Intelligent Lighting Control Method and System along Urban Traffic Roads

Abstract

The present invention provides a method and system for intelligent lighting control along urban traffic roads. By performing big data analysis on the urban traffic road network, information on the traffic flow status of the road network is obtained, and based on this, the areas along the target roads that require active lighting adjustment are identified. Information on the presence status of objects is visually recognized from the areas along the target roads, and based on this, the sub-areas that require lighting adjustment are determined, providing accurate position guidance for subsequent lighting devices to perform directional lighting adjustment on the areas along the target roads. Also, based on the distribution position information of all lighting devices in the areas along the target roads and the sub-areas that require lighting adjustment, the matching lighting devices for performing lighting adjustment on the sub-areas that require lighting adjustment are identified, and in combination with their lighting characteristic information, the lighting status of the sub-areas is adjusted, effectively combining the traffic conditions of urban traffic roads to accurately adjust the operation of lighting devices along the roads, and realizing intelligent lighting control of the areas along the roads.

Description

Technical Field

[0001] The present invention relates to the field of smart city management, and particularly to a smart lighting control method and system for urban traffic roads along the line. Background Art

[0002] As a vehicle driving road, urban traffic roads have specific requirements for lighting. Under normal circumstances, if the traffic flow on a certain traffic road can maintain a high level for a long time, then even if the street lamp equipment along the road is damaged and cannot provide normal lighting at night, the lighting provided by the vehicle lights on the traffic road can meet the corresponding lighting requirements, and there is no need to increase the luminous intensity of the street lamp equipment additionally; if the traffic flow on a certain traffic road continues to be at a low level, the lighting provided by the vehicle lights on the traffic road cannot meet the most basic lighting needs. At this time, it is necessary to adjust the lighting of the street lamp equipment specifically, and maximize the lighting function of the street lamp equipment under its limited lighting capacity to ensure that effective lighting conditions can be maintained along the traffic road. The existing technology lacks a lighting adjustment control scheme for urban traffic roads along the line, and cannot effectively combine the traffic conditions of urban traffic roads to accurately adjust the work of the street lamp equipment along the road, and cannot achieve intelligent and efficient lighting control of the area along the road. Summary of the Invention

[0003] The purpose of the present invention is to provide a smart lighting control method and system for urban traffic roads along the line, perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, thereby identifying the target road along the line area that needs to be actively adjusted for lighting, and screening different road along the line areas in combination with the traffic flow of the road to determine the areas that require auxiliary lighting by lighting equipment; identify the presence state information of objects from the visual recognition of the target road along the line area, thereby determining the sub-areas that need to be adjusted for lighting, and providing accurate position guidance for the subsequent lighting equipment to perform directional lighting adjustment on the target road along the line area; also based on the distribution position information of all lighting equipment in the target road along the line area and the sub-areas that need to be adjusted for lighting, identify the matching lighting equipment for lighting adjustment of the sub-areas that need to be adjusted for lighting, and combine its lighting characteristic information to adjust its lighting state for the sub-areas, effectively combine the traffic conditions of urban traffic roads to accurately adjust the work of the lighting equipment along the road, and achieve intelligent lighting control of the area along the road.

[0004] The present invention is realized through the following technical solutions:

[0005] A smart lighting control method for urban traffic roads along the line, comprising:

[0006] Perform big data analysis on the urban traffic road network to obtain traffic flow status information of the road network; based on the traffic flow status information of the road network, identify the target road along areas within the urban traffic road network that require active lighting adjustment;

[0007] Perform visual recognition on the target road along areas to obtain object presence status information of the target road along areas; based on the object presence status information, identify sub-areas within the target road along areas that require lighting adjustment;

[0008] Based on the distribution location information of all lighting devices in the target road along areas and the sub-areas that require lighting adjustment, identify the matching lighting devices for lighting adjustment of the sub-areas that require lighting adjustment; based on the lighting characteristic information of the matching lighting devices, adjust the lighting status of the matching lighting devices for the sub-areas that require lighting adjustment.

[0009] Optionally, performing big data analysis on the urban traffic road network to obtain traffic flow status information of the road network; based on the traffic flow status information of the road network, identifying the target road along areas within the urban traffic road network that require active lighting adjustment includes:

[0010] Based on the location information of the urban traffic road network, obtain the traffic big data corresponding to the preset time interval of the urban traffic road network; wherein, the traffic big data includes the global vehicle driving path big data and the global vehicle driving speed big data within the urban traffic road network; analyze the traffic big data to obtain the traffic flow density change status information of each road section within the urban traffic road network, and use this as the traffic flow status information of the road network; wherein, the traffic flow density change status information includes the change information of the number of vehicles per unit length within each road section over time;

[0011] Based on the traffic flow density change status information of each road section and the vehicle lighting range information, determine whether each road section along area obtains sufficient vehicle lighting; if not, determine the corresponding road section along area as the target road along area within the urban traffic road network that requires active lighting adjustment; if so, do not determine the corresponding road section along area as the target road along area within the urban traffic road network that requires active lighting adjustment.

[0012] Optionally, based on the traffic flow density change status information of each road section and the vehicle lighting range information, determining whether each road section along area obtains sufficient vehicle lighting includes:

[0013] Extract the change information of the number of vehicles per unit length within each road section included in the traffic flow density change status information over time;

[0014] Obtain a first lighting evaluation coefficient according to the variation information of the number of vehicles within a unit length in each road section over time;

[0015] Among them, the first lighting evaluation coefficient is obtained through the following formula:

[0016]

[0017] Among them, K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; F ij represents the number of vehicles passing through the j-th unit time of the i-th unit length; F ij-1 represents the number of vehicles passing through the (j - 1)-th unit time of the i-th unit length; F bi represents the standard deviation of the number of vehicles corresponding to the m unit times of the i-th unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to the m unit times of the (i - 1)-th unit length; F zi represents the median of the number of vehicles corresponding to the m unit times of the i-th unit length; F maxi represents the maximum value of the number of vehicles corresponding to the m unit times of the i-th unit length; F bz represents the median of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths; F bmax represents the maximum value of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths;

[0018] Extract vehicle lighting range information;

[0019] Obtain a second lighting evaluation coefficient by using the vehicle lighting range information;

[0020] Among them, the second lighting evaluation coefficient is obtained through the following formula:

[0021]

[0022] Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; S ij represents the average area of the vehicle lighting range of the vehicles passing through the j-th unit time of the i-th unit length; S ij-1represents the average area of the vehicle lighting range of the vehicles passing through the (j - 1)-th unit time of the i-th unit length; S bi represents the standard deviation of the vehicle lighting range area of the i-th unit length; S bi-1 represents the standard deviation of the vehicle lighting range area of the (i - 1)-th unit length;

[0023] Obtain a comprehensive lighting evaluation coefficient by using the first lighting evaluation coefficient and the second lighting evaluation coefficient;

[0024] Among them, the comprehensive lighting evaluation coefficient is obtained through the following formula:

[0025] K = w 01 ·K 01 + w 02 ·K 02

[0026] Among them, K represents the comprehensive lighting evaluation coefficient; K 01 represents the first lighting evaluation coefficient; K 02 represents the second lighting evaluation coefficient;

[0027] Compare the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold;

[0028] When the comprehensive lighting evaluation coefficient exceeds the preset evaluation coefficient threshold, it indicates that the area along the road section obtains sufficient vehicle lighting;

[0029] When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

[0030] Optionally, perform visual recognition on the area along the target road to obtain the object presence status information of the area along the target road; based on the object presence status information, identify the sub-areas that need to adjust lighting within the area along the target road, including:

[0031] Perform three-dimensional shooting on the area along the target road to obtain a three-dimensional image of the area along the target road; perform object contour recognition on the three-dimensional image of the area along the target road to obtain the three-dimensional spatial presence status information of the objects in the area along the target road; among them, the three-dimensional spatial presence status information of the objects includes the information on the occupied area range of each static object in the three-dimensional space in the area along the target road;

[0032] Based on the three-dimensional spatial presence status information of the objects, locate and identify the shielding ranges formed by all static objects within the area along the target road, so as to obtain the sub-areas that need to adjust lighting within the area along the target road.

