Urban road lighting intelligent management system and method based on big data

Abstract

The invention discloses an urban road lighting intelligent management system and method based on big data, and relates to the technical field of urban road lighting management. Urban roads are divided based on urban road surface brightness monitoring data; acquiring operation states and position information of all street lamps in the urban road; performing abnormal marking and alarm reminding based on the road surface brightness condition of each urban road section and the loss illumination brightness of the urban road; according to the maximum correction illumination brightness and the difference comparison condition between the loss illumination brightness of the faulty urban road section and a preset loss brightness threshold value, alarm reminding is carried out, and the brightness of the street lamps in the faulty urban road section is regulated and controlled; and selecting the fault road section inspection scheme with the minimum inspection reference value as an optimal inspection scheme, and sending the optimal inspection scheme to a corresponding administrator. Potential safety hazards caused by abnormal road surface brightness are eliminated; the service life of the normal street lamp is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of urban road lighting management, and in particular to an intelligent management system and method for urban road lighting based on big data. Background Art

[0002] Road lighting is an important part of urban transportation infrastructure. It not only affects traffic safety, but also affects the city's economic development, social security and environmental quality. Road lighting refers to the installation of lighting equipment on roads and their ancillary facilities to improve the safety of vehicles and pedestrians at night and prevent traffic accidents.

[0003] The existing intelligent lighting management system can monitor the operating status of street lamps and detect lighting anomalies of street lamps. However, there is a certain window period from the time a street lamp fails to the time when repairs are completed. The urban roads corresponding to the faulty street lamps will continue to have light and dark areas, and passing vehicles and personnel are prone to visual blind spots, posing a high safety hazard.

[0004] Therefore, the present invention discloses an intelligent management system and method for urban road lighting based on big data to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an urban road lighting intelligent management system and method based on big data to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for intelligent management of urban road lighting based on big data, the method comprising the following steps:

[0007] S1: uniformly numbering urban roads in the monitored city, monitoring the urban roads according to the numbers, obtaining road surface brightness monitoring data of the urban roads, and dividing the urban roads into sections based on the road surface brightness monitoring data of the urban roads;

[0008] S2: Extract road surface brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road sections with street lamps with operating failures; analyze the road surface brightness of each urban road section and calculate the lost light brightness of the urban road; based on the road surface brightness of each urban road section and the lost light brightness of the urban road, mark abnormalities and issue alarm reminders;

[0009] S3: Analyze the maximum corrected light brightness of each urban road; according to the comparison between the maximum corrected light brightness and the loss light brightness of the faulty urban road section and the preset loss brightness threshold, issue an alarm and adjust the brightness of the street lamps in the faulty urban road section;

[0010] S4: Arrange and combine the obtained faulty urban road sections to generate a faulty road section inspection plan corresponding to each arrangement and combination plan; analyze the inspection reference values ​​of the faulty road section inspection plans; select the faulty road section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0011] According to the above scheme, S1 includes the following contents:

[0012] S101: Number the urban roads of the city to be monitored and generate a set of monitored urban roads, denoted as MUR = {MUR 1 , MUR 2 , …, MUR i , …, MUR I}; where MUR i represents an urban road numbered i in the city to be monitored, i∈[1,I]; I represents the total number of urban roads in the city to be monitored; the urban roads are monitored according to the numbers to obtain road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes road surface brightness and corresponding position information;

[0013] S102: Urban roads MUR i The corresponding road surface brightness monitoring data is mapped in the coordinate system, and the position coordinates corresponding to the same road surface brightness are extracted to form a position coordinate set; two consecutive position coordinates with odd numbers in the position coordinate set are used to generate a coordinate group; two adjacent coordinate groups are randomly extracted and recorded as (X (y，1) , X (y，2) ) and (X (y+1，1) , X (y+1，2) ), where y represents the urban road MUR i The coordinate group number, y∈[1,y], y represents the urban road MUR i The total number of corresponding coordinate groups; based on urban roads MUR i Calculate the correlation S for the corresponding coordinate group:

[0014] S = exp[(X (y+1，1) -X (y，1) )÷(X (y+1，2) -X (y，2 )-1];

[0015] Where exp[] represents an exponential function with natural numbers as base;

[0016] S103: Analysis of Urban Road MUR i All coordinate groups of the urban road MUR i The area is divided according to the correlation degree of the coordinate group; when S 阈1 ≤S≤S 阈2When S 阈2 <S or S>S 阈1 , the urban roads corresponding to the yth and y+1th coordinate groups are divided into two different urban road segments; where S 阈1 and S 阈2 represents the correlation threshold, which is a system preset constant; the urban road sections are numbered, and the urban road MUR i The j-th urban road segment is recorded as URS (i，j) , where j∈[1,J], J represents the urban road MUR i The total number of road segments in the city.

