An intelligent lighting method and system based on big data

Abstract

The present application provides an intelligent lighting method and system based on big data. The method includes: obtaining real-time vehicle information and real-time environmental information corresponding to a target road section, where the real-time environmental information represents visual field data in the current environment, and the real-time vehicle information represents traffic flow parameters in the current environment; determining an environmental deviation parameter according to the real-time environmental information and reference environmental information, where the environmental deviation parameter includes the deviation between the visual field data in the current environment and that in a haze-free weather; obtaining a vehicle deviation parameter according to the real-time vehicle information and reference vehicle information, where the vehicle deviation parameter represents the deviation between the traffic flow parameters in the current environment and that in a haze-free weather; obtaining a real-time lighting requirement according to the vehicle deviation parameter and the environmental deviation parameter, and generating lighting information corresponding to the target road section according to the real-time lighting requirement, where the lighting information is used to control lighting devices of the target road section for road lighting.

Description

Technical Field

[0001] The present application relates to the technical field of road lighting, and particularly to an intelligent lighting method and system based on big data. Background Art

[0002] With the development of modern society, the traffic flow is continuously increasing. As an important means to ensure the safety of night driving and pedestrians, road lighting cannot be ignored. Good road lighting can not only improve the visibility of drivers, but also play a crucial role in traffic safety in some special environments, such as foggy weather, where appropriate lighting intensity is of vital importance.

[0003] Traditional road lighting systems mainly rely on time control or photosensitive sensors to turn on and off street lights. Although this method can meet the basic lighting needs to a certain extent, it lacks flexibility and intelligence.

[0004] However, the light emitted by ordinary street lights will be scattered in large amounts when encountering high-concentration fog, making it difficult to penetrate to a distance, resulting in a reduced effective lighting range. Moreover, although existing intelligent control systems can adjust the brightness according to the ambient light to a certain extent, they are not sensitive enough to respond to rapidly changing and complex foggy weather and cannot make the most optimized lighting adjustments in a timely manner. In summary, there is a problem in the existing technology that lighting devices are not sensitive enough to respond to foggy weather. Summary of the Invention

[0005] The present application provides an intelligent lighting method and system based on big data to solve the technical problem that lighting devices are not sensitive enough to respond to foggy weather.

[0006] In a first aspect, the present application provides an intelligent lighting method based on big data, including:

[0007] Obtaining real-time vehicle information and real-time environment information corresponding to a target road section, where the real-time environment information represents visual data in the current environment, and the real-time vehicle information represents traffic flow parameters in the current environment;

[0008] Determining an environmental deviation parameter according to the real-time environment information and reference environment information, where the reference environment information represents visual data of the target road section in a fog-free weather, and the environmental deviation parameter includes the deviation between the visual data in the current environment and that in the fog-free weather;

[0009] Obtaining a vehicle deviation parameter according to the real-time vehicle information and reference vehicle information, where the reference vehicle information represents traffic flow parameters of the target road section in a fog-free weather, and the vehicle deviation parameter represents the deviation between the traffic flow parameters in the current environment and those in the fog-free weather;

[0010] Based on the vehicle deviation parameters and the environmental deviation parameters, obtain the real-time lighting requirement, and generate the lighting information corresponding to the target road section according to the real-time lighting requirement, where the lighting information is used to control the lighting equipment of the target road section for road lighting.

[0011] Optionally, the obtaining of the vehicle deviation parameters according to the real-time vehicle information and the reference vehicle information includes:

[0012] Correct the real-time vehicle information according to the environmental deviation parameters to obtain target vehicle information;

[0013] Obtain the vehicle deviation parameters according to the target vehicle information and the reference vehicle information.

[0014] Optionally, after obtaining the vehicle deviation parameters according to the target vehicle information and the reference vehicle information, it further includes:

[0015] Correct the real-time environmental information according to the vehicle deviation parameters to obtain target environmental information; and obtain the corrected environmental deviation according to the target environmental information and the reference environmental information;

[0016] Update the environmental deviation parameters according to the corrected environmental deviation to obtain new environmental deviation parameters.

