Intelligent lighting method and system based on big data

Abstract

The invention provides an intelligent lighting method and system based on big data. The method comprises the steps that real-time vehicle information and real-time environment information corresponding to a target road section are acquired, the real-time environment information represents view data in the current environment, and the real-time vehicle information represents traffic flow parameters in the current environment; according to the real-time environment information and the reference environment information, environment deviation parameters are determined, and the environment deviation parameters comprise deviation between the view data in the current environment and the view data in the haze-free weather; according to the real-time vehicle information and the reference vehicle information, vehicle deviation parameters are obtained, and the vehicle deviation parameters represent the deviation between the traffic flow parameters in the current environment and the traffic flow parameters in the haze-free weather; and according to the vehicle deviation parameter and the environment deviation parameter, obtaining a real-time illumination demand, and according to the real-time illumination demand, generating illumination information corresponding to the target road section, the illumination information being used for controlling an illumination device of the target road section to perform road illumination.

Description

Technical Field

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

[0002] With the development of modern society, traffic volume continues to increase. As an important means to ensure nighttime driving safety and pedestrian safety, road lighting plays an important role. Good road lighting can not only improve the driver's vision clarity, but also in some special environments, such as foggy weather, appropriate lighting intensity plays a vital role in traffic safety.

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

[0004] However, the light from ordinary street lamps will be scattered in large quantities when encountering high concentrations of haze, making it difficult to penetrate far away, resulting in a reduction in the effective lighting range. In addition, although the existing intelligent control system can adjust the brightness according to the ambient light to a certain extent, it is not sensitive enough to the rapidly changing and complex haze weather, and cannot make the most optimized lighting adjustment in time. In summary, the existing technology has the problem that lighting equipment is not sensitive enough to haze 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 equipment is not sensitive enough to haze weather.

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

[0007] Optionally, 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.

[0008] Optionally, 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.

[0009] Optionally, 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.

[0010] Optionally, the reference object includes 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.

[0011] Optionally, obtaining the real-time lighting requirement 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 brought to the target road section by the vehicle under the current environment; 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.

[0012] In a second aspect, the present application provides an intelligent lighting system based on big data, which is applied to an intelligent lighting method based on big data, including: 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.

[0013] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the intelligent lighting method based on big data as described in any one of the first aspects.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the intelligent lighting method based on big data as described in any one of the first aspects.

[0015] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the intelligent lighting method based on big data as described in any one of the first aspects.

[0016] The big data-based intelligent lighting method provided in the present application determines the visual field data and traffic flow parameters in the current environment by acquiring real-time vehicle information and real-time environmental information corresponding to the target road section; determines the environmental deviation parameter based on the real-time environmental information and the reference environmental information, so as to obtain the environmental deviation parameter including the deviation between the visual field data in the current environment and the visual field data in the haze-free weather; obtains the vehicle deviation parameter based on the real-time vehicle information and the reference vehicle information, so as to obtain the vehicle deviation parameter to characterize the deviation between the traffic flow parameters in the current environment and the traffic flow parameters in the haze-free weather; obtains the real-time lighting demand based on the vehicle deviation parameter and the environmental deviation parameter, and generates the lighting information corresponding to the target road section based on the real-time lighting demand, thereby realizing the control of the lighting equipment of the target road section for road lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, are used to explain the principles of the present application.

[0018] Figure 1 A schematic diagram of an application scenario of an intelligent lighting method based on big data provided in an embodiment of the present application; Figure 2 A schematic diagram of a process of an intelligent lighting method based on big data provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an intelligent lighting system based on big data provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in this application.

[0019] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope 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

[0020] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0021] 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 users, a handheld device with wireless connection function, or other processing equipment connected to a wireless modem. A wireless terminal can communicate with one or more core network devices via a radio access network (Radio Access Network, referred to as RAN). A wireless terminal can be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, for example, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges language and / or data with a radio access network. For another example, a wireless terminal can also be a personal communication service (Personal Communication Service, referred to as PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (Wireless Local Loop, referred to as WLL) station, a Personal Digital Assistant (Personal Digital Assistant, referred to as PDA) and other devices. A wireless terminal may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, an access terminal, a user terminal, a user agent, a user device or user equipment, without limitation. Optionally, the terminal device may also be a smart watch, a tablet computer or other device.

