CN112738959A - Urban road street lamp energy saving and emission reduction control system based on smart city - Google Patents

Urban road street lamp energy saving and emission reduction control system based on smart city Download PDF

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CN112738959A
CN112738959A CN202110156985.7A CN202110156985A CN112738959A CN 112738959 A CN112738959 A CN 112738959A CN 202110156985 A CN202110156985 A CN 202110156985A CN 112738959 A CN112738959 A CN 112738959A
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street lamp
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柳旭
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Jiangsu Future Urban Public Space Development And Operation Co ltd
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    • H—ELECTRICITY
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Abstract

The invention discloses an urban road street lamp energy-saving emission-reducing control system based on a smart city, and belongs to the technical field of smart city traffic energy conservation. The invention provides an urban road street lamp energy-saving emission-reducing control system based on a smart city, which comprises an acquisition module, a processing module, an adjusting module, a prediction module and an instruction adjusting module, wherein the acquisition module is used for acquiring a street lamp energy-saving emission-reducing control signal; the acquisition module is used for acquiring relevant state information of a target vehicle; the processing module is used for processing and judging the information of the target vehicle; the adjusting module is used for adjusting the irradiation angle of the street lamp; the prediction module is used for predicting the starting density of the street lamp according to the current environment; the instruction adjusting module is used for adjusting the starting of the street lamp according to the prediction result; according to the invention, on the premise of ensuring traffic safety, brand-new regulation and restriction on the starting density of the street lamps are carried out, the deflection angle of the street lamps is defined, and the effects of energy conservation and emission reduction of the street lamps in smart cities are achieved.

Description

Urban road street lamp energy saving and emission reduction control system based on smart city
Technical Field
The invention relates to the technical field of smart city traffic energy conservation, in particular to an urban road street lamp energy conservation and emission reduction control system based on a smart city.
Background
With the development of new-generation information technologies represented by the internet of things, cloud computing and the mobile internet, the social innovation concept of the smart city is gradually developed and opened. The smart city utilizes various information technologies or innovative concepts to open and integrate the system and service of the city so as to improve the efficiency of resource application, optimize city management and service and improve the quality of life of citizens.
In urban roads, street lamps are one of indispensable elements, but a large amount of resources are wasted; firstly, in some road sections or some time, almost no vehicle passes at night, and street lamps can be used for a long time, so that great waste is caused; secondly, in the street lamp irradiation range, at night, the vehicle traffic rate is low, in order to cover the illumination range of large area, it is often realized to adopt the light pole of increase, in order to improve luminance, often adopt the mode of high power, but in the actual need, the vehicle is passed and is only needed a lane, and need not illuminate all lanes, consequently also can cause the extravagant condition of certain degree.
Therefore, in the construction of smart cities, people urgently need an urban road street lamp control system capable of saving energy and reducing emission.
Disclosure of Invention
The invention aims to provide an urban road street lamp energy-saving emission-reducing control system based on a smart city, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an urban road street lamp energy-saving emission-reducing control system based on a smart city comprises an acquisition module, a processing module, an adjusting module, a prediction module and an instruction adjusting module;
the acquisition module is used for acquiring relevant state information of a target vehicle; the processing module is used for processing and judging the information of the target vehicle; the adjusting module is used for adjusting the irradiation angle of the street lamp; the prediction module is used for predicting the starting density of the street lamp according to the current environment; the instruction adjusting module is used for adjusting the starting of the street lamp according to the prediction result;
the output end of the acquisition module is electrically connected with the input end of the processing module; the output end of the processing module is electrically connected with the input end of the adjusting module; the output end of the prediction module is electrically connected with the input end of the instruction adjusting module.
According to the technical scheme, the acquisition module comprises a first acquisition unit, a second acquisition unit and a third acquisition unit; the first acquisition unit is used for acquiring the linear distance between a target vehicle and the street lamp in the lane line direction; the second acquisition unit is used for acquiring initial state information of the target vehicle; the third acquisition unit is used for acquiring information in the running state of the target vehicle.