[0017] Optionally, there are reference objects set on the target road section, the real-time environmental information is composed of the real-time images of the reference objects, and the reference environmental information is composed of the reference images of the reference objects.

[0018] Optionally, the reference objects include signal lights and non-luminous components set within the target road section, and the real-time environmental information includes first environmental information corresponding to the signal lights and second environmental information corresponding to the non-luminous components;

[0019] Determine the environmental deviation parameters according to the first environmental information, the second environmental information and the reference environmental information, including:

[0020] Obtain a first environmental deviation according to the first environmental information and the reference environmental information, where the first environmental deviation characterizes the degree to which the light source is weakened in the current environment;

[0021] Obtain a second environmental deviation according to the second environmental information and the reference environmental information, where the second environmental deviation characterizes the degree of influence of the current environment on the field of vision of people;

[0022] Obtain the environmental deviation parameters according to the first environmental deviation and the second environmental deviation.

[0023] Optionally, obtaining the real-time lighting requirement according to the vehicle deviation parameter and the environmental deviation parameter includes:

[0024] Obtaining a real-time lighting compensation value according to the vehicle deviation parameter, where the real-time lighting compensation value represents the lighting compensation brought by the vehicle to the target road section under the current environment;

[0025] Obtaining the real-time lighting requirement according to the real-time lighting compensation value, the first environmental deviation and the second environmental deviation included in the environmental deviation parameter.

[0026] In a second aspect, the present application provides an intelligent lighting system based on big data, which is applied to the intelligent lighting method based on big data, and includes:

[0027] An acquisition module, configured to acquire real-time vehicle information and real-time environmental information corresponding to a target road section, where the real-time environmental information represents visual field data under the current environment, and the real-time vehicle information represents traffic flow parameters under the current environment;

[0028] A first processing module, configured to determine an environmental deviation parameter according to the real-time environmental information and reference environmental information, where the reference environmental information represents visual field data of the target road section under haze-free weather, and the environmental deviation parameter includes the deviation between the visual field data under the current environment and that under haze-free weather;

[0029] A second processing module, configured to obtain a vehicle deviation parameter according to the real-time vehicle information and reference vehicle information, where the reference vehicle information represents traffic flow parameters of the target road section under haze-free weather, and the vehicle deviation parameter represents the deviation between the traffic flow parameters under the current environment and those under haze-free weather;

[0030] A third processing module, configured to obtain a real-time lighting requirement according to the vehicle deviation parameter and the environmental deviation parameter, and generate lighting information corresponding to the target road section according to the real-time lighting requirement, where the lighting information is used to control lighting devices of the target road section for road lighting.

[0031] In a third aspect, the present application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0032] The memory stores computer execution instructions;

[0033] The processor executes the computer execution instructions stored in the memory to implement the intelligent lighting method based on big data according to any item in the first aspect.

[0034] Fourthly, the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the big data-based intelligent lighting method according to any one of the first aspect when executed by a processor.

[0035] Fifthly, the present application provides a computer program product including a computer program, which implements the big data-based intelligent lighting method according to any one of the first aspect when executed by a processor.

[0036] The big data-based intelligent lighting method provided by the present application determines the visual field data and traffic flow parameters in the current environment by obtaining the real-time vehicle information and real-time environment information corresponding to the target road section; determines the environmental deviation parameters according to the real-time environment information and the reference environment information to obtain the environmental deviation parameters including the deviation between the visual field data in the current environment and that in the haze-free weather; obtains the vehicle deviation parameters according to the real-time vehicle information and the reference vehicle information to obtain the vehicle deviation parameters representing the deviation between the traffic flow parameters in the current environment and that in the haze-free weather; obtains the real-time lighting demand according to the vehicle deviation parameters and the environmental deviation parameters, and generates the lighting information corresponding to the target road section according to the real-time lighting demand, thereby realizing controlling the lighting equipment of the target road section for road lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings here are incorporated into the description and form a part of this description, showing embodiments consistent with the present application and used together with the description to explain the principles of the present application.

