A method and system for energy-saving control of urban lighting based on edge computing

Abstract

The present invention discloses an urban lighting energy-saving control method and system based on edge computing, which relates to the field of urban lighting technology, including: forming an illumination impact model; forming a nighttime illumination period; establishing a model of illumination intensity and visible distance; forming an illumination superposition prediction model; forming an illumination intensity setting value of a highway section; obtaining a preliminary illumination intensity of a street lamp; obtaining a highway section where an accident is caused by a target accident cause as a target highway section; obtaining a target illumination intensity of a street lamp; setting the illumination intensity of the street lamps of the target highway section according to the target illumination intensity, and setting the illumination intensity of the street lamps of the highway sections other than the target highway section according to the preliminary illumination intensity. By setting a model building module, a preliminary calculation module, a secondary calculation module and a street lamp adjustment module, it is ensured that the set illumination intensity is the minimum value that meets the demand, thereby achieving an energy-saving effect.

Description

Technical Field

[0001] The present invention relates to the field of urban lighting technology, and in particular to an urban lighting energy-saving control method and system based on edge computing. Background Art

[0002] Urban roads are a major component of the overall urban planning, and they are related to the organic activities of the entire city. Street lights should be set up on the sidewalks of urban roads. The setting of street lights generally requires reasonable lighting distribution. The light source should preferably have a very long life and be maintenance-free for all-year-round use; have high light efficiency and good color rendering, and be able to instantly start normal operation in ultra-low temperature environments. In order to save electricity, the brightness of street lights needs to be adjusted.

[0003] Road lights need to ensure the normal driving of vehicles and avoid collisions due to low brightness. However, existing control technologies are relatively inadequate in balancing the brightness of road lights with the normal driving of vehicles, resulting in insufficient safety in its energy-saving control. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for energy-saving control of urban lighting based on edge computing are provided. This technical solution solves the problem that the existing control technology proposed in the above background technology is relatively insufficient in balancing the brightness of road lights and the normal driving of vehicles, resulting in insufficient safety of its energy-saving control.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An urban lighting energy-saving control method based on edge computing, comprising:

[0007] Based on the weather, form a model of the impact of light;

[0008] Form night lighting periods based on seasons;

[0009] Model light intensity and viewing distance;

[0010] Divide the night lighting period into gradual lighting period, full lighting period and reduced lighting period;

[0011] During the progressive lighting period, the light brightness is gradually increased to the light intensity of the full lighting period;

[0012] During the dimming period, the light intensity of the full lighting period is gradually reduced to 0;

[0013] Based on historical data, a light superposition prediction model is formed;

[0014] Evenly divide the urban highway into at least one highway section, count the traffic volume in the highway section, and form a light intensity setting value for the highway section based on the vehicle statistics, wherein the light intensity setting value refers to the light intensity projected onto the highway surface by the street lamp, not the light intensity of the street lamp;

[0015] During the full lighting period, the real-time moonlight intensity is obtained at every preset time, and the lighting intensity of the street lights on the highway section is set at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the street lights;

[0016] For accident-prone areas, trace the causes of accidents, determine the target accident causes related to lighting, and obtain the highway sections where accidents are caused by the target accident causes as the target highway sections;

[0017] Based on the target accident cause and the model of light intensity and visible distance, the preliminary lighting intensity of the street lamps on the target highway section is adjusted to obtain the target lighting intensity of the street lamps;

[0018] For street lamps on the target highway section, the street lamp illumination intensity is set according to the target illumination intensity; for street lamps on highway sections other than the target highway section, the street lamp illumination intensity is set according to the reserve illumination intensity.

[0019] Preferably, forming a light impact model based on weather comprises the following steps:

[0020] Obtain a rainfall value range, divide the rainfall value range into equal intervals, and obtain at least one rainfall point;

[0021] Under the condition that the rainfall is the value at the rainfall point, the effect coefficient after the light is weakened is obtained, and the effect coefficient is the ratio of the light intensity after weakening to the light intensity before weakening;

[0022] The rainfall points and the action coefficients are paired and fitted to obtain the illumination influence fitting function, where the rainfall points are the independent variables and the action coefficients are the dependent variables;

[0023] The lighting effect fitting function is used as the lighting effect model.

[0024] Preferably, forming the night lighting period based on the season comprises the following steps:

[0025] In each season of spring, summer, autumn and winter, the initial time when the sunlight intensity is insufficient and the initial time when the sunlight intensity is sufficient are counted;

[0026] The average of the initial time when the sunlight intensity is insufficient is taken to get the initial time of the night, and the average of the initial time when the sunlight intensity is sufficient is taken to get the end time of the night;

[0027] The period between the beginning of the night and the end of the night constitutes the night lighting period.

