illumination area left after deletion in the current illumination area, and then calculating the average value to obtain the target environment characteristic of the current illumination area; the target environment characteristics comprise four target environment characteristic parameters, namely a target junction temperature mean value, a target haze concentration, a target environment humidity and a target noise intensity, namely:
wherein the content of the first and second substances,
is a target environmental characteristic of the area,
is the average of the target junction temperatures for that region,
is the target haze concentration for the area,
is the target ambient humidity for the area,
the target noise intensity for that region.
In the present embodiment, the first vehicle flow rate of the current illumination area and the sequentially adjacent vehicle flow rate before the current illumination area
The second traffic of each first lighting area is obtained by a camera through RGB images of roads in the area, and then a key point detection network is adopted to detect and identify vehicles on the roads, wherein the number of key points of the vehicles is the traffic of the area; as another embodiment, the traffic flow may also be determined by other existing manners, for example, a license plate of the vehicle is identified by an erected monitoring camera, and the traffic flow is determined according to the number of the license plates obtained by identification.
It should be noted that, for the obtained target environmental characteristics of the current illumination area, the first vehicle flow rate of the current illumination area, and the sequentially adjacent target environmental characteristics before the current illumination area
The second vehicle flow of the first lighting area is normalized to be in [0,1 ]]To (c) to (d); the normalization process has many methods and is a well-known technique, and this embodiment will not be described in detail.
Step S002, inputting the target environment characteristic into a preset first control model, and obtaining a first street lamp illumination degree index and a first color temperature index of a predicted illumination area, where the predicted illumination area is a next illumination area adjacent to the current illumination area.
In the embodiment, target environment characteristics are input into a preset first regulation and control model to obtain a first street lamp illumination degree index and a first color temperature index of a predicted illumination area, wherein the predicted illumination area is a next illumination area adjacent to the current illumination area; the method specifically comprises the following steps:
calculating a first street lamp illumination degree index of a predicted illumination area according to the following model:
wherein the content of the first and second substances,
to predict the first of the illumination areasThe index of the illumination degree of the street lamp,
as the first of the target environmental features of the currently illuminated area
The characteristic parameter of the target environment is measured,
is the current illumination area
The weight corresponding to each target environment characteristic parameter; and the sum of the weights is 1, the weight corresponding to the target environment characteristic parameter is obtained by an analytic hierarchy process, or the importance degree of the first street lamp illumination degree index is directly set according to each target environment characteristic parameter.
Calculating a first color temperature index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
is the first color temperature index of the current illumination area,
is the target environment characteristic of the current illumination area
The characteristic parameter of the target environment is measured,
is the target environment characteristic of the current illumination area
The weight corresponding to each target environment characteristic parameter; and the sum of the weights is 1, the weight corresponding to the target environment characteristic parameter is obtained by an analytic hierarchy process, or the importance degree of the first color temperature index is directly set according to each target environment characteristic parameter.
Step S003, inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area; wherein the second regulatory model comprises: the second road lamp illumination degree index and the second color temperature index are equal to the weighted sum of the first vehicle flow and each second vehicle flow, and the closer the distance between the second road lamp illumination degree index and the second color temperature index and the predicted illumination area is, the larger the illumination degree weight value and the color temperature weight value of the vehicle flow corresponding to the illumination area are.
In this embodiment, the first traffic flow of the current illumination area and the traffic flow before the current illumination area that are adjacent in sequence are used
And inputting the second vehicle flow of the first illumination area into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the prediction illumination area.
Wherein the second regulatory model comprises: the second road lamp illumination degree index and the second color temperature index are equal to the weighted sum of the first vehicle flow and each second vehicle flow, and the closer the distance to the predicted illumination area is, the larger the illumination degree weight value and the color temperature weight value of the vehicle flow corresponding to the illumination area are, the more concrete:
and calculating a second road lamp illumination degree index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
to prepareMeasuring the second street lamp illumination degree index of the illumination area,
is the first traffic volume of the currently illuminated area,
the illumination degree weight corresponding to the first traffic flow of the current illumination area,
is the sequentially adjacent first before the current lighting area
A second vehicle flow of the first illumination area,
the number of the first illumination areas which are adjacent in sequence before the current illumination area,
is the sequentially adjacent first before the current lighting area
The illumination degree weight corresponding to each first illumination area; and is
。
A second color temperature indicator of the predicted illumination area is calculated according to the following model:
wherein the content of the first and second substances,
to predict the second color temperature indicator for the illuminated area,
is the first traffic volume of the currently illuminated area,
the color temperature weight corresponding to the first vehicle flow of the current illumination area,
is the first one adjacent to the current lighting area in sequence
Color temperature weights corresponding to second vehicle flows in the first illumination areas; and is
。
It should be noted that, when the distance between the current illumination area and the first illumination area before the current illumination area and in the sequential vicinity of the current illumination area and the predicted illumination area is shorter, the illumination degree weight value and the color temperature weight value of the traffic flow corresponding to the illumination area are larger, for example, when the current illumination area is closest to the predicted area, the illumination degree weight value of the second road lamp of the traffic flow corresponding to the current illumination area is the largest, and the weight value of the second color temperature of the traffic flow corresponding to the current illumination area is the largest.