[0017] Through the above analysis, the correlation between two adjacent bright coordinate groups of data can be effectively displayed. In order to facilitate the tunnel lamp lighting control system to control the brightness of the street lamp, the urban road corresponding to the correlation in the threshold interval is regarded as a road section, which improves the control efficiency of the system.

[0018] According to the above scheme, S2 includes the following contents:

[0019] S201: extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; extract the urban road MUR from the historical road surface brightness monitoring data i The highest road brightness is recorded as ABRS i max ;

[0020] S202: Get the urban road MUR i The operating status and location information of all street lamps in the system are obtained; the urban road sections with street lamps with operating failures are marked as faulty urban road sections; the average road surface brightness of the pth faulty urban road section is recorded as ABRS (i，p) ; The lowest road brightness of the pth faulty urban road section is recorded as ABRS (i，p) min ; p∈[1,P]; where P represents the urban road MUR i The number of urban road sections with medium faults;

[0021] The urban road sections without street lamps with operating failures are recorded as normal urban road sections, and the average road surface brightness of all normal urban road sections is recorded as ABRS. (i，Q) ; Q represents urban road MUR i The number of normal urban road segments in; P+Q=J;

[0022] Calculate MUR for urban roads i The loss of light brightness:

[0023] S203: When the city road MUR i The loss of light illuminance GMUR i Less than the preset loss brightness threshold GMUR 阈 , and there is no minimum road surface brightness ABRS in the urban road section with faults (i，p) min If the brightness is lower than the preset minimum road surface brightness threshold, the street lamps with operating failures will be marked and uploaded to the system background for recording;

[0024] When the city road MUR i The loss of light illuminance GMUR i Greater than or equal to the preset loss brightness threshold GMUR 阈 When there is a fault, the minimum road surface brightness of the urban road section is ABRS (i，p) min Less than the preset minimum road brightness threshold ABRS min 阈 , to issue an alarm reminder.

[0025] The loss of light brightness of each urban road is calculated. According to the comparison between the loss of light brightness of the urban road and the minimum road brightness and the threshold, it can effectively show the situation where the loss of light brightness of the urban road is large and the minimum road brightness of some urban road sections is low. The above situation is warned, providing data support for the subsequent elimination of safety hazards caused by low road brightness.

[0026] According to the above scheme, S3 includes the following:

[0027] Calculate MUR for urban roads i Maximum corrected light intensity:

[0028] When the city road MUR i The maximum corrected light illuminance is less than that of urban roads MUR i When the difference between the loss of light brightness and the loss brightness threshold is reached, an alarm will be issued; when the urban road MUR i The maximum corrected light illuminance is greater than or equal to the urban road MUR i When the difference between the loss light illumination and the loss brightness threshold is calculated, the brightness of the street lamps in the faulty urban road section is adjusted; the urban road MUR is calculated i The minimum control brightness G of each faulty urban road section p :

[0029] G p =(GMUR i -GMUR 阈 )÷P+ABRS(i，p) min -ABRS min 阈 ;

[0030] And satisfy G p ≤ABRS i max , otherwise an alarm will be issued.

[0031] Analyze the minimum controlled brightness to ensure that the average and minimum road brightness of the urban road section after control is greater than the corresponding threshold value, eliminating the potential safety hazards caused by abnormal road brightness;

[0032] According to the above scheme, S4 includes the following contents:

[0033] S401: extracting all faulty urban road sections with alarm reminders in the city to be monitored; performing permutations and combinations on the obtained faulty urban road sections with alarm reminders, and generating a faulty road section inspection plan corresponding to each permutation and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads in the city to be monitored of each adjacent faulty urban road section in the permutation and combination of the corresponding faulty urban road sections;

[0034] S402: Analyze the inspection reference value LRV of each fault section inspection plan:

[0035]