[0028] Preferably, the step of establishing a model of light intensity and visual distance comprises the following steps:

[0029] Obtain a value range of light intensity, divide the value range of light intensity into equal intervals, and obtain at least one light point;

[0030] Under the condition that the light intensity is the value at the light point, obtain the maximum visual distance of the driver;

[0031] Pair and fit the illumination point with the maximum visible distance to obtain a visible distance fitting function, where the illumination point is the independent variable and the maximum visible distance is the dependent variable;

[0032] The visible distance fitting function is used as a model of light intensity and visible distance.

[0033] Preferably, dividing the nighttime lighting period into a progressive lighting period, a full lighting period and a reduced lighting period comprises the following steps:

[0034] Based on historical data, the time from the beginning of the night to the complete sunset is obtained as the characteristic time interval;

[0035] The part of the night lighting period whose distance from the initial time of night is less than the characteristic time interval is regarded as the progressive lighting period;

[0036] The part of the night lighting period whose distance from the night end time is less than the characteristic time interval is regarded as the reduced lighting period;

[0037] The part of the night lighting period except the gradual lighting period and the dimmed lighting period is regarded as the full lighting period.

[0038] Preferably, forming a light superposition prediction model based on historical data comprises the following steps:

[0039] Obtain the distance range from the street lamp to the illuminated part of the urban highway, and equally space the distance range of the illuminated part to obtain at least one distance point;

[0040] Under the condition that the distance to the street lamp is the value at the distance point, an intensity coefficient is obtained, where the intensity coefficient is the ratio of the light intensity of the point whose distance to the street lamp is the value at the distance point to the actual intensity of the street lamp;

[0041] Pairing and fitting the distance points and the intensity coefficients to obtain at least one intensity fitting function;

[0042] Randomly combining at least one illumination point to obtain at least one illumination group;

[0043] Under the condition that the two light sources are the values ​​of the light points in the light group, the comprehensive light intensity of the two light sources is obtained;

[0044] Pair and fit the illumination group with the comprehensive illumination intensity to obtain an illumination superposition fitting function;

[0045] The intensity fitting function and the illumination superposition fitting function are used as illumination superposition prediction models.

[0046] Preferably, forming the illumination intensity setting value of the highway section based on vehicle statistics comprises the following steps:

[0047] Obtain the length of the highway section, the traffic volume per unit time of the highway section, and the average time for vehicles to pass through the highway section, and use the vehicle spacing formula to calculate the average vehicle spacing on the highway section;

[0048] Substitute the average distance between vehicles as the braking distance into the visual distance fitting function, and inversely solve to obtain the light intensity setting value of the highway section;

[0049] The formula for vehicle distance is as follows: ,

[0050] Among them, k is the average distance between vehicles, s is the length of the highway section, a is the traffic flow per unit time, and t is the average time for vehicles to pass through the highway section.

[0051] Preferably, the step of setting the lighting intensity of the street lamps on the highway section to obtain the preliminary lighting intensity of the street lamps comprises the following steps:

[0052] Get any point in the road section as a feature point;

[0053] Take at least one sampling point evenly on the highway section;

[0054] Get the real-time rainfall of the day, substitute the real-time rainfall into the light impact fitting function, and get the real-time action coefficient;

[0055] Substituting the distance from the feature point to at least one street lamp on the highway section into the intensity fitting function to obtain the actual intensity coefficient;

[0056] The lighting intensity of the street lamp is taken as an unknown number and superimposed with the real-time moonlight intensity. After superposition, it is multiplied with the real-time action coefficient and the actual intensity coefficient to obtain the actual action intensity.

[0057] The comprehensive light intensity of at least one street lamp is combined using an intensity fitting function. When combining, the comprehensive light intensity of any two street lamps is substituted into the intensity fitting function. The result covers the comprehensive light intensity of the two street lamps used for combination to form a new comprehensive light intensity. This step is repeated until the number of comprehensive light intensities of at least one street lamp becomes 1, and the comprehensive light intensity is used as the characteristic light intensity.

[0058] Let the characteristic light intensity be equal to the light intensity setting value, and inversely solve the lighting intensity of the street lamp;

[0059] When the feature point traverses at least one sampling point, the lighting intensity of at least one street lamp is obtained, and the maximum value of the lighting intensity of at least one street lamp is used as the preliminary lighting intensity of the street lamp.