[0033] Optionally, based on the distribution location information of all lighting devices along the target road area and the sub - area that needs lighting adjustment, identify the matching lighting devices for lighting adjustment of the sub - area that needs lighting adjustment; based on the lighting characteristic information of the matching lighting devices, adjust the lighting state of the matching lighting devices for the sub - area that needs lighting adjustment, including:

[0034] Based on the distribution location information of all lighting devices along the target road area and the sub - area that needs lighting adjustment, estimate the lighting influence degree value of each lighting device on the sub - area that needs lighting adjustment; based on the lighting influence degree value, identify the matching lighting devices for lighting adjustment of the sub - area that needs lighting adjustment;

[0035] Based on the adjustable range of the lighting emission angle and the adjustable range of the lighting intensity of the matching lighting devices, adjust the lighting angle and / or lighting intensity of the matching lighting devices for the sub - area that needs lighting adjustment.

[0036] The intelligent lighting control system along urban traffic roads includes:

[0037] A traffic flow state determination module, which is used to perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network;

[0038] A target road area identification module, which is used to identify the target road area along the urban traffic road network that needs active lighting adjustment based on the traffic flow state information of the road network;

[0039] An object presence state identification module, which is used to perform visual identification on the target road area to obtain the object presence state information of the target road area;

[0040] A lighting adjustment sub - area identification module, which is used to identify the sub - area that needs lighting adjustment within the target road area based on the object presence state information;

[0041] A lighting device matching determination module, which is used to identify the matching lighting devices for lighting adjustment of the sub - area that needs lighting adjustment based on the distribution location information of all lighting devices along the target road area and the sub - area that needs lighting adjustment;

[0042] A lighting state adjustment module, which is used to adjust the lighting state of the matching lighting devices for the sub - area that needs lighting adjustment based on the lighting characteristic information of the matching lighting devices.

[0043] Optionally, the traffic flow state determination module is used to perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, including:

[0044] Based on the location information of the urban traffic road network, traffic big data corresponding to a preset time interval of the urban traffic road network is obtained; wherein, the traffic big data includes global vehicle driving path big data and global vehicle driving speed big data inside the urban traffic road network; the traffic big data is analyzed to obtain the traffic flow density change state information of each road section inside the urban traffic road network, and this is used as the traffic flow state information of the road network; wherein, the traffic flow density change state information includes the change information of the number of vehicles within a unit length range in each road section over time.

[0045] The target road along - region recognition module is used to identify the target road along - region inside the urban traffic road network that needs active lighting adjustment based on the traffic flow state information of the road network, including:

[0046] Based on the traffic flow density change state information of all road sections and the vehicle lighting range information, it is determined whether each road - section along - region obtains sufficient vehicle lighting; if not, the road - section along - region is determined as the target road along - region inside the urban traffic road network that needs active lighting adjustment; if so, the road - section along - region is not determined as the target road along - region inside the urban traffic road network that needs active lighting adjustment.

[0047] Optionally, based on the traffic flow density change state information of all road sections and the vehicle lighting range information, determining whether each road - section along - region obtains sufficient vehicle lighting includes:

[0048] Extract the change information of the number of vehicles within a unit length range in each road section over time included in the traffic flow density change state information;

[0049] According to the change information of the number of vehicles within a unit length range in each road section over time, a first lighting evaluation coefficient is obtained;

[0050] Wherein, the first lighting evaluation coefficient is obtained through the following formula:

[0051]

[0052] Where K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1min - 6min; F ij represents the number of vehicles passing through the j - th unit time of the i - th unit length; F ij-1 represents the number of vehicles passing through the (j - 1) - th unit time of the i - th unit length; Fbi represents the standard deviation of the number of vehicles corresponding to m unit times of the i-th unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to m unit times of the (i - 1)-th unit length; F zi represents the median value of the number of vehicles corresponding to m unit times of the i-th unit length; F maxi represents the maximum value of the number of vehicles corresponding to m unit times of the i-th unit length; F bz represents the median value of the standard deviations of the number of vehicles corresponding to m unit times of n unit lengths; F bmax represents the maximum value of the standard deviations of the number of vehicles corresponding to m unit times of n unit lengths;

[0053] Extract vehicle lighting range information;

[0054] Obtain a second lighting evaluation coefficient by using the vehicle lighting range information;

[0055] Among them, the second lighting evaluation coefficient is obtained through the following formula:

[0056]

[0057] Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; S ij represents the average area of the vehicle lighting range of the vehicles passing through the j-th unit time of the i-th unit length; S ij-1 represents the average area of the vehicle lighting range of the vehicles passing through the (j - 1)-th unit time of the i-th unit length; S bi represents the standard deviation of the vehicle lighting range area of the i-th unit length; S bi-1 represents the standard deviation of the vehicle lighting range area of the (i - 1)-th unit length;

[0058] Obtain a comprehensive lighting evaluation coefficient by using the first lighting evaluation coefficient and the second lighting evaluation coefficient;

[0059] Among them, the comprehensive lighting evaluation coefficient is obtained through the following formula:

[0060] K = w 01 ·K 01 +w 02 ·K 02

[0061] Among them, K represents the comprehensive lighting evaluation coefficient; K 01 represents the first lighting evaluation coefficient; K 02Represents the second lighting evaluation coefficient;

[0062] Compare the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold;

[0063] When the comprehensive lighting evaluation coefficient exceeds the preset evaluation coefficient threshold, it indicates that the area along the road section obtains sufficient vehicle lighting;

[0064] When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

[0065] Optionally, the object presence state recognition module is used to perform visual recognition on the area along the target road to obtain the object presence state information of the area along the target road, including:

[0066] Perform three-dimensional shooting on the area along the target road to obtain a three-dimensional image of the area along the target road; perform object contour recognition on the three-dimensional image of the area along the target road to obtain the object three-dimensional space presence state information of the area along the target road; wherein, the object three-dimensional space presence state information includes the occupied area range information of all static objects in the area along the target road in three-dimensional space respectively;

[0067] The lighting adjustment sub-region recognition module is used to identify the sub-regions that need to adjust lighting within the area along the target road based on the object presence state information, including:

[0068] Based on the object three-dimensional space presence state information, locate and identify the shielding range formed by all static objects within the area along the target road, so as to obtain the sub-regions that need to adjust lighting within the area along the target road.