[0036] Where L represents the total route length of the fault section inspection plan; L n Indicates the length of the route that the nth faulty urban road segment has passed in the faulty road segment inspection plan; n∈[1, N]; N indicates the number of faulty urban road segments in the faulty road segment inspection plan; ABRS n min It indicates the lowest road surface brightness before brightness adjustment of the nth faulty urban road section in the faulty road section inspection plan;

[0037] S403: Select the faulty road section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0038] After adjusting the low road brightness corresponding to the faulty urban road section, the street lamp will be in a high-power working state. Through the above calculation formula, the original faulty urban road section with low road brightness can be arranged to the front position of the optimal inspection plan, and the faulty urban road section can be repaired as soon as possible, which can reduce the waste of electricity resources and increase the service life of normal street lamps.

[0039] Another aspect of the present application provides an intelligent management system for urban road lighting based on big data, which is applied to the above-mentioned intelligent management method for urban road lighting based on big data, and the system includes a road section division module, a road surface brightness loss analysis module, a lighting correction and control module, and an optimal inspection plan generation module;

[0040] The road section division module is used to uniformly number the urban roads in the monitored city, monitor the urban roads according to the numbers, obtain the road surface brightness monitoring data of the urban roads, and divide the urban roads into sections based on the road surface brightness monitoring data of the urban roads;

[0041] The road brightness loss analysis module is used to extract road brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road section with street lamps with operating failures; analyze the road brightness conditions of each urban road section and calculate the loss of light brightness of the urban road; based on the road brightness conditions of each urban road section and the loss of light brightness of the urban road, perform abnormal marking and alarm reminders;

[0042] The illumination correction and control module is used to analyze the maximum corrected light brightness of each urban road; according to the comparison between the maximum corrected light brightness and the loss light brightness of the faulty urban road section and the preset loss brightness threshold, an alarm is issued and the brightness of the street lamps in the faulty urban road section is adjusted;

[0043] The optimal inspection plan generation module is used to arrange and combine the obtained faulty urban road sections to generate a faulty road section inspection plan corresponding to each arrangement and combination plan; analyze the inspection reference values ​​of each faulty road section inspection plan; select the faulty road section inspection plan with the smallest inspection reference value as the optimal inspection plan, and send it to the corresponding administrator.

[0044] According to the above solution, the road segment division module includes a road data extraction unit and an association splitting unit;

[0045] The road data extraction unit is used to number the urban roads of the monitored city, monitor the urban roads according to the numbers, and obtain the road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes the road surface brightness and the corresponding position information;

[0046] The association splitting unit is used to analyze the association degree of adjacent coordinate groups, and split the urban roads corresponding to the coordinate groups according to the comparison between the association degree of the adjacent coordinate groups and the association degree threshold.

[0047] According to the above scheme, the road surface brightness loss analysis module includes a loss illumination calculation unit and an abnormality mark alarm unit;

[0048] The lost light calculation unit is used to extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; calculate the lost light brightness of each urban road;

[0049] The abnormal marking and warning unit performs abnormal marking and warning reminders based on the road surface brightness conditions of each urban road section and the loss of light brightness of the urban road.

[0050] According to the above solution, the illumination correction and control module includes a correction brightness analysis unit and a brightness control unit;

[0051] The modified brightness analysis unit is used to calculate the maximum modified light brightness of the urban road;

[0052] The brightness control unit controls the brightness of the street lamps in the faulty urban road section according to the comparison between the maximum corrected light brightness of the urban road and the difference between the loss light brightness of the urban road and the loss brightness threshold.

[0053] According to the above solution, the optimal inspection plan generation module includes an inspection plan generation unit and an inspection reference value analysis unit;

[0054] The inspection plan generating unit is used to extract all faulty urban road sections with alarm reminders in the city to be monitored; arrange and combine the obtained faulty urban road sections with alarm reminders to generate a faulty road section inspection plan corresponding to each arrangement and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads in the city to be monitored of each adjacent faulty urban road section in the arrangement and combination of the corresponding faulty urban road sections;

