A highway intelligent fog lamp centralized control method based on a modular architecture

Abstract

The present application relates to the field of intelligent fog lamp control, and relates to a highway intelligent fog lamp centralized control method based on a modular architecture. The present application acquires meteorological data in each control partition in real time through a centralized management platform, matches the corresponding basic induction mode for each control partition in combination with the basic induction mode corresponding to each fog warning level; collects the real-time position and rear distance of each vehicle in combination with the meteorological data of the corresponding partition to determine whether to start the dynamic tail trace induction function, and determines the tail trace display fog lamp of each control partition when the dynamic tail trace induction function needs to be started; generates a fusion instruction in combination with the basic induction mode of the corresponding control partition, and each cloud box controls the corresponding fog lamp based on the received fusion instruction; the platform analyzes the health assessment score and identifies the abnormal state fog lamp based on the equipment state data of each fog lamp collected by each cloud box in real time, and performs corresponding maintenance on the abnormal state fog lamp, thereby improving the management and control efficiency and traffic safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent fog light control, and specifically to a centralized control method for intelligent fog lights on highways based on a modular architecture. Background Technology

[0002] Low visibility weather conditions such as fog, haze, rain, and snow pose a serious threat to highway driving safety. To address these risks, the installation of fog lights along highways has become a key facility for ensuring driving safety in low visibility conditions. However, fog light equipment comes in a variety of models and communication protocols, and there is a lack of a unified digital monitoring platform, resulting in inconvenient operation and maintenance management, delayed fault response, and low maintenance efficiency. Therefore, how to efficiently and centrally control fog lights has become an urgent problem to be solved.

[0003] Existing technologies, such as Chinese Patent Publication No. CN115116252A, disclose a method, device, and system for guiding vehicle safety. By checking the current road brightness, visibility, and fog conditions, the system adopts corresponding control modes for fog lights on both sides of the road to provide early warning based on the actual road conditions, effectively preventing traffic accidents.

[0004] However, the existing technology has the following problems: 1. The existing technology activates the corresponding mode according to the brightness and visibility of the road based on a fixed threshold. When displaying the vehicle trajectory, it does not take into account the vehicle speed and distance to set a different activation threshold, which leads to low guidance coverage and control efficiency. It is easy for the warning effect to be mismatched with the actual driving danger level, resulting in delayed or invalid warnings.

[0005] 2. Existing technologies do not take into account real-time analysis of equipment health status and actual operational feedback, resulting in delayed fault detection, low maintenance efficiency, and easy occurrences of fog light failure and abnormal response, thus reducing the fog lights' ability to ensure traffic safety. Summary of the Invention

[0006] This invention aims to address the shortcomings of existing technologies by providing a centralized control method for intelligent fog lights on highways based on a modular architecture. This method enables standardized management and precise control of fog light equipment, thereby improving road safety.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a centralized control method for intelligent fog lights on highways based on a modular architecture, comprising: a centralized management platform acquiring meteorological data in each control zone in real time, and matching the corresponding basic guidance mode for each control zone by combining the basic guidance mode corresponding to each major fog warning level.

[0008] The system collects the real-time location and distance to vehicles behind each vehicle, and combines this with meteorological data for the corresponding zone to determine whether to activate the dynamic taillight guidance function. When the dynamic taillight guidance function needs to be activated, the system uses the real-time location of the vehicles to determine the taillight display fog lights.

[0009] Based on the taillight display fog lights of each vehicle and the basic guidance mode of the corresponding control zone, a fusion command is generated and transmitted to the corresponding cloud box of the control zone. Each cloud box controls the corresponding fog lights based on the received fusion command.

[0010] The system collects real-time device status data for each fog light from each cloud box and uploads it to the centralized management platform. Based on the device status data of each fog light, it analyzes the health assessment score and identifies fog lights with abnormal status, and performs corresponding maintenance on fog lights with abnormal status.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention collects the real-time position and rear distance of each vehicle and combines the meteorological data of the corresponding zone to determine whether to activate the dynamic tail track guidance function, and displays the tail track function in a targeted manner to avoid ineffective guidance and untimely guidance, thereby improving the accuracy of early warning and driving safety while achieving energy saving.