[0022] The embodiment of the present application provides an intelligent lighting method based on big data, which 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. The wireless terminal can be a device that provides voice and / or other business data connectivity to users, a handheld device with 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 (Radio Access Network, referred to as 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, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges language and / or data with the wireless access network. For another example, the wireless terminal can also be a personal communication service (Personal Communication Service, referred to as PCS) phone, a cordless phone, a Session Initiation Protocol (Session Initiation Protocol, referred to as SIP) phone, a wireless local loop (Wireless Local Loop, referred to as WLL) station, a personal digital assistant (Personal Digital Assistant, referred to as PDA) and other devices. A wireless terminal may also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, an access terminal, a user terminal, a user agent, a user device or user equipment, without limitation herein.

[0023] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments 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 in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, Figure 1A schematic diagram of an application scenario of an intelligent lighting method based on big data provided in this application: a camera device collects real-time images; a traffic monitoring device collects real-time vehicle information; an intelligent lighting system based on big data obtains real-time images and real-time vehicle information, and obtains real-time lighting requirements based on the real-time images and real-time vehicle information, and then generates lighting information based on the real-time lighting requirements, and controls the operation of lighting equipment based on the lighting information.

[0025] like Figure 2 As shown, Figure 2 A schematic flow chart of an intelligent lighting method based on big data provided in an embodiment of the present application may specifically include steps S201 to S204, wherein: S201. 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.

[0026] Real-time environmental information is collected through high-precision cameras, LiDAR, weather stations and other equipment installed on streetlight poles or other appropriate locations. These devices can monitor and record the visual field data in the current environment, including but not limited to visibility, haze concentration, air humidity and other factors, and represent the degree of impact of current environmental conditions on visual clarity in digital form. At the same time, real-time vehicle information is collected by geomagnetic induction coils, video detectors and intelligent traffic lights distributed on the road. They can accurately sense and count the number, speed, model and other parameters of motor vehicles on the target road section, so as to quantitatively describe the traffic flow conditions in the current environment.

[0027] S202. Determine an environmental deviation parameter based on the real-time environmental information and the reference environmental information. The reference environmental information represents the visual field data of the target road section in the haze-free weather. The environmental deviation parameter includes the deviation between the visual field data in the current environment and the visual field data in the haze-free weather.

[0028] It is understandable that haze weather will limit people's vision. Therefore, it is necessary to make up for the difference between the vision of people in the current environment and the vision of people in a clear state, so as to ensure the clarity of the vision of people. Moreover, lighting equipment can improve the range of people's vision through illumination. Therefore, this embodiment determines the light compensation demand of the current environment by determining the environmental deviation parameter.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] S204. Obtain real-time lighting requirements based on vehicle deviation parameters and environmental deviation parameters, and generate lighting information corresponding to the target road section based on the real-time lighting requirements, wherein the lighting information is used to control lighting equipment on the target road section for road lighting.

[0034] It can be understood that the difference between the light compensation demand and the light compensation in the current environment is the amount of light that the lighting device needs to compensate, that is, the illumination brightness of the lighting device, which can be obtained based on "light compensation demand - light compensation".

[0035] First, the vehicle deviation parameter calculated in the above steps and the environmental deviation parameter obtained by real-time monitoring of the environmental sensor are received, wherein the vehicle deviation parameter represents the difference between the traffic flow parameters in the current environment and in the haze-free weather, while the environmental deviation parameter covers factors that affect the road lighting effect, such as light intensity and visibility. Next, the intelligent algorithm module integrated in the system combines the vehicle deviation parameter and the environmental deviation parameter for comprehensive analysis to evaluate the actual lighting demand of the current road section. The intelligent algorithm is based on a preset model or machine learning training results, and takes into account the minimum lighting standards required to ensure driving safety under different traffic conditions and environmental conditions, so as to accurately predict and determine the real-time lighting demand of the target road section. Subsequently, according to the determined real-time lighting demand, the system automatically generates the corresponding target road section lighting information, which specifically defines control instructions including but not limited to the on state of the lighting equipment, the brightness adjustment level, and the color temperature selection. Finally, these lighting information are transmitted to the lighting control system deployed on the target road section, and are directly used to adjust the working state of each lighting equipment to realize the intelligent management of road lighting, ensure that appropriate road lighting can be provided under various meteorological conditions and traffic conditions, and ensure driving safety and efficiency.