[0055] The inspection reference value analysis unit is used to analyze the inspection reference values ​​of each fault section inspection plan, select the fault section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention effectively displays the correlation between two adjacent bright coordinate group data, and in order to facilitate the tunnel lamp lighting control system to control the brightness of the street lamp, the urban road corresponding to the correlation in the threshold interval is taken as a road section; the control efficiency of the system is improved; the lost light brightness of each urban road is calculated, and according to the comparison between the lost light brightness of the urban road and the minimum road surface brightness and the threshold, it can effectively display the situation that the lost light brightness of the urban road is large and the minimum road surface brightness of individual urban road sections is low, and an alarm is given for the above situation, which provides data support for the subsequent elimination of safety hazards caused by low road surface brightness; the minimum control brightness is analyzed to ensure that the average road surface brightness and the minimum road surface brightness of the urban road section after control are greater than the corresponding threshold value, and the safety hazards caused by abnormal road surface brightness are eliminated; after adjusting the low road surface brightness corresponding to the faulty urban road section, the street lamp will be in a high-power working state, and through the above calculation formula, the original faulty urban road section with low road surface brightness can be arranged to the front position of the optimal inspection plan, and the faulty urban road section can be repaired as soon as possible, which can reduce the waste of power resources and increase the service life of normal street lamps. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 A schematic diagram of a process of an intelligent management method for urban road lighting based on big data according to the present invention;

[0059] Figure 2 This is a structural schematic diagram of an urban road lighting intelligent management system based on big data in the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 The present invention provides a technical solution: a method for intelligent management of urban road lighting based on big data, the method comprising the following steps:

[0062] S1: uniformly numbering urban roads in the monitored city, monitoring the urban roads according to the numbers, obtaining road surface brightness monitoring data of the urban roads, and dividing the urban roads into sections based on the road surface brightness monitoring data of the urban roads;

[0063] In S1, the following contents are included:

[0064] S101: Number the urban roads of the city to be monitored and generate a set of monitored urban roads, denoted as MUR = {MUR 1 , MUR 2 , …, MUR i , …, MUR I}; where MUR i represents the urban road numbered i in the city to be monitored, i∈[1,I]; I represents the total number of urban roads in the city to be monitored; the urban roads are monitored according to the numbers to obtain the road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes the road surface brightness and the corresponding position information;

[0065] S102: Urban roads MUR i The corresponding road surface brightness monitoring data is mapped in the coordinate system, and the position coordinates corresponding to the same road surface brightness are extracted to form a position coordinate set; two consecutive position coordinates with odd numbers in the position coordinate set are used to generate a coordinate group; two adjacent coordinate groups are randomly extracted and recorded as (X (y，1) , X (y，2) ) and (X (y+1，1) , X (y+1，2) ), where y represents the urban road MUR i The coordinate group number, y∈[1,y], y represents the urban road MUR i The total number of corresponding coordinate groups; based on urban roads MUR i Calculate the correlation S for the corresponding coordinate group:

[0066] S = exp[(X (y+1，1) -X (y，1) )÷(X (y+1，2) -X (y，2 )-1];

[0067] Where exp[] represents an exponential function with natural numbers as base;

[0068] S103: Analysis of Urban Road MUR i All coordinate groups of the urban road MUR i The area is divided according to the correlation degree of the coordinate group; when S 阈1 ≤S≤S 阈2 When S阈2 <S or S>S 阈1 , the urban roads corresponding to the yth and y+1th coordinate groups are divided into two different urban road segments; where S 阈1 and S 阈2 Represents the correlation threshold, which is a system preset constant; the urban road sections are numbered and the urban road MUR i The j-th urban road segment is recorded as URS (i，j) , where j∈[1,J], J represents the urban road MUR i The total number of road segments in the city.

[0069] Example 1: In this example, two adjacent coordinate groups are (1, 2) and (3, 3.9);

[0070] Therefore, S = exp[(3-1)÷(3.9-2)-1]≈1.05;

[0071] In this embodiment, S 阈1 =0.5, S 阈2 =2; therefore, the urban roads corresponding to the coordinate groups in this embodiment are classified into the same urban road segment;

[0072] S2: Extract road surface brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road sections with street lamps with operating failures; analyze the road surface brightness of each urban road section and calculate the lost light brightness of the urban road; based on the road surface brightness of each urban road section and the lost light brightness of the urban road, mark abnormalities and issue alarm reminders;

[0073] In S2, the following are included:

[0074] S201: extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; extract the urban road MUR from the historical road surface brightness monitoring data i The highest road brightness is recorded as ABRS i max ;