[0069] Optionally, the lighting device matching determination module is used to identify the matching lighting devices for adjusting the lighting of the sub-regions that need to adjust lighting based on the distribution position information of all lighting devices in the area along the target road and the sub-regions that need to adjust lighting, including:

[0070] Based on the distribution position information of all lighting devices in the area along the target road and the sub-regions that need to adjust lighting, estimate the lighting influence degree value of each lighting device on the sub-regions that need to adjust lighting; based on the lighting influence degree value, identify the matching lighting devices for adjusting the lighting of the sub-regions that need to adjust lighting;

[0071] The lighting state adjustment module is used to adjust the lighting state of the matching lighting devices for the sub-regions that need to adjust lighting based on the lighting characteristic information of the matching lighting devices, including:

[0072] Based on the adjustable range of the illumination light emission angle and the adjustable range of the illumination light intensity of the matching illumination device, adjust the illumination angle and / or the illumination intensity of the matching illumination device for the sub-region that needs illumination adjustment.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The intelligent lighting control method and system provided in this application perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, thereby identifying the target road along areas that need active lighting adjustment, and screening different road along areas in combination with the road traffic conditions to determine the areas with the need for auxiliary lighting of lighting devices; visually identify the object existence state information from the target road along areas, thereby determining the sub-regions that need lighting adjustment, providing accurate position guidance for subsequent lighting devices to perform directional lighting adjustment on the target road along areas; also based on the distribution position information of all lighting devices in the target road along areas and the sub-regions that need lighting adjustment, identify the matching lighting devices for lighting adjustment of the sub-regions that need lighting adjustment, and in combination with their lighting characteristic information, adjust their lighting states for the sub-regions, effectively combining the traffic conditions of urban traffic roads to accurately adjust the operation of road along lighting devices, and realizing intelligent lighting control of road along areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0076] Figure 1 It is a schematic flow chart of the intelligent lighting control method for urban traffic roads provided by the present invention.

[0077] Figure 2 It is a schematic structural diagram of the intelligent lighting control system for urban traffic roads provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will describe in detail the specific embodiments of the present application with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only for explaining the present application and not for limiting the present application. Additionally, it should be noted that for the convenience of description, only the parts related to the present application rather than all the structures are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0079] The terms "including" and "having" and any variations thereof in the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0080] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0081] Please refer to Figure 1 As shown, a smart lighting control method for urban traffic road along is provided in an embodiment of the present application. The smart lighting control method for urban traffic road along includes:

[0082] Perform big data analysis on the urban traffic road network to obtain road network traffic flow state information; based on the road network traffic flow state information, identify the target road along areas within the urban traffic road network that need to perform active lighting adjustment;

[0083] Perform visual recognition on the target road along areas to obtain the object presence state information of the target road along areas; based on the object presence state information, identify the sub-areas within the target road along areas that need to adjust lighting;

[0084] Based on the distribution position information of all lighting devices in the target road along areas and the sub-areas that need to adjust lighting, identify the matching lighting devices for lighting adjustment of the sub-areas that need to adjust lighting; based on the lighting characteristic information of the matching lighting devices, adjust the lighting state of the matching lighting devices for the sub-areas that need to adjust lighting.

[0085] Beneficial effects of the above embodiments. The intelligent lighting control method for urban traffic road sections conducts big data analysis on the urban traffic road network to obtain traffic flow state information of the road network, thereby identifying target road sections along the line that require active lighting adjustment. Based on the traffic flow conditions of the roads, different road sections along the line are screened to determine areas with auxiliary lighting requirements for lighting equipment. Object presence state information is visually recognized from the target road sections along the line to determine sub-areas that require lighting adjustment, providing accurate position guidance for subsequent lighting equipment to perform directional lighting adjustment on the target road sections along the line. Additionally, based on the distribution position information of all lighting equipment in the target road sections along the line and the sub-areas that require lighting adjustment, matching lighting equipment for lighting adjustment of the sub-areas that require lighting adjustment is identified, and their lighting state is adjusted in combination with their lighting characteristic information, effectively combining the traffic conditions of urban traffic roads to accurately adjust the operation of lighting equipment along the roads and achieve intelligent lighting control of the road sections along the line.

[0086] In another embodiment, big data analysis is conducted on the urban traffic road network to obtain traffic flow state information of the road network. Based on this traffic flow state information of the road network, target road sections along the line within the urban traffic road network that require active lighting adjustment are identified, including:

[0087] Based on the location information of the urban traffic road network, traffic big data for the corresponding preset time interval of the urban traffic road network is obtained. Among them, the traffic big data includes global vehicle driving path big data and global vehicle driving speed big data within the urban traffic road network. The traffic big data is analyzed to obtain the traffic flow density change state information of each road section within the urban traffic road network, which is used as the traffic flow state information of the road network. Among them, the traffic flow density change state information includes the change information of the number of vehicles per unit length within each road section over time.

[0088] Based on the traffic flow density change state information and vehicle lighting range information of each road section, it is determined whether the areas along each road section receive sufficient vehicle lighting. If not, the areas along the corresponding road section are determined as target road sections along the line within the urban traffic road network that require active lighting adjustment. If so, the areas along the corresponding road section are not determined as target road sections along the line within the urban traffic road network that require active lighting adjustment.

[0089] Beneficial effects of the above embodiments. To ensure the normal operation of the urban traffic road network, lighting devices such as street lights are installed along each traffic road. These lighting devices usually work automatically at a preset time point or when the external environmental brightness is less than a preset brightness threshold, so as to achieve lighting of the corresponding area along the traffic road. In the actual traffic road lighting control scenario, the light emitted by the vehicle headlights of the vehicles driving on the traffic road can also provide effective lighting for the area along the traffic road to a certain extent. In order to minimize the power consumption of the lighting devices along the traffic road, when adjusting and controlling the lighting devices, it is also necessary to consider the light intensity generated by the vehicles driving on the traffic road. Based on the location information of the urban traffic road network, traffic big data for the urban traffic road network corresponding to a preset time interval (such as a week or a month, etc.) is obtained, and big data characterization is carried out on the vehicle driving path big data and vehicle driving speed big data within the entire scope of the urban traffic road network, and the driving dynamics of vehicles on all road sections within the urban traffic road network are characterized. And by analyzing the traffic big data, the change state information of the traffic flow density of each road section within the urban traffic road network is obtained, and this is used as the traffic flow state information of the road network. When the traffic flow density of a road section can remain at a relatively high level for a long period of time, the light emitted by the vehicles driving on the road section can provide stable and sufficient lighting for the area along the road. It can be seen that determining the change information of the number of vehicles per unit length within each road section over time can provide a reliable data basis for judging whether the area along the road section obtains sufficient vehicle lighting.

