Multi-parameter coordinated adjustment system for highway electromechanical equipment

Abstract

The present invention relates to the field of electromechanical equipment for highways, and specifically to a multi-parameter coordinated adjustment system for electromechanical equipment for highways. The system achieves refined monitoring and display control of different sections of highways through the coordinated work of a video acquisition module, a video splitter, a coordinated adjustment module, an adjustment module, and an LED dimming control module. The system utilizes regional segmentation dynamic perception technology to improve its adaptability to complex environments. The data acquisition module is responsible for collecting exposure data from the video acquisition module and related information from an LED controller, while the control module coordinates the operations of the various modules. The video switching module outputs an exposure-adjusted video image according to instructions from the control module. The coordinated adjustment module not only processes brightness information but also calculates the brightness change and ratio of the exposure-adjusted video image and feeds this information back to the video acquisition module to adjust the exposure. This significantly improves the intelligence and response speed of the system and enhances the operational safety and efficiency of highways.

Description

Technical Field

[0001] The present invention relates to the field of highway electromechanical equipment, and in particular to a multi-parameter coordinated adjustment system for highway electromechanical equipment. Background Art

[0002] As a crucial component of modern intelligent transportation systems, highway electromechanical equipment, including video surveillance equipment, LED displays, signal lights, and other devices, plays a vital role in ensuring traffic safety and improving traffic efficiency. With the continuous development of intelligent transportation systems, the variety and quantity of highway electromechanical equipment has gradually increased, and the coordination between these devices has become increasingly important.

[0003] Existing electromechanical equipment on highways typically operates independently, lacking effective coordination mechanisms. For example, cameras and LED displays are adjusted based on their own parameters, without considering the interaction between them. In complex environmental conditions, such as scenes with large variations in light intensity, cameras may over- or underexpose images, while LED displays may display unclear information due to improper brightness settings, impacting traffic safety and efficiency.

[0004] In addition, the existing highway electromechanical equipment adjustment system generally has the following problems: First, the equipment parameter adjustment lacks holistic consideration, and each device sets parameters independently, resulting in poor overall system effect; second, the ability to adapt to environmental changes is limited, and parameters cannot be dynamically adjusted according to real-time environmental conditions; third, energy utilization efficiency is low, and equipment power consumption cannot be optimized according to actual needs.

[0005] Therefore, there is an urgent need for a system that can realize the coordinated adjustment of multiple parameters of highway electromechanical equipment to improve equipment operation performance and energy utilization efficiency. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-parameter coordinated adjustment system for highway electromechanical equipment. By constructing a coordinated adjustment mechanism for video acquisition and LED display, it can achieve refined perception and control of the environment of different road sections, and solve the problems of poor performance and high energy consumption caused by independent operation of equipment in traditional systems.

[0007] The present invention proposes a multi-parameter coordinated adjustment system for electromechanical equipment of highways, comprising:

[0008] Video acquisition module, used to acquire multiple frames of original video images;

[0009] A video splitter, connected to the video acquisition module, is used to split the multiple frames of original video images into multiple pictures, where different pictures correspond to different roadside LED modules;

[0010] a collaborative adjustment module, connected to the video splitter, configured to calculate brightness information corresponding to each of the plurality of images, perform fusion processing on the brightness information, and generate an exposure adjustment instruction;

[0011] an adjustment module connected to the collaborative adjustment module, configured to adjust the exposure corresponding to different frames of each frame of the multiple frames of original video images according to the exposure adjustment instruction to obtain an exposure-adjusted video image;

[0012] an LED dimming control module, connected to the video acquisition module and the collaborative adjustment module, configured to receive the multiple frames of original video images and feed back exposure adjustment values ​​to the video acquisition module;

[0013] A data acquisition module, configured to acquire exposure data from the video acquisition module and information about the LED drive current and LED current adjustment coefficient of the LED controller;

[0014] a control module, connected to the video acquisition module, the LED dimming control module, and the data acquisition module, respectively, for controlling the video acquisition module to acquire images, and collecting exposure data of the video acquisition module and information about the LED drive current and LED current adjustment coefficient of the LED controller through the data acquisition module;