[0060] Preferably, the step of adjusting the preliminary lighting intensity of the street lamps on the target highway section to obtain the target lighting intensity of the street lamps comprises the following steps:

[0061] Obtaining a driving record video of a vehicle involved in an accident on a target highway section, and obtaining a safe braking distance based on the driving record video, wherein the safe braking distance satisfies the requirement that braking is performed when two vehicles meet the safe braking distance without causing a collision;

[0062] Substitute the safe braking distance into the visual distance fitting function and inversely solve to obtain the target intensity setting value;

[0063] Acquire the characteristic light intensity of the target highway section as the target characteristic light intensity;

[0064] Set the target characteristic light intensity equal to the target intensity setting value, and inversely solve the street lamp lighting intensity as the street lamp target lighting intensity;

[0065] When the characteristic point of the target highway section traverses at least one sampling point of the target highway section, the target lighting intensity of the street lamp is obtained, and the maximum value of the target lighting intensities of the street lamp is used as the target lighting intensity of the street lamp.

[0066] An urban lighting energy-saving control system based on edge computing, used to implement the above-mentioned urban lighting energy-saving control method based on edge computing, comprising:

[0067] A model building module, wherein the model building module forms a light impact model based on the weather, forms a nighttime lighting period based on the season, establishes a model of light intensity and visible distance, and forms a light superposition prediction model based on historical data;

[0068] A time period classification module, wherein the time period classification module divides the nighttime lighting period into a gradual lighting period, a full lighting period, and a reduced lighting period;

[0069] A lighting adjustment module, which gradually increases the light brightness to the light intensity of the full lighting period during the progressive lighting period, and gradually reduces the light intensity of the full lighting period to 0 during the reduced lighting period;

[0070] A preliminary calculation module, wherein the preliminary calculation module evenly divides the urban highway into at least one highway section, performs traffic statistics on the highway section, and forms a light intensity setting value for the highway section based on the vehicle statistics;

[0071] A secondary calculation module, wherein the secondary calculation module obtains the real-time moonlight intensity at every preset time during the full lighting period, and sets the lighting intensity of the streetlights on the highway section at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the streetlights;

[0072] A target determination module, wherein the target determination module traces the causes of accidents in the accident-prone statistical areas, determines the target accident causes related to lighting, and obtains the highway section where the target accident causes the accidents as the target highway section;

[0073] A street light adjustment module, which adjusts the preliminary lighting intensity of street lights on a target highway section based on a target accident cause and a model of light intensity and visible distance to obtain a target lighting intensity of the street lights;

[0074] A streetlight setting module is used to set the streetlight illumination intensity of the target highway section according to the target illumination intensity, and to set the streetlight illumination intensity of the highway section other than the target highway section according to the reserve illumination intensity.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] By setting up a model building module, a preliminary calculation module, a secondary calculation module and a street light adjustment module, various driving conditions of vehicles on the road are summarized, and the light intensity is set based on the driving distance to ensure that the light intensity is sufficient to see adjacent vehicles clearly. At the same time, the light is further enhanced for accident-prone sections of the road to ensure that drivers can reasonably increase the distance between vehicles while still seeing adjacent vehicles clearly, thereby avoiding accidents. When setting, the weakening effect of light with distance and weather is taken into account, and the light superposition effect of multiple close street lights is considered to ensure that the set light intensity is the minimum value that meets the needs, thereby achieving energy-saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of the urban lighting energy-saving control method based on edge computing of the present invention;

[0078] Figure 2 It is a schematic diagram of the process of forming a light impact model based on weather in the present invention;

[0079] Figure 3 It is a schematic diagram of the process of forming nighttime lighting periods based on seasons according to the present invention;

[0080] Figure 4 A schematic diagram of a process for establishing a model of light intensity and visible distance according to the present invention;

[0081] Figure 5 A schematic diagram of a process for dividing a nighttime lighting period into a gradual lighting period, a full lighting period and a reduced lighting period according to the present invention;

[0082] Figure 6 It is a schematic diagram of the process of forming a light superposition prediction model based on historical data of the present invention;

[0083] Figure 7 A schematic diagram of a process for forming a light intensity setting value for a highway section based on vehicle statistics according to the present invention;

[0084] Figure 8 A schematic diagram of a process of setting the lighting intensity of street lamps on a highway section to obtain the preliminary lighting intensity of the street lamps according to the present invention;

[0085] Fig. 9 The present invention is a flow chart of adjusting the preliminary lighting intensity of street lamps on a target highway section to obtain the target lighting intensity of the street lamps. DETAILED DESCRIPTION