[0090] In addition, whether the light emitted by the vehicle can provide stable and sufficient lighting for the area along the road is also affected by the size of the vehicle lighting range. The larger the vehicle lighting range, the greater the probability that the corresponding area along the road obtains sufficient vehicle lighting. Therefore, based on the change state information of the traffic flow density of each road section and the vehicle lighting range information, fitting analysis is carried out on the lighting range, intensity and duration of the light emitted by the vehicle for the area along each road section, so as to judge whether the area along each road section obtains sufficient vehicle lighting, and thus accurately identify the target road areas along the urban traffic road network that need to be actively adjusted for lighting. By discriminating different road areas along the road in combination with the traffic flow situation to determine the areas with the need for auxiliary lighting by lighting devices, the intelligent level of lighting control for the areas along the urban traffic road can be improved.

[0091] In another embodiment, based on the change state information of the traffic flow density of each road section and the vehicle lighting range information, judging whether the area along each road section obtains sufficient vehicle lighting includes:

[0092] Extract the variation information of the number of vehicles per unit length in each road section included in the traffic flow density variation state information over time;

[0093] Obtain a first lighting evaluation coefficient based on the variation information of the number of vehicles per unit length in each road section over time;

[0094] Among them, the first lighting evaluation coefficient is obtained through the following formula:

[0095]

[0096] Among them, K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; F ij represents the number of vehicles passing through the j-th unit time of the i-th unit length; F ij-1 represents the number of vehicles passing through the (j - 1)-th unit time of the i-th unit length; F bi represents the standard deviation of the number of vehicles corresponding to the m unit times of the i-th unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to the m unit times of the (i - 1)-th unit length; F zi represents the median of the number of vehicles corresponding to the m unit times of the i-th unit length; F maxi represents the maximum value of the number of vehicles corresponding to the m unit times of the i-th unit length; F bz represents the median of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths; F bmax represents the maximum value of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths;

[0097] Extract the vehicle lighting range information;

[0098] Obtain a second lighting evaluation coefficient using the vehicle lighting range information;

[0099] Among them, the second lighting evaluation coefficient is obtained through the following formula:

[0100]

[0101] Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; S ij represents the average area of the vehicle lighting range of the vehicles passing through the j-th unit time of the i-th unit length; Sij-1 represents the average area of the vehicle lighting range of the vehicles passing through in the (j - 1)-th unit time of the i-th unit length; S bi represents the standard deviation of the vehicle lighting range area of the i-th unit length; S bi-1 represents the standard deviation of the vehicle lighting range area of the (i - 1)-th unit length;

[0102] Obtain a comprehensive lighting evaluation coefficient by using the first lighting evaluation coefficient and the second lighting evaluation coefficient;

[0103] wherein, the comprehensive lighting evaluation coefficient is obtained through the following formula:

[0104] K = w 01 ·K 01 + w 02 ·K 02

[0105] wherein, K represents the comprehensive lighting evaluation coefficient; K 01 represents the first lighting evaluation coefficient; K 02 represents the second lighting evaluation coefficient;

[0106] Compare the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold;

[0107] When the comprehensive lighting evaluation coefficient exceeds the preset evaluation coefficient threshold, it indicates that the area along the road section obtains sufficient vehicle lighting;

[0108] When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

[0109] The beneficial effects of the above embodiments are as follows. By comprehensively considering the information on the changing state of traffic flow density and the vehicle lighting range information, this solution can more accurately evaluate the lighting conditions in the areas along each road section. This comprehensive evaluation method is more comprehensive and accurate than only considering a single factor (such as the number of vehicles or the lighting range). The evaluation coefficients in the solution (including the first lighting evaluation coefficient and the second lighting evaluation coefficient) are dynamically calculated based on the number of vehicles and the lighting range data per unit time and per unit length. This means that this solution can adaptively adjust as the road traffic conditions change, thus providing more real-time lighting evaluation results. The first lighting evaluation coefficient, the second lighting evaluation coefficient, and the comprehensive lighting evaluation coefficient calculated through the formula provide quantitative indicators for the evaluation of road lighting conditions. This enables decision-makers to more objectively and scientifically judge whether the road lighting is sufficient and provides data support for subsequent improvement measures. By accurately evaluating the road lighting conditions, this solution helps to timely detect areas with insufficient lighting and take corresponding improvement measures. This can not only improve the driving safety of drivers but also reduce traffic accidents caused by insufficient lighting. Based on the results of the comprehensive lighting evaluation coefficient, decision-makers can more accurately determine which road sections need to increase lighting facilities or improve lighting conditions, thereby optimizing the allocation of lighting resources and avoiding unnecessary waste. This solution combines the information on traffic flow density and vehicle lighting range, reflecting the idea of data fusion and intelligent decision-making in the intelligent transportation system. This helps to promote the development and application of intelligent transportation technologies and improve the intelligent level of road traffic.

[0110] In another embodiment, visual recognition is performed on the area along the target road to obtain the object presence status information of the area along the target road; based on the object presence status information, sub-areas within the area along the target road that need to adjust lighting are identified, including:

[0111] The area along the target road is photographed in three dimensions to obtain a three-dimensional image of the area along the target road; object contour recognition is performed on the three-dimensional image of the area along the target road to obtain the three-dimensional spatial presence status information of the objects in the area along the target road; wherein, the three-dimensional spatial presence status information of the objects includes the occupied area range information of all static objects in the area along the target road in three-dimensional space respectively.

[0112] Based on the three-dimensional spatial presence status information of the objects, the shielding range formed by all static objects within the area along the target road is located and recognized, so as to obtain the sub-areas within the area along the target road that need to adjust lighting.

[0113] The beneficial effect of the above-mentioned embodiment is that a large number of trees and other vegetation will be planted along the urban traffic roads. These vegetation will block the light emitted by lighting equipment such as street lamps during their growth, making it impossible for certain sub-areas inside the target road area to be effectively illuminated by the lighting equipment, seriously reducing the lighting stability and sustainability of the target road area. In order to accurately determine the effect of vegetation on the light blocking of lighting equipment in the target road area, the target road area is photographed in three dimensions to obtain a three-dimensional image of the target road; the object contours of the three-dimensional image of the target road are identified to obtain the three-dimensional space existence status information of the objects in the target road area, thereby globally and quantitatively characterizing the area occupied by all static objects in the target road area in three-dimensional space. In addition, the three-dimensional existence status information of the object is compared with the occupancy range information of the area along the target road, so as to locate and identify the shielding range formed by all static objects in the area along the target road, thereby obtaining the sub-area in the area along the target road that needs to adjust the lighting. In this way, the sub-area in the area along the target road that cannot obtain sufficient light from the current lighting status of the lighting equipment can be accurately located from within the area along the target road, providing accurate position guidance for the subsequent lighting equipment to perform directional lighting adjustments on the area along the target road.

[0114] In another embodiment, based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, identifying a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required; and based on the lighting feature information of the matching lighting device, adjusting the lighting state of the sub-area where lighting adjustment is required by the matching lighting device, comprises:

[0115] Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, estimating the lighting impact value of each lighting device on the sub-area where lighting adjustment is required; based on the lighting impact value, identifying a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required;

[0116] Based on the adjustable range of the illumination light emission angle and the adjustable range of the illumination light intensity of the matching illumination device, the illumination angle and / or illumination intensity of the matching illumination device for the sub-area requiring illumination adjustment is adjusted.