[0038] Figure 1 It is a schematic diagram of an application scenario of a big data-based intelligent lighting method provided by an embodiment of the present application;

[0039] Figure 2 It is a schematic flowchart of a big data-based intelligent lighting method provided by an embodiment of the present application;

[0040] Figure 3 It is a schematic structural diagram of a big data-based intelligent lighting system provided by an embodiment of the present application;

[0041] Figure 4 It is a schematic structural diagram of an electronic device provided by the present application.

[0042] Through the above accompanying drawings, the clear embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and the textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0044] The terminal device can be a wireless terminal or a wired terminal. A wireless terminal can be a device that provides voice and / or other service data connectivity to a user, a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core network devices via a Radio Access Network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For another example, the wireless terminal can also be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), and other devices. The wireless terminal can also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, a remote terminal, an access terminal, a user terminal, a user agent, a user device or user equipment, which is not limited herein. Optionally, the above terminal device can also be a smart watch, a tablet computer, and other devices.

[0045] An intelligent lighting method based on big data provided by an embodiment of the present application aims to solve the above technical problems of the prior art and is executed by an intelligent lighting system based on big data. The intelligent lighting system based on big data can be a wireless terminal or a wired terminal. A wireless terminal can be a device that provides voice and / or other service data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core network devices via a Radio Access Network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For another example, the wireless terminal can also be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), and other devices. The wireless terminal can also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, a remote terminal, an access terminal, a user terminal, a user agent, a user device or user equipment, which is not limited herein.

[0046] The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0047] As Figure 1 shown, Figure 1Schematic diagram of an application scenario of an intelligent lighting method based on big data provided by this application: The camera device collects real-time images; the traffic flow monitoring device collects real-time vehicle information; the intelligent lighting system based on big data obtains the real-time images and real-time vehicle information, and based on the real-time images and real-time vehicle information, obtains the real-time lighting demand, then generates lighting information based on the real-time lighting demand, and controls the lighting equipment to work based on the lighting information.

[0048] As Figure 2 shown, Figure 2 Schematic diagram of a process of an intelligent lighting method based on big data provided by an embodiment of this application, which may specifically include steps S201 to S204, where:

[0049] S201. Obtain real-time vehicle information and real-time environment information corresponding to the target section. The real-time environment information represents the vision data in the current environment, and the real-time vehicle information represents the traffic flow parameters in the current environment.

[0050] The real-time environment information is collected by devices such as high-precision cameras, lidar (LiDAR), and weather stations installed on street lamp poles or other appropriate positions. These devices can monitor and record the vision data in the current environment, including but not limited to factors such as visibility, haze concentration, and air humidity, and represent the degree of influence of the current environmental conditions on visual clarity in digital form. At the same time, the real-time vehicle information is collected by facilities such as geomagnetic induction coils, video detectors, and intelligent traffic signal lights distributed on the road. They can accurately sense and count parameters such as the number, speed, and vehicle type of motor vehicles passing through the target section, so as to quantitatively describe the traffic flow conditions in the current environment.

[0051] S202. Determine the environmental deviation parameter according to the real-time environment information and the reference environment information. The reference environment information represents the vision data of the target section in a haze-free weather, and the environmental deviation parameter includes the deviation between the vision data in the current environment and that in the haze-free weather.

[0052] It can be understood that haze weather will limit people's vision. Therefore, it is necessary to make up the difference between the human vision in the current environment and that in the clear state to ensure the clarity of the human vision. Moreover, lighting equipment can improve the human vision range through light. Therefore, in this embodiment, the light compensation demand of the current environment is determined by determining the environmental deviation parameter.

[0053] First, the preset reference environment information is retrieved from the storage module, which represents the standard visual field data of the target road section under haze-free weather conditions. These data may come from historical records or one-time measurements under specific conditions. Then, the visual field data in the current environment collected in real time by the sensor equipment (such as high-precision cameras, laser radars, etc.) deployed on the target road section is compared and analyzed with the above-mentioned reference environment information. The difference between the two is calculated by a special algorithm to determine the environmental deviation parameter, which specifically quantifies the degree of change of the visual field data in the current environment compared with the visual field data in haze-free weather, including but not limited to the proportion of reduced visibility, the increase in light scattering intensity, etc., wherein the above-mentioned special algorithm is differential image analysis or spatiotemporal sequence analysis, which can perform differential analysis on the visual field data and the reference environment information, and this embodiment does not make specific limitations. The calculation result of the environmental deviation parameter not only reflects the impact of haze on visual clarity, but also provides a scientific basis for the subsequent adjustment of road lighting brightness, color temperature, etc., to ensure that the best road traffic safety and traffic efficiency can be maintained under different weather conditions.