[0086] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0087] Reference Figure 1 As shown, a method for energy-saving control of urban lighting based on edge computing includes:

[0088] Based on the weather, form a model of the impact of light;

[0089] Form night lighting periods based on seasons;

[0090] Model light intensity and viewing distance;

[0091] Divide the night lighting period into gradual lighting period, full lighting period and reduced lighting period;

[0092] During the progressive lighting period, the light brightness is gradually increased to the light intensity of the full lighting period;

[0093] During the dimming period, the light intensity of the full lighting period is gradually reduced to 0;

[0094] Based on historical data, a light superposition prediction model is formed;

[0095] Evenly divide the urban highway into at least one highway section, count the traffic volume in the highway section, and form a light intensity setting value for the highway section based on the vehicle statistics, wherein the light intensity setting value refers to the light intensity projected onto the highway surface by the street lamp, not the light intensity of the street lamp;

[0096] During the full lighting period, the real-time moonlight intensity is obtained at every preset time, and the lighting intensity of the street lights on the highway section is set at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the street lights;

[0097] For accident-prone areas, trace the causes of accidents, determine the target accident causes related to lighting, and obtain the highway sections where accidents are caused by the target accident causes as the target highway sections;

[0098] Based on the target accident cause and the model of light intensity and visible distance, the preliminary lighting intensity of the street lamps on the target highway section is adjusted to obtain the target lighting intensity of the street lamps;

[0099] For street lamps on the target highway section, the street lamp illumination intensity is set according to the target illumination intensity; for street lamps on highway sections other than the target highway section, the street lamp illumination intensity is set according to the reserve illumination intensity.

[0100] There are usually warning signs in accident-prone areas. Therefore, drivers can control the distance between vehicles when they see the warning signs. Intensifying the lighting in this area will enable drivers to see the situation of the vehicle in front clearly after increasing the distance between vehicles, and thus brake in advance. Since the braking distance is sufficient, a collision can be avoided.

[0101] Reference Figure 2 As shown, based on the weather, forming a light impact model includes the following steps:

[0102] Obtain a rainfall value range, divide the rainfall value range into equal intervals, and obtain at least one rainfall point;

[0103] Under the condition that the rainfall is the value at the rainfall point, the effect coefficient after the light is weakened is obtained, and the effect coefficient is the ratio of the light intensity after weakening to the light intensity before weakening;

[0104] The rainfall points and the action coefficients are paired and fitted to obtain the illumination influence fitting function, where the rainfall points are the independent variables and the action coefficients are the dependent variables;

[0105] The lighting effect fitting function is used as the lighting effect model.

[0106] Under different weather conditions, the impact on lighting is different. Here we only consider the situation of rainfall, because cloudy days have little impact on lighting at night and can be ignored, while rainfall has a greater weakening effect on the propagation of light and cannot be ignored. Therefore, it is necessary to predict the weakening of light due to different rainfall amounts, so as to adjust the intensity of street lights in a targeted manner.

[0107] Reference Figure 3 As shown, forming a night lighting period based on the season includes the following steps:

[0108] In each season of spring, summer, autumn and winter, the initial time when the sunlight intensity is insufficient and the initial time when the sunlight intensity is sufficient are counted;

[0109] The average of the initial time when the sunlight intensity is insufficient is taken to get the initial time of the night, and the average of the initial time when the sunlight intensity is sufficient is taken to get the end time of the night;

[0110] The period between the beginning of the night and the end of the night constitutes the night lighting period.

[0111] Nighttime lighting is not always at full power. It exists before dawn and just after dark at night. Therefore, in this case, due to the effect of sunlight, the intensity of street lights can be appropriately reduced, thereby achieving energy-saving effects.

[0112] Reference Figure 4 As shown, establishing a model of light intensity and viewing distance includes the following steps:

[0113] Obtain a value range of light intensity, divide the value range of light intensity into equal intervals, and obtain at least one light point;

[0114] Under the condition that the light intensity is the value at the light point, obtain the maximum visual distance of the driver;

[0115] Pair and fit the illumination point with the maximum visible distance to obtain a visible distance fitting function, where the illumination point is the independent variable and the maximum visible distance is the dependent variable;

[0116] The visible distance fitting function is used as a model of light intensity and visible distance.

[0117] Different light intensities have different maximum visible distances, and the maximum distance plays an important role in braking. Therefore, using the model of light intensity and visible distance can ensure that the visible distance under different light intensities is almost consistent with the braking distance, thereby leaving enough braking distance to avoid collision.