[0015] a video switching module, connected to the control module, and configured to output the exposure-adjusted video image under the control of the control module;

[0016] Among them, the collaborative adjustment module is also used to receive and store the LED driving current and the LED current adjustment coefficient information, and after adjusting the exposure corresponding to different frames of the exposure adjustment video image, calculate the brightness change of multiple frames of the exposure adjustment video image and the previous frame of the exposure adjustment video image and the brightness ratio of the exposure adjustment video image to the multiple frames of original video images, and feed back the brightness change and the brightness ratio to the video acquisition module; the video acquisition module adjusts the exposure according to the exposure adjustment value feedback from the LED dimming control module.

[0017] Preferably, it also includes a collaborative data packet module; the collaborative adjustment module sends the brightness change of multiple frames of exposure-adjusted video images and the previous frame of exposure-adjusted video image to the collaborative data packet module, the collaborative data packet module sends the brightness change to the control module, and the control module stores the brightness change.

[0018] Preferably, the collaborative adjustment module transmits the exposure corresponding to different frames of the exposure-adjusted video image to the collaborative data packet module, the collaborative data packet module transmits the exposure to the control module, and the control module stores the exposure.

[0019] Preferably, it further includes a display; the collaborative data packet module sends the brightness change to the display; and the control module sends the exposure corresponding to different frames of the exposure-adjusted video image to the display.

[0020] Preferably, it further includes a video display module; the control module inputs the exposure-adjusted video image output by the video switching module into the video display module for display.

[0021] Preferably, it further includes a video storage module; the control module controls the video display module to display the multiple frames of original video images, and controls the video storage module to store the multiple frames of original video images.

[0022] Preferably, the collaborative adjustment module calculates the LED brightness through the LED driving current and the LED current adjustment coefficient; the video acquisition module acquires the multiple frames of original video images, calculates the brightness average of the original video images corresponding to different frames, and the video acquisition module adjusts the brightness corresponding to different frames of each frame of the original video image to obtain the exposure-adjusted video image.

[0023] Preferably, the video acquisition module uses the original video image to adjust the brightness corresponding to different pictures; the adjusted image is the exposure-adjusted video image; when the brightness ratio of the exposure-adjusted video image to the multiple frames of original video image is 1±m, m is an artificially set constant; when the brightness ratio is less than 1, the video acquisition module uses the original video image to adjust the brightness corresponding to different pictures; when the brightness ratio is greater than 1, the collaborative adjustment module adjusts the exposure corresponding to different pictures of the exposure-adjusted video image.

[0024] Preferably, the collaborative adjustment module includes a parameter detection module, a signal transmission module, an edge processing module and a model training module; the parameter detection module is used to read the collaborative parameter value list and compare the parameters and parameter values ​​in the collaborative parameter value list. When abnormal parameters and parameter values ​​are detected, the corresponding abnormal information is sent to the signal transmission module; the edge processing module receives the abnormal information of the signal transmission module, and generates control information after processing the abnormal information and sends it to the control module; the model training module is used to train the collaborative control model according to the brightness change and brightness ratio data, so that the collaborative control model can make real-time predictions on the collaborative parameter value list.

[0025] Preferably, it further includes a safety detection module; the safety detection module is used to analyze various parameter values ​​and various images, and perform safety detection based on the brightness change and the brightness ratio. When a safety abnormality is detected, the safety detection module generates a prompt message and a stop message and sends it to the control module; the safety detection module also includes a video recognition unit, which is used to identify and analyze various images and convert the analysis results into parameters.

[0026] The beneficial effects of the present invention include:

[0027] 1. Through regional segmentation dynamic perception technology, refined monitoring and display control of different sections of the highway are achieved, improving the system's adaptability to complex environments;

[0028] 2. Through a multi-dimensional brightness information fusion algorithm, the brightness data of different regions are weighted and integrated to generate an overall brightness adjustment strategy that takes into account both regional characteristics and global balance;

[0029] 3. The adaptive exposure adjustment loop design enables dynamic adaptive adjustment of the camera system’s exposure parameters, improving video image quality.