[0054] S203. Obtain a vehicle deviation parameter based on the real-time vehicle information and the reference vehicle information. The reference vehicle information represents the traffic flow parameter of the target road section in non-haze weather. The vehicle deviation parameter represents the deviation between the traffic flow parameter in the current environment and that in the non-haze weather.

[0055] It is understandable that in haze weather, drivers generally turn on the headlights to increase their field of vision. It can be considered that all vehicles turn on the headlights in haze weather; due to light propagation, the illumination brought by the headlights plays a role of light compensation in the current environment.

[0056] First, the reference vehicle information stored in the database is obtained, which accurately represents the standard traffic flow parameters of the target road section under non-smog weather conditions, such as vehicle flow, average vehicle speed and lane occupancy, etc. These data can be obtained through historical data of the same period or field measurements on specific clear days. Then, the system collects the current vehicle information monitored by various sensors (such as traffic cameras, ground sensing coils, radar speed meters, etc.) deployed on the target road section in real time, including but not limited to real-time vehicle flow, vehicle speed distribution and queue length, etc. Next, the real-time vehicle information and the reference vehicle information are compared and analyzed by the data analysis module, and the difference between the two is calculated by a pre-set algorithm to obtain the vehicle deviation parameter, wherein the pre-set algorithm is a graph theory and network flow algorithm or an ARIMA model, which can perform a difference analysis on the reference vehicle information and the real-time vehicle information, and this embodiment does not make specific restrictions. This parameter accurately quantifies the changes in traffic flow parameters in the current environment relative to the standard values ​​under non-smog weather conditions, such as the increase or decrease ratio of vehicle flow, the change range of average vehicle speed, etc.

[0057] S204. Obtain the real-time lighting requirement based on the vehicle deviation parameter and the environmental deviation parameter, and generate the lighting information corresponding to the target road section according to the real-time lighting requirement, where the lighting information is used to control the lighting equipment of the target road section for road lighting.

[0058] It can be understood that the difference between the light compensation requirement and the light compensation in the current environment is the amount of light that the lighting equipment needs to compensate, that is, the illumination brightness of the lighting equipment, which can be obtained based on "light compensation requirement - light compensation".

[0059] First, receive the vehicle deviation parameter calculated in the previous step and the environmental deviation parameter obtained by real-time monitoring of the environmental sensor. The vehicle deviation parameter represents the difference between the traffic flow parameters in the current environment and those in a haze-free weather, while the environmental deviation parameter covers factors such as light intensity and visibility that affect the road lighting effect. Next, through the intelligent algorithm module integrated in the system, comprehensively analyze the vehicle deviation parameter and the environmental deviation parameter, and evaluate the actual lighting requirement of the current road section. This intelligent algorithm is based on a preset model or the result of machine learning training, and takes into account the minimum lighting standard required to ensure driving safety under different traffic conditions and environmental conditions, so as to accurately predict and determine the real-time lighting requirement of the target road section. Subsequently, according to the determined real-time lighting requirement, the system automatically generates the corresponding lighting information for the target road section. This lighting information specifically defines control instructions including but not limited to the on / off state of the lighting equipment, the brightness adjustment level, and the color temperature selection. Finally, this lighting information is transmitted to the lighting control system deployed on the target road section, directly used to regulate the working state of each lighting equipment, so as to realize the intelligent management of road lighting, ensure appropriate road lighting under various meteorological conditions and traffic conditions, and guarantee driving safety and efficiency.