[0012] (2) When the dynamic taillight guidance function needs to be activated, the present invention determines the taillight display fog lights in combination with the real-time position of the vehicle, thereby ensuring the stability and continuity of the cross-zone fog light display, significantly improving the recognizability of the vehicle in front, and reducing the probability of rear-end collision accidents.

[0013] (3) The present invention generates a fusion command based on the taillight display fog lights of each vehicle and the basic guidance mode of the corresponding control zone. The generated fusion command is transmitted to the corresponding cloud box of the control zone. Each cloud box controls the corresponding fog lights based on the received fusion command, taking into account both the overall road contour guidance and the local vehicle risk warning, improving the comprehensiveness of the guidance effect and better meeting the actual safety needs.

[0014] (4) The present invention collects the equipment status data of each fog light in real time through each cloud box, analyzes the health assessment score based on the equipment status data of each fog light and identifies fog lights with abnormal status, performs corresponding maintenance on fog lights with abnormal status, realizes automatic identification of faulty and unresponsive equipment, monitors and prevents sudden equipment damage in advance, reduces manual emergency road maintenance, and improves system stability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0017] Figure 2 This is a schematic diagram illustrating the specific steps of the method for determining the distance threshold in this invention;

[0018] Figure 3 This is a schematic diagram illustrating the specific steps of the health assessment score acquisition method in this invention. Detailed Implementation

[0019] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0020] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0021] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0022] Please see Figure 1 As shown, the present invention provides a centralized control method for intelligent fog lights on highways based on a modular architecture, including: S1, a centralized management platform acquires meteorological data in each control zone in real time, and matches the corresponding basic guidance mode for each control zone in combination with the basic guidance mode corresponding to each major fog warning level.

[0023] It should be noted that considering the long length and wide coverage of highways, and the large number of fog light devices, different sections of the highway use fog lights with different models and communication protocols. Therefore, a modular, partitioned deployment and layered management using the cloud box intermediate layer devices are implemented to improve the overall stability and maintainability of the system. In this embodiment, the cloud box and fog lights use a wireless communication network based on the 2.4GHz frequency band to ensure highly reliable and low-latency data transmission. Simultaneously, the cloud box and the intelligent fog light centralized management platform use a unified standardized communication protocol, such as MQTT, with JSON data format to ensure interoperability.

[0024] Furthermore, considering the significant differences in weather conditions and visibility levels across different sections of highways, and the regional and sudden nature of severe weather, a fixed and uniform fog light control mode cannot adapt to the ever-changing road environment. At the same time, different fog warning levels correspond to different driving risks, requiring differentiated fog light guidance methods to achieve effective visibility and safe guidance.

[0025] If control is based solely on a single brightness or visibility threshold, a mismatch between warning intensity and the degree of danger will occur, failing to provide drivers with clear and timely road outlines and safety alerts. Therefore, it is necessary to acquire real-time weather and visibility data by zone, and accurately match the basic guidance mode according to official fog warning levels and visibility standards to ensure that basic guidance is highly adapted to the road environment.

[0026] Based on this, in a preferred embodiment of the present invention, the matching method for the corresponding basic guidance modes includes: S11, extracting the basic guidance modes corresponding to the major fog warning levels from the backend database of the intelligent fog light centralized management platform, and matching the visibility range corresponding to each basic guidance mode in combination with the visibility range of the major fog warning levels. The basic guidance modes include off, breathing flash, constant on, and strobe.

[0027] S12. Obtain the corresponding meteorological data based on the geographical location of each control zone, and extract the current weather type and visibility from the meteorological data within each control zone.

[0028] S13. If the current weather type of a certain control zone is foggy, the corresponding basic guidance mode is matched from the basic guidance modes corresponding to the major fog warning levels based on the fog warning level.