[0075] S202: Get the urban road MUR i The operating status and location information of all street lamps in the system are obtained; the urban road sections with street lamps with operating failures are marked as faulty urban road sections; the average road surface brightness of the pth faulty urban road section is recorded as ABRS (i，p) ; The lowest road brightness of the pth faulty urban road section is recorded as ABRS (i，p) min ; p∈[1,P]; where P represents the urban road MUR iThe number of urban road sections with medium faults;

[0076] The urban road sections without street lamps with operating failures are recorded as normal urban road sections, and the average road surface brightness of all normal urban road sections is recorded as ABRS. (i，Q) ; Q represents urban road MUR i The number of normal urban road segments in; P+Q=J;

[0077] Calculate MUR for urban roads i The loss of light brightness:

[0078] S203: When the city road MUR i GMUR i Less than the preset loss brightness threshold GMUR 阈 , and there is no minimum road surface brightness ABRS in the urban road section with faults (i，p) min If the brightness is lower than the preset minimum road surface brightness threshold, the street lamps with operating failures will be marked and uploaded to the system background for recording;

[0079] When the city road MUR i The loss of light illuminance GMUR i Greater than or equal to the preset loss brightness threshold GMUR 阈 When there is a fault, the minimum road surface brightness of the urban road section is ABRS (i，p) min Less than the preset minimum road brightness threshold ABRS min 阈 , to issue an alarm reminder.

[0080] S3: Analyze the maximum corrected light brightness of each urban road; according to the comparison between the maximum corrected light brightness and the loss light brightness of the faulty urban road section and the preset loss brightness threshold, issue an alarm and adjust the brightness of the street lamps in the faulty urban road section;

[0081] In S3, include the following:

[0082] Calculate MUR for urban roads i Maximum corrected light intensity:

[0083] When the city road MUR i The maximum corrected light illuminance is less than that of urban roads MUR i When the difference between the loss of light brightness and the loss brightness threshold is reached, an alarm will be issued; when the urban road MUR i The maximum corrected light illuminance is greater than or equal to the urban road MUR iWhen the difference between the loss light illumination and the loss brightness threshold is calculated, the brightness of the street lamps in the faulty urban road section is adjusted; the urban road MUR is calculated i The minimum control brightness G of each faulty urban road section p :

[0084] G p =(GMUR i -GMUR 阈 )÷P+ABRS (i，p) min -ABRS min 阈 ;

[0085] And satisfy G p ≤ABRS i max , otherwise an alarm will be issued.

[0086] S4: Arrange and combine the obtained faulty urban road sections to generate a faulty road section inspection plan corresponding to each arrangement and combination plan; analyze the inspection reference values ​​of the faulty road section inspection plans; select the faulty road section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0087] In S4, the following are included:

[0088] S401: extracting all faulty urban road sections with alarm reminders in the city to be monitored; performing permutations and combinations on the obtained faulty urban road sections with alarm reminders, and generating a faulty road section inspection plan corresponding to each permutation and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads in the city to be monitored of each adjacent faulty urban road section in the permutation and combination of the corresponding faulty urban road sections;

[0089] S402: Analyze the inspection reference value LRV of each fault section inspection plan:

[0090]

[0091] Where L represents the total route length of the fault section inspection plan; L n Indicates the length of the route that the nth faulty urban road segment has passed in the faulty road segment inspection plan; n∈[1, N]; N indicates the number of faulty urban road segments in the faulty road segment inspection plan; ABRS n min It indicates the lowest road surface brightness before brightness adjustment of the nth faulty urban road section in the faulty road section inspection plan;

[0092] S403: Select the faulty road section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0093] See also Figure 2 , the present invention provides a technical solution: an intelligent management system for urban road lighting based on big data, the system includes a road section division module, a road surface brightness loss analysis module, a lighting correction and control module and an optimal inspection plan generation module;

[0094] The road section division module is used to uniformly number the urban roads in the monitored city, monitor the urban roads according to the numbers, obtain the road surface brightness monitoring data of the urban roads, and divide the urban roads into sections based on the road surface brightness monitoring data of the urban roads;

[0095] The road brightness loss analysis module is used to extract road brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road sections with street lamps with operating failures; analyze the road brightness conditions of each urban road section and calculate the loss of light brightness of the urban road; based on the road brightness conditions of each urban road section and the loss of light brightness of the urban road, perform abnormal marking and alarm reminders;