[0060] The intelligent lighting method based on big data provided by the embodiments of this application determines the visual field data and traffic flow parameters in the current environment by obtaining the real-time vehicle information and real-time environmental information corresponding to the target road section; determines the environmental deviation parameter according to the real-time environmental information and the reference environmental information, so as to obtain that the environmental deviation parameter includes the deviation between the visual field data in the current environment and that in a haze-free weather; obtains the vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information, so as to obtain that the vehicle deviation parameter represents the deviation between the traffic flow parameters in the current environment and those in a haze-free weather; obtains the real-time lighting requirement according to the vehicle deviation parameter and the environmental deviation parameter, and generates the lighting information corresponding to the target road section according to the real-time lighting requirement, thereby achieving the beneficial effect of controlling the lighting equipment of the target road section for road lighting.

[0061] In an implementable manner, the above S203 obtains the vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information, which may specifically include S203-1 and S203-2, wherein:

[0062] S203-1. Correct the real-time vehicle information according to the environmental deviation parameter to obtain the target vehicle information.

[0063] The original real-time vehicle information is adjusted using the above environmental deviation parameters through the built-in correction algorithm module. The operation process of the correction algorithm module includes: obtaining the real-time air quality index (AQI) and the images taken by the traffic camera; using the historical AQI and its corresponding historical images to train the LSTM model to obtain the target LSTM, which can predict the impact on traffic flow and vehicle speed based on the AQI; specifically, when the AQI increases, the LSTM model predicts that this may cause the recognition error of the traffic camera to increase. At this time, the image processing parameters of the camera, such as contrast and brightness, are automatically adjusted to compensate for the limited field of view caused by haze. At the same time, the speed estimation algorithm based on image analysis may also need to be adjusted to take into account the misjudgment that may be caused by haze. The correction process takes into account the impact of environmental factors on vehicle driving behavior, for example, appropriately reducing the vehicle speed estimated based on the ideal state under severe weather conditions, or adjusting the estimated safe distance of the vehicle according to the slippery road conditions. In addition, for certain types of vehicles, such as large trucks or buses, additional corrections may be required to ensure safety. The target vehicle information obtained after correction more accurately reflects the vehicle operation status under actual road conditions.

[0064] S203-2. Obtain vehicle deviation parameters according to the target vehicle information and the reference vehicle information.

[0065] Vehicle deviation parameter = reference vehicle information - target vehicle information.

[0066] It is understandable that as the severity of haze increases, vehicle speed decreases and vehicle density increases, and both vehicle speed and vehicle density have an impact on traffic flow parameters. Therefore, the real-time vehicle information is corrected by the haze level to improve the accuracy of vehicle deviation parameters.

[0067] In an implementable manner, after obtaining the vehicle deviation parameter according to the target vehicle information and the reference vehicle information, the method further includes:

[0068] According to the vehicle deviation parameter, the real-time environmental information is corrected to obtain the target environmental information; and according to the target environmental information and the reference environmental information, the corrected environmental deviation is obtained;

[0069] According to the corrected environmental deviation, the environmental deviation parameter is updated to obtain a new environmental deviation parameter.

[0070] First, based on the vehicle deviation parameters calculated previously, the real-time environmental information obtained from devices such as sensors is corrected. The error components in the original collected data are adjusted through a compensation algorithm to obtain target environmental information that is closer to the actual situation. Subsequently, the above-mentioned target environmental information is compared and analyzed with the pre-stored or synchronously collected reference environmental information, and a specific algorithm is used to calculate the difference between the two to determine the corrected environmental deviation. Based on the obtained corrected environmental deviation, the system further executes an update process to correct the existing environmental deviation parameters, introduce the latest deviation correction factor, and after a series of arithmetic processes, finally output the updated environmental deviation parameters to ensure that they can accurately reflect the deviation status under the current environmental conditions for use in subsequent vehicle control and decision-making processes.

[0071] It can be understood that as the vehicle speed decreases, the vehicle density increases, and the vehicle exhaust emissions increase, which will aggravate the haze. Therefore, the real-time environmental information is corrected through the vehicle deviation parameters to accurately obtain the real-time environmental information, thereby improving the accuracy of the environmental deviation parameters.

