Smart city lighting control method and system

Abstract

The embodiment of the invention relates to the technical field of smart city lighting, and particularly discloses a smart city lighting control method and system. According to the embodiment of the invention, a target driving vehicle is determined, and driving navigation data and real-time positioning data are obtained; verifying and updating the driving navigation data; according to the real-time positioning data, an illumination opening road section is extracted from the driving navigation data, and illumination opening control is carried out on the illumination opening road section; and according to the real-time positioning data, extracting a lighting turn-off road section from the driving navigation data, and performing lighting turn-off control on the lighting turn-off road section. The system can obtain driving navigation data and real-time positioning data, verify and update the driving navigation data, extract an illumination turn-on road section in real time, perform illumination turn-on control on the illumination turn-on road section, extract an illumination turn-off road section in real time, and perform illumination turn-off control on the illumination turn-off road section, so that the driving safety is ensured on the premise of ensuring the driving safety. The energy consumption is effectively reduced, and the lighting efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart city lighting, and particularly relates to a smart city lighting control method and system. Background Art

[0002] A smart city is a modern urban model that uses advanced information and communication technologies, the Internet of Things, big data, artificial intelligence and other modern scientific and technological means to sense, analyze and integrate urban operations, so as to optimize urban management, improve public services, enhance the quality of life of citizens, and achieve sustainable development.

[0003] Smart city lighting is to intelligently manage and optimize the urban lighting system through advanced information technology and Internet of Things means. Its core lies in improving lighting efficiency, reducing energy consumption, improving the urban environment, and enhancing the quality of life of citizens.

[0004] In the prior art, for the lighting control of smart cities, it is usually just a simple setting of control time periods to turn on or off the lighting of road sections at different times. Although it can effectively reduce energy consumption, it cannot ensure effective driving safety and the lighting efficiency needs to be improved. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a smart city lighting control method and system, aiming to solve the problems raised in the background art.

[0006] To achieve the above purpose, the embodiments of the present invention provide the following technical solutions: A smart city lighting control method, the method includes the following steps: Determine the target driving vehicle and obtain driving navigation data and real-time positioning data; Verify and update the driving navigation data according to the real-time positioning data; Extract the lighting-on road sections from the driving navigation data according to the real-time positioning data, and perform lighting-on control on the lighting-on road sections; Extract the lighting-off road sections from the driving navigation data according to the real-time positioning data, and perform lighting-off control on the lighting-off road sections.

[0007] A smart city lighting control system, the system includes a navigation positioning acquisition unit, a navigation verification and update unit, a lighting-on control unit and a lighting-off control unit, wherein: The navigation positioning acquisition unit is used to determine the target driving vehicle and obtain driving navigation data and real-time positioning data; The navigation verification and update unit is used to verify and update the driving navigation data according to the real-time positioning data; The lighting turn-on control unit is configured to extract the lighting turn-on sections from the driving navigation data according to the real-time positioning data, and perform lighting turn-on control on the lighting turn-on sections; The lighting turn-off control unit is configured to extract the lighting turn-off sections from the driving navigation data according to the real-time positioning data, and perform lighting turn-off control on the lighting turn-off sections.

[0008] Compared with the prior art, the beneficial effects of the present invention are as follows: In the embodiment of the present invention, by determining the target driving vehicle, the driving navigation data and the real-time positioning data are obtained; the driving navigation data is verified and updated; according to the real-time positioning data, the lighting turn-on sections are extracted from the driving navigation data, and lighting turn-on control is performed on the lighting turn-on sections; according to the real-time positioning data, the lighting turn-off sections are extracted from the driving navigation data, and lighting turn-off control is performed on the lighting turn-off sections. It is possible to obtain the driving navigation data and the real-time positioning data, verify and update the driving navigation data, extract the lighting turn-on sections in real time, perform lighting turn-on control on the lighting turn-on sections, and extract the lighting turn-off sections in real time, and perform lighting turn-off control on the lighting turn-off sections, so as to effectively reduce energy consumption and improve lighting efficiency on the premise of ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0010] Figure 1 The flowchart of the method provided by the embodiment of the present invention is shown.

[0011] Figure 2 The flowchart of obtaining the driving navigation data and the real-time positioning data in the method provided by the embodiment of the present invention is shown.

[0012] Figure 3 The flowchart of selecting the driving navigation data in the method provided by the embodiment of the present invention is shown.

[0013] Figure 4 The flowchart of verifying and updating the driving navigation data in the method provided by the embodiment of the present invention is shown.

[0014] Figure 5 The flowchart of performing lighting turn-on control in the method provided by the embodiment of the present invention is shown.