[0029] S14. If the current weather type of a certain control zone is non-foggy, the corresponding basic guidance mode is matched from the visibility range corresponding to each basic guidance mode based on visibility.

[0030] In this embodiment, the basic guidance modes corresponding to the various fog warning levels are set based on existing road management experience, specifically as follows: a breathing flash is activated for a yellow fog warning, a constant light is activated for an orange fog warning, and a strobe light is activated for a red fog warning; otherwise, the warning is turned off. The visibility range for each fog warning level is a standard range defined by meteorological management departments; for example, visibility less than 500 meters but greater than or equal to 200 meters qualifies as a yellow fog warning. Therefore, in this embodiment, the breathing flash mode corresponds to visibility less than 500 meters but greater than or equal to 200 meters, the constant light mode corresponds to visibility less than 200 meters but greater than or equal to 50 meters, the strobe light mode corresponds to visibility less than 50 meters, and the off mode corresponds to visibility greater than or equal to 500 meters.

[0031] This invention uses a centralized management platform to acquire meteorological data in each control zone in real time and match it with the corresponding basic guidance mode. This enables matching of the basic guidance mode with the environment under different visibility conditions, improves the matching degree between the basic guidance mode and the environment, and enhances the reliability and timeliness of road demarcation and safety guidance in adverse environments such as fog.

[0032] S2. Determine whether each vehicle has activated the dynamic taillight guidance function and confirm that the taillight display fog lights are on.

[0033] Considering that following too closely is the main cause of rear-end collisions, and that the safe following distance varies with different vehicle speeds and visibility, simply using a fixed threshold cannot reflect real-time driving risks. If fog light guidance is activated only based on a fixed threshold without taking into account vehicle speed, real-time distance, and road visibility for differentiated judgment, it is easy to cause delayed warnings, ineffective guidance, or excessive guidance.

[0034] Furthermore, considering that dynamic trail guidance needs to accurately cover the dangerous area behind the vehicle, and that continuity of guidance must be maintained when driving across zones to avoid trail breaks and warning failure due to zone boundaries, the system dynamically calculates the guidance activation threshold by combining the vehicle's real-time position, minimum rear distance, zone visibility, and speed difference between the vehicles in front and behind. Based on the vehicle's zone and boundary position, continuous trail fog lights are then activated to achieve precise, dynamic, and continuous safety warnings.

[0035] Based on this, the specific steps of step S2 are as follows: S21. Collect the real-time position and rear distance of each vehicle, and combine this with the meteorological data of the corresponding zone to determine whether to activate the dynamic wake guidance function. The specific implementation steps are as follows: S211. When the basic guidance mode of a certain control zone is in the activation mode, the real-time position, speed, and distance to surrounding vehicles of each vehicle on the highway are collected in real-time by radar. The activation mode includes any one of the following: breathing flash, constant light, and strobe mode.

[0036] S212. Taking the direction of vehicle movement as the front, filter the distances of vehicles behind based on the real-time positions of each vehicle, and record the minimum distance between each vehicle and the vehicles behind as the corresponding distance to the rear vehicle.

[0037] It should be further clarified that the term "rear vehicles" does not refer only to vehicles in the same lane, but rather to all vehicles behind the vehicle in the same or adjacent lanes. Considering that when the distance between vehicles in adjacent lanes reaches a certain threshold, the illuminated taillights provide a direct warning of safe distance, naturally encouraging drivers to maintain a safe following distance. This invention uses the minimum distance between vehicles in the same or adjacent lanes as the criterion for judgment, effectively preventing dangerous behaviors such as lane changes and overtaking by drivers of vehicles behind in low visibility conditions, thereby providing more comprehensive protection for driving safety.

[0038] S213. Based on the current visibility of each control zone, obtain the corresponding prescribed following distance, and based on the prescribed following distance and the speed of the following vehicle, determine the distance threshold for activating the dynamic wake guidance function. For example... Figure 2 As shown, the specific implementation steps are as follows: S2131, when the speed of the following vehicle is equal to the speed of the vehicle, the buffer distance is determined by the set ratio of the specified following distance.