And step S004, integrating the first street lamp illumination degree index and the second street lamp illumination degree index to obtain a final street lamp illumination degree index, and integrating the first color temperature index and the second color temperature index to obtain a final color temperature index.
In the embodiment, the first street lamp illumination degree index and the second street lamp illumination degree index are integrated to obtain a final street lamp illumination degree index, and the first color temperature index and the second color temperature index are integrated to obtain a final color temperature index; the method specifically comprises the following steps:
calculating the final street lamp illumination degree index according to the following formula:
wherein the content of the first and second substances,
to predict the final street light lighting level indicator for the lighting area,
to predict a first street light illumination level indicator for an illumination area,
the second street lamp illumination degree index of the illumination area is predicted;
calculating a final color temperature index according to the following formula:
wherein the content of the first and second substances,
to predict the final color temperature index of the illumination area,
to predict a first color temperature indicator within an illumination area,
to predict a second color temperature indicator within the illumination area.
And S005, regulating and controlling the street lamps in the predicted illumination area according to the final street lamp illumination degree index and the final color temperature index.
In the embodiment, the street lamps in the predicted illumination area are regulated and controlled according to the final street lamp illumination degree index and the final color temperature index; the method specifically comprises the following steps: and regulating and controlling the illumination degrees and the color temperatures of all the street lamps in the predicted illumination area according to the obtained final street lamp illumination degree index and the final color temperature index.
And then obtaining the average vehicle speed and the lane occupancy of the vehicle flow of the current illumination area according to the first vehicle flow of the current illumination area, and obtaining the street lamp regulation and control time and the street lamp maintenance time of the predicted illumination area according to the average vehicle speed and the lane occupancy of the vehicle flow of the current illumination area.
In this embodiment, the average vehicle speed of the vehicle flow in the current illumination area is obtained by the key point detection network through the same key point in the consecutive multiple frames
The detailed process of (2) is not specifically described in this embodiment because it is a prior art.
In this embodiment, the lane occupancy of the current illumination area is calculated according to the following formula:
wherein the content of the first and second substances,
as the lane occupancy of the current illumination area,
for the length of the road segment of the current illumination area,
the total length of the vehicles running on the road section with the unit length of the current illumination area is the maximum distance between key points in the running direction in the unit length of the current illumination area.
Inputting the average vehicle speed and the lane occupancy of the vehicle flow of the current illumination area into a prediction network model to obtain the street lamp regulation and control time and the street lamp maintenance time of the prediction illumination area; the average vehicle speed and the lane occupancy of the vehicle flow are related to the congestion condition of the road, and the street lamp regulation and control time and the street lamp maintenance time are related to the congestion condition of the road; the output value of the prediction network model is transmitted to the control equipment of the prediction area through a wireless network, and the control equipment realizes the pre-adjustment of each street lamp in the prediction illumination area according to the received data; the slower the average vehicle speed of the vehicle flow and the larger the lane occupancy, the longer the street lamp regulation and control time and the street lamp maintenance time.
The prediction network model may be a neural network, or may be a prediction model such as a support vector machine.