[0015] Figure 6 The flowchart of performing lighting turn-off control in the method provided by the embodiment of the present invention is shown.

[0016] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0017] Figure 8 The structural block diagram of the navigation positioning acquisition unit in the system provided by the embodiment of the present invention is shown.

[0018] Figure 9 The structural block diagram of the navigation verification and update unit in the system provided by the embodiment of the present invention is shown.

[0019] Figure 10 The structural block diagram of the lighting turn-on control unit in the system provided by the embodiment of the present invention is shown. Detailed implementation manners

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] It can be understood that in the prior art, for the lighting control of a smart city, usually only the control time period is simply set, and the road section lighting is turned on or off at different time periods. Although it can effectively reduce energy consumption, however, it cannot ensure effective driving safety and the lighting efficiency needs to be improved.

[0022] To solve the above problems, the embodiment of the present invention determines a target driving vehicle, obtains driving navigation data and real-time positioning data; verifies and updates the driving navigation data according to the real-time positioning data; extracts the lighting turn-on road sections from the driving navigation data according to the real-time positioning data, and performs lighting turn-on control on the lighting turn-on road sections; extracts the lighting turn-off road sections from the driving navigation data according to the real-time positioning data, and performs lighting turn-off control on the lighting turn-off road sections. It can obtain the driving navigation data and real-time positioning data, verify and update the driving navigation data, extract the lighting turn-on road sections in real time, perform lighting turn-on control on the lighting turn-on road sections, and extract the lighting turn-off road sections in real time, and perform lighting turn-off control on the lighting turn-off road sections, so as to effectively reduce energy consumption and improve the lighting efficiency on the premise of ensuring driving safety.

[0023] Figure 1 The flowchart of the method provided by the embodiment of the present invention is shown.

[0024] Specifically, a method for lighting control of a smart city, the method includes the following steps: Step S101, determine a target driving vehicle, and obtain driving navigation data and real-time positioning data.

[0025] In an embodiment of the present invention, when a moving vehicle turns on the navigation, vehicle navigation data will be generated and uploaded. By receiving multiple vehicle navigation data in real time, data recognition is performed on the multiple vehicle navigation data, the vehicle navigation time in the multiple vehicle navigation data is extracted, and then based on a preset lighting control period, the multiple vehicle navigation times are matched, and the time matching result is recorded. Furthermore, according to the time matching result, the vehicle navigation time within the lighting control period is determined, and the corresponding vehicle navigation data is marked as driving navigation data, so that the driving navigation data can be selected from the multiple vehicle navigation data, and then identity recognition is performed according to the driving navigation data to determine the target driving vehicle, and real-time positioning monitoring is performed on the target driving vehicle to obtain the real-time positioning data of the target driving vehicle.

[0026] It can be understood that the vehicle navigation data can be generated and uploaded by the vehicle itself, or can be data generated and uploaded by the driver in the vehicle when navigating through intelligent mobile terminals such as mobile phones, tablets, and smart watches.

[0027] Specifically, Figure 2 FIG. shows a flowchart of obtaining driving navigation data and real-time positioning data in the method provided by an embodiment of the present invention.

[0028] Among them, in a preferred embodiment provided by the present invention, the determining the target driving vehicle, obtaining the driving navigation data and the real-time positioning data specifically includes the following steps: Step S1011, receiving multiple vehicle navigation data.

[0029] Step S1012, selecting driving navigation data from the multiple vehicle navigation data.

[0030] Specifically, Figure 3 FIG. shows a flowchart of selecting driving navigation data in the method provided by an embodiment of the present invention.

[0031] Among them, in a preferred embodiment provided by the present invention, the selecting driving navigation data from the multiple vehicle navigation data specifically includes the following steps: Step S10121, extracting the vehicle navigation time in the multiple vehicle navigation data; Step S10122, matching the multiple vehicle navigation times based on a preset lighting control period, and recording the time matching result; Step S10123, selecting driving navigation data from the multiple vehicle navigation data according to the time matching result.