[0030] 4. Through the two-way coordination mechanism between LED and camera systems, a composite system with mutual adjustment and mutual influence between LED and camera systems is constructed, solving the problem of mutual interference between LED display and video surveillance in traditional systems;

[0031] 5. Through the collaborative data packet model and historical data learning system, continuous optimization and long-term self-evolution of system parameters are achieved, and the intelligence level of the system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of the overall structure of the multi-parameter coordinated adjustment system for electromechanical equipment on highways according to the present invention;

[0033] Figure 2 This is a data interaction flow chart of the video acquisition module and the collaborative adjustment module in the present invention;

[0034] Figure 3 This is a flow chart of the multi-dimensional brightness information fusion algorithm in the present invention;

[0035] Figure 4 Schematic diagram of the working principle of the adaptive exposure adjustment loop in the present invention;

[0036] Figure 5 This is a diagram of the internal structure of the collaborative regulation module in the present invention;

[0037] Figure 6This is a workflow diagram of the safety detection module in the present invention. DETAILED DESCRIPTION

[0038] Please refer to the attached Figure 1-6 , the specific implementation of the present invention is further described in detail below with reference to the accompanying drawings.

[0039] like Figure 1 As shown, the multi-parameter coordinated adjustment system for highway electromechanical equipment provided by the present invention includes a video acquisition module 1, a video splitter 2, a coordinated adjustment module 3, an adjustment module 4, an LED dimming control module 5, a data acquisition module 6, a control module 7, a video switching module 8, and an optional coordinated data packet module 9, a display 10, a video display module 11, a video storage module and a safety detection module.

[0040] Video acquisition module 1 is used to capture multiple frames of raw video images. Preferably, video acquisition module 1 uses a high-dynamic-range industrial-grade camera that supports regionalized exposure control. The acquisition frequency can be set to 15-30 fps (frames per second) to meet real-time monitoring requirements. In highway monitoring scenarios, a higher frame rate can better capture the process of high-speed vehicle movement, especially in sections with heavy traffic. A frame rate of 15 fps or higher can effectively avoid image blur and information loss. The raw video images captured by video acquisition module 1 typically have a resolution of 1920×1080 or higher to ensure image clarity and detail, facilitating the subsequent identification of key information such as vehicle license plates.

[0041] The video splitter 2 is connected to the video acquisition module 1 and is used to split multiple frames of original video images into multiple screens, where different screens correspond to different roadside LED modules. In one embodiment of the present invention, the video splitter 2 can split a complete highway monitoring screen into 3-8 sub-areas, such as the entrance area, main line area, exit area, etc., each area corresponds to an LED display module on the roadside. Taking a certain highway toll station as an example, the monitoring screen can be divided into three parts: the toll station entrance area, the waiting area, and the exit area, corresponding to three groups of LED information screens respectively, so that the display content and brightness can be adjusted according to the actual situation of each area. The segmentation method can be flexibly set according to actual monitoring needs, and can be either uniform segmentation or non-uniform segmentation based on importance. The video splitter 2 uses real-time image processing technology to ensure that the segmentation process does not affect the real-time performance of image acquisition.

[0042] The collaborative adjustment module 3 is connected to the video splitter 2 and is used to calculate the brightness information corresponding to each of the multiple images, fuse the brightness information, and generate exposure adjustment instructions. The collaborative adjustment module 3 adopts a multi-dimensional brightness information fusion algorithm, the core of which is to perform weighted fusion of brightness features of different regions. Specifically, let the average brightness value of the i-th region be , the regional weight is , then the brightness index after fusion is The calculation is as follows:

[0043] ,

[0044] in: is the brightness index after fusion, the unit is brightness value (range 0-255); is the average brightness value of the ith region, in units of brightness values ​​(range 0-255); For the The weight coefficient of each region is dimensionless and ranges from 0 to 1; is the total number of regions, that is, the number of regions after the video is segmented;

[0045] represents the summation operation from i=1 to i=N. The weight coefficient can be dynamically adjusted based on factors such as regional importance and traffic volume. For example, at the entrance area of ​​a highway ramp, where frequent lane changes pose a higher safety risk, the weight of this area can be set to 0.3-0.5, while the weight of ordinary straight sections can be set to 0.1-0.2. Furthermore, in areas with drastic changes in lighting conditions, such as tunnel entrances and exits, the weight coefficient should be increased accordingly to ensure the system's monitoring quality in these critical areas.