The technical effects of the city street lamp data processing and joint regulation and control system based on artificial intelligence provided by the embodiment include: firstly, compared with the traditional mode of relying on manual regulation or a single control mode, the urban street lamp data processing and combined regulation and control system based on artificial intelligence provided by the embodiment avoids the problems of subjectivity, low working efficiency, no real-time property, untimely regulation and control and poor regulation effect of manual regulation and control, improves the working efficiency, improves the precision of system regulation and control, realizes the purpose of energy conservation, and enables the overall management to be more flexible; furthermore, the parameters used for the regulation were: the environmental characteristic and the traffic flow of the current illumination area and the traffic flow of at least two adjacent illumination areas in proper order before the current illumination area, respectively with the environmental characteristic of the current illumination area, and the traffic flow of the current illumination area and a plurality of illumination areas before it is input respectively into the corresponding regulation and control model, just can obtain two street lamp illumination degree indexes and colour temperature index, obtain final street lamp illumination degree index and final colour temperature index at last, in order to carry out street lamp regulation and control, promote regulation and control accuracy and reliability, can avoid appearing adjusting the condition of undulant too big in the accommodation process, improve the adaptability of system to the vehicle.
It should be noted that the order of the above-mentioned embodiments of the present invention is merely for description and does not represent the merits of the embodiments, and in some cases, actions or steps recited in the claims may be executed in an order different from the order of the embodiments and still achieve desirable results.
The embodiments in the present specification are all described in a progressive manner, and the same and similar parts among the various embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments.
Claims (5)
1. An artificial intelligence-based urban street lamp data processing and joint regulation and control system comprises a memory and a processor, and is characterized in that the processor executes a computer program stored in the memory to realize the following steps:
acquiring a target environmental characteristic and a first traffic flow of a current illumination area, and sequentially adjacent target environmental characteristics and first traffic flow before the current illumination area
A second vehicle flow of the first illumination area,
；
inputting the target environment characteristics into a preset first regulation and control model to obtain a first street lamp illumination degree index and a first color temperature index of a predicted illumination area, wherein the predicted illumination area is a next illumination area adjacent to the current illumination area;
inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area; wherein the second regulatory model comprises: the second road lamp illumination degree index and the second color temperature index are equal to the first vehicle flow and each second vehicle flow in a weighted sum, and the closer the distance between the second road lamp illumination degree index and the second color temperature index and the predicted illumination area is, the larger the illumination degree weight value and the color temperature weight value of the vehicle flow corresponding to the illumination area are;
integrating the first street lamp illumination degree index and the second street lamp illumination degree index to obtain a final street lamp illumination degree index, and integrating the first color temperature index and the second color temperature index to obtain a final color temperature index;
regulating and controlling the street lamps in the predicted illumination area according to the final street lamp illumination degree index and the final color temperature index;
the step of inputting the first vehicle flow and each second vehicle flow into a preset second regulation and control model to obtain a second road lamp illumination degree index and a second color temperature index of the predicted illumination area includes:
and calculating a second road lamp illumination degree index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
to predict the second street light illumination level indicator for the illumination area,
is the first traffic volume of the currently illuminated area,
the illumination degree weight corresponding to the first traffic flow of the current illumination area,
is the sequentially adjacent first before the current lighting area
A second volume of vehicle in the first illumination zone,
is the sequentially adjacent first before the current lighting area
The illumination degree weight corresponding to the first illumination area, and
；
a second color temperature indicator of the predicted illumination area is calculated according to the following model:
wherein the content of the first and second substances,
to predict the second color temperature indicator for the illuminated area,
is the first traffic volume of the currently illuminated area,
the color temperature weight corresponding to the first vehicle flow of the current illumination area,
is the first one adjacent to the current lighting area in sequence
Color temperature weights corresponding to second vehicle flows in the first illumination areas; and is
。
2. The system of claim 1, wherein the acquiring of the target environment characteristics of the current illumination area comprises:
acquiring a first environment characteristic sequence of each street lamp in a current illumination area according to a preset sampling period, wherein the first environment characteristic sequence comprises four first characteristic parameter sequences which are respectively a first temperature mean value sequence, a first haze concentration sequence, a first environment humidity sequence and a first noise intensity sequence;
carrying out error parameter filtering on the first environment characteristic sequence of each street lamp in the current illumination area according to the following error parameter filtering model, and reserving parameters which accord with the error parameter filtering model in the first environment characteristic sequence to obtain a second environment characteristic sequence of each street lamp in the current illumination area; the second environment characteristic sequence comprises four second characteristic parameter sequences, namely a second junction temperature mean value sequence, a second haze concentration sequence, a second environment humidity sequence and a second noise intensity sequence:
| F i,j,v _F i,j med) (|< Q j
wherein the content of the first and second substances,F i,j med) (is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The median of each element in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element; wherein the content of the first and second substances,
，
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
The number of elements in the first sequence of characteristic parameters,
is the first in the current illumination area
In the first environment characteristic sequence of the individual street lamp
A first characteristic parameter sequence
An element and a
A gradient between the individual elements;
calculated according to the following formula
：
Wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The number of elements in the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
In a second characteristic parameter sequence
The number of the elements is one,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
A first oneSecond in two characteristic parameter sequence
The weight corresponding to each element;
for the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of each second characteristic parameter sequence is subjected to reliability judgment according to the following reliability judgment model:
wherein the content of the first and second substances,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is the first in the current illumination area
Second environment characteristic sequence of individual street lamp
The value of the second sequence of characteristic parameters,
is a decision factor;
when the current illumination area is within
Second environment characteristic sequence of individual street lamp
When the second characteristic parameter sequence does not meet the reliability judgment model, the first characteristic parameter sequence is used for judging the first characteristic parameter sequence in the current illumination area
Deleting the second environment characteristic sequence of each street lamp;
respectively summing the values of the second environment characteristic sequences of all the street lamps in the current illumination area which are left in the current illumination area after deletion, and then calculating the average value to obtain the target environment characteristic of the current illumination area; the target environment characteristics comprise four target environment characteristic parameters, namely a target junction temperature mean value, a target haze concentration, a target environment humidity and a target noise intensity.