[0032] Among them, in a preferred embodiment provided by the present invention, matching the multiple vehicle navigation times based on a preset lighting control period and recording the time matching result specifically includes the following steps: Obtain the vehicle navigation time data and the preset lighting control period parameters, and normalize the vehicle navigation time data and the lighting control period parameters to obtain the normalized navigation time value and the lighting period; Dynamically set the importance of different time periods according to the type of time period in a day to generate the weight coefficients of the start boundary and the end boundary in the lighting period; Offset and correct the center point of the lighting period according to the historical traffic flow data to obtain the adjusted period characteristics; Calculate the distances between the navigation time value and the start boundary and the end boundary in the adjusted period characteristics, and adjust them respectively using the weight coefficients of the corresponding start boundary and end boundary to obtain the sensitivity scores of the start boundary and the end boundary; Calculate the variance between each time point in the adjusted period characteristics and the navigation time value, and apply exponential decay to the variance result to obtain the core area strengthening coefficient; Perform weighted synthesis on the sensitivity scores of the start boundary and the end boundary and the core area strengthening coefficient, and calculate to obtain the comprehensive matching degree score; Traverse all lighting periods and repeat the above process to generate the comprehensive matching degree scores of each period, and obtain several comprehensive matching degree scores; Set a matching threshold, select the highest comprehensive matching degree score from several comprehensive matching degree scores and verify it with the matching threshold. If the highest comprehensive matching degree score is greater than the matching threshold, generate the final matching result.

[0033] In the above solution, the present invention automatically corrects the period center point through the historical traffic flow data, and then adapts the offset range. Moreover, the combination of the weight coefficients of the start boundary and the end boundary enables the system to have a buffering ability for the period mutation during the morning and evening rush hours, which can realize the elasticity of the period boundary and adjust the boundary error tolerance rate. And, a dual decision-making mechanism, the first-level decision: determine the candidate period based on the maximum matching degree score, and then filter out the low-confidence matches through the matching threshold to ensure the accuracy of the matching. And by offsetting and correcting the center point of the lighting period to control the influence range of the core area, the concentration of lighting energy consumption can be improved. And through the matching threshold, unnecessary lighting requests can be effectively filtered.

[0034] Further, the determining the target driving vehicle, obtaining the driving navigation data and the real-time positioning data further includes the following steps: Determine the target driving vehicle according to the driving navigation data.

[0035] Step S1014, obtain the real-time positioning data of the target driving vehicle.

[0036] Further, the smart city lighting control method further includes the following steps: Step S102: Verify and update the driving navigation data according to the real-time positioning data.

[0037] In the embodiment of the present invention, according to the real-time positioning data, real-time verification and analysis are performed on the driving navigation data to determine whether the target driving vehicle deviates from the navigation route. According to different judgment results, different data processing is performed. Specifically, when the target driving vehicle does not deviate from the navigation route, the driving navigation data remains unchanged; while when the target driving vehicle deviates from the navigation route, according to the real-time positioning data and the end position in the driving navigation data, the navigation route is re-planned to realize the update processing of the driving navigation data.

[0038] Specifically, Figure 4 The flowchart of the verification and update of the driving navigation data in the method provided by the embodiment of the present invention is shown.

[0039] Among them, in the preferred embodiment provided by the present invention, the verification and update of the driving navigation data according to the real-time positioning data specifically include the following steps: Step S1021: Verify whether the driving navigation data deviates according to the real-time positioning data; Step S1022: Keep the driving navigation data when there is no driving deviation; Step S1023: Update the driving navigation data when there is a driving deviation.