[0096] The illumination correction and control module is used to analyze the maximum corrected illumination brightness of each urban road; according to the comparison between the maximum corrected illumination brightness and the loss illumination brightness of the faulty urban road section and the preset loss brightness threshold, an alarm is issued and the brightness of the street lamps in the faulty urban road section is adjusted;

[0097] The optimal inspection plan generation module is used to arrange and combine the obtained faulty urban road sections, generate a faulty section inspection plan corresponding to each arrangement and combination plan; analyze the inspection reference values ​​of each faulty section inspection plan; select the faulty section inspection plan with the smallest inspection reference value as the optimal inspection plan, and send it to the corresponding administrator.

[0098] The road segmentation module includes a road data extraction unit and an associated splitting unit;

[0099] The road data extraction unit is used to number the urban roads of the monitored city, monitor the urban roads according to the numbers, and obtain the road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes the road surface brightness and the corresponding position information;

[0100] The association splitting unit is used to analyze the association degree of adjacent coordinate groups, and split the urban roads corresponding to the coordinate groups according to the comparison between the association degree of adjacent coordinate groups and the association degree threshold.

[0101] The road surface brightness loss analysis module includes a loss illumination calculation unit and an abnormality mark alarm unit;

[0102] The lost light calculation unit is used to extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; calculate the lost light brightness of each urban road;

[0103] The abnormal marking and warning unit performs abnormal marking and warning reminders based on the road surface brightness conditions of each urban road section and the loss of light brightness of the urban road.

[0104] The illumination correction and control module includes a correction brightness analysis unit and a brightness control unit;

[0105] The corrected brightness analysis unit is used to calculate the maximum corrected light brightness of urban roads;

[0106] The brightness control unit controls the brightness of the street lamps in the faulty urban road section according to the comparison between the maximum corrected light brightness of the urban road and the difference between the loss light brightness of the urban road and the loss brightness threshold.

[0107] The optimal inspection plan generation module includes an inspection plan generation unit and an inspection reference value analysis unit;

[0108] The inspection plan generating unit is used to extract all faulty urban road sections with alarm reminders in the city to be monitored; arrange and combine the obtained faulty urban road sections with alarm reminders, and generate a faulty road section inspection plan corresponding to each arrangement and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads in the city to be monitored of each adjacent faulty urban road section in the arrangement and combination of the corresponding faulty urban road sections;

[0109] The inspection reference value analysis unit is used to analyze the inspection reference values ​​of each fault section inspection plan, select the fault section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for intelligent management of urban road lighting based on big data, characterized in that: The method comprises the following steps: S1: uniformly numbering urban roads in the monitored city, monitoring the urban roads according to the numbers, obtaining road surface brightness monitoring data of the urban roads, and dividing the urban roads into sections based on the road surface brightness monitoring data of the urban roads; S2: Extract road surface brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road sections with street lamps with operating failures; analyze the road surface brightness of each urban road section and calculate the lost light brightness of the urban road; based on the road surface brightness of each urban road section and the lost light brightness of the urban road, mark abnormalities and issue alarm reminders; S3: Analyze the maximum corrected light illumination of each urban road; According to the comparison between the maximum corrected light brightness and the loss light brightness of the faulty urban road section and the preset loss brightness threshold, an alarm is issued and the brightness of the street lamps in the faulty urban road section is adjusted; S4: performing permutations and combinations on the obtained faulty urban road sections, and generating a faulty road section inspection plan corresponding to each permutation and combination plan; Analyze the inspection reference values ​​of each fault section inspection plan; The inspection plan for the faulty section with the smallest inspection reference value is selected as the best inspection plan and sent to the corresponding administrator.