3. The system of claim 1, wherein the step of inputting the target environmental characteristics into a preset first control model to obtain a first street lamp illumination level indicator and a first color temperature indicator of a predicted illumination area comprises:
calculating a first street lamp illumination degree index of a predicted illumination area according to the following model:
wherein the content of the first and second substances,
to predict the area of illuminationA first street light illumination level indicator for a domain,
as the first of the target environmental features of the currently illuminated area
The characteristic parameter of the target environment is measured,
is the current illumination area
The weight corresponding to each target environment characteristic parameter;
calculating a first color temperature index of the predicted illumination area according to the following model:
wherein the content of the first and second substances,
is the first color temperature index of the current illumination area,
is the target environment characteristic of the current illumination area
The characteristic parameter of the target environment is measured,
is the target environment characteristic of the current illumination area
And the weight corresponding to each target environment characteristic parameter.
4. The system of claim 1, wherein the integrating the first street lamp illumination degree indicator and the second street lamp illumination degree indicator to obtain a final street lamp illumination degree indicator, and the integrating the first color temperature indicator and the second color temperature indicator to obtain a final color temperature indicator comprises:
calculating the final street lamp illumination degree index according to the following formula:
wherein the content of the first and second substances,
to predict the final street light lighting level indicator for the lighting area,
to predict a first street light illumination level indicator for an illumination area,
the second street lamp illumination degree index of the illumination area is predicted;
calculating a final color temperature index according to the following formula:
wherein the content of the first and second substances,
to predict the final color temperature index of the illumination area,
to predict a first color temperature indicator for an illumination area,
a second color temperature indicator for the predicted illumination area.
5. The system as claimed in claim 1, wherein after the controlling of the street lamps in the predicted lighting area according to the final street lamp lighting level indicator and the final color temperature indicator, the system further comprises:
obtaining the average vehicle speed and the lane occupancy of the vehicle flow in the current illumination area according to the first vehicle flow in the current illumination area;
calculating the lane occupancy of the current illumination area according to the following formula:
wherein the content of the first and second substances,
as the lane occupancy of the current illumination area,
for the length of the road segment of the current illumination area,
the total length of the vehicles running on the road section with the unit length of the current illumination area is obtained;
and inputting the average vehicle speed of the vehicle flow and the lane occupancy into a prediction network model to obtain the street lamp regulation and control time and the street lamp maintenance time of the prediction illumination area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111058581.0A CN113505346B (en) | 2021-09-10 | 2021-09-10 | Urban street lamp data processing and combined regulation and control system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111058581.0A CN113505346B (en) | 2021-09-10 | 2021-09-10 | Urban street lamp data processing and combined regulation and control system based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113505346A CN113505346A (en) | 2021-10-15 |
| CN113505346B true CN113505346B (en) | 2021-11-26 |
Family
ID=78017069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111058581.0A Active CN113505346B (en) | 2021-09-10 | 2021-09-10 | Urban street lamp data processing and combined regulation and control system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113505346B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115942564B (en) * | 2023-03-15 | 2023-07-18 | 广东中诚科技有限公司 | Municipal street lamp brightness control method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106304483A (en) * | 2016-08-19 | 2017-01-04 | 江苏长路能源科技发展有限公司 | Vcehicular tunnel wisdom green lighting system |