[0040] Among them, in the preferred embodiment provided by the present invention, verifying whether the driving navigation data deviates according to the real-time positioning data specifically includes the following steps: Obtain road information and historical traffic cycle numbers, where the road information includes road curvature and road slope; Eliminate the jitter error of the real-time positioning data through Kalman filtering, and use differential calculation to obtain the instantaneous speed to obtain the purified motion state; Obtain the key turning points in the driving navigation data and mark the starting positions of special sections to obtain enhanced path data with feature marks; Establish a motion equation including vehicle mass, and establish an acceleration prediction curve according to vehicle state parameters. The corresponding process has the following relationship: ; Among them, represents the predicted acceleration, represents the engine power output, represents the vehicle mass, represents the vehicle speed, represents the gravitational acceleration, Road slope angle; Then, the theoretical acceleration value of the acceleration prediction curve is corrected according to the road slope to obtain the predicted acceleration curve after environmental compensation. The corresponding process has the following relational expression: ; Among them, represents the predicted acceleration after environmental compensation, represents the air density, represents the air resistance coefficient, represents the frontal area of the vehicle; Obtain the driver behavior pattern, and generate a predicted corridor band according to the predicted acceleration curve after environmental compensation to obtain a three-dimensional space prediction corridor; Project the real-time positioning data into the three-dimensional coordinate system of the three-dimensional space prediction corridor, and calculate the Euclidean distance between the real-time positioning data and the center line in the three-dimensional space prediction corridor to obtain the spatial deviation distance. The corresponding process has the following relational expression: ; Among them, represents the spatial offset distance, represents the real-time positioning point, , represents the three-dimensional coordinates of the real-time positioning point; represents the projection point parameter on the center line, , represents the center line trajectory, , represents the predicted path length, represents the arc length parameter along the predicted path, represents the road elevation coordinate, represents the coordinates of the path center line in the two-dimensional plane; Obtain the measured speed and measured heading angle of the vehicle, and calculate the difference rate between the measured speed and the predicted acceleration curve after environmental compensation to obtain the speed matching degree index. The corresponding process has the following relational expression: ; Among them, represents the speed matching degree index, represents the sampling time interval, represents the measured speed; Calculate the included angle deviation between the measured heading angle and the road curvature within the time window before and after the key turning point to obtain the heading mutation coefficient. The corresponding process has the following relational expression: ; Among them, represents the heading mutation coefficient, represents the number of heading angle sampling points; Fuse the spatial deviation distance, speed matching degree index, and heading mutation coefficient to obtain a deviation index. The corresponding process has the following relational expression: ; Among them, represents the deviation index, represents the spatial offset distance, represents three different weight coefficients, and ; represents the maximum tolerance threshold of the spatial deviation distance, represents the maximum allowable route deviation; Take the deviation indices of several adjacent sampling periods and use the least squares method to fit the change rate to obtain the deviation trend slope, and use the deviation trend slope to verify whether the driving navigation data deviates. The corresponding process has the following relational expression: ; Among them, represents the intercept of the linear fit, represents the error term, represents the deviation index sequence, represents the slope parameter of the linear fit, represents the sampling time point; ; Among them, represents the deviation trend slope, represents the deviation trend determination threshold, represents the number of sampling points, represents the deviation index sequence, represents the slope parameter of the linear fit, represents the sampling time point.

[0041] In the above solution, the present invention forms a three-dimensional analysis ability by establishing a multi-dimensional data fusion mechanism, organically integrating multi-source information such as real-time positioning, vehicle dynamics, road characteristics, and environmental variables, effectively avoiding the defect of single data dimension.

[0042] Furthermore, the smart city lighting control method further includes the following steps: Step S103: Extract the lighting turn-on sections from the driving navigation data according to the real-time positioning data, and perform lighting turn-on control on the lighting turn-on sections.

[0043] In an embodiment of the present invention, taking the real-time positioning data as the starting position of the line segment, according to the preset lighting section length, extract the lighting-on section from the driving navigation data, based on the preset urban street lamp data, perform street lamp recognition, determine a plurality of first lighting street lamps in the lighting-on section, then obtain the lighting control addresses of the plurality of first lighting street lamps corresponding to Internet of Things communication control, and according to the plurality of lighting control addresses, perform lighting-on control on the plurality of first lighting street lamps in the lighting-on section.

[0044] Specifically, Figure 5 Fig. 5 shows a flowchart of performing lighting-on control in the method provided by an embodiment of the present invention.

[0045] Among them, in a preferred embodiment provided by the present invention, the extracting the lighting-on section from the driving navigation data according to the real-time positioning data and performing lighting-on control on the lighting-on section specifically includes the following steps: Step S1031, extract the lighting-on section from the driving navigation data according to the real-time positioning data and the preset lighting section length; Step S1032, determine a plurality of first lighting street lamps in the lighting-on section; Step S1033, obtain the lighting control addresses of the plurality of first lighting street lamps; Step S1034, according to the plurality of lighting control addresses, perform lighting-on control on the plurality of first lighting street lamps in the lighting-on section.

[0046] Among them, in a preferred embodiment provided by the present invention, the extracting the lighting-on section from the driving navigation data according to the real-time positioning data and the preset lighting section length specifically includes the following steps: Obtain the vehicle plane coordinate point according to the real-time positioning data; Obtain the vehicle plane coordinate sequence of the moving trajectory, and use the least squares method to fit the driving direction angle to obtain the vehicle movement direction; Taking the vehicle real-time positioning data as the center, obtain the road network data to get the local road network topology model; Calculate the included angle between each road in the local road network topology model and the vehicle movement direction, and generate the cosine correlation degree between the road and the vehicle direction; Project the vehicle plane coordinate point onto each road in the local road network topology model along the vehicle movement direction, and calculate the perpendicular distance between the projection point and the road center line to obtain the projection matching degree of each road; Select the road with the highest road projection matching degree and the cosine correlation degree between the road and the vehicle direction greater than the set threshold as the main traffic road; Obtain the measured speed of the vehicle, and dynamically set the lighting opening span of the main traffic road according to the measured speed of the vehicle to obtain the lighting opening section.