[0036] The big data-based intelligent lighting method provided in the embodiment of the present application determines the visual field data and traffic flow parameters in the current environment by acquiring real-time vehicle information and real-time environmental information corresponding to the target road section; determines the environmental deviation parameter based on the real-time environmental information and the reference environmental information, so as to obtain the environmental deviation parameter including the deviation between the visual field data in the current environment and the visual field data in the haze-free weather; obtains the vehicle deviation parameter based on the real-time vehicle information and the reference vehicle information, so as to obtain the vehicle deviation parameter representing the deviation between the traffic flow parameters in the current environment and the traffic flow parameters in the haze-free weather; obtains the real-time lighting demand based on the vehicle deviation parameter and the environmental deviation parameter, and generates the lighting information corresponding to the target road section based on the real-time lighting demand, thereby achieving the beneficial effect of controlling the lighting equipment of the target road section to perform road lighting.

[0037] 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: S203-1. Correct the real-time vehicle information according to the environmental deviation parameter to obtain the target vehicle information.

[0038] 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.

[0039] S203-2. Obtain vehicle deviation parameters according to the target vehicle information and the reference vehicle information.

[0040] Vehicle deviation parameter = reference vehicle information - target vehicle information.

[0041] 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.

[0042] 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: 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; According to the corrected environmental deviation, the environmental deviation parameter is updated to obtain a new environmental deviation parameter.

[0043] First, based on the previously calculated vehicle deviation parameters, the real-time environmental information obtained from sensors and other devices is corrected, and the error components in the original collected data are adjusted through compensation algorithms to obtain target environmental information that is closer to the actual situation. Subsequently, the above target environmental information is compared and analyzed with the pre-stored or synchronously collected reference environmental information, and the difference between the two is calculated using a specific algorithm to determine the corrected environmental deviation. Based on the corrected environmental deviation obtained, the system further executes an update process to correct the existing environmental deviation parameters and introduce the latest deviation correction factor. After a series of calculations, the updated environmental deviation parameters are finally output 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.

[0044] It is understandable that as vehicle speed decreases, vehicle density increases, and vehicle exhaust emissions increase, smog will worsen. Therefore, the real-time environmental information is corrected through vehicle deviation parameters to accurately obtain real-time environmental information, thereby improving the accuracy of the environmental deviation parameters.

[0045] In one possible implementation, a reference object is provided on the target road section, the real-time environment information is at least composed of a real-time image of the reference object, and the reference environment information is at least composed of a reference image of the reference object.

[0046] In an implementable manner, the reference object includes a traffic 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 traffic light and second environmental information corresponding to the non-luminous component. In the above S202, the environmental deviation parameter is determined according to the first environmental information, the second environmental information and the reference environmental information, which may specifically include S202-1 to S202-3, wherein: S202-1. Obtain a first environment deviation according to the first environment information and the reference environment information, wherein the first environment deviation represents the degree to which the light source is weakened in the current environment.

[0047] It is understandable 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 light intensity. Therefore, by determining the degree of weakening of light intensity by haze, the light loss caused by haze can be obtained.

[0048] It should be noted that traffic lights operate based on the three colors of red, green and yellow, and the three colors of light travel a long distance in haze weather. Therefore, using traffic lights as a reference to judge the degree to which the light source is weakened is sufficiently stable.

[0049] S202-2. Obtain a second environment deviation according to the second environment information and the reference environment information, wherein the second environment deviation represents the degree of influence of the current environment on the person's field of vision.