[0072] In an implementable manner, reference objects are set on the target road section, and the real-time environmental information consists of at least the real-time images of the reference objects, and the reference environmental information consists of at least the reference images of the reference objects.

[0073] In an implementable manner, the reference objects include traffic lights and non-luminous components set within the target road section. The real-time environmental information includes the first environmental information corresponding to the traffic lights and the second environmental information corresponding to the non-luminous components. In the above S202, according to the first environmental information, the second environmental information, and the reference environmental information, the environmental deviation parameters are determined, which may specifically include S202-1 to S202-3, where:

[0074] S202-1. According to the first environmental information and the reference environmental information, the first environmental deviation is obtained, where the first environmental deviation represents the degree to which the light source is weakened under the current environment.

[0075] It can be understood that since haze can scatter and absorb light, haze has a weakening effect on the light intensity provided by the light source. Different degrees of haze have different weakening effects on the light intensity. Therefore, by determining the degree of weakening of the light intensity by haze, the light loss caused by haze can be obtained.

[0076] It should be noted that the traffic lights operate based on the three colors of red, green, and yellow, and the light of the above three colors has a relatively long propagation distance in haze weather. Therefore, using traffic lights as reference objects to judge the degree of weakening of the light source is sufficiently stable.

[0077] S202-2. Obtain a second environmental deviation based on the second environmental information and the reference environmental information, where the second environmental deviation characterizes the degree of influence of the current environment on the field of view of the person.

[0078] The non-luminous component can be any fixed road administration object that can be monitored by the camera device within the target section.

[0079] S202-3. Obtain an environmental deviation parameter based on the first environmental deviation and the second environmental deviation.

[0080] By setting the reference objects as signal lights and non-luminous components, the environmental deviation parameter can be obtained more stably.

[0081] In one implementable manner, in the above S204, obtaining the real-time light demand according to the vehicle deviation parameter and the environmental deviation parameter may specifically include:

[0082] Obtain a real-time light compensation value according to the vehicle deviation parameter, where the real-time light compensation value characterizes the light compensation brought by the vehicle in the current environment to the target section.

[0083] Obtain the real-time light demand according to the real-time light compensation value, the first environmental deviation and the second environmental deviation included in the environmental deviation parameter.

[0084] In one implementable manner, the real-time light demand = real-time light compensation value - first environmental deviation + second environmental deviation.

[0085] In another implementable manner, dynamically calculate the real-time light compensation value by analyzing the deviation parameter of the vehicle, and this compensation value reflects the adjustment amount of the influence of the vehicle on the light of the target section in the current environment; subsequently, combine the first environmental deviation (such as weather conditions) and the second environmental deviation (such as terrain features) in the environmental deviation parameter, comprehensively evaluate and determine the real-time light demand, so as to guide the real-time brightness adjustment of the vehicle lighting system and ensure that the target section obtains suitable and stable lighting conditions.

[0086] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0087] Further, it should be noted that although the steps in the flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description 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 the flowchart 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 in turn with at least a part of other steps or sub-steps or stages of other steps.

[0088] Figure 3 FIG. is a schematic structural diagram of an intelligent lighting system based on big data provided by an embodiment of the present application, which is applied to the intelligent lighting method based on big data as Figure 3 shown. The intelligent lighting system 40 based on big data provided by an embodiment of the present application includes:

[0089] An acquisition module 401, configured to acquire real-time vehicle information and real-time environment information corresponding to a target road section. The real-time environment information represents visual data in the current environment, and the real-time vehicle information represents traffic flow parameters in the current environment;

[0090] A first processing module 402, configured to determine an environment deviation parameter according to the real-time environment information and the reference environment information. The reference environment information represents visual data of the target road section in a haze-free weather, and the environment deviation parameter includes the deviation between the visual data in the current environment and that in the haze-free weather;

[0091] A second processing module 403, configured to obtain a vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information. The reference vehicle information represents traffic flow parameters of the target road section in a haze-free weather, and the vehicle deviation parameter represents the deviation between the traffic flow parameters in the current environment and those in the haze-free weather;

[0092] A third processing module 404, configured to obtain a real-time lighting requirement according to the vehicle deviation parameter and the environment deviation parameter, and generate lighting information corresponding to the target road section according to the real-time lighting requirement, where the lighting information is used to control the lighting equipment of the target road section for road lighting.