[0047] In the above solution, the present invention matches the positioning data with the projection of the road network topology, and abstracts the vehicle as a dynamic node in the road network. By calculating the projection point of the vehicle coordinates on the road center line in real time, centimeter-level accuracy path binding is realized, and the problem of incorrect road matching caused by positioning drift in the traditional solution is solved. And by combining the instantaneous speed of the vehicle with the preset response time, the length of the front lighting area is dynamically adjusted, which can ensure the visual safety guarantee during high-speed driving and also take into account the advantage of energy saving. And Further, the smart city lighting control method further includes the following steps: Step S104, according to the real-time positioning data, extract the lighting-off section from the driving navigation data, and perform lighting-off control on the lighting-off section.

[0048] In the embodiment of the present invention, taking the real-time positioning data as the line segment termination position, according to the preset length of the light-off section, extract the lighting-off section from the driving navigation data, identify the street lights based on the preset urban street light data, determine multiple second lighting street lights in the lighting-off section, and then obtain the lighting-off control addresses corresponding to the multiple second lighting street lights for Internet of Things communication control, and perform lighting-off control on the multiple second lighting street lights in the lighting-off section according to the multiple lighting-off control addresses.

[0049] Specifically, Figure 6 Shows the flowchart of performing lighting-off control in the method provided by the embodiment of the present invention.

[0050] Among them, in the preferred embodiment provided by the present invention, the step of extracting the lighting-off section from the driving navigation data according to the real-time positioning data and performing lighting-off control on the lighting-off section specifically includes the following steps: Step S1041, extract the lighting-off section from the driving navigation data according to the real-time positioning data and the preset length of the light-off section; Step S1042, determine multiple second lighting street lights in the lighting-off section; Step S1043, obtain the lighting-off control addresses of the multiple second lighting street lights; Step S1044, perform lighting-off control on the multiple second lighting street lights in the lighting-off section according to the multiple lighting-off control addresses.

[0051] Among them, in the preferred embodiment provided by the present invention, extracting the lighting-off section from the driving navigation data according to the real-time positioning data and the preset length of the lighting-off section specifically includes the following steps: The road information also includes the road grade. A reference value is set according to the current road grade, and then the reference value is adjusted according to the current time period to obtain the basic lighting-off length reference value; Then, the basic lighting-off length reference is fused with the square term of the real-time speed to obtain the length of the lighting-off area with adaptive change; According to the current position of the vehicle in the real-time positioning data, a dynamic length value is extended along the driving reverse direction with the current position of the vehicle as the end coordinate to obtain the vehicle rear space projection interval; Retrieve the data of the set of currently illuminated roads, extract the start and end coordinates and elevation information of each road from the road set data to obtain the activated road space attributes; Compare the vehicle rear space projection interval with the activated road space attributes, and screen out the roads that overlap with the projection interval to obtain a preliminary candidate lighting-off road set; Detect the real-time lighting state of the candidate roads to obtain the lighting detection result. Exclude the sections that are already in the closed state from the preliminary candidate lighting-off road set according to the lighting detection result, and calculate the delay-off time threshold according to the length of the lighting-off area with adaptive change and the real-time speed to obtain the final lighting-off section.

[0052] In the above solution, the present invention constructs a non-linear response model through the square term of the real-time speed. When the vehicle speed reaches 80 km / h, the length of the lighting-off area automatically expands to 3.2 times the reference value. This design is based on the kinetic energy theorem in kinematics, enabling the system to predict the braking distance requirements of high-speed vehicles and maximizing the energy-saving effect under the premise of ensuring safety. And the reverse space projection technology is adopted to map the vehicle movement trajectory into a three-dimensional geometric interval. By introducing the elevation coordinate (Z-axis) component, the misjudgment problem of traditional two-dimensional projection in three-dimensional traffic scenarios such as overpasses and underground tunnels is successfully solved. And the delay-off time threshold is set according to the length of the lighting-off area with adaptive change according to the real-time speed. When the vehicle decelerates suddenly (such as from 60 km / h to 20 km / h suddenly), the system automatically extends the lighting maintenance time to 1.8 times the normal value to ensure the driver's visual adaptation time in case of emergencies.

[0053] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0054] Among them, in another preferred embodiment provided by the present invention, a smart city lighting control system includes: A navigation positioning acquisition unit 101, configured to determine a target driving vehicle and acquire driving navigation data and real-time positioning data.