[0117] The beneficial effects of the above embodiments are as follows. In actual operation, the lighting coverage ranges of lighting devices at different positions in the area along the target road for the sub-area that needs lighting adjustment are not the same. Generally speaking, the lighting device closer to the sub-area that needs lighting adjustment or the lighting device with a larger lighting range has a greater lighting impact on the sub-area that needs lighting adjustment. Therefore, based on the distribution position information of all lighting devices in the area along the target road and the area range of the sub-area that needs lighting adjustment, a corresponding neural network model is used to calculate the lighting impact degree value of each lighting device on the sub-area that needs lighting adjustment. If the lighting impact degree value is larger, it indicates that the corresponding lighting device has a greater lighting impact on the sub-area that needs lighting adjustment. If the lighting impact degree value is greater than or equal to a preset degree threshold, the corresponding lighting device is determined as the matching lighting device for lighting adjustment of the sub-area that needs lighting adjustment, which is convenient for subsequent lighting control only for the matching lighting devices, improving the centralization and intelligence of lighting control. Additionally, based on the adjustable range of the lighting emission angle and the adjustable range of the lighting intensity of the matching lighting device, the lighting angle and / or lighting intensity of the matching lighting device for the sub-area that needs lighting adjustment are adjusted, so that the matching lighting device can aim at the sub-area that needs lighting adjustment to emit light, realizing intelligent and efficient lighting control of the area along the road.

[0118] Please refer to Figure 2 as shown. A smart lighting control system for urban traffic roads along the line provided by an embodiment of the present application. The smart lighting control system for urban traffic roads along the line includes:

[0119] A traffic flow state determination module, configured to perform big data analysis on the urban traffic road network to obtain road network traffic flow state information;

[0120] A target road along - line area identification module, configured to identify the target road along - line area within the urban traffic road network that needs active lighting adjustment based on the road network traffic flow state information;

[0121] An object presence state identification module, configured to perform visual identification on the target road along - line area to obtain the object presence state information of the target road along - line area;

[0122] A lighting adjustment sub - area identification module, configured to identify the sub - area within the target road along - line area that needs lighting adjustment based on the object presence state information;

[0123] A lighting device matching determination module, configured to identify the matching lighting device for lighting adjustment of the sub - area that needs lighting adjustment based on the distribution position information of all lighting devices in the target road along - line area and the sub - area that needs lighting adjustment;

[0124] A lighting state adjustment module, configured to adjust the lighting state of the matching lighting device for the sub-region where lighting needs to be adjusted based on the lighting feature information of the matching lighting device.

[0125] The beneficial effects of the above embodiments are as follows: The intelligent lighting control system for urban traffic roadways performs big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, thereby identifying the target roadway areas along the road that require active lighting adjustment. Based on the traffic flow conditions on the road, different roadway areas are screened to determine the areas with auxiliary lighting requirements for lighting devices; the presence state information of objects is visually recognized from the target roadway areas to determine the sub-regions where lighting needs to be adjusted, providing an accurate position guide for subsequent lighting devices to perform directional lighting adjustment on the target roadway areas; also, based on the distribution position information of all lighting devices in the target roadway areas and the sub-regions where lighting needs to be adjusted, the matching lighting devices for lighting adjustment of the sub-regions where lighting needs to be adjusted are identified, and their lighting state is adjusted in combination with their lighting feature information, effectively combining the traffic conditions of urban traffic roads to accurately adjust the operation of lighting devices along the road, and realizing intelligent lighting control of the roadway areas.

[0126] In another embodiment, the traffic flow state determination module is configured to perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, including:

[0127] Based on the position information of the urban traffic road network, traffic big data corresponding to a preset time interval of the urban traffic road network is obtained; wherein, the traffic big data includes global vehicle driving path big data and global vehicle driving speed big data inside the urban traffic road network; the traffic big data is analyzed to obtain the traffic flow density change state information of each road section inside the urban traffic road network, and this is used as the traffic flow state information of the road network; wherein, the traffic flow density change state information includes the change information of the number of vehicles per unit length in each road section over time.

[0128] The target roadway area identification module is configured to identify the target roadway areas along the urban traffic road network that require active lighting adjustment based on the traffic flow state information of the road network, including:

[0129] Based on the traffic flow density change state information and vehicle lighting range information of each road section, it is determined whether the areas along each road section receive sufficient vehicle lighting; if not, the areas along the corresponding road section are determined as the target roadway areas along the urban traffic road network that require active lighting adjustment; if so, the areas along the corresponding road section are not determined as the target roadway areas along the urban traffic road network that require active lighting adjustment.

[0130] Beneficial effects of the above embodiments. To ensure the normal operation of the urban traffic road network, lighting devices such as street lights are installed along each traffic road. These lighting devices usually work automatically at preset time points or when the ambient brightness is less than a preset brightness threshold, so as to achieve lighting for the areas along the corresponding traffic roads. In the actual traffic road lighting control scenario, the light emitted by the vehicle headlights on the traffic road also provides effective lighting for the areas along the traffic road to a certain extent. In order to minimize the power consumption of the lighting devices along the traffic road, when adjusting and controlling the lighting devices, it is also necessary to consider the light intensity generated by the vehicles driving on the traffic road. Based on the location information of the urban traffic road network, traffic big data for the urban traffic road network corresponding to a preset time interval (such as one week or one month, etc.) is obtained, and big data representation is carried out on the vehicle driving path big data and vehicle driving speed big data within the entire scope of the urban traffic road network, and the driving dynamics of vehicles on all road sections within the urban traffic road network are analyzed. And by analyzing the traffic big data, the change state information of the traffic flow density of each road section within the urban traffic road network is obtained, and this is used as the traffic flow state information of the road network. When the traffic flow density of a road section can remain at a relatively high level for a long period of time, the light emitted by the vehicles driving on the road section can provide stable and sufficient lighting for the areas along the road. It can be seen that determining the change information of the number of vehicles per unit length within each road section over time can provide a reliable data basis for judging whether the areas along the road section obtain sufficient vehicle lighting.

[0131] In addition, whether the light emitted by the vehicle can provide stable and sufficient lighting for the areas along the road is also affected by the lighting range of the vehicle. The larger the vehicle lighting range, the greater the probability that the corresponding areas along the road obtain sufficient vehicle lighting. Therefore, based on the change state information of the traffic flow density of each road section and the vehicle lighting range information, fitting analysis is carried out on the lighting range, intensity, and duration of the light emitted by the vehicle for the areas along each road section, so as to judge whether the areas along each road section obtain sufficient vehicle lighting, and thus accurately identify the target road areas along the urban traffic road network that need to be actively adjusted for lighting. By differentiating different road areas along the road in combination with the traffic flow situation to determine the areas with the need for auxiliary lighting by lighting devices, the intelligence level of lighting control for the areas along the urban traffic road can be improved.