[0118] Reference Figure 5 As shown, dividing the night lighting period into a gradual lighting period, a full lighting period and a reduced lighting period includes the following steps:

[0119] Based on historical data, the time from the beginning of the night to the complete sunset is obtained as the characteristic time interval;

[0120] The part of the night lighting period whose distance from the initial time of night is less than the characteristic time interval is regarded as the progressive lighting period;

[0121] The part of the night lighting period whose distance from the night end time is less than the characteristic time interval is regarded as the reduced lighting period;

[0122] The part of the night lighting period except the gradual lighting period and the dimmed lighting period is regarded as the full lighting period.

[0123] Reference Figure 6 As shown, based on historical data, forming a light superposition prediction model includes the following steps:

[0124] Obtain the distance range from the street lamp to the illuminated part of the urban highway, and equally space the distance range of the illuminated part to obtain at least one distance point;

[0125] Under the condition that the distance to the street lamp is the value at the distance point, an intensity coefficient is obtained, where the intensity coefficient is the ratio of the light intensity of the point whose distance to the street lamp is the value at the distance point to the actual intensity of the street lamp;

[0126] Pairing and fitting the distance points and the intensity coefficients to obtain at least one intensity fitting function;

[0127] Randomly combining at least one illumination point to obtain at least one illumination group;

[0128] Under the condition that the two light sources are the values ​​of the light points in the light group, the comprehensive light intensity of the two light sources is obtained;

[0129] Pair and fit the illumination group with the comprehensive illumination intensity to obtain an illumination superposition fitting function;

[0130] The intensity fitting function and the illumination superposition fitting function are used as illumination superposition prediction models.

[0131] There are multiple street lamps along the highway, and the light intensity of nearby street lamps will have a superimposed effect. Therefore, when adjusting the light intensity of the street lamps, it is necessary to consider the superimposed effect rather than simply considering a certain street lamp. Considering only a single street lamp will result in a greater light intensity and cause energy waste.

[0132] Reference Figure 7 As shown, forming a light intensity setting value for a highway segment based on vehicle statistics includes the following steps:

[0133] Obtain the length of the highway section, the traffic volume per unit time of the highway section, and the average time it takes for vehicles to pass through the highway section, and use the vehicle spacing formula to calculate the average vehicle spacing on the highway section;

[0134] Substitute the average distance between vehicles as the braking distance into the visual distance fitting function, and inversely solve to obtain the light intensity setting value of the highway section;

[0135] The formula for vehicle distance is as follows: ,

[0136] Among them, k is the average distance between vehicles, s is the length of the highway section, a is the traffic flow per unit time, and t is the average time for vehicles to pass through the highway section.

[0137] The flow of vehicles will determine the distance between them. The greater the flow, the greater the distance between vehicles. The brightness of the lights must at least ensure that the front vehicle can be seen clearly by the rear vehicle. Otherwise, when the front vehicle encounters a problem, the rear vehicle cannot make a judgment in time, resulting in a collision. Therefore, the brightness of the street lights must ensure that the front vehicle can be seen clearly.

[0138] Reference Figure 8 As shown, setting the lighting intensity of the street lamps on the highway section to obtain the preliminary lighting intensity of the street lamps includes the following steps:

[0139] Get any point in the road section as a feature point;

[0140] Take at least one sampling point evenly on the highway section;

[0141] Get the real-time rainfall of the day, substitute the real-time rainfall into the light impact fitting function, and get the real-time action coefficient;

[0142] Substituting the distance from the feature point to at least one street lamp on the highway section into the intensity fitting function to obtain the actual intensity coefficient;

[0143] The lighting intensity of the street lamp is taken as an unknown number and superimposed with the real-time moonlight intensity. After superposition, it is multiplied with the real-time action coefficient and the actual intensity coefficient to obtain the actual action intensity.

[0144] The comprehensive light intensity of at least one street lamp is combined using an intensity fitting function. When combining, the comprehensive light intensity of any two street lamps is substituted into the intensity fitting function. The result covers the comprehensive light intensity of the two street lamps used for combination to form a new comprehensive light intensity. This step is repeated until the number of comprehensive light intensities of at least one street lamp becomes 1, and the comprehensive light intensity is used as the characteristic light intensity.

[0145] Let the characteristic light intensity be equal to the light intensity setting value, and inversely solve the lighting intensity of the street lamp;

[0146] When the feature point traverses at least one sampling point, the lighting intensity of at least one street lamp is obtained, and the maximum value of the lighting intensity of at least one street lamp is used as the preliminary lighting intensity of the street lamp.