[0093] Optionally, when the second processing module 403 executes to obtain a vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information, it is configured to:

[0094] Correct the real-time vehicle information according to the environment deviation parameter to obtain target vehicle information;

[0095] Based on the target vehicle information and the reference vehicle information, vehicle deviation parameters are obtained.

[0096] Optionally, the above system 40 further includes a correction module;

[0097] The correction module is used for:

[0098] According to the vehicle deviation parameters, the real-time environment information is corrected to obtain target environment information; and according to the target environment information and the reference environment information, a corrected environment deviation is obtained;

[0099] According to the corrected environment deviation, the environment deviation parameters are updated to obtain new environment deviation parameters.

[0100] Optionally, there are reference objects set on the target road section, the real-time environment information consists of the real-time images of the reference objects, and the reference environment information consists of the reference images of the reference objects.

[0101] Optionally, the reference objects include traffic lights and non-luminous components set within the target road section, the real-time environment information includes first environment information corresponding to the traffic lights and second environment information corresponding to the non-luminous components;

[0102] When the first processing module 402 executes to determine the environment deviation parameters according to the first environment information, the second environment information and the reference environment information, it is used for:

[0103] According to the first environment information and the reference environment information, a first environment deviation is obtained, where the first environment deviation represents the degree to which the light source is weakened in the current environment;

[0104] According to the second environment information and the reference environment information, a second environment deviation is obtained, where the second environment deviation represents the degree of influence of the current environment on the field of vision of people;

[0105] According to the first environment deviation and the second environment deviation, the environment deviation parameters are obtained.

[0106] Optionally, when the third processing module 404 executes to obtain the real-time lighting requirement according to the vehicle deviation parameters and the environment deviation parameters, it is used for:

[0107] According to the vehicle deviation parameters, a real-time lighting compensation value is obtained, where the real-time lighting compensation value represents the lighting compensation brought by the vehicle to the target road section in the current environment;

[0108] According to the real-time lighting compensation value, the first environment deviation and the second environment deviation included in the environment deviation parameters, the real-time lighting requirement is obtained.

[0109] The intelligent lighting system based on big data provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar, which will not be elaborated here in this embodiment.

[0110] It should be understood that the above system embodiments are merely illustrative, and the system of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0111] In addition, without special instructions, in each embodiment of the present application, each functional unit / module can be integrated in one unit / module, or each unit / module can exist physically alone, or two or more units / modules can be integrated together. The above integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0112] When the integrated unit / module is implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc.

[0113] Figure 4 It is a schematic structural diagram of the electronic device provided by the present application. As Figure 4 shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. Among them, the processor 501, the memory 502, and the communication component 503 are connected through a bus 504.

[0114] In the specific implementation process, at least one processor 501 executes the computer execution instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0115] For the specific implementation process of the processor 501, reference can be made to the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0116] Unless otherwise specified, the processor 501 can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, ASIC, etc. Unless otherwise specified, the memory 502 can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0117] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical discs and other media that can store program codes.

[0118] The embodiments of this application also provide a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the processor executes the computer-executable instructions, the redundant field update method for a distributed system as described above is implemented.

[0119] The embodiments of this application also provide a computer program product, including a computer program. When the computer program is executed by the processor, the redundant field update method for a distributed system as described above is implemented.

[0120] In the above embodiments, the descriptions of the various embodiments each have their own emphasis. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. 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 falling within the scope described in this specification.