[0055] In an embodiment of the present invention, when a moving vehicle turns on the navigation, vehicle navigation data is generated and uploaded. The navigation positioning acquisition unit 101 receives multiple vehicle navigation data in real time, performs data identification on the multiple vehicle navigation data, extracts the vehicle navigation time in the multiple vehicle navigation data, then matches the multiple vehicle navigation times based on a preset lighting control period, records the time matching result, and further determines the vehicle navigation time in the lighting control period according to the time matching result, and marks the corresponding vehicle navigation data as driving navigation data, so that the driving navigation data can be selected from the multiple vehicle navigation data, and then identity recognition is performed according to the driving navigation data to determine the target driving vehicle, and real-time positioning monitoring is performed on the target driving vehicle to obtain the real-time positioning data of the target driving vehicle.

[0056] Specifically, Figure 8 Fig. shows the structural block diagram of the navigation positioning acquisition unit 101 in the system provided by the embodiment of the present invention.

[0057] Among them, in the preferred embodiment provided by the present invention, the navigation positioning acquisition unit 101 specifically includes: A navigation receiving module 1011, configured to receive multiple vehicle navigation data; A data selection module 1012, configured to select driving navigation data from the multiple vehicle navigation data; A vehicle determination module 1013, configured to determine a target driving vehicle according to the driving navigation data; A positioning acquisition module 1014, configured to acquire the real-time positioning data of the target driving vehicle.

[0058] Furthermore, the smart city lighting control system further includes: A navigation verification and update unit 102, configured to verify and update the driving navigation data according to the real-time positioning data.

[0059] In an embodiment of the present invention, the navigation verification and update unit 102 performs real-time verification and analysis on the driving navigation data according to the real-time positioning data, determines whether the target driving vehicle deviates from the driving route of the navigation route, and performs different data processing according to different judgment results. Specifically, when the target driving vehicle does not deviate from the driving route of the navigation route, the driving navigation data remains unchanged; and when the target driving vehicle deviates from the driving route of the navigation route, a new navigation route is planned according to the real-time positioning data and the end position in the driving navigation data to implement the update processing of the driving navigation data.

[0060] Specifically, Figure 9 Fig. shows the structural block diagram of the navigation verification and update unit 102 in the system provided by the embodiment of the present invention.

[0061] Among them, in the preferred embodiment provided by the present invention, the navigation verification and update unit 102 specifically includes: A deviation judgment module 1021, configured to verify whether the driving navigation data deviates according to the real-time positioning data; A navigation holding module 1022, configured to hold the driving navigation data when no driving deviation occurs; A navigation update module 1023, configured to update the driving navigation data when a driving deviation occurs.

[0062] Furthermore, the smart city lighting control system further includes: A lighting turn-on control unit 103, configured to extract a lighting turn-on section from the driving navigation data according to the real-time positioning data, and perform lighting turn-on control on the lighting turn-on section.

[0063] In the embodiment of the present invention, the lighting turn-on control unit 103 uses the real-time positioning data as the starting position of the line segment, extracts the lighting turn-on section from the driving navigation data according to the preset lighting section length, performs street lamp identification based on the preset urban street lamp data, determines a plurality of first lighting street lamps in the lighting turn-on section, and then obtains the lighting control addresses corresponding to the plurality of first lighting street lamps for Internet of Things communication control, and performs lighting turn-on control on the plurality of first lighting street lamps in the lighting turn-on section according to the plurality of lighting control addresses.

[0064] Specifically, Figure 10 shows a structural block diagram of the lighting turn-on control unit 103 in the system provided by the embodiment of the present invention.

[0065] Among them, in the preferred embodiment provided by the present invention, the lighting turn-on control unit 103 specifically includes: An on-section extraction module 1031, configured to extract a lighting turn-on section from the driving navigation data according to the real-time positioning data and the preset lighting section length; A lighting street lamp determination module 1032, configured to determine a plurality of first lighting street lamps in the lighting turn-on section; A control address acquisition module 1033, configured to acquire the lighting control addresses of the plurality of first lighting street lamps; A lighting turn-on control module 1034, configured to perform lighting turn-on control on the plurality of first lighting street lamps in the lighting turn-on section according to the plurality of lighting control addresses.

[0066] Furthermore, the smart city lighting control system further includes: The lighting-off control unit 104 is configured to extract a lighting-off section from the driving navigation data according to the real-time positioning data, and perform lighting-off control on the lighting-off section.

[0067] In an embodiment of the present invention, the lighting-off control unit 104 uses the real-time positioning data as the line segment termination position, extracts the lighting-off section from the driving navigation data in advance according to the preset length of the lights-out section, identifies street lights based on the preset urban street light data, determines a plurality of second lighting street lights in the lighting-off section, then obtains the lighting-off control addresses of the Internet of Things communication control corresponding to the plurality of second lighting street lights, and performs lighting-off control on the plurality of second lighting street lights in the lighting-off section according to the plurality of lighting-off control addresses.