| CN109874202A (en) * | 2018-12-29 | 2019-06-11 | 中国计量大学 | The adaptive lighting system of kindergarten one classroom scene type, control device and control method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10499477B2 (en) * | 2013-03-18 | 2019-12-03 | Signify Holding B.V. | Methods and apparatus for information management and control of outdoor lighting networks |
| CN112738959B (en) * | 2021-02-04 | 2022-11-01 | 江苏未来城市公共空间开发运营有限公司 | Urban road street lamp energy saving and emission reduction control system based on smart city |
| CN113015297B (en) * | 2021-02-22 | 2022-08-26 | 上海工程技术大学 | Road intelligent lighting system based on traffic flow prediction |
-
2021
- 2021-09-10 CN CN202111058581.0A patent/CN113505346B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106304483A (en) * | 2016-08-19 | 2017-01-04 | 江苏长路能源科技发展有限公司 | Vcehicular tunnel wisdom green lighting system |
| CN109874202A (en) * | 2018-12-29 | 2019-06-11 | 中国计量大学 | The adaptive lighting system of kindergarten one classroom scene type, control device and control method |
Non-Patent Citations (2)
| Title |
|---|
| Boris A等."Interactive Scenario-Based Assessment Approach of Urban Street Lighting and Its Application to Estimating Energy Saving Benefits".《ResearchGate》.2021, * |
| 谭童."基于环境及道路状态的智慧照明控制策略研究".《中国硕士学位论文全文数据库》.2020, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113505346A (en) | 2021-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109754597B (en) | Urban road regional congestion regulation and control strategy recommendation system and method | |
| CN110969871B (en) | Intelligent traffic light control system and control method | |
| CN111586944B (en) | Highway tunnel intelligent illumination control system and method based on ETC portal system | |
| WO2019214016A1 (en) | Lora technology-based multi-functional led smart street lamp system | |
| CN111739315B (en) | Intelligent green wave adjusting method and intelligent green wave adjusting system for traffic signal lamp | |
| CN113505346B (en) | Urban street lamp data processing and combined regulation and control system based on artificial intelligence | |
| CN108966448A (en) | A kind of light dynamic regulation method based on adaptive fuzzy decision tree | |
| CN113096418A (en) | Traffic network traffic light control method and system based on edge calculation and computer readable storage medium | |
| CN115767842A (en) | Intelligent lighting control method and system based on real-time environment information | |
| CN112738959A (en) | Urban road street lamp energy saving and emission reduction control system based on smart city | |
| CN115862315B (en) | Traffic light control method and device for smart city multi-source heterogeneous data stream | |
| CN109615885A (en) | A kind of intelligent traffic signal control method, apparatus and system | |
| CN111899537A (en) | Intersection signal control mobile tuning device and method based on edge calculation | |
| CN114333359A (en) | Artificial intelligence-based self-adaptive traffic signal lamp control method and system | |
| CN117152981B (en) | Intelligent traffic light control method and system | |
| CN118075299A (en) | Smart city system based on Internet of things | |
| CN117915526A (en) | Road tunnel illumination control model construction and control method based on multiple parameters | |
| CN118168106A (en) | Air conditioner control system and method for terminal building based on Internet of things | |
| Xie et al. | Design of intelligent traffic light system based on genetic algorithm to optimize Elman neural network | |
| CN114913683A (en) | Traffic signal lamp monitoring system | |
| CN116311999A (en) | Bus-priority traffic light real-time timing optimization method, system and device | |
| CN117334042A (en) | Intelligent traffic management system and method based on artificial intelligence | |
| CN114786317A (en) | Intelligent control method of intelligent street lamp | |
| Sun et al. | Intelligent Highway Traffic Detection Algorithm based on Deep Learning | |
| CN118338507A (en) | Intelligent traffic street lamp detection big data analysis processing system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 226200 group 11, Chengbei Village, Huilong Town, Qidong City, Nantong City, Jiangsu Province Patentee after: Jiangsu Dianboshi Energy Equipment Co.,Ltd. Address before: 226000 group 11, Chengbei Village, Huilong Town, Qidong City, Nantong City, Jiangsu Province Patentee before: Nantong Electric doctor automation equipment Co.,Ltd. |
|
| CP03 | Change of name, title or address |