[0121] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0122] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. An intelligent lighting method based on big data, characterized in that: in: Acquire real-time vehicle information and real-time environment information corresponding to the target road section, wherein the real-time environment information represents the visual field data in the current environment, and the real-time vehicle information represents the traffic flow parameters in the current environment; Determining an environmental deviation parameter according to the real-time environmental information and the reference environmental information, wherein the reference environmental information represents the visual field data of the target road section in non-haze weather, and the environmental deviation parameter includes the deviation between the visual field data in the current environment and the visual field data in non-haze weather; According to the real-time vehicle information and the reference vehicle information, a vehicle deviation parameter is obtained, wherein the reference vehicle information represents the traffic flow parameter of the target road section in the weather without haze, and the vehicle deviation parameter represents the deviation between the traffic flow parameter in the current environment and the traffic flow parameter in the weather without haze; According to the vehicle deviation parameter and the environment deviation parameter, a real-time lighting requirement is obtained, and according to the real-time lighting requirement, lighting information corresponding to the target road section is generated, wherein the lighting information is used to control the lighting equipment of the target road section for road lighting.

2. The intelligent lighting method based on big data according to claim 1, characterized in that: The step of obtaining a vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information includes: Correcting the real-time vehicle information according to the environmental deviation parameter to obtain target vehicle information; A vehicle deviation parameter is obtained according to the target vehicle information and the reference vehicle information.

3. The intelligent lighting method based on big data according to claim 2 is characterized in that: After obtaining the vehicle deviation parameter according to the target vehicle information and the reference vehicle information, the method further includes: According to the vehicle deviation parameter, the real-time environmental information is corrected to obtain target environmental information; and according to the target environmental information and the reference environmental information, a corrected environmental deviation is obtained; The environmental deviation parameter is updated according to the corrected environmental deviation to obtain a new environmental deviation parameter.

4. The intelligent lighting method based on big data according to claim 1, characterized in that: The target road section is provided with a reference object, the real-time environment information is composed of a real-time image of the reference object, and the reference environment information is composed of a reference image of the reference object.

5. The intelligent lighting method based on big data according to claim 4 is characterized in that: The reference objects include a signal light and a non-luminous component set in the target road section, and the real-time environmental information includes first environmental information corresponding to the signal light and second environmental information corresponding to the non-luminous component; Determining the environmental deviation parameter according to the first environmental information, the second environmental information and the reference environmental information includes: Obtaining a first environmental deviation according to the first environmental information and the reference environmental information, wherein the first environmental deviation represents the degree to which the light source is weakened in the current environment; Obtaining a second environmental deviation according to the second environmental information and the reference environmental information, wherein the second environmental deviation represents the degree of influence of the current environment on the person's field of vision; The environmental deviation parameter is obtained according to the first environmental deviation and the second environmental deviation.

6. The intelligent lighting method based on big data according to claim 5, characterized in that: The obtaining of real-time lighting requirements according to the vehicle deviation parameter and the environment deviation parameter includes: According to the vehicle deviation parameter, a real-time illumination compensation value is obtained, wherein the real-time illumination compensation value represents the illumination compensation effect brought by the illumination brought by the vehicle lights in the current environment to the target road section; The real-time lighting requirement is obtained according to the real-time lighting compensation value, the first environmental deviation and the second environmental deviation included in the environmental deviation parameter.

7. An intelligent lighting system based on big data, applied to the intelligent lighting method based on big data according to any one of claims 1 to 6, characterized in that: include: An acquisition module, used to acquire real-time vehicle information and real-time environment information corresponding to a target road section, wherein the real-time environment information represents visual field data in a current environment, and the real-time vehicle information represents traffic flow parameters in a current environment; A first processing module, configured to determine an environmental deviation parameter according to the real-time environmental information and reference environmental information, wherein the reference environmental information represents the visual field data of the target road section in non-haze weather, and the environmental deviation parameter includes a deviation between the visual field data in the current environment and the visual field data in non-haze weather; A second processing module is used to obtain a vehicle deviation parameter according to the real-time vehicle information and reference vehicle information, wherein the reference vehicle information represents the traffic flow parameter of the target road section in non-haze weather, and the vehicle deviation parameter represents the deviation between the traffic flow parameter in the current environment and the traffic flow parameter in non-haze weather; The third processing module is used to obtain the real-time lighting demand according to the vehicle deviation parameter and the environmental deviation parameter, and generate lighting information corresponding to the target road section according to the real-time lighting demand, wherein the lighting information is used to control the lighting equipment of the target road section to perform road lighting.

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