According to the technical scheme, when the linear distance between the target vehicle and the street lamp in the lane line direction, acquired by the first acquisition unit, is lower than a threshold value A, the second acquisition unit and the third acquisition unit are started;
the initial state information of the target vehicle collected in the second collecting unit comprises the distance between the target vehicle and a lane line closest to the street lamp and the initial running speed of the target vehicle;
the information in the running state of the target vehicle collected by the third collecting unit comprises the running time of the target vehicle, the steering angle of the front wheels of the target vehicle in a fixed preset time period and the running acceleration of the target vehicle.
In the first acquisition unit, the linear distance between the target vehicle and the street lamp in the lane line direction is the distance between the target vehicle and the street lamp in the parallel direction, and a distance threshold is set here, which is intended for two reasons, firstly, the acquisition unit is prevented from being started all the time when no vehicle passes, so that the resource waste is caused; secondly, the system is started at a certain distance, so that the prediction can be effectively carried out in advance, and the prediction time is prevented from being insufficient due to too short distance; in the second acquisition unit and the third acquisition unit, the initial state and the advancing state of the whole target vehicle are acquired, the current state of the target vehicle is determined, the lane where the target vehicle runs is judged in the later stage, and data support is provided for the accurate judgment in the later stage.
According to the technical scheme, the processing module comprises a data processing unit and a lane change judging unit;
the data processing unit is used for further processing the data in the acquisition module to obtain the change condition of the target vehicle in the running process; and the lane change judging unit is used for judging whether the target vehicle is ready to change lanes or not according to the result of the data processing unit.
According to the technical scheme, the data processing unit performs the following data processing process:
according to the formula: dx＝tanβi((v0+at1)t2+at2 2)+D0；
Wherein D isxThe horizontal distance between the front wheel and the lane line closest to the street lamp in the running process of the target vehicle; beta is aiFor a front wheel steering angle of the target vehicle for a fixed preset time period, tan β is specifiediTaking a positive value when deflecting left, and taking a negative value when deflecting right; d0The horizontal distance between the front wheels of the target vehicle and the street lamp in the initial state is obtained; v. of0Is the initial running speed of the target vehicle; t is t1The running time when the steering angle of the front wheels of the target vehicle is not changed; t is t2The driving time after the steering angle of the front wheels of the target vehicle is changed; a is the running acceleration of the target vehicle;
in the formula, the current running speed of the automobile is solved, and the automobile is regarded as uniform acceleration; after deflection, the original driving path forms an included angle beta with the deflection path directioniThus according to tan betaiSolving the horizontal distance between the front wheels and the lane line closest to the street lamp in the running process of the target vehicle, wherein the horizontal distance is increased or decreased relative to the original horizontal distance due to different deflection directions, and in the domestic practical situation, the right-hand traffic is mainly used, so that a rule is made, a positive value is taken during left deflection, and a negative value is taken during right deflection, so that the lane where the target vehicle runs near the street lamp is solved;
the lane change judging unit judges the following process:
when beta isiAnd when the threshold value B is exceeded, judging that the vehicle changes the lane.
A threshold value is set, and the threshold value is mainly used for preventing the automobile from deflecting a little bit during running and being misjudged as lane change by a system, so that the judgment accuracy is improved by setting the threshold value.
According to the technical scheme, the adjusting mode of the adjusting module is as follows:
when the vehicle changes lanes, the street lamp is deflected, so that the irradiation range of the maximum brightness of the street lamp covers the target vehicle;
according to the formula:
Figure BDA0002934056950000041
wherein theta is1Is the deflection angle of the street lamp, D1The horizontal distance between the light irradiation critical point close to one side of the street lamp and the street lamp; h is the height of the street lamp;
when D is presentx＝D1In time, the irradiation range of the street lamp just covers the target vehicle.
After the horizontal distance between the target vehicle and the street lamp is obtained, the street lamp is deflected, so that the street lamp can just irradiate the lane where the vehicle runs.
According to the technical scheme, the prediction module comprises a large database, a prediction unit and a function unit;
the big database is used for providing and storing various historical big data of a city; the prediction unit is used for predicting the lighting density of the street lamps at night every day according to historical big data; the function unit is used for generating a function model and realizing automatic control.