2. According to the big data-based intelligent management method for urban road lighting of claim 1, it is characterized by: In S1, the following contents are included: S101: Number the urban roads of the city to be monitored and generate a set of monitored urban roads, recorded as MUR = {MUR1, MUR2, ..., MUR i , …, MUR I }; where MUR i represents the urban road numbered i in the city to be monitored, i∈[1,I]; I represents the total number of urban roads in the city to be monitored; the urban roads are monitored according to the number to obtain the road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes the road surface brightness and the corresponding position information; S102: Urban roads MUR i The corresponding road surface brightness monitoring data is mapped in the coordinate system, and the position coordinates corresponding to the same road surface brightness are extracted to form a position coordinate set; two consecutive position coordinates with odd numbers in the position coordinate set are used to generate a coordinate group; two adjacent coordinate groups are randomly extracted and recorded as (X (y，1) , X (y，2) ) and (X (y+1，1) , X (y+1，2) ), where y represents the urban road MUR i The coordinate group number, y∈[1,y], y represents the urban road MUR i The total number of corresponding coordinate groups; based on urban roads MUR i Calculate the correlation S for the corresponding coordinate group: S=exp[(X (y+1，1) -X (y，1) )÷(X (y+1，2) -X (y，2 )-1]; Where exp[] represents an exponential function with natural numbers as base; S103: Analysis of Urban Road MUR i All coordinate groups of the city road MUR i The area is divided according to the correlation degree of the coordinate group; when S 阈1 ≤S≤S 阈2 When S 阈2 <S or S>S 阈1 , the urban roads corresponding to the yth and y+1th coordinate groups are divided into two different urban road segments; where S 阈1 and S 阈2 represents the correlation threshold, which is a system preset constant; the urban road sections are numbered, and the urban road MUR i The j-th urban road segment is recorded as URS (i，j) , where j∈[1,J], J represents the urban road MUR i The total number of road segments in the city.

3. According to claim 2, a method for intelligent management of urban road lighting based on big data is characterized in that: In S2, the following are included: S201: extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; extract the urban road MUR from the historical road surface brightness monitoring data i The highest road brightness is recorded as ABRS i max ; S202: Get the urban road MUR i The operating status and location information of all street lamps in the system are obtained; the urban road sections with street lamps with operating failures are marked as faulty urban road sections; the average road surface brightness of the pth faulty urban road section is recorded as ABRS (i，p) ; The lowest road brightness of the pth faulty urban road section is recorded as ABRS (i，p) min ; p∈[1,P]; Where P represents urban road MUR i The number of urban road sections with medium faults; The urban road sections without street lamps with operating failures are recorded as normal urban road sections, and the average road surface brightness of all normal urban road sections is recorded as ABRS. (i，Q) ; Q represents urban road MUR i The number of normal urban road segments in; P+Q=J; Calculate MUR for urban roads i The loss of light brightness: S203: When the city road MUR i GMUR i Less than the preset loss brightness threshold GMUR 阈 , and there is no minimum road surface brightness ABRS in the urban road section with faults (i，p) min If the brightness is lower than the preset minimum road surface brightness threshold, the street lamps with operating failures will be marked and uploaded to the system background for recording; When the city road MUR i GMUR i Greater than or equal to the preset loss brightness threshold GMUR 阈 When there is a fault, the minimum road surface brightness of the urban road section is ABRS (i，p) min Less than the preset minimum road brightness threshold ABRS min 阈 , to issue an alarm reminder.

4. The method for intelligent management of urban road lighting based on big data according to claim 3 is characterized in that: In S3, include the following: Calculate MUR for urban roads i Maximum corrected light intensity: When the city road MUR i The maximum corrected light illuminance is less than that of urban roads MUR i When the difference between the loss of light brightness and the loss brightness threshold is reached, an alarm will be issued; when the urban road MUR i The maximum corrected light illuminance is greater than or equal to the urban road MUR i When the difference between the loss light illumination and the loss brightness threshold is calculated, the brightness of the street lamps in the faulty urban road section is adjusted; the urban road MUR is calculated i The minimum control brightness G of each faulty urban road section p : G p =(GMUR i -GMUR 阈 )÷P+ABRS (i，p) min -ABRS min 阈 ; And satisfy G p ≤ABRS i max , otherwise an alarm will be issued.