[0147] The preparatory lighting intensity of a street lamp must be the maximum value among the lighting intensities of at least one street lamp, because it can ensure that the brightness at each point is sufficient to clearly see the situation of the vehicle in front, so that timely operation can be made to avoid collision.

[0148] Reference Fig. 9 As shown, adjusting the preliminary lighting intensity of the street lamps on the target highway section to obtain the target lighting intensity of the street lamps includes the following steps:

[0149] Obtaining a driving record video of a vehicle involved in an accident on a target highway section, and obtaining a safe braking distance based on the driving record video, wherein the safe braking distance satisfies the requirement that braking is performed when two vehicles meet the safe braking distance without causing a collision;

[0150] Substitute the safe braking distance into the visual distance fitting function and inversely solve to obtain the target intensity setting value;

[0151] Acquire the characteristic light intensity of the target highway section as the target characteristic light intensity;

[0152] Set the target characteristic light intensity equal to the target intensity setting value, and inversely solve the street lamp lighting intensity as the street lamp target lighting intensity;

[0153] When the characteristic point of the target highway section traverses at least one sampling point of the target highway section, the target lighting intensity of the street lamp is obtained, and the maximum value of the target lighting intensities of the street lamp is used as the target lighting intensity of the street lamp.

[0154] The reason why accidents often occur on the target highway section may be that the road conditions are poor or the speed of vehicles on this road section is fast. Therefore, after setting the warning and enhancing the light intensity, the visible distance will increase. Most drivers will increase the distance according to the warning signs. Due to the enhanced light intensity, they can still see the vehicle in front clearly, so they can avoid it in time, thereby reducing the occurrence of collisions.

[0155] An urban lighting energy-saving control system based on edge computing, used to implement the above-mentioned urban lighting energy-saving control method based on edge computing, comprising:

[0156] A model building module, wherein the model building module forms a light impact model based on the weather, forms a nighttime lighting period based on the season, establishes a model of light intensity and visible distance, and forms a light superposition prediction model based on historical data;

[0157] A time period classification module, wherein the time period classification module divides the nighttime lighting period into a gradual lighting period, a full lighting period, and a reduced lighting period;

[0158] A lighting adjustment module, which gradually increases the light brightness to the light intensity of the full lighting period during the progressive lighting period, and gradually reduces the light intensity of the full lighting period to 0 during the reduced lighting period;

[0159] A preliminary calculation module, wherein the preliminary calculation module evenly divides the urban highway into at least one highway section, performs traffic statistics on the highway section, and forms a light intensity setting value for the highway section based on the vehicle statistics;

[0160] A secondary calculation module, wherein the secondary calculation module obtains the real-time moonlight intensity at every preset time during the full lighting period, and sets the lighting intensity of the streetlights on the highway section at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the streetlights;

[0161] A target determination module, wherein the target determination module traces the causes of accidents in the accident-prone statistical areas, determines the target accident causes related to lighting, and obtains the highway section where the target accident causes the accidents as the target highway section;

[0162] A street light adjustment module, which adjusts the preliminary lighting intensity of street lights on a target highway section based on a target accident cause and a model of light intensity and visible distance to obtain a target lighting intensity of the street lights;

[0163] A streetlight setting module is used to set the streetlight illumination intensity of the target highway section according to the target illumination intensity, and to set the streetlight illumination intensity of the highway section other than the target highway section according to the reserve illumination intensity.

[0164] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored, and when the computer-readable program is called, the above-mentioned edge computing-based urban lighting energy-saving control method is executed.

[0165] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0166] In summary, the advantages of the present invention are: by setting a model building module, a preliminary calculation module, a secondary calculation module and a street light adjustment module, various situations of vehicles driving on the road are summarized, and the light intensity is set based on the driving distance to ensure that the light intensity is sufficient to see adjacent vehicles clearly. At the same time, the lighting is further enhanced for sections prone to accidents, thereby ensuring that the driver can reasonably increase the distance between vehicles while still seeing adjacent vehicles clearly, thereby avoiding accidents. When setting, the weakening effect of light with distance and weather is taken into account, and the light superposition effect of multiple close street lights is taken into account, thereby ensuring that the set light intensity is the minimum value that meets the needs, thereby achieving energy-saving effects.