According to the above technical solution, the prediction unit prediction process is as follows:
s8-1, acquiring a correlation function of each environmental factor in the city in a conventional state and an influence function of each environmental factor on the starting density of the street lamps through a large database;
s8-2, collecting data of each environmental factor in the city in real time under the current state, determining the relevance of each environmental factor in the city, and determining whether the environmental factors are consistent with a historical data model; predicting the street lamp density of a city needing to be started, and determining whether the street lamp density is matched with a historical data model;
s8-3, if the road lamp density is consistent with the road lamp density, directionally monitoring each environment factor of the city in the current state according to the correlation function of each environment factor in the city in the conventional state in the big database and the influence function of each environment factor on the road lamp density, and predicting the road lamp density of the city needing to be started;
and S8-4, if the road lamp density is not matched with the road lamp density, acquiring a plurality of groups of dynamic data of each environmental factor in the city under the current state, continuously improving and perfecting the original model, adjusting the influence weight, and predicting the road lamp density which needs to be started in the city.
According to the technical scheme, in the steps S8-1 to S8-4, each environment factor inside the city comprises the accident rate k1And the number k of vehicles running at night2Holiday time k3Environmental visibility k4；
Wherein, the accident rate k1Is the main influencing factor, and forms a correlation function with other environmental factors as follows: k is a radical of1＝m1k2+b1；k1＝m2k3+b2；k1＝m3k4+b3；
Wherein m is1、m2、m3Is a proportionality coefficient; b1、b2、b3Is a tuning constant;
number k of vehicles running at night2And holiday time k3There is also a correlation function, as follows:
k2＝m4k3+b4；
wherein m is4Is a proportionality coefficient; b4Is a tuning constant;
according to a formula, predicting the street lamp density required to be switched on in a city:
Figure BDA0002934056950000051
Figure BDA0002934056950000052
Figure BDA0002934056950000053
Figure BDA0002934056950000054
wherein L is1-L4Respectively predicting the influence level of each environmental factor on each day;
Figure BDA0002934056950000055
respectively weighting the influence of each environmental factor of the ith day in the historical data;
Figure BDA0002934056950000056
respectively predicting the respective influence weight of each environmental factor on the day;
setting a threshold value for the influence level of each environmental factor;
setting the opening density of the street lamp to be 1-5; respectively corresponding to the number 0-4 of the influence levels of the environmental factors exceeding the threshold value;
predicting the street lamp opening density grade according to the number exceeding the threshold value;
and if the current data are found to have mutation conditions in the prediction process, the current data do not conform to the original model, and the street lamp opening density grade is predicted after the influence weight is adjusted.
In the whole prediction process, a model is established by using the historical data in a normal state, then the current environment is collected in real time, the night condition is predicted in the day, the weight ratio is used as an adjusting factor, the influence level of each environment factor is used as a judgment standard, and the street lamp opening density is adjusted;
the street lamp opening density grade is 1-5 grade;
wherein the starting density of the street lamps is 1 in every 3 busy road sections and one in every 5 cold clear road sections at the level of 1;
the street lamp starting density is 2 grades, 1 is opened for every 2 busy road sections, and one is opened for every 4 cold clear road sections;
the street lamp starting density is 3 grades, namely the street lamps in the busy road section are fully opened, and one street lamp is opened in each 3 street lamps in the cold and clear road sections;
the street lamp starting density is 4 grades, the street lamps in a busy road section are fully opened, and one street lamp is opened in each 2 street lamps in a cold and clear road section;
the street lamp opening density level is that all street lamps are fully opened;
according to the technical scheme, the instruction adjusting module comprises a receiving unit and an adjusting unit;
the receiving unit is used for receiving the real-time prediction information of the prediction module; the adjusting unit is used for continuously adjusting the opening density grade of the street lamp.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the corresponding lane girth on which the target vehicle runs is illuminated by adjusting the deflection angle of the street lamp, the height of a street lamp pole and the street lamp power can be reduced to a certain extent, the illumination brightness is improved, the night vehicle passing is facilitated, and the effects of energy conservation and emission reduction of the street lamp under an intelligent city are achieved on the premise of ensuring the traffic safety; meanwhile, the invention also predicts the starting density of the street lamp according to the historical environmental factors and the current environmental factors, and comprehensively regulates the starting mode of the street lamp, thereby realizing energy conservation and emission reduction and avoiding waste.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an urban road street lamp energy conservation and emission reduction control system based on a smart city;
FIG. 2 is a schematic diagram of the prediction step of the urban road street lamp energy-saving emission-reducing control system based on the smart city;
FIG. 3 is a flow chart of an urban road street lamp energy conservation and emission reduction control system based on a smart city;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the 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, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides the following technical solutions: in fig. 1, an urban road street lamp energy conservation and emission reduction control system based on a smart city comprises an acquisition module, a processing module, an adjustment module, a prediction module and an instruction adjustment module;
the acquisition module is used for acquiring relevant state information of a target vehicle; the processing module is used for processing and judging the information of the target vehicle; the adjusting module is used for adjusting the irradiation angle of the street lamp; the prediction module is used for predicting the starting density of the street lamp according to the current environment; the instruction adjusting module is used for adjusting the starting of the street lamp according to the prediction result;
the output end of the acquisition module is electrically connected with the input end of the processing module; the output end of the processing module is electrically connected with the input end of the adjusting module; the output end of the prediction module is electrically connected with the input end of the instruction adjusting module.