[0132] In another embodiment, based on the change state information of the traffic flow density of each road section and the vehicle lighting range information, judging whether the areas along each road section obtain sufficient vehicle lighting includes:

[0133] Extract the information on the change over time of the number of vehicles within the range of a unit length in each road section included in the traffic flow density change state information;

[0134] Obtain a first lighting evaluation coefficient based on the information on the change over time of the number of vehicles within the range of a unit length in each road section;

[0135] Among them, the first lighting evaluation coefficient is obtained through the following formula:

[0136]

[0137] Among them, K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; F ij represents the number of vehicles passing through the jth unit time of the ith unit length; F ij-1 represents the number of vehicles passing through the (j - 1)th unit time of the ith unit length; F bi represents the standard deviation of the number of vehicles corresponding to the m unit times of the ith unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to the m unit times of the (i - 1)th unit length; F zi represents the median of the number of vehicles corresponding to the m unit times of the ith unit length; F maxi represents the maximum value of the number of vehicles corresponding to the m unit times of the ith unit length; F bz represents the median of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths; F bmax represents the maximum value of the standard deviations of the number of vehicles corresponding to the m unit times of the n unit lengths;

[0138] Extract the vehicle lighting range information;

[0139] Obtain a second lighting evaluation coefficient by using the vehicle lighting range information;

[0140] Among them, the second lighting evaluation coefficient is obtained through the following formula:

[0141]

[0142] Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths included in each road section; m represents the number of unit times experienced by each unit length, and the value of the unit time is 1 min - 6 min; S ij represents the average area of the vehicle lighting range of the vehicles passing through the jth unit time of the ith unit length; Sij-1 represents the average area of the vehicle lighting range of the vehicles passing through in the (j - 1)-th unit time of the i-th unit length; S bi represents the standard deviation of the vehicle lighting range area of the i-th unit length; S bi-1 represents the standard deviation of the vehicle lighting range area of the (i - 1)-th unit length;

[0143] Obtain a comprehensive lighting evaluation coefficient by using the first lighting evaluation coefficient and the second lighting evaluation coefficient;

[0144] Among them, the comprehensive lighting evaluation coefficient is obtained through the following formula:

[0145] K = w 01 ·K 01 + w 02 ·K 02

[0146] Among them, K represents the comprehensive lighting evaluation coefficient; K 01 represents the first lighting evaluation coefficient; K 02 represents the second lighting evaluation coefficient;

[0147] Compare the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold;

[0148] When the comprehensive lighting evaluation coefficient exceeds the preset evaluation coefficient threshold, it indicates that the area along the road section obtains sufficient vehicle lighting;

[0149] When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

[0150] The beneficial effects of the above embodiments, by comprehensively considering the change state information of the traffic flow density and the vehicle lighting range information, this solution can more accurately evaluate the lighting situation of each area along the road section. This comprehensive evaluation method is more comprehensive and accurate than only considering a single factor (such as the number of vehicles or the lighting range).

[0151] The evaluation coefficients in the solution (including the first lighting evaluation coefficient and the second lighting evaluation coefficient) are dynamically calculated based on the number of vehicles per unit time and unit length and the lighting range data. This means that the solution can adaptively adjust with the changes in road traffic conditions, thereby providing more real-time lighting evaluation results. The first lighting evaluation coefficient, the second lighting evaluation coefficient, and the comprehensive lighting evaluation coefficient calculated through formulas provide quantitative indicators for the evaluation of road lighting conditions. This enables decision-makers to more objectively and scientifically judge whether the road lighting is sufficient and provides data support for subsequent improvement measures. By accurately evaluating the road lighting conditions, this solution helps to promptly identify areas with insufficient lighting and take corresponding improvement measures. This can not only improve the driving safety of motorists but also reduce traffic accidents caused by insufficient lighting. Based on the results of the comprehensive lighting evaluation coefficient, decision-makers can more accurately determine which road sections need to increase lighting facilities or improve lighting conditions, thereby optimizing the allocation of lighting resources and avoiding unnecessary waste. This solution combines the information of traffic flow density and vehicle lighting range, embodying the idea of data fusion and intelligent decision-making in intelligent transportation systems. This helps to promote the development and application of intelligent transportation technologies and improve the intelligent level of road traffic.

[0152] In another embodiment, the object presence state recognition module is used to perform visual recognition on the area along the target road to obtain the object presence state information of the area along the target road, including:

[0153] Perform three-dimensional shooting on the area along the target road to obtain a three-dimensional image of the area along the target road; perform object contour recognition on the three-dimensional image of the area along the target road to obtain the three-dimensional spatial presence state information of the objects in the area along the target road; wherein, the three-dimensional spatial presence state information of the objects includes the occupancy area range information of all static objects in the area along the target road in three-dimensional space respectively.

[0154] The lighting adjustment sub-region recognition module is used to identify the sub-regions that need to adjust lighting within the area along the target road based on the object presence state information, including:

[0155] Based on the three-dimensional spatial presence state information of the objects, locate and identify the shielding range formed by all static objects within the area along the target road, so as to obtain the sub-regions that need to adjust lighting within the area along the target road.

[0156] The beneficial effect of the above-mentioned embodiment is that a large number of trees and other vegetation will be planted along the urban traffic roads. These vegetation will block the light emitted by lighting equipment such as street lamps during their growth, making it impossible for certain sub-areas inside the target road area to be effectively illuminated by the lighting equipment, seriously reducing the lighting stability and sustainability of the target road area. In order to accurately determine the effect of vegetation on the light blocking of lighting equipment in the target road area, the target road area is photographed in three dimensions to obtain a three-dimensional image of the target road; the object contours of the three-dimensional image of the target road are identified to obtain the three-dimensional space existence status information of the objects in the target road area, thereby globally and quantitatively characterizing the area occupied by all static objects in the target road area in three-dimensional space. In addition, the three-dimensional existence status information of the object is compared with the occupancy range information of the area along the target road, so as to locate and identify the shielding range formed by all static objects in the area along the target road, thereby obtaining the sub-area in the area along the target road that needs to adjust the lighting. In this way, the sub-area in the area along the target road that cannot obtain sufficient light from the current lighting status of the lighting equipment can be accurately located from within the area along the target road, providing accurate position guidance for the subsequent lighting equipment to perform directional lighting adjustments on the area along the target road.

[0157] In another embodiment, the lighting device matching determination module is used to identify a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, including:

[0158] Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, estimating the lighting impact value of each lighting device on the sub-area where lighting adjustment is required; based on the lighting impact value, identifying a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required;

[0159] The lighting state adjustment module is used to adjust the lighting state of the matching lighting device for the sub-area that needs to adjust the lighting based on the lighting feature information of the matching lighting device, including:

[0160] Based on the adjustable range of the illumination light emission angle and the adjustable range of the illumination light intensity of the matching illumination device, the illumination angle and / or illumination intensity of the matching illumination device for the sub-area requiring illumination adjustment is adjusted.