[0039] The specified following distance is determined based on the following distances corresponding to various visibility conditions as stipulated by the traffic management department. Specifically, when the visibility is less than 500 meters but greater than or equal to 200 meters, the specified following distance is 150 meters or more; when the visibility is less than 200 meters but greater than or equal to 100 meters, the specified following distance is 100 meters or more; and when the visibility is less than 100 meters but greater than or equal to 50 meters, the specified following distance is 50 meters or more.

[0040] In this embodiment, the set ratio is 10%. The implementer can also set other specific thresholds, but they should not be too large, so as to cause the buffer distance to be too large and result in frequent invalid activation of the dynamic wake induction function.

[0041] S2132. When the speed of the following vehicle is greater than the speed of the current vehicle, obtain the absolute difference between the speed of the following vehicle and the speed of the current vehicle and analyze the distance reduction per unit time.

[0042] Specifically, the distance reduction is calculated as follows: the product of the absolute difference between the speed of the following vehicle and the speed of the current vehicle and the unit time is recorded as the distance reduction, where the unit time in this invention is 1 second.

[0043] S2133. Determine the initial buffer distance according to the set ratio of the specified following distance, and record the sum of the initial buffer distance and the distance reduction per unit time as the buffer distance.

[0044] S2134. When the speed of the following vehicle is less than the speed of the current vehicle, obtain the absolute difference between the speed of the following vehicle and the speed of the current vehicle and analyze the distance increase per unit time. If the distance increase is greater than the initial buffer distance, then record the buffer distance as zero.

[0045] S2135. If the increase in distance is less than the initial buffer distance, the difference between the initial buffer distance and the increase in distance shall be recorded as the buffer distance.

[0046] S2136. The sum of the buffer distance and the specified following distance shall be recorded as the distance threshold.

[0047] S214. If the distance to a vehicle behind it is less than a distance threshold, it is determined that the dynamic tailrace guidance function needs to be activated for that vehicle.

[0048] This invention collects the real-time location and distance to other vehicles of each vehicle, combined with meteorological data of the corresponding zone, to determine whether to activate the dynamic wake guidance function. It then displays the wake function in a targeted manner, avoiding ineffective or untimely guidance, improving the accuracy of warnings and driving safety while achieving energy conservation.

[0049] S22. When it is necessary to activate the dynamic taillight guidance function, determine the taillight display fog lights based on the real-time position of the vehicle. The specific implementation steps are as follows: S221. When it is necessary to activate the dynamic taillight guidance function for a certain vehicle, obtain the rear boundary position of the control zone to which the vehicle belongs, and record the distance between it and the real-time position of the vehicle as the taillight length within the zone.

[0050] S222. The prescribed following distance of the control zone to which the vehicle belongs shall be used as the initial tailrace control length of the vehicle.

[0051] S223. Analyze the final trailing length of the vehicle based on the comparison between the trailing length within the zone and the initial trailing control length. The specific implementation steps are as follows: S2231. When the trailing length within the zone is greater than the initial trailing control length, the initial trailing control length is taken as the final trailing control length.

[0052] S2232. When the length of the wake within the zone is less than the initial wake control length, and the basic guidance mode of the control zone adjacent to the control zone to which the vehicle belongs is the closed mode, the length of the wake within the zone shall be taken as the final wake control length.

[0053] S2233. When the trail length within a zone is less than the initial trail control length, and the basic guidance mode of the adjacent rear control zone of the control zone to which the vehicle belongs is the start mode, the final trail control length is comprehensively analyzed based on the initial trail control length of the vehicle and the prescribed following distance of the adjacent rear control zone. The specific implementation steps are as follows: First, if the prescribed following distance of the adjacent rear control zone of the control zone to which the vehicle belongs is greater than or equal to the initial trail control length of the vehicle, the prescribed following distance of the adjacent rear control zone of the control zone to which the vehicle belongs is taken as the final trail control length.