[0046] In addition, the collaborative adjustment module 3 also considers the characteristics of time series and smoothes the brightness data of M consecutive frames (usually M = 5-10) to filter out transient interference. In highway scenes, fast-moving vehicles can cause transient brightness fluctuations. In particular, when large vehicles pass through the monitoring area, reflective surfaces may cause sudden changes in brightness. Time series smoothing uses a weighted moving average algorithm:

[0047] ,

[0048] in: The brightness value after smoothing at the current moment t, in units of brightness (range 0-255); is the fused brightness value at the current moment t, in units of brightness (range 0-255); is the smoothed brightness value at the previous moment t-1, in units of brightness values ​​(range 0-255); is the smoothing coefficient, dimensionless, ranging from 0 to 1; Indicates the current moment; Indicates the previous moment. Smoothing coefficient The value is usually 0.2-0.3, and the smaller The value provides smoother output but increases system response delay. The choice of value should balance the smoothing effect and response speed. For example, during the period of day and night (such as dusk or dawn), the lighting conditions change relatively slowly, so Set the value to 0.2 to obtain a smoother brightness transition; in scenes with sudden changes in traffic flow or tunnel entrances and exits, The value is set to 0.3 to increase the system's response speed to environmental changes.

[0049] Adjustment module 4 is connected to collaborative adjustment module 3 and is used to adjust the exposure corresponding to different frames of each frame of multiple frames of original video images according to the exposure adjustment instruction, thereby obtaining an exposure-adjusted video image. Adjustment module 4 receives the exposure adjustment instruction generated by collaborative adjustment module 3 and calculates the optimal exposure parameters for each segmented area. Exposure parameter adjustment follows the following rules: when the regional brightness is lower than the target brightness, the exposure time is increased or the exposure gain is increased; when the regional brightness is higher than the target brightness, the exposure time is reduced or the exposure gain is reduced. Target brightness values ​​may vary in different functional areas of the highway. For example, in toll booth areas, due to the need for clear identification of vehicle license plates, the target brightness is typically set in the range of 110-130 (based on a brightness range of 0-255); in normal driving areas, the target brightness can be slightly lower, set in the range of 90-110, to reduce the burden on the monitoring system.

[0050] The adjustment module 4 also implements the automatic scene recognition and strategy adaptation mechanism based on the brightness change (ΔL). The system sets two thresholds ΔL_low=20 and ΔL_high=50 for scene type judgment:

[0051] (1) When ΔL < ΔL_low, it is determined to be a general scene (ambient light changes slowly), and the standard adjustment strategy is adopted: the upper limit of exposure parameter adjustment is 15% / frame of the current parameter; the target brightness value is 110-130 in the toll station area and 90-110 in the ordinary driving area; the smoothing coefficient α = 0.2, providing a smoother transition effect

[0052] (2) When ΔL_low≤ΔL≤ΔL_high, it is determined to be a transition scene and a gradual adjustment strategy is adopted: the upper limit of the exposure parameter adjustment is (15%+0.3×(ΔL-ΔL_low)) / frame of the current parameter; the target brightness is linearly adjusted according to ΔL; the smoothing coefficient α=0.2+0.1×(ΔL-ΔL_low) / (ΔL_high-ΔL_low)

[0053] (3) When ΔL>ΔL_high, it is determined to be a special scene (such as tunnel entrance / exit, strong light sudden change area), and a fast response strategy is adopted: the upper limit of exposure parameter adjustment is 30% / frame of the current parameter; the target brightness of the tunnel entrance area is 80-100, and the target brightness of the exit area is 120-140; the smoothing coefficient α=0.3 to improve the system's response speed to environmental changes;

[0054] The system also learns and classifies common road sections based on historical data. By analyzing brightness change patterns over 30 consecutive frames, the system can identify specific road sections such as toll booths, tunnels, and ramps, automatically loading preset optimized parameter configurations. For example, if the system detects a vehicle approaching a tunnel entrance, it will adjust parameters 3-5 seconds in advance to account for the impending sudden change in lighting.