[0161] The beneficial effects of the above embodiments are as follows. In actual operation, for the lighting devices located in different positions along the target road, the light illumination coverage ranges of the lighting devices for the sub-region that needs to adjust the lighting are different. Generally speaking, the lighting devices closer to the sub-region that needs to adjust the lighting or the lighting devices with a larger lighting range have a greater lighting influence on the sub-region that needs to adjust the lighting. Therefore, based on the distribution position information of all lighting devices along the target road and the area range of the sub-region that needs to adjust the lighting, a corresponding neural network model is used to calculate the lighting influence degree value of each lighting device on the sub-region that needs to adjust the lighting. If the lighting influence degree value is larger, it indicates that the corresponding lighting device has a greater lighting influence on the sub-region that needs to adjust the lighting. If the lighting influence degree value is greater than or equal to the preset degree threshold, the corresponding lighting device is determined as the matching lighting device for lighting adjustment of the sub-region that needs to adjust the lighting, which is convenient for subsequent lighting control only for the matching lighting devices, improving the concentration and intelligence of lighting control. Additionally, based on the adjustable range of the lighting emission angle and the adjustable range of the lighting intensity of the matching lighting device, the lighting angle and / or lighting intensity of the matching lighting device for the sub-region that needs to adjust the lighting are adjusted, so that the matching lighting device can aim at the sub-region that needs to adjust the lighting and emit light, realizing intelligent and efficient lighting control for the area along the road.

[0162] Generally speaking, the intelligent lighting control method and system for the urban traffic road network perform big data analysis on the urban traffic road network to obtain the traffic flow state information of the road network, identify the area along the target road that needs to actively adjust the lighting based on this, and screen different areas along the road to determine the areas with auxiliary lighting requirements for lighting devices in combination with the traffic flow situation on the road; visually identify the object presence state information in the area along the target road to determine the sub-region that needs to adjust the lighting, providing an accurate position guide for subsequent lighting devices to perform directional lighting adjustment on the area along the target road; also, based on the distribution position information of all lighting devices in the area along the target road and the sub-region that needs to adjust the lighting, identify the matching lighting devices for lighting adjustment of the sub-region that needs to adjust the lighting, and combine their lighting characteristic information to adjust their lighting states for the sub-region, effectively combining the traffic conditions of the urban traffic road to accurately adjust the operation of the lighting devices along the road and realizing intelligent lighting control for the area along the road.

[0163] The above is only a specific embodiment of the present invention. Any improvement made on the premise of the present invention's concept is regarded as the protection scope of the present invention.

Claims

1. A smart lighting control method along urban traffic roads, characterized in that: include: Performing big data analysis on the urban traffic road network to obtain traffic status information of the road network; based on the traffic status information of the road network, identifying target road areas within the urban traffic road network that require active lighting adjustment; Performing visual recognition on the target road area to obtain object existence status information in the target road area; based on the object existence status information, identifying a sub-area within the target road area that needs to adjust lighting; Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, identifying a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required; based on the lighting feature information of the matching lighting device, adjusting the lighting state of the sub-area where lighting adjustment is required by the matching lighting device; Among them, the identifying of target road areas along the urban traffic road network that require active lighting adjustment based on the traffic status information of the road network includes: judging whether the area along each road section obtains sufficient vehicle lighting based on the traffic density change status information and vehicle lighting range information of all road sections; if not, determining the area along the corresponding road section as the target road area along the urban traffic road network that requires active lighting adjustment; if so, not determining the area along the corresponding road section as the target road area along the urban traffic road network that requires active lighting adjustment.

2. The intelligent lighting control method along urban traffic roads according to claim 1, characterized in that: Performing big data analysis on the urban traffic road network to obtain road network traffic status information; based on the road network traffic status information, identifying target road areas within the urban traffic road network that require active lighting adjustment, including: Based on the location information of the urban traffic road network, traffic big data of the urban traffic road network corresponding to a preset time interval is obtained; wherein, the traffic big data includes global vehicle travel path big data and global vehicle travel speed big data within the urban traffic road network; the traffic big data is analyzed to obtain the traffic density change status information of all road sections within the urban traffic road network, and use it as the traffic status information of the road network; wherein, the traffic density change status information includes the change information of the number of vehicles within a unit length range in each road section over time.

3. The intelligent lighting control method along urban traffic roads as claimed in claim 2, characterized in that: Based on the traffic density change status information and vehicle lighting range information of all road sections, determine whether the area along each road section has sufficient vehicle lighting, including: Extracting the information on the change of the number of vehicles per unit length in each road section contained in the traffic density change state information; Obtaining a first lighting evaluation coefficient according to information on changes in the number of vehicles within a unit length range in each road section over time; Wherein, the first lighting evaluation coefficient is obtained by the following formula: Among them, K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths contained in each road section; m represents the number of unit time experienced by each unit length, and the value of unit time is 1min-6min; F ij F represents the number of vehicles that pass through the jth unit time of the i-th unit length; ij-1 F represents the number of vehicles that pass through the i-th unit length in the j-1th unit time; bi represents the standard deviation of the number of vehicles corresponding to m units of time of the i-th unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to m units of time of the i-1th unit length; F zi represents the median value of the number of vehicles corresponding to m units of time of the i-th unit length; F maxi F represents the maximum number of vehicles corresponding to m units of time of the i-th unit length; bz represents the median standard deviation of the number of vehicles corresponding to m unit time of n unit length; F bmax It represents the maximum value of the standard deviation of the number of vehicles corresponding to m unit time of n unit length; Extract vehicle lighting range information; Obtaining a second lighting evaluation coefficient using the vehicle lighting range information; The second lighting evaluation coefficient is obtained by the following formula: Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths contained in each road section; m represents the number of unit time experienced by each unit length, and the value of the unit time is 1min-6min; S ij S represents the average area of ​​the vehicle lighting range of the vehicle passing through the i-th unit length in the j-th unit time; ij-1 S represents the average area of ​​the vehicle lighting range of the vehicle passing through the i-th unit length in the j-1th unit time; bi S represents the standard deviation of the vehicle lighting range area of ​​the i-th unit length; bi-1 represents the standard deviation of the vehicle lighting range area of ​​the i-1th unit length; Obtaining a comprehensive lighting evaluation coefficient using the first lighting evaluation coefficient and the second lighting evaluation coefficient; The comprehensive lighting evaluation coefficient is obtained by the following formula: K=w 01 ·K 01 +w 02 ·K 02 Wherein, K represents the comprehensive lighting evaluation coefficient; K 01 Represents the first lighting evaluation coefficient; K 02 represents the second lighting evaluation coefficient; Comparing the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold; When the comprehensive lighting evaluation coefficient exceeds a preset evaluation coefficient threshold, it indicates that the area along the road section has sufficient vehicle lighting; When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

4. The intelligent lighting control method along urban traffic roads according to claim 1, characterized in that: Performing visual recognition on the area along the target road to obtain information on the existence status of objects in the area along the target road; Based on the object existence status information, identifying a sub-area within the target road area where lighting needs to be adjusted, including: The target road area is photographed in three dimensions to obtain a three-dimensional image of the target road area; object contours are recognized on the three-dimensional image of the target road area to obtain three-dimensional space existence status information of objects in the target road area; wherein the three-dimensional space existence status information of objects includes the area range information of all static objects in the target road area in three-dimensional space; Based on the three-dimensional space existence status information of the object, the shielding range formed by all static objects in the area along the target road is located and identified, so as to obtain the sub-area in the area along the target road where the lighting needs to be adjusted.

5. The intelligent lighting control method along urban traffic roads according to claim 1, characterized in that: Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, identifying a matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required; Adjusting the lighting state of the sub-area requiring lighting adjustment by the matching lighting device based on the lighting characteristic information of the matching lighting device includes: Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, estimating the lighting influence degree value of each lighting device on the sub-area where lighting adjustment is required; based on the lighting influence degree value, identifying the matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required; Based on the adjustable range of the illumination light emission angle and the adjustable range of the illumination light intensity of the matching illumination device, the illumination angle and / or illumination intensity of the matching illumination device for the sub-area requiring illumination adjustment is adjusted.