[0054] Secondly, if the prescribed following distance of the adjacent rear control zone to which the vehicle belongs is less than the initial tailrace control length of the vehicle, then the initial tailrace control length shall be used as the final tailrace control length.

[0055] S224. Starting from the fog lights on both sides of the vehicle's real-time position, all fog lights included within the final trail control length extended backward are designated as trail display fog lights.

[0056] When the dynamic trail guidance function needs to be activated, this invention determines the trail display fog lights based on the real-time position of the vehicle, thereby ensuring the stability and continuity of the fog light display across zones, significantly improving the visibility of the vehicle in front, and reducing the probability of rear-end collisions.

[0057] S3. Based on the basic guidance mode and the wake display fog light, generate fusion control commands, and each cloud box controls each fog light based on the generated fusion commands.

[0058] Considering that the basic guidance mode is responsible for guiding the overall road outline, while dynamic wake guidance is responsible for local vehicle risk warnings, both functions need to be active simultaneously. Furthermore, given that each control zone is managed by an independent cloud box, the zone's basic guidance commands and vehicle wake guidance commands need to be merged into a unified fusion command to ensure accurate and synchronized execution of fog lights by the cloud box. In addition, within the same control zone, some fog lights need to implement wake display while the rest maintain the basic mode. This fusion command enables differentiated and coordinated control of fog lights within each zone, balancing global outline guidance with local risk warnings.

[0059] Based on this, the specific implementation of step S3 is as follows: S31, generate a fusion command based on the taillight display fog lights combined with the basic guidance mode of the corresponding control zone. The specific implementation steps are as follows: S311, count the taillight display fog lights in each control zone in real time, and when there are no taillight display fog lights in a certain control zone, control all fog lights in that zone to be set to the corresponding basic guidance mode command.

[0060] It should be noted that if the same fog light is identified as a trail indicator fog light by multiple vehicles, when counting trail indicator fog lights in each control zone, the fog light will only be counted once. After the cloud box receives the fusion command, it can uniformly execute the trail indicator command for the fog light without distinguishing its triggering entity.

[0061] S312. When there are trail display fog lights in a certain control partition, set the control commands for each trail display fog light to trail display commands, and set the control commands for the remaining fog lights to commands corresponding to the basic guidance mode.

[0062] S313. Calculate the commands corresponding to each fog light in each control zone to obtain the fusion command.

[0063] The fusion command mentioned above is a set of commands corresponding to all fog lights within the control zone. Specifically, the command content includes, but is not limited to, device ID, type, operating mode, flashing frequency, and color. For example, for a fog light with a basic guidance mode command, the command content is {"Device ID": "LD-001", "Type": "Basic", "Operating Mode": "Strobe", "Flashing Frequency": "3Hz", "Color": "Yellow"}. For a fog light with a trail display command, the command content is {"Device ID": "LD-001", "Type": "Trail", "Operating Mode": "Constant On", "Color": "Red"}.

[0064] S32. The generated fusion command is transmitted to the corresponding cloud box in the control partition, and each cloud box controls the corresponding fog lights based on the received fusion command.

[0065] This invention generates a fusion command based on the taillight display fog lights of each vehicle and the basic guidance mode of the corresponding control zone. The generated fusion command is transmitted to the corresponding cloud box of the control zone. Each cloud box controls the corresponding fog lights based on the received fusion command, taking into account both overall road contour guidance and local vehicle risk warning, improving the comprehensiveness of the guidance effect and better meeting actual safety needs.

[0066] S4. Identify fog lights in abnormal condition and perform corresponding maintenance on them.

[0067] Considering that highway fog lights are exposed to the elements for extended periods, they are prone to problems such as light intensity attenuation, abnormal command response, and equipment damage. Traditional manual inspections cannot monitor the health status of the equipment in real time, resulting in delayed fault detection, low maintenance efficiency, and a high risk of sudden fog light failure leading to safety accidents. Therefore, by quantitatively assessing the health status of the equipment, we can predict aging and failure risks in advance, and achieve automatic identification and graded handling of abnormal equipment.