[0055] Adjustment module 4 adopts a hierarchical adaptive gradient adjustment strategy to dynamically adjust the exposure parameter change limit according to the ambient light change rate:

[0056] In standard environment: maintain the adjustment limit of 15% to ensure smooth transition of video images;

[0057] Areas with rapidly changing lighting: When a brightness change of ΔL>20 is detected, the adjustment limit is automatically increased to a maximum of 30% / frame;

[0058] Pre-adaptation for special scenarios: For areas with predicted sudden changes in illumination, such as tunnel entrances, the system pre-calculates parameter change curves to enable fast and precise adjustment.

[0059] The optimized adaptive adjustment formula is:

[0060] Adjustment limit value = basic limit value (15%) + (ΔL-5) / 50 × 15% (when ΔL>5);

[0061] This adaptive adjustment mechanism significantly improves the system's response speed to lighting changes while maintaining visual comfort. It should be emphasized that controlling exposure during the acquisition phase is necessary for the following reasons:

[0062] Dynamic range preservation: Camera sensors have a limited dynamic range. Correct exposure control can avoid the irreversible loss of detail information that cannot be restored in post-processing.

[0063] Real-time requirements: Highway monitoring systems need to provide real-time video streams to operators and automatic identification systems. Optimization during the acquisition phase is more in line with real-time requirements than post-processing.

[0064] System resource optimization: Real-time post-processing of brightness of multiple HD video streams requires a lot of computing resources. It is more efficient to optimize exposure during the acquisition phase.

[0065] This optimized gradient adjustment strategy ensures that it meets the visual comfort requirements of the human eye without affecting video quality, while also being able to quickly adapt to changes in ambient lighting.

[0066] The LED dimming control module 5 is connected to the video capture module 1 and the coordinated adjustment module 3. It receives multiple frames of raw video images and provides exposure adjustment values ​​to the video capture module 1. The LED dimming control module 5 calculates the optimal LED brightness level based on the brightness information of the raw video images and uses PWM (pulse width modulation) technology to achieve precise brightness control. In highway LED display systems, brightness levels are typically divided into 10-16 levels, which are dynamically adjusted based on ambient light conditions. For example, in strong light environments (such as direct midday sunlight), the LED brightness is set to levels 14-16 (equivalent to 6000-8000 cd / m2) to ensure clear visibility. In normal lighting conditions (such as cloudy weather or early morning or evening hours), the setting is 8-12 (equivalent to 3000-5000 cd / m2). In low light environments (such as at night or in tunnels), the setting is 5-7 (equivalent to 1000-2500 cd / m2). These brightness level settings are based on extensive experimental data and research on driver visual perception, ensuring that the information displayed by the LEDs can be clearly seen by the driver under various lighting conditions, while avoiding glare and discomfort caused by excessive brightness.

[0067] The data acquisition module 6 is used to collect exposure data from the video acquisition module 1 and the LED drive current and LED current adjustment coefficient information of the LED controller. In a preferred embodiment of the present invention, the data acquisition module 6 adopts a multi-level sampling frequency design to distinguish different parameter types and application scenarios:

[0068] Real-time sampling of key parameters: Exposure time, exposure gain, and other key parameters that directly affect image quality are sampled at a frequency of 60-120Hz, synchronized with the video frame rate, ensuring that parameter changes in each frame are captured.

[0069] LED control parameter sampling: The sampling frequency of LED drive current and LED current regulation coefficient is increased to above 60Hz to ensure synchronization with exposure changes;

[0070] Environmental status parameters: Parameters that change relatively slowly, such as ambient light intensity, can maintain a sampling frequency of 10-20Hz to save system resources;

[0071] It is important to distinguish that the PWM modulation frequency of LEDs and the parameter sampling frequency are two different concepts:

[0072] LED PWM modulation frequency: Maintained in the range of 2-20kHz, which is the operating frequency of the LED driver circuit;

[0073] LED parameter control frequency: This refers to the frequency of adjusting the LED drive current and adjustment coefficient. This frequency must be increased to at least 60Hz, in sync with the video frame rate.