[0068] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0069] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0070] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0071] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0072] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart city lighting control method, characterized in that: The method comprises the following steps: Determine the target vehicle and obtain driving navigation data and real-time positioning data; Verifying and updating the driving navigation data according to the real-time positioning data; Extracting the lighting-on section from the driving navigation data according to the real-time positioning data, and performing lighting-on control on the lighting-on section; Extracting the lighting-off road section from the driving navigation data according to the real-time positioning data, and performing lighting-off control on the lighting-off road section; Determining the target driving vehicle and obtaining driving navigation data and real-time positioning data specifically includes the following steps: receiving a plurality of vehicle navigation data; Selecting driving navigation data from the plurality of vehicle navigation data; Determining a target driving vehicle according to the driving navigation data; Acquire real-time positioning data of the target moving vehicle.

2. A smart city lighting control method according to claim 1, characterized in that: The step of selecting the driving navigation data from the plurality of vehicle navigation data specifically comprises the following steps: extracting vehicle navigation time from a plurality of vehicle navigation data; Based on a preset lighting control period, matching the plurality of vehicle navigation times, and recording the time matching results; According to the time matching result, driving navigation data is selected from a plurality of vehicle navigation data.

3. The smart city lighting control method according to claim 2, characterized in that: Based on the preset lighting control period, matching the plurality of vehicle navigation times and recording the time matching results specifically comprises the following steps: Obtaining vehicle navigation time data and preset lighting control period parameters, and normalizing the vehicle navigation time data and lighting control period parameters to obtain normalized navigation time values ​​and lighting period values; The importance of different time periods is dynamically set according to the type of time period in each day to generate weight coefficients of the start boundary and the end boundary in the lighting period; According to the historical traffic flow data, the center point of the lighting period is offset and corrected to obtain the adjusted period characteristics; Calculate the distance between the navigation time value and the start boundary and the end boundary in the adjusted time period feature, and adjust them using the corresponding weight coefficients of the start boundary and the end boundary to obtain the sensitivity scores of the start boundary and the end boundary; Calculate the variance of each time point and the navigation time value in the adjusted time period feature, and apply exponential decay to the variance result to obtain the core area reinforcement coefficient; The sensitivity scores of the start and end boundaries are weighted and synthesized with the core area reinforcement coefficient to calculate the comprehensive matching score; Repeat the above process through all lighting periods to generate a comprehensive matching score for each period, and obtain several comprehensive matching scores; A matching threshold is set, and the highest comprehensive matching score is selected from several comprehensive matching scores for verification with the matching threshold. If the highest comprehensive matching score is greater than the matching threshold, the final matching result is generated.

4. The smart city lighting control method according to claim 3, characterized in that: The verification and updating of the driving navigation data according to the real-time positioning data specifically includes the following steps: Verifying whether the driving navigation data deviates according to the real-time positioning data; When no driving deviation occurs, maintaining the driving navigation data; When driving deviation occurs, the driving navigation data is updated.

5. The smart city lighting control method according to claim 4, characterized in that: Verifying whether the driving navigation data deviates according to the real-time positioning data specifically comprises the following steps: Obtain road information and historical traffic cycle numbers, the road information includes road curvature and road slope; The jitter error of real-time positioning data is eliminated by Kalman filtering, and the instantaneous speed is obtained by differential calculation to obtain the purified motion state; Obtain key turning points in driving navigation data and mark the starting positions of special road sections to obtain enhanced path data with feature markers; Establish the motion equation including the vehicle mass and establish the acceleration prediction curve according to the vehicle state parameters; The theoretical acceleration value of the acceleration prediction curve is corrected according to the road slope to obtain the predicted acceleration curve after environmental compensation; The driver behavior pattern is obtained, and a prediction corridor is generated according to the predicted acceleration curve after environmental compensation to obtain a three-dimensional space prediction corridor; The real-time positioning data is projected into the three-dimensional coordinate system of the three-dimensional space prediction corridor, and the Euclidean distance between the real-time positioning data and the center line of the three-dimensional space prediction corridor is calculated to obtain the spatial deviation distance; Obtain the measured speed and measured heading angle of the vehicle, and calculate the difference rate between the measured speed and the predicted acceleration curve after environmental compensation to obtain the speed consistency index; Calculate the angle deviation between the measured heading angle and the road curvature in the time window before and after the key turning point to obtain the heading mutation coefficient; The spatial deviation distance, speed consistency index and heading mutation coefficient are integrated to obtain the deviation index; The deviation indexes of several adjacent sampling periods are taken to fit the change rate by the least square method to obtain the deviation trend slope, and the deviation trend slope is used to verify whether the driving navigation data deviates.