The acquisition module comprises a first acquisition unit, a second acquisition unit and a third acquisition unit; the first acquisition unit is used for acquiring the linear distance between a target vehicle and the street lamp in the lane line direction; the second acquisition unit is used for acquiring initial state information of the target vehicle; the third acquisition unit is used for acquiring information in the running state of the target vehicle.
When the linear distance between the target vehicle and the street lamp in the lane line direction, acquired by the first acquisition unit, is lower than a threshold value A, starting a second acquisition unit and a third acquisition unit;
the initial state information of the target vehicle collected in the second collecting unit comprises the distance between the target vehicle and a lane line closest to the street lamp and the initial running speed of the target vehicle;
the information in the running state of the target vehicle collected by the third collecting unit comprises the running time of the target vehicle, the steering angle of the front wheels of the target vehicle in a fixed preset time period and the running acceleration of the target vehicle.
The processing module comprises a data processing unit and a lane change judging unit;
the data processing unit is used for further processing the data in the acquisition module to obtain the change condition of the target vehicle in the running process; and the lane change judging unit is used for judging whether the target vehicle is ready to change lanes or not according to the result of the data processing unit.
The data processing unit performs the following data processing procedures:
according to the formula: dx＝tanβi((v0+at1)t2+at2 2)+D0；
Wherein D isxThe horizontal distance between the front wheel and the lane line closest to the street lamp in the running process of the target vehicle; beta is aiFor a front wheel steering angle of the target vehicle for a fixed preset time period, tan β is specifiediTaking a positive value when deflecting left, and taking a negative value when deflecting right; d0The horizontal distance between the front wheels of the target vehicle and the street lamp in the initial state is obtained; v. of0Is the initial running speed of the target vehicle; t is t1The running time when the steering angle of the front wheels of the target vehicle is not changed; t is t2The driving time after the steering angle of the front wheels of the target vehicle is changed; a is the running acceleration of the target vehicle;
the lane change judging unit judges the following process:
when beta isiAnd when the threshold value B is exceeded, judging that the vehicle changes the lane.
According to the technical scheme, the adjusting mode of the adjusting module is as follows:
when the vehicle changes lanes, the street lamp is deflected, so that the irradiation range of the maximum brightness of the street lamp covers the target vehicle;
according to the formula:
Figure BDA0002934056950000091
wherein theta is1Is the deflection angle of the street lamp, D1Is near the critical point of light irradiation on one side of the street lampHorizontal distance from the street lamp; h is the height of the street lamp;
when D is presentx＝D1In time, the irradiation range of the street lamp just covers the target vehicle.
The prediction module comprises a large database, a prediction unit and a function unit;
the big database is used for providing and storing various historical big data of a city; the prediction unit is used for predicting the lighting density of the street lamps at night every day according to historical big data; the function unit is used for generating a function model and realizing automatic control.