[0068] Based on this, the specific implementation steps of S4 are as follows: S41, collect the device status data of each fog light in real time through each cloud box and upload it to the centralized management platform, and analyze the health assessment score based on the device status data of each fog light. Figure 3 As shown, the specific implementation steps are as follows: S411, based on the fusion instructions of each cloud box, each fog light corresponding to each cloud box with the basic induction mode as the start mode is recorded as the working fog light.

[0069] S412. Extract the operating current from the equipment status data of each working fog light. When the operating current of a working fog light is zero at each time point within the set historical time window, record the health assessment score of the working fog light as zero.

[0070] The historical time window can be set based on the cycle of the breathing flashing fog light. For example, in this embodiment, the cycle of the breathing flashing fog light is 2.5 seconds, so the historical time window is set to within 5 seconds. The implementer can also set other specific values, but it should not be too long, so as not to accurately capture the current real attenuation level and reduce the real-time performance. It should not be too short either. If the historical time window is set to be less than the cycle of breathing flashing, it will lead to data loss and thus affect the analysis results.

[0071] S413. Based on the specifications of the remaining working fog lights, extract the maximum luminous flux of the corresponding fog lights during normal operation from the backend database of the intelligent fog light centralized management platform. The remaining fog lights refer to those excluding those whose health assessment scores are recorded as zero.

[0072] S414. Extract the peak luminous flux within the set historical time window from the device status data of the remaining working fog lights, calculate the absolute difference between it and the corresponding maximum luminous flux, and record the ratio of it to the maximum luminous flux as the luminous flux attenuation rate.

[0073] Considering that the luminous flux changes in real time during the breathing flash and strobe modes, the actual attenuation of the device can be accurately reflected by collecting the peak luminous flux. Specifically, the real-time luminous flux data of the fog light is collected by the cloud box at a sampling frequency of not less than 50Hz, and the maximum value of the luminous flux in each breathing flash or strobe cycle is taken as the peak luminous flux of that cycle.

[0074] S415. The product of the luminous flux attenuation rate and the total set health assessment score is recorded as the health attenuation score, and the difference between the total set health assessment score and the health attenuation score is recorded as the health assessment score. In this embodiment, the total health assessment score is 100 points.

[0075] S42. Identify fog lights with abnormal status based on the health assessment scores of each fog light and the equipment status data.

[0076] In a preferred embodiment of the present invention, the method for identifying fog lights in abnormal states includes: S421, extracting working status feedback data from the device status data of each fog light, comparing it with the corresponding instruction data, and determining that the fog light is in abnormal response if the working status feedback of a fog light is inconsistent with the corresponding instruction. The feedback data and instructions are both in JSON format. For example, the instruction for a fog light is as follows: Instruction content = {"Device ID": "LD-001", "Target Mode": "Flicker", "Flicker Frequency": "3Hz", "Color": "Yellow"}, and its feedback data is as follows: Feedback data = {"Device ID": "LD-001", "Current Mode": "Flicker", "Flicker Frequency": "2Hz", "Color": "Yellow"}. By comparing the two, a difference in flicker frequency is found, thus determining that the fog light is in abnormal response.

[0077] S422. When the health assessment score of a working fog light is lower than the set scoring threshold or the fog light responds abnormally, the fog light is recorded as an abnormal fog light. In this embodiment, the scoring threshold is set to 80 points. When the health assessment score of a fog light is lower than 80 points, the fog light is determined to be an abnormal fog light.

[0078] S43. Perform appropriate maintenance on fog lights in abnormal condition.

[0079] In this embodiment, the specific method for maintaining fog lights in abnormal states is as follows: if the health assessment score of a fog light is below 80 points, the fog light can be marked as aged and degraded, and it can be replaced when the fog light is in the off mode; when the health assessment score of a fog light is 0 points, the fog light can be repaired urgently; if a fog light responds abnormally, it can be restarted and reset through the intelligent fog light centralized management platform.