6. The smart city lighting control method according to claim 5, characterized in that: The extracting of the lighting-on road section from the driving navigation data according to the real-time positioning data and performing lighting-on control on the lighting-on road section specifically comprises the following steps: Extracting the lighting-on section from the driving navigation data according to the real-time positioning data and the preset lighting section length; Determining a plurality of first lighting street lamps in the lighting-on road section; Obtaining lighting control addresses of a plurality of the first lighting street lamps; According to the plurality of lighting control addresses, lighting-on control is performed on the plurality of first lighting street lamps in the lighting-on section.

7. The smart city lighting control method according to claim 6, characterized in that: According to the real-time positioning data and the preset length of the lighting section, extracting the lighting-on section from the driving navigation data specifically comprises the following steps: Obtain the plane coordinate points of the vehicle according to the real-time positioning data; Obtain the vehicle plane coordinate sequence of the moving trajectory, use the least squares method to fit the driving direction angle, and obtain the vehicle movement direction; Acquire road network data with vehicle real-time positioning data as the center to obtain a local road network topology model; Calculate the angle between each road in the local road network topology model and the vehicle's direction of movement, and generate the cosine correlation between the road and the vehicle's direction; Project the plane coordinate points of the vehicle along the direction of vehicle movement onto each road in the local road network topology model, and calculate the vertical distance between the projection point and the center line of the road to obtain the projection matching degree of each road; The road with the highest road projection matching degree and the cosine correlation between the road and the vehicle direction greater than the set threshold condition is selected as the main traffic road; The measured speed of the vehicle is obtained, and the lighting-on span of the main road is dynamically set according to the measured speed of the vehicle to obtain the lighting-on road section.

8. The smart city lighting control method according to claim 7, characterized in that: The extracting of the lighting-off road section from the driving navigation data according to the real-time positioning data and performing lighting-off control on the lighting-off road section specifically comprises the following steps: Extracting the lighting-off road section from the driving navigation data according to the real-time positioning data and the preset length of the lighting-off road section; Determining a plurality of second lighting street lamps in the lighting-off road section; Obtaining a plurality of light-off control addresses of the second lighting street lamps; According to the plurality of light-off control addresses, lighting-off control is performed on the plurality of the second lighting street lamps in the lighting-off section.

9. The smart city lighting control method according to claim 8, characterized in that: According to the real-time positioning data and the preset length of the road section with lights off, extracting the road section with lights off from the driving navigation data specifically comprises the following steps: The road information also includes the road grade. The reference value is set according to the current road grade, and then the reference value is adjusted according to the current time period to obtain the basic light-off length reference value; Then, the basic lights-off length benchmark value is combined with the square term of the real-time speed to obtain the adaptively changing lights-off area length; According to the current position of the vehicle in the real-time positioning data, the dynamic length value is extended in the opposite direction of the driving with the current position of the vehicle as the end point coordinate to obtain the projection interval of the space behind the vehicle; Retrieve the road collection data of the currently turned-on lighting, extract the start and end coordinates and elevation information of each road from the road collection data, and obtain the activated road space attributes; Compare the space projection interval behind the vehicle with the spatial attributes of the activated road, filter out the roads that overlap with the projection interval, and obtain a preliminary candidate set of light-off roads; The real-time lighting status of the candidate roads is detected to obtain the lighting detection results. The preliminary candidate light-off road set is excluded from the sections that are already in the closed state according to the lighting detection results. The delayed closing time threshold is calculated according to the adaptively changing light-off area length and real-time speed to obtain the final lighting-off section.

10. A smart city lighting control system, the system being applied to the smart city lighting control method according to any one of claims 1 to 9, characterized in that: The system comprises a navigation positioning acquisition unit, a navigation verification update unit, a lighting on control unit and a lighting off control unit, wherein: A navigation positioning acquisition unit is used to determine the target vehicle and obtain driving navigation data and real-time positioning data; A navigation verification and updating unit, used to verify and update the driving navigation data according to the real-time positioning data; A lighting on control unit, configured to extract a lighting on section from the driving navigation data according to the real-time positioning data, and perform lighting on control on the lighting on section; The lighting shutoff control unit is used to extract the lighting shutoff section from the driving navigation data according to the real-time positioning data, and perform lighting shutoff control on the lighting shutoff section.

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