[0050] The non-luminous component may be any fixed road administration object that can be monitored by the camera equipment in the target road section.

[0051] S202-3. Obtain an environmental deviation parameter according to the first environmental deviation and the second environmental deviation.

[0052] By setting the reference objects as signal lights and non-luminous components, the environmental deviation parameters can be obtained more stably.

[0053] In one possible implementation, in the above S204, obtaining the real-time lighting requirement according to the vehicle deviation parameter and the environment deviation parameter may specifically include: A real-time illumination compensation value is obtained according to the vehicle deviation parameter, wherein the real-time illumination compensation value represents the illumination compensation brought to the target road section by the vehicle under the current environment.

[0054] The real-time lighting requirement is obtained according to the real-time lighting compensation value and the first environment deviation and the second environment deviation included in the environment deviation parameter.

[0055] In one implementable manner, the real-time lighting requirement=the real-time lighting compensation value-the first environment deviation+the second environment deviation.

[0056] In another possible implementation, the real-time lighting compensation value is dynamically calculated by analyzing the vehicle's deviation parameters. The compensation value reflects the adjustment amount of the vehicle's impact on the lighting of the target road section under the current environment. Subsequently, the real-time lighting demand is comprehensively evaluated and determined based on the first environmental deviation (such as weather conditions) and the second environmental deviation (such as terrain features) in the environmental deviation parameters, thereby guiding the real-time brightness adjustment of the vehicle's lighting system to ensure that the target road section obtains suitable and stable lighting conditions.

[0057] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0058] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0059] Figure 3 A schematic diagram of the structure of an intelligent lighting system based on big data provided in an embodiment of the present application is applied to the intelligent lighting method based on big data, such as Figure 3 As shown, the intelligent lighting system 40 based on big data provided in the embodiment of the present application includes: An acquisition module 401 is 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 the visual field data in the current environment, and the real-time vehicle information represents the traffic flow parameters in the current environment; A first processing module 402 is used to determine 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 the weather without haze, and the environmental deviation parameter includes the deviation between the visual field data in the current environment and the visual field data in the weather without haze; The second processing module 403 is used to obtain a vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information, 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; The third processing module 404 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 for road lighting.

[0060] Optionally, the second processing module 403, when obtaining the vehicle deviation parameter according to the real-time vehicle information and the reference vehicle information, is used to: According to the environmental deviation parameters, the real-time vehicle information is corrected to obtain the target vehicle information; According to the target vehicle information and the reference vehicle information, a vehicle deviation parameter is obtained.

[0061] Optionally, the above system 40 further includes a correction module; Correction module for: 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; According to the corrected environmental deviation, the environmental deviation parameter is updated to obtain a new environmental deviation parameter.

[0062] Optionally, a reference object is provided on the target road section, 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.

[0063] Optionally, the reference object includes 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; The first processing module 402, when determining the environmental deviation parameter according to the first environmental information, the second environmental information and the reference environmental information, is used to: 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 field of vision of the person; An environmental deviation parameter is obtained according to the first environmental deviation and the second environmental deviation.

[0064] Optionally, the third processing module 404, when obtaining the real-time lighting requirement according to the vehicle deviation parameter and the environment deviation parameter, is used to: 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 brought to the target road section by the vehicle in the current environment; The real-time lighting requirement is obtained according to the real-time lighting compensation value and the first environment deviation and the second environment deviation included in the environment deviation parameter.

[0065] The intelligent lighting system based on big data provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0066] It should be understood that the above system embodiments are only 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 may 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.

[0067] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0068] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to a transistor, a memristor, etc.

[0069] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the intelligent lighting system 50 based on big data further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0070] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0071] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0072] If not otherwise specified, the processor 501 may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not otherwise specified, the memory 502 may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc.

[0073] 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0074] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above redundant field updating method for a distributed system is implemented.

[0075] An embodiment of the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the above redundant field updating method for a distributed system.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0078] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only 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 brought to the target road section by the vehicle under the current environment; 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.

Images