[0080] This invention collects real-time device status data for each fog light from each cloud box, analyzes the health assessment score based on the device status data of each fog light, identifies fog lights with abnormal status, and performs corresponding maintenance on fog lights with abnormal status. This enables automatic identification of faulty and unresponsive devices, early monitoring and prevention of sudden equipment damage, reduces the need for manual emergency roadside repairs, and improves system stability.

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0082] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0085] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A centralized control method for intelligent fog lights on highways based on a modular architecture, characterized in that, include: The centralized management platform acquires meteorological data in each control zone in real time, and matches the corresponding basic guidance mode for each control zone based on the basic guidance mode corresponding to the major fog warning levels. The system collects the real-time location and distance to vehicles behind each vehicle, and combines this with the meteorological data of the corresponding zone to determine whether to activate the dynamic taillight guidance function. When the dynamic taillight guidance function needs to be activated, the system determines the taillight display fog lights based on the real-time location of the vehicles. Methods for determining the presence of fog lights in a trail include: When it is necessary to activate the dynamic wake guidance function for a vehicle, the rear boundary position of the control zone to which the vehicle belongs is obtained, and the distance between the boundary position and the real-time position of the vehicle is recorded as the wake length within the zone. The prescribed following distance for the control zone to which the vehicle belongs shall be used as the initial tailrace control length for the vehicle. The final trailing length of the vehicle is analyzed based on a comparison between the trailing length within the zone and the initial trailing control length. Starting from the fog lights on both sides of the vehicle's real-time position, all fog lights within the final trail control length extended backward are used as trail display fog lights. Based on the taillight display fog lights of each vehicle and the basic guidance mode of the corresponding control zone, a fusion command is generated and transmitted to the corresponding cloud box of the control zone. Each cloud box controls the corresponding fog lights based on the received fusion command. The specific steps for generating the fusion instruction include: Real-time statistics of the fog lights in each control zone; when there are no fog lights in a certain control zone, control all fog lights in that zone to be set to the corresponding basic guidance mode command. When a trail display fog light exists in a certain control partition, set the control command for each trail display fog light to the trail display command, and set the control command for the remaining fog lights to the command for the corresponding basic guidance mode. The fusion command is obtained by statistically analyzing the commands corresponding to each fog light in each control zone; The system collects real-time device status data for each fog light from each cloud box and uploads it to a centralized management platform. Based on the device status data of each fog light, it analyzes the health assessment score and identifies fog lights with abnormal status, and performs corresponding maintenance on fog lights with abnormal status.

2. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 1, characterized in that: The basic induction patterns include off, breathing flash, constant light, and strobe, wherein the matching method for the corresponding basic induction patterns includes: Extract the basic guidance modes corresponding to the major fog warning levels from the backend database of the intelligent fog light centralized management platform, and match the visibility range corresponding to each basic guidance mode with the visibility range of each major fog warning level. Based on the geographical location of each control zone, obtain the corresponding meteorological data, and extract the current weather type and visibility from the meteorological data within each control zone; If the current weather type of a certain control zone is foggy, obtain the fog warning level of that control zone, and match the corresponding basic guidance mode from the basic guidance modes corresponding to the major fog warning levels. If the current weather type of a certain control zone is non-foggy, the corresponding basic guidance mode is matched from the visibility range corresponding to each basic guidance mode based on visibility.

3. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 2, characterized in that: The specific steps for determining whether to activate the dynamic wake guidance function include: When the basic guidance mode of a certain control zone is the start mode, the real-time position, speed and distance to surrounding vehicles of each vehicle on the highway are collected by radar. Taking the direction of vehicle travel as the front, extract the minimum value among the distances between each vehicle and the vehicles around it and the distances between each vehicle and the vehicles behind it, and record it as the corresponding rear distance. The corresponding prescribed following distance is obtained based on the current visibility of each control zone, and the distance threshold for activating the dynamic wake guidance function is determined based on the prescribed following distance and the speed of the following vehicle. If the distance behind a vehicle is less than a distance threshold, it is determined that the dynamic tailrace guidance function needs to be activated for that vehicle.

4. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 3, characterized in that: The method for determining the distance threshold includes: When the speed of the following vehicle is equal to the speed of this vehicle, the buffer distance is determined according to the set ratio of the prescribed following distance. When the speed of the following vehicle is greater than the speed of the current vehicle, the absolute difference between the speeds of the following vehicle and the current vehicle is obtained to analyze the amount of distance reduction per unit time. The initial buffer distance is determined by a set ratio of the prescribed following distance, and the sum of the initial buffer distance and the distance reduction per unit time is recorded as the buffer distance. When the speed of the following vehicle is less than the speed of the current vehicle, the absolute difference between the speed of the following vehicle and the speed of the current vehicle is obtained to analyze the increase in distance per unit time. If the increase in distance is greater than the initial buffer distance, the buffer distance is recorded as zero. If the increase in distance is less than the initial buffer distance, the difference between the initial buffer distance and the increase in distance is recorded as the buffer distance. The sum of the buffer distance and the prescribed following distance is recorded as the distance threshold.

5. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 1, characterized in that: The method for analyzing the final wake control length includes: When the length of the trail within the partition is greater than the initial trail control length, the initial trail control length is used as the final trail control length. When the length of the wake within the zone is less than the initial wake control length, and the basic guidance mode of the control zone adjacent to the control zone to which the vehicle belongs is the closed mode, the length of the wake within the zone is taken as the final wake control length. When the tailrace length within a zone is less than the initial tailrace control length, and the basic guidance mode of the adjacent rear control zone to which the vehicle belongs is the start mode, the final tailrace control length is comprehensively analyzed based on the initial tailrace control length of the vehicle and the prescribed following distance of the adjacent rear control zone.

6. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 5, characterized in that: The comprehensive analysis ultimately controls the length of the wake. If the prescribed following distance of the adjacent rear control zone to which the vehicle belongs is greater than or equal to the initial tailrace control length of the vehicle, the prescribed following distance of the adjacent rear control zone to which the vehicle belongs shall be used as the final tailrace control length. If the following distance specified by the adjacent rear control zone to which the vehicle belongs is less than the initial tailrace control length of the vehicle, then the initial tailrace control length shall be used as the final tailrace control length.

7. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 1, characterized in that: The methods for obtaining the health assessment score include: Based on the fusion instructions of each cloud box, each fog light corresponding to each cloud box with the basic induction mode as the start mode is recorded as the working fog light; Extract the operating current from the equipment status data of each fog light. When the operating current of a fog light is zero at each time point within a set historical time window, the health assessment score of that fog light is recorded as zero. Based on the specifications of the remaining working fog lights, the maximum luminous flux of the corresponding fog lights during normal operation is extracted from the backend database of the intelligent fog light centralized management platform. Extract the peak luminous flux within the set historical time window from the device status data of the remaining working fog lights, calculate the absolute difference between it and the corresponding maximum luminous flux, and record the ratio of it to the maximum luminous flux as the luminous flux attenuation rate; The product of the luminous flux attenuation rate and the total set health assessment score is recorded as the health attenuation score, and the difference between the total set health assessment score and the health attenuation score is recorded as the health assessment score.

8. The centralized control method for intelligent fog lights on highways based on a modular architecture according to claim 1, characterized in that: The method for identifying abnormal fog lights is as follows: Extract the working status feedback data from the device status data of each fog light, compare it with the corresponding instruction data, and if the working status feedback of a fog light is inconsistent with the corresponding instruction, the fog light is determined to be in abnormal response. When the health assessment score of a working fog light is lower than the set scoring threshold or a fog light responds abnormally, the fog light is recorded as an abnormal fog light.

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