[0167] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for energy-saving control of urban lighting based on edge computing, characterized in that: include: Based on the weather, form a model of the impact of light; Form night lighting periods based on seasons; Model light intensity and viewing distance; Divide the night lighting period into gradual lighting period, full lighting period and reduced lighting period; During the progressive lighting period, the light brightness is gradually increased to the light intensity of the full lighting period; During the dimming period, the light intensity of the full lighting period is gradually reduced to 0; Based on historical data, a light superposition prediction model is formed; Evenly divide the urban highway into at least one highway section, count the traffic volume in the highway section, and form a light intensity setting value for the highway section based on the vehicle statistics, wherein the light intensity setting value refers to the light intensity projected onto the highway surface by the street lamp, not the light intensity of the street lamp; During the full lighting period, the real-time moonlight intensity is obtained at every preset time, and the lighting intensity of the street lights on the highway section is set at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the street lights; For accident-prone areas, trace the causes of accidents, determine the target accident causes related to lighting, and obtain the highway sections where accidents are caused by the target accident causes as the target highway sections; Based on the target accident cause and the model of light intensity and visible distance, the preliminary lighting intensity of the street lamps on the target highway section is adjusted to obtain the target lighting intensity of the street lamps; For street lamps on the target highway section, the street lamp illumination intensity is set according to the target illumination intensity; for street lamps on highway sections other than the target highway section, the street lamp illumination intensity is set according to the reserve illumination intensity; The model of establishing light intensity and visual distance comprises the following steps: Obtain a value range of light intensity, divide the value range of light intensity into equal intervals, and obtain at least one light point; Under the condition that the light intensity is the value at the light point, obtain the maximum visual distance of the driver; Pair and fit the illumination point with the maximum visible distance to obtain a visible distance fitting function, where the illumination point is the independent variable and the maximum visible distance is the dependent variable; The visible distance fitting function is used as a model of light intensity and visible distance; The method of forming a light superposition prediction model based on historical data includes the following steps: Obtain the distance range from the street lamp to the illuminated part of the urban highway, and equally space the distance range of the illuminated part to obtain at least one distance point; Under the condition that the distance to the street lamp is the value at the distance point, an intensity coefficient is obtained, where the intensity coefficient is the ratio of the light intensity of the point whose distance to the street lamp is the value at the distance point to the actual intensity of the street lamp; Pairing and fitting the distance points and the intensity coefficients to obtain at least one intensity fitting function; Randomly combining at least one illumination point to obtain at least one illumination group; Under the condition that the two light sources are the values ​​of the light points in the light group, the comprehensive light intensity of the two light sources is obtained; Pair and fit the illumination group with the comprehensive illumination intensity to obtain an illumination superposition fitting function; The intensity fitting function and the illumination superposition fitting function are used as illumination superposition prediction models; The method of forming a light intensity setting value for a highway section based on vehicle statistics comprises the following steps: Obtain the length of the highway section, the traffic volume per unit time of the highway section, and the average time it takes for vehicles to pass through the highway section, and use the vehicle spacing formula to calculate the average vehicle spacing on the highway section; Substitute the average distance between vehicles as the braking distance into the visual distance fitting function, and inversely solve to obtain the light intensity setting value of the highway section; The formula for vehicle distance is as follows: , Among them, k is the average distance between vehicles, s is the length of the highway section, a is the traffic volume per unit time, and t is the average time for vehicles to pass through the highway section; The step of setting the lighting intensity of the street lamps on the highway section to obtain the preliminary lighting intensity of the street lamps comprises the following steps: Get any point in the road section as a feature point; Take at least one sampling point evenly on the highway section; Get the real-time rainfall of the day, substitute the real-time rainfall into the light impact fitting function, and get the real-time action coefficient; Substituting the distance from the feature point to at least one street lamp on the highway section into the intensity fitting function to obtain the actual intensity coefficient; The lighting intensity of the street lamp is taken as an unknown number and superimposed with the real-time moonlight intensity. After superposition, it is multiplied with the real-time action coefficient and the actual intensity coefficient to obtain the actual action intensity. The comprehensive light intensity of at least one street lamp is combined using an intensity fitting function. When combining, the comprehensive light intensity of any two street lamps is substituted into the intensity fitting function. The result covers the comprehensive light intensity of the two street lamps used for combination to form a new comprehensive light intensity. This step is repeated until the number of comprehensive light intensities of at least one street lamp becomes 1, and the comprehensive light intensity is used as the characteristic light intensity. Let the characteristic light intensity be equal to the light intensity setting value, and inversely solve the lighting intensity of the street lamp; When the feature point traverses at least one sampling point, the lighting intensity of at least one street lamp is obtained, and the maximum value of the lighting intensity of at least one street lamp is used as the preliminary lighting intensity of the street lamp; The step of adjusting the preliminary lighting intensity of the street lamps on the target highway section to obtain the target lighting intensity of the street lamps comprises the following steps: Obtaining a driving record video of a vehicle involved in an accident on a target highway section, and obtaining a safe braking distance based on the driving record video, wherein the safe braking distance satisfies the requirement that braking is performed when two vehicles meet the safe braking distance without causing a collision; Substitute the safe braking distance into the visual distance fitting function and inversely solve to obtain the target intensity setting value; Acquire the characteristic light intensity of the target highway section as the target characteristic light intensity; Set the target characteristic light intensity equal to the target intensity setting value, and inversely solve the street lamp lighting intensity as the street lamp target lighting intensity; When the characteristic point of the target highway section traverses at least one sampling point of the target highway section, the target lighting intensity of the street lamp is obtained, and the maximum value of the target lighting intensities of the street lamp is used as the target lighting intensity of the street lamp.