[0074] To solve the problem of synchronization between exposure changes and LED control, the system introduces an adaptive control mechanism based on event triggering:

[0075] When it detects that the exposure parameter changes exceed the preset threshold, the system automatically enters high-frequency sampling mode and adopts a predictive control algorithm to calculate and adjust the LED parameters in advance according to the exposure change trend, compensate for control delays, and establish a parameter change buffer to ensure that rapidly changing parameters are not lost due to sampling intervals. Through this multi-level sampling frequency design and event-triggered adaptive control mechanism, the system can ensure real-time acquisition and control of key parameters while maintaining resource efficiency, effectively solving the problem that LED current changes cannot keep up with changes in exposure values.

[0076] These optimization measures enable the system to respond more sensitively to changes in lighting in the complex and ever-changing environment of highways, ensuring the simultaneous optimization of video surveillance quality and LED display effects, further improving the overall performance and adaptability of the system.

[0077] This sampling frequency is chosen based on the typical time constants of highway ambient lighting changes, ensuring real-time performance without generating excessive redundant data. Data collected includes exposure time (typically ranging from 1 / 10,000 to 1 / 30 second), exposure gain (typically ranging from 0dB to 20dB), LED drive current (typically ranging from 100mA to 500mA), and LED current regulation coefficient (typically ranging from 0.5 to 1.5). These parameter ranges are based on the technical specifications of modern highway surveillance cameras and LED displays, covering a wide range of operating conditions, from extremely low light levels to strong light levels.

[0078] The control module 7 is connected to the video acquisition module 1, the LED dimming control module 5, and the data acquisition module 6. It controls the video acquisition module 1 for image acquisition and, through the data acquisition module 6, collects exposure data from the video acquisition module 1, as well as information about the LED drive current and LED current adjustment coefficient from the LED controller. As the system's central control unit, the control module 7 coordinates the operations of various functional modules to ensure stable system operation. The control module 7 utilizes a real-time operating system with a response time typically less than 10ms, meeting the real-time requirements of highway monitoring. In practical applications, vehicles on highways may travel at speeds of 120 km / h (approximately 33.3 m / s). A response time of 10ms means that the vehicle only moves approximately 33.3 cm, which is sufficient to ensure a rapid system response to emergencies and is crucial for ensuring highway safety.

[0079] The video switching module 8 is connected to the control module 7 and is used to output the exposure-adjusted video image under the control of the control module 7. The video switching module 8 supports multiple video inputs and outputs and can switch between the original video image and the exposure-adjusted video image according to control commands, or output both images simultaneously for comparative display. In the highway monitoring center, operators can evaluate the system's adjustment effects by switching between different view modes. This switching display function helps operators quickly assess the extent of video quality improvement, especially in situations where lighting conditions change dramatically (such as a sudden change from cloudy to sunny, or when vehicles enter or exit a tunnel). The response time for the switching operation is typically less than 50ms, ensuring a smooth switching process and avoiding image interruptions or delays at critical moments.

[0080] The collaborative adjustment module 3 is also used to receive and store LED driving current and LED current adjustment coefficient information, adjust the exposure corresponding to different frames of the exposure adjustment video image, calculate the brightness change of multiple frames of the exposure adjustment video image and the previous frame of the exposure adjustment video image, and the brightness ratio of the exposure adjustment video image to multiple frames of the original video image, and feed back the brightness change and the brightness ratio to the video acquisition module 1.