6. Intelligent lighting control system along urban traffic roads, characterized by: include: The traffic flow status determination module is used to perform big data analysis on the urban traffic road network to obtain the traffic flow status information of the road network; A target road area identification module, used to identify target road areas that require active lighting adjustment within the urban traffic road network based on the road network traffic state information; An object existence status recognition module is used to visually recognize the area along the target road to obtain object existence status information in the area along the target road; A lighting adjustment sub-area identification module, used to identify the sub-area within the area along the target road where lighting needs to be adjusted based on the object existence status information; A lighting equipment matching determination module, configured to identify a matching lighting equipment for performing lighting adjustment on the sub-area requiring lighting adjustment based on the distribution position information of all lighting equipment in the area along the target road and the sub-area requiring lighting adjustment; A lighting state adjustment module, configured to adjust the lighting state of the sub-area requiring lighting adjustment by the matching lighting device based on the lighting feature information of the matching lighting device; The target road area identification module is used to identify the target road area that needs active lighting adjustment within the urban traffic road network based on the road network traffic state information, including: Based on the traffic density change status information and vehicle lighting range information of all road sections, determine whether the area along each road section obtains sufficient vehicle lighting; if not, determine the area along the corresponding road section as the target road area along the urban traffic road network that requires active lighting adjustment; if so, do not determine the area along the corresponding road section as the target road area along the urban traffic road network that requires active lighting adjustment.

7. The intelligent lighting control system along the urban traffic road as claimed in claim 6, characterized in that: The vehicle flow state determination module is used to perform big data analysis on the urban traffic road network to obtain road network vehicle flow state information, including: Based on the location information of the urban traffic road network, traffic big data of the urban traffic road network corresponding to a preset time interval is obtained; wherein, the traffic big data includes global vehicle travel path big data and global vehicle travel speed big data within the urban traffic road network; the traffic big data is analyzed to obtain the traffic density change status information of all road sections within the urban traffic road network, and use it as the traffic status information of the road network; wherein, the traffic density change status information includes the change information of the number of vehicles within a unit length range in each road section over time.

8. The intelligent lighting control system along the urban traffic road as claimed in claim 7, characterized in that: Based on the traffic density change status information and vehicle lighting range information of all road sections, determine whether the area along each road section has sufficient vehicle lighting, including: Extracting the information on the change of the number of vehicles per unit length in each road section contained in the traffic density change state information; Obtaining a first lighting evaluation coefficient according to information on changes in the number of vehicles within a unit length range in each road section over time; Wherein, the first lighting evaluation coefficient is obtained by the following formula: Among them, K 01 represents the first lighting evaluation coefficient; n represents the number of unit lengths contained in each road section; m represents the number of unit time experienced by each unit length, and the value of unit time is 1min-6min; F ij F represents the number of vehicles that pass through the jth unit time of the i-th unit length; ij-1 F represents the number of vehicles that pass through the i-th unit length in the j-1th unit time; bi represents the standard deviation of the number of vehicles corresponding to m units of time of the i-th unit length; F bi-1 represents the standard deviation of the number of vehicles corresponding to m units of time of the i-1th unit length; F zi represents the median value of the number of vehicles corresponding to m units of time of the i-th unit length; F maxi F represents the maximum number of vehicles corresponding to m units of time of the i-th unit length; bz represents the median standard deviation of the number of vehicles corresponding to m unit time of n unit length; F bmax It represents the maximum value of the standard deviation of the number of vehicles corresponding to m unit time of n unit length; Extract vehicle lighting range information; Obtaining a second lighting evaluation coefficient using the vehicle lighting range information; The second lighting evaluation coefficient is obtained by the following formula: Among them, K 02 represents the second lighting evaluation coefficient; n represents the number of unit lengths contained in each road section; m represents the number of unit time experienced by each unit length, and the value of the unit time is 1min-6min; S ij S represents the average area of ​​the vehicle lighting range of the vehicle passing through the i-th unit length in the j-th unit time; ij-1 S represents the average area of ​​the vehicle lighting range of the vehicle passing through the i-th unit length in the j-1th unit time; bi S represents the standard deviation of the vehicle lighting range area of ​​the i-th unit length; bi-1 represents the standard deviation of the vehicle lighting range area of ​​the i-1th unit length; Obtaining a comprehensive lighting evaluation coefficient using the first lighting evaluation coefficient and the second lighting evaluation coefficient; The comprehensive lighting evaluation coefficient is obtained by the following formula: K=w 01 ·K 01 +w 02 ·K 02 Wherein, K represents the comprehensive lighting evaluation coefficient; K 01 Represents the first lighting evaluation coefficient; K 02 represents the second lighting evaluation coefficient; Comparing the comprehensive lighting evaluation coefficient with a preset evaluation coefficient threshold; When the comprehensive lighting evaluation coefficient exceeds a preset evaluation coefficient threshold, it indicates that the area along the road section has sufficient vehicle lighting; When the comprehensive lighting evaluation coefficient does not exceed the preset evaluation coefficient threshold, it indicates that the area along the road section does not obtain sufficient vehicle lighting.

9. The intelligent lighting control system along the urban traffic road as claimed in claim 6, characterized in that: The object existence status recognition module is used to perform visual recognition on the area along the target road to obtain object existence status information in the area along the target road, including: The target road area is photographed in three dimensions to obtain a three-dimensional image of the target road area; object contours are recognized on the three-dimensional image of the target road area to obtain three-dimensional space existence status information of objects in the target road area; wherein the three-dimensional space existence status information of objects includes the area range information of all static objects in the target road area in three-dimensional space; The lighting adjustment sub-area identification module is used to identify the sub-area that needs to adjust the lighting inside the area along the target road based on the object existence state information, including: Based on the three-dimensional space existence status information of the object, the shielding range formed by all static objects in the area along the target road is located and identified, so as to obtain the sub-area in the area along the target road where the lighting needs to be adjusted.

10. The intelligent lighting control system along the urban traffic road as claimed in claim 6, characterized in that: The lighting device matching determination module is used to identify a matching lighting device for performing lighting adjustment on the sub-area requiring lighting adjustment based on the distribution position information of all lighting devices in the area along the target road and the sub-area requiring lighting adjustment, including: Based on the distribution position information of all lighting devices in the area along the target road and the sub-area where lighting adjustment is required, estimating the lighting influence degree value of each lighting device on the sub-area where lighting adjustment is required; based on the lighting influence degree value, identifying the matching lighting device for performing lighting adjustment on the sub-area where lighting adjustment is required; The lighting status adjustment module is used to adjust the lighting status of the sub-area that needs to be adjusted by the matching lighting device based on the lighting characteristic information of the matching lighting device, including: adjusting the lighting angle and / or lighting intensity of the matching lighting device for the sub-area that needs to be adjusted based on the adjustable range of the lighting light emission angle and the adjustable range of the lighting light intensity of the matching lighting device.

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