In fig. 2, the prediction unit prediction process is as follows:
s8-1, acquiring a correlation function of each environmental factor in the city in a conventional state and an influence function of each environmental factor on the starting density of the street lamps through a large database;
s8-2, collecting data of each environmental factor in the city in real time under the current state, determining the relevance of each environmental factor in the city, and determining whether the environmental factors are consistent with a historical data model; predicting the street lamp density of a city needing to be started, and determining whether the street lamp density is matched with a historical data model;
s8-3, if the road lamp density is consistent with the road lamp density, directionally monitoring each environment factor of the city in the current state according to the correlation function of each environment factor in the city in the conventional state in the big database and the influence function of each environment factor on the road lamp density, and predicting the road lamp density of the city needing to be started;
and S8-4, if the road lamp density is not matched with the road lamp density, acquiring a plurality of groups of dynamic data of each environmental factor in the city under the current state, continuously improving and perfecting the original model, adjusting the influence weight, and predicting the road lamp density which needs to be started in the city.
In steps S8-1 to S8-4, each environmental factor inside the city includes an accident occurrence rate k1And the number k of vehicles running at night2Holiday time k3Environmental visibility k4；
Wherein, the accident rate k1Is the main influencing factor, and forms a correlation function with other environmental factors as follows: k is a radical of1＝m1k2+b1；k1＝m2k3+b2；k1＝m3k4+b3；
Wherein m is1、m2、m3Is a proportionality coefficient; b1、b2、b3Is a tuning constant;
number k of vehicles running at night2And holiday time k3There is also a correlation function, as follows:
k2＝m4k3+b4；
wherein m is4Is a proportionality coefficient; b4Is a tuning constant;
according to a formula, predicting the street lamp density required to be switched on in a city:
Figure BDA0002934056950000101
Figure BDA0002934056950000102
Figure BDA0002934056950000103
Figure BDA0002934056950000104
wherein L is1-L4Respectively predicting the influence level of each environmental factor on each day;
Figure BDA0002934056950000105
respectively weighting the influence of each environmental factor of the ith day in the historical data;
Figure BDA0002934056950000106
respectively predicting the respective influence weight of each environmental factor on the day;
setting a threshold value for the influence level of each environmental factor;
setting the opening density of the street lamp to be 1-5; respectively corresponding to the number 0-4 of the influence levels of the environmental factors exceeding the threshold value;
predicting the street lamp opening density grade according to the number exceeding the threshold value;
and if the current data are found to have mutation conditions in the prediction process, the current data do not conform to the original model, and the street lamp opening density grade is predicted after the influence weight is adjusted.
The instruction adjusting module comprises a receiving unit and an adjusting unit;
the receiving unit is used for receiving the real-time prediction information of the prediction module; the adjusting unit is used for continuously adjusting the opening density grade of the street lamp.
In this embodiment 1, the target vehicle initial speed is set to 7m/s, the distance threshold a is 2000m, the acceleration is 0, the front wheel deflection angle is 15 degrees, the horizontal distance between the target vehicle front wheel and the street lamp in the initial state is 4m, and the running time after the target vehicle front wheel steering angle is changed is 4s, according to the formula:
Dx＝tanβi((v0+at1)t2+at2 2)+D0＝tan15°*7*4+4＝11.28
setting the height of the street lamp to be 2.5m according to a formula:
Figure BDA0002934056950000111
when D is present1＝DxWhile, the deflection angle theta1The street lamp deflection angle is 12 degrees, namely the street lamp deflection angle is 12 degrees, so that the street lamp can just irradiate a target vehicle driving lane;
in this embodiment 2, the environmental factors in the city are set to include the accident rate k1And the number k of vehicles running at night2Holiday time k3Environmental visibility k4；
Wherein, the accident rate k1Is a main influence factor and forms a correlation function with other environmental factorsThe following were used: k is a radical of1＝m1k2+b1；k1＝m2k3+b2；k1＝m3k4+b3；
Wherein m is1、m2、m3Is a proportionality coefficient; b1、b2、b3Is a tuning constant;
randomly taking 10 groups of historical data, predicting that the respective influence weights of the environmental factors on the day are respectively 75%, 10%, 5% and 10%, and according to a formula:
Figure BDA0002934056950000121
Figure BDA0002934056950000122
Figure BDA0002934056950000123
Figure BDA0002934056950000124
wherein L is1-L4Respectively predicting the influence level of each environmental factor on each day;
Figure BDA0002934056950000125
respectively weighting the influence of each environmental factor of the ith day in the historical data;
Figure BDA0002934056950000126
respectively predicting the respective influence weight of each environmental factor on the day;
solve to L1-L4Setting the influence level setting threshold of each environmental factor to be F1-F4In which L is found1And L3Exceeds a threshold level F1And F3Therefore, the street lamp is judged to be in the opening density grade of 3, namely the street lamp is fully opened in a busy road section, and one street lamp is opened in every 3 cold and clear road sections.