[0081] Brightness change ( ) reflects the dynamic characteristics of brightness between adjacent frames, and the calculation formula is as follows:

[0082] ,

[0083] in: The brightness change is in units of brightness value (range 0-255); The brightness value of the i-th region in the current frame, in units of brightness values ​​(range 0-255); The brightness value of the ith region in the previous frame, in units of brightness values ​​(range 0-255); is the total number of regions; Indicates finding the average value; Represents the sum operation from i=1 to i=N; Indicates the absolute value of the brightness difference between the current frame and the previous frame. In highway monitoring applications, brightness change is an important dynamic indicator. >20 (based on the brightness range of 0-255) indicates that the ambient light conditions change dramatically, such as the rapid movement of clouds causing the sun to flicker, or vehicles entering and exiting tunnels, and the system needs to respond quickly; the brightness change is small (usually <5) indicates that ambient light conditions are relatively stable and the system can maintain current parameter settings. These thresholds are based on extensive real-world monitoring data and have been validated in monitoring systems across multiple highways.

[0084] The brightness ratio (R) reflects the effect of exposure adjustment, and the calculation formula is as follows:

[0085] ,

[0086] in: is the brightness ratio, dimensionless; The brightness value of the ith area after exposure adjustment, in units of brightness values ​​(range 0-255); The brightness value of the i-th region of the original image before adjustment, in units of brightness values ​​(range 0-255); is the total number of regions; Indicates finding the average value;

[0087] Represents the sum operation from i=1 to i=N; R represents the ratio of adjusted brightness to original brightness. Ideally, the R value should be close to 1, indicating that the exposure adjustment is neither over- nor under-adjusted. In practical applications, exposure adjustment is considered ideal when the R value is within the range of 1 ± m (m is a manually set constant, typically 0.1-0.2). For example, empirical data from a highway monitoring system shows that when the R value is within the range of 0.8-1.2, the exposure-adjusted video image quality is optimal, clearly displaying vehicle details and road conditions. When the R value is below 0.8, the image is dark, making it easy to miss details. When the R value is above 1.2, the image is bright and prone to overexposure. Therefore, the m value of 0.2 is an empirical value based on practical application results.

[0088] The video capture module 1 adjusts the exposure based on the exposure adjustment value fed back by the LED dimming control module 5. Specifically, upon receiving the exposure adjustment instruction, the video capture module 1 adjusts the exposure parameters of each area according to the parameter settings in the instruction. The exposure parameter adjustment follows the principle of smooth transition to avoid fluctuations in image quality caused by sudden parameter changes. In highway monitoring systems, the adjustment process is typically completed within 3-5 frames to ensure the continuity of the visual effect. For example, in a scene where a vehicle enters a tunnel during the day, the video capture module needs to quickly increase the exposure parameters to adapt to the sudden decrease in light. However, if the adjustment is too abrupt, the video screen will cause flickering. By smoothly transitioning within 3-5 frames (approximately 0.1-0.3 seconds, based on a frame rate of 15fps), visual discomfort can be avoided while ensuring response speed.

[0089] In the second embodiment of the present invention, the system further includes a collaborative data packet module 9. As previously described, the collaborative adjustment module 3 sends the brightness change between the multiple exposure-adjusted video frames and the previous exposure-adjusted video frame to the collaborative data packet module 9. The collaborative data packet module 9 sends the brightness change to the control module 7, which stores the brightness change.

[0090] Collaborative Data Packet Module 9 uses a standardized data packet format, including fields such as timestamp, data type, data value, and checksum information. The total length of the data packet is typically 64-128 bytes to accommodate the transmission requirements of different types of data. In the network environment of a highway monitoring system, the selection of data packet size requires a balance between transmission efficiency and network load. Smaller data packets (such as 64 bytes) are suitable for scenarios with high real-time requirements, while larger data packets (such as 128 bytes) are suitable for scenarios that require more information to be transmitted. Collaborative Data Packet Module 9 supports data compression. When transmitting large amounts of data, the data compression rate can be set to 50% to 70% to reduce the network transmission burden. In large highway network monitoring systems, the amount of data generated by multiple monitoring points may reach tens of MB per second. Data compression can effectively reduce network bandwidth requirements and improve system scalability.

[0091] Control module 7 stores the received brightness change data in an internal cache, typically with a storage period of one hour and a storage interval of five seconds, meaning approximately 720 data points are stored per hour. This storage strategy, based on the typical time constants of highway lighting changes, captures important trends while minimizing redundant data. This data is used for system performance evaluation and parameter optimization, supporting system self-adjustment and long-term evolution. For example, by analyzing brightness change patterns at different times of the day, the system can predictively adjust parameters, such as optimizing configurations before sunrise and sunset to account for dramatic changes in lighting conditions.