5. The method for intelligent management of urban road lighting based on big data according to claim 4 is characterized in that: In S4, the following are included: S401: extracting all faulty urban road sections with alarm reminders in the city to be monitored; performing permutations and combinations on the obtained faulty urban road sections with alarm reminders, and generating a faulty road section inspection plan corresponding to each permutation and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads in the city to be monitored of each adjacent faulty urban road section in the permutation and combination of the corresponding faulty urban road sections; S402: Analyze the inspection reference value LRV of each fault section inspection plan: Where L represents the total route length of the fault section inspection plan; L n Indicates the length of the route that the nth faulty urban road segment has passed in the faulty road segment inspection plan; n∈[1, N]; N indicates the number of faulty urban road segments in the faulty road segment inspection plan; ABRS n min It indicates the lowest road surface brightness before brightness adjustment of the nth faulty urban road section in the faulty road section inspection plan; S403: Select the faulty road section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

6. An intelligent management system for urban road lighting based on big data, the system is applied to the intelligent management method for urban road lighting based on big data as described in any one of claims 1 to 5, characterized in that: The system includes a road section division module, a road surface brightness loss analysis module, a lighting correction and control module, and an optimal inspection plan generation module; The road section division module is used to uniformly number the urban roads in the monitored city, monitor the urban roads according to the numbers, obtain the road surface brightness monitoring data of the urban roads, and divide the urban roads into sections based on the road surface brightness monitoring data of the urban roads; The road brightness loss analysis module is used to extract road brightness monitoring data in each urban road section; obtain the operating status and location information of all street lamps in the urban road; mark the urban road section with street lamps with operating failures; analyze the road brightness conditions of each urban road section and calculate the loss of light brightness of the urban road; based on the road brightness conditions of each urban road section and the loss of light brightness of the urban road, perform abnormal marking and alarm reminders; The illumination correction and control module is used to analyze the maximum corrected illumination of each urban road; According to the comparison between the maximum corrected light brightness and the loss light brightness of the faulty urban road section and the preset loss brightness threshold, an alarm is issued and the brightness of the street lamps in the faulty urban road section is adjusted; The optimal inspection plan generation module is used to arrange and combine the obtained faulty urban road sections to generate a faulty road section inspection plan corresponding to each arrangement and combination plan; Analyze the inspection reference values ​​of each fault section inspection plan; The inspection plan for the faulty section with the smallest inspection reference value is selected as the best inspection plan and sent to the corresponding administrator.

7. The urban road lighting intelligent management system based on big data according to claim 6 is characterized by: The road segment division module includes a road data extraction unit and an association splitting unit; The road data extraction unit is used to number the urban roads of the monitored city, monitor the urban roads according to the numbers, and obtain the road surface brightness monitoring data of the urban roads; the road surface brightness monitoring data includes the road surface brightness and the corresponding position information; The association splitting unit is used to analyze the association degree of adjacent coordinate groups, and split the urban roads corresponding to the coordinate groups according to the comparison between the association degree of the adjacent coordinate groups and the association degree threshold.

8. The urban road lighting intelligent management system based on big data according to claim 6 is characterized by: The road surface brightness loss analysis module includes a loss illumination calculation unit and an abnormality mark alarm unit; The lost light calculation unit is used to extract the road surface brightness monitoring data of each urban road section, calculate the average road surface brightness of each urban road section; calculate the lost light brightness of each urban road; The abnormal marking and warning unit performs abnormal marking and warning reminders based on the road surface brightness conditions of each urban road section and the loss of light brightness of the urban road.

9. The urban road lighting intelligent management system based on big data according to claim 6 is characterized by: The illumination correction and control module includes a correction brightness analysis unit and a brightness control unit; The modified brightness analysis unit is used to calculate the maximum modified light brightness of the urban road; The brightness control unit controls the brightness of the street lamps in the faulty urban road section according to the comparison between the maximum corrected light brightness of the urban road and the difference between the loss light brightness of the urban road and the loss brightness threshold.

10. The urban road lighting intelligent management system based on big data according to claim 6, characterized in that: The optimal inspection plan generation module includes an inspection plan generation unit and an inspection reference value analysis unit; The inspection plan generation unit is used to extract all faulty urban road sections with alarm reminders in the city to be monitored; Arrange and combine the obtained faulty urban road sections with alarm reminders, and generate a faulty road section inspection plan corresponding to each arrangement and combination plan; the faulty road section inspection plan is a splicing route of the shortest urban roads of each adjacent faulty urban road section in the arrangement and combination of the corresponding faulty urban road sections in the city to be monitored; The inspection reference value analysis unit is used to analyze the inspection reference values ​​of each fault section inspection plan, select the fault section inspection plan with the smallest inspection reference value as the best inspection plan, and send it to the corresponding administrator.

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