The working principle of the invention is as follows: the invention utilizes the acquisition module to acquire the basic state information of the target vehicle and provide the basic state information to the processing module; analyzing and processing data by using a processing module, and accurately judging a driving lane of a target vehicle; the adjusting module is used for adjusting the deflection angle of the street lamp, so that the irradiation range of the street lamp is accurately irradiated on a driving lane of a target vehicle, and the irradiation brightness can be improved; the forecasting module is used for analyzing each influence factor, forecasting the street lamp opening density grade in advance, and the accuracy of the model can be guaranteed; and adjusting the starting density of the street lamps in real time by using the instruction adjusting module.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides an urban road street lamp energy saving and emission reduction control system based on wisdom city which characterized in that: the system comprises an acquisition module, a processing module, an adjustment module, a prediction module and an instruction regulation module;
the acquisition module is used for acquiring relevant state information of a target vehicle; the processing module is used for processing and judging the information of the target vehicle; the adjusting module is used for adjusting the irradiation angle of the street lamp; the prediction module is used for predicting the starting density of the street lamp according to the current environment; the instruction adjusting module is used for adjusting the starting of the street lamp according to the prediction result;
the output end of the acquisition module is electrically connected with the input end of the processing module; the output end of the processing module is electrically connected with the input end of the adjusting module; the output end of the prediction module is electrically connected with the input end of the instruction adjusting module.
2. The urban road street lamp energy-saving emission-reducing control system based on smart city according to claim 1, characterized in that: the acquisition module comprises a first acquisition unit, a second acquisition unit and a third acquisition unit; the first acquisition unit is used for acquiring the linear distance between a target vehicle and the street lamp in the lane line direction; the second acquisition unit is used for acquiring initial state information of the target vehicle; the third acquisition unit is used for acquiring information in the running state of the target vehicle.
3. The urban road street lamp energy-saving emission-reduction control system based on smart city according to claim 2, characterized in that: when the linear distance between the target vehicle and the street lamp in the lane line direction, acquired by the first acquisition unit, is lower than a threshold value A, starting a second acquisition unit and a third acquisition unit;
the initial state information of the target vehicle collected in the second collecting unit comprises the distance between the target vehicle and a lane line closest to the street lamp and the initial running speed of the target vehicle;
the information in the running state of the target vehicle collected by the third collecting unit comprises the running time of the target vehicle, the steering angle of the front wheels of the target vehicle in a fixed preset time period and the running acceleration of the target vehicle.
4. The urban road street lamp energy-saving emission-reducing control system based on smart city according to claim 1, characterized in that: the processing module comprises a data processing unit and a lane change judging unit;
the data processing unit is used for further processing the data in the acquisition module to obtain the change condition of the target vehicle in the running process; and the lane change judging unit is used for judging whether the target vehicle is ready to change lanes or not according to the result of the data processing unit.
5. The urban road street lamp energy-saving emission-reduction control system based on smart city according to claim 4, characterized in that: the data processing unit performs the following data processing procedures:
according to the formula: dx＝tanβi((v0+at1)t2+at2 2)+D0；
Wherein D isxThe horizontal distance between the front wheel and the lane line closest to the street lamp in the running process of the target vehicle; beta is aiFor a front wheel steering angle of the target vehicle for a fixed preset time period, tan β is specifiediTaking a positive value when deflecting left, and taking a negative value when deflecting right; d0The horizontal distance between the front wheels of the target vehicle and the street lamp in the initial state is obtained; v. of0Is the initial running speed of the target vehicle; t is t1The running time when the steering angle of the front wheels of the target vehicle is not changed; t is t2The driving time after the steering angle of the front wheels of the target vehicle is changed; a is the running acceleration of the target vehicle;
the lane change judging unit judges the following process:
when beta isiAnd when the threshold value B is exceeded, judging that the vehicle changes the lane.