2. According to the edge computing-based urban lighting energy-saving control method of claim 1, it is characterized in that: The step of forming a light impact model based on weather includes the following steps: Obtain a rainfall value range, divide the rainfall value range into equal intervals, and obtain at least one rainfall point; Under the condition that the rainfall is the value at the rainfall point, the effect coefficient after the light is weakened is obtained, and the effect coefficient is the ratio of the light intensity after weakening to the light intensity before weakening; The rainfall points and the action coefficients are paired and fitted to obtain the illumination influence fitting function, where the rainfall points are the independent variables and the action coefficients are the dependent variables; The lighting effect fitting function is used as the lighting effect model.

3. According to the edge computing-based urban lighting energy-saving control method of claim 2, it is characterized in that: The formation of night lighting periods based on seasons includes the following steps: In each season of spring, summer, autumn and winter, the initial time when the sunlight intensity is insufficient and the initial time when the sunlight intensity is sufficient are counted; The average of the initial time when the sunlight intensity is insufficient is taken to get the initial time of the night, and the average of the initial time when the sunlight intensity is sufficient is taken to get the end time of the night; The period between the beginning of the night and the end of the night constitutes the night lighting period.

4. According to the edge computing-based urban lighting energy-saving control method of claim 3, it is characterized in that: The method of dividing the nighttime lighting period into a gradual lighting period, a full lighting period and a reduced lighting period comprises the following steps: Based on historical data, the time from the beginning of the night to the complete sunset is obtained as the characteristic time interval; The part of the night lighting period whose distance from the initial time of night is less than the characteristic time interval is regarded as the progressive lighting period; The part of the night lighting period whose distance from the night end time is less than the characteristic time interval is regarded as the reduced lighting period; The part of the night lighting period except the gradual lighting period and the dimmed lighting period is regarded as the full lighting period.

5. An urban lighting energy-saving control system based on edge computing, used to implement the urban lighting energy-saving control method based on edge computing as described in any one of claims 1 to 4, characterized in that: include: A model building module, wherein the model building module forms a light impact model based on the weather, forms a nighttime lighting period based on the season, establishes a model of light intensity and visible distance, and forms a light superposition prediction model based on historical data; A time period classification module, wherein the time period classification module divides the nighttime lighting period into a gradual lighting period, a full lighting period, and a reduced lighting period; A lighting adjustment module, which gradually increases the light brightness to the light intensity of the full lighting period during the progressive lighting period, and gradually reduces the light intensity of the full lighting period to 0 during the reduced lighting period; A preliminary calculation module, wherein the preliminary calculation module evenly divides the urban highway into at least one highway section, performs traffic statistics on the highway section, and forms a light intensity setting value for the highway section based on the vehicle statistics; A secondary calculation module, wherein the secondary calculation module obtains the real-time moonlight intensity at every preset time during the full lighting period, and sets the lighting intensity of the streetlights on the highway section at every preset time based on the light intensity setting value, the real-time moonlight intensity and the light impact model to obtain the preliminary lighting intensity of the streetlights; A target determination module, wherein the target determination module traces the causes of accidents in the accident-prone statistical areas, determines the target accident causes related to lighting, and obtains the highway section where the target accident causes the accidents as the target highway section; A street light adjustment module, which adjusts the preliminary lighting intensity of street lights on a target highway section based on a target accident cause and a model of light intensity and visible distance to obtain a target lighting intensity of the street lights; A streetlight setting module is used to set the streetlight illumination intensity of the target highway section according to the target illumination intensity, and to set the streetlight illumination intensity of the highway section other than the target highway section according to the reserve illumination intensity.

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