6. The urban road street lamp energy-saving emission-reducing control system based on smart city according to claim 5, characterized in that: the adjustment mode of the adjustment module is as follows:
when the vehicle changes lanes, the street lamp is deflected, so that the irradiation range of the maximum brightness of the street lamp covers the target vehicle;
according to the formula:
Figure FDA0002934056940000021
wherein theta is1Is the deflection angle of the street lamp, D1The horizontal distance between the light irradiation critical point close to one side of the street lamp and the street lamp; h is the height of the street lamp;
when D is presentx＝D1In time, the irradiation range of the street lamp just covers the target vehicle.
7. The urban road street lamp energy-saving emission-reducing control system based on smart city according to claim 1, characterized in that: the prediction module comprises a large database, a prediction unit and a function unit;
the big database is used for providing and storing various historical big data of a city; the prediction unit is used for predicting the lighting density of the street lamps at night every day according to historical big data; the function unit is used for generating a function model and realizing automatic control.
8. The urban road street lamp energy-saving emission-reduction control system based on smart city according to claim 7, characterized in that: the prediction unit prediction process is as follows:
s8-1, acquiring a correlation function of each environmental factor in the city in a conventional state and an influence function of each environmental factor on the starting density of the street lamps through a large database;
s8-2, collecting data of each environmental factor in the city in real time under the current state, determining the relevance of each environmental factor in the city, and determining whether the environmental factors are consistent with a historical data model; predicting the street lamp density of a city needing to be started, and determining whether the street lamp density is matched with a historical data model;
s8-3, if the road lamp density is consistent with the road lamp density, directionally monitoring each environment factor of the city in the current state according to the correlation function of each environment factor in the city in the conventional state in the big database and the influence function of each environment factor on the road lamp density, and predicting the road lamp density of the city needing to be started;
and S8-4, if the road lamp density is not matched with the road lamp density, acquiring a plurality of groups of dynamic data of each environmental factor in the city under the current state, continuously improving and perfecting the original model, adjusting the influence weight, and predicting the road lamp density which needs to be started in the city.
9. The urban road street lamp energy-saving emission-reduction control system based on smart city according to claim 8, characterized in that: in steps S8-1 to S8-4, each environmental factor inside the city includes an accident occurrence rate k1And the number k of vehicles running at night2Holiday time k3Environmental visibility k4；
Wherein, the accident rate k1Is the main influencing factor, and forms a correlation function with other environmental factors as follows: k is a radical of1＝m1k2+b1；k1＝m2k3+b2；k1＝m3k4+b3；
Wherein m is1、m2、m3Is a proportionality coefficient; b1、b2、b3Is a tuning constant;
number k of vehicles running at night2And holiday time k3There is also a correlation function, as follows:
k2＝m4k3+b4；
wherein m is4Is a proportionality coefficient; b4Is a tuning constant;
according to a formula, predicting the street lamp density required to be switched on in a city:
Figure FDA0002934056940000041
Figure FDA0002934056940000042
Figure FDA0002934056940000043
Figure FDA0002934056940000044
wherein L is1-L4Respectively predicting the influence level of each environmental factor on each day;
Figure FDA0002934056940000045
respectively weighting the influence of each environmental factor of the ith day in the historical data;
Figure FDA0002934056940000046
respectively predicting the respective influence weight of each environmental factor on the day;
setting a threshold value for the influence level of each environmental factor;
setting the opening density of the street lamp to be 1-5; respectively corresponding to the number 0-4 of the influence levels of the environmental factors exceeding the threshold value;
predicting the street lamp opening density grade according to the number exceeding the threshold value;
and if the current data are found to have mutation conditions in the prediction process, the current data do not conform to the original model, and the street lamp opening density grade is predicted after the influence weight is adjusted.
10. The urban road street lamp energy-saving emission-reducing control system based on smart city according to claim 1, characterized in that: the instruction adjusting module comprises a receiving unit and an adjusting unit;
the receiving unit is used for receiving the real-time prediction information of the prediction module; the adjusting unit is used for continuously adjusting the opening density grade of the street lamp.
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