[0092] In the third embodiment of the present invention, the collaborative adjustment module 3 transmits the exposure corresponding to different frames of the exposure-adjusted video image to the collaborative data packet module 9, the collaborative data packet module 9 transmits the exposure to the control module 7, and the control module 7 stores the exposure.

[0093] Exposure information includes exposure time, exposure gain, and white balance parameters for each area. These parameters are encapsulated in JSON format to facilitate data parsing and storage.

[0094] In highway surveillance systems, exposure parameters can vary significantly between different areas. For example, exposure times between tunnel entrances and exits can differ by a factor of 5-10 due to significant differences in lighting conditions. For example, in a mountain highway tunnel, on a sunny day at noon, the exposure time for the tunnel's exterior is typically 0.001-0.002 seconds, while the interior might require 0.01-0.02 seconds. By storing independent exposure parameters for each area, the system can more precisely control image quality, ensuring clear surveillance images under varying lighting conditions.

[0095] Control module 7 stores this exposure information in a system database over a 24-hour period to support the system's circadian learning and parameter optimization. This 24-hour period, chosen based on the cyclical nature of natural light, fully records parameter changes throughout the day, providing a data foundation for long-term system optimization. By analyzing historical exposure parameter data, the system identifies optimal parameter configuration patterns and automatically applies them under similar conditions, reducing the need for manual intervention.

[0096] In the fourth embodiment of the present invention, the system further includes a display 10. The coordinated data packet module 9 sends the brightness change to the display 10; the control module 7 sends the exposure corresponding to different frames of the exposure-adjusted video image to the display 10.

[0097] The display 10 usually adopts a high-resolution LCD display with a resolution of not less than 1920×1080, supports multi-window display, and can simultaneously display information such as the original video image, exposure-adjusted video image, brightness change curve, and exposure parameters of each area. In the highway monitoring center, operators need to monitor video images and system parameters of multiple areas at the same time. The high-resolution display can provide richer visual information to facilitate problem identification and decision-making. The display interface adopts ergonomic design to ensure that operators can intuitively monitor the system operation status and adjustment effect. For example, color coding is used to display brightness changes, and green indicates a stable state ( <5), yellow indicates moderate change (5≤ ≤20), red indicates drastic changes ( >20), so that operators can quickly identify abnormal conditions.

[0098] Brightness change data is displayed as a line graph, with time plotted on the horizontal axis and brightness change values ​​plotted on the vertical axis. The display time range is adjustable from 1 minute to 1 hour. In highway monitoring applications, different time scales are suitable for different analysis needs. For example, a 1-minute time scale is suitable for observing short-term brightness fluctuations, such as vehicle traffic or cloud cover blocking sunlight, while a 1-hour time scale is suitable for observing long-term trends, such as brightness changes during sunrise and sunset. Exposure parameters are displayed in a table or dashboard format, allowing operators to monitor exposure settings for each area in real time. Critical sections (such as accident-prone areas or areas affected by severe weather) can be marked with special markers to alert operators to their particular attention.

[0099] In the fifth embodiment of the present invention, the system further comprises a video display module 11. The control module 7 inputs the exposure-adjusted video image output by the video switching module 8 into the video display module 11 for display.

[0100] The video display module 11 utilizes a professional-grade monitor that supports multiple video input formats, including HDMI, SDI, and IP streaming. In highway monitoring centers, different video sources may utilize different transmission protocols. Multi-format support ensures system compatibility and scalability. The display screen typically measures 21-27 inches, with a resolution of at least 1920×1080 and 8-bit or higher color accuracy to ensure accurate detail and color reproduction in the video image. High color accuracy is crucial for identifying specific highway conditions, such as waterlogging, oil stains, or ice. These conditions often manifest as subtle color and texture variations, requiring high-quality display devices for accurate representation. The video display module 11 supports multi-screen split-screen display, allowing operators to simultaneously display video images from multiple areas, facilitating comparative analysis. For example, in severe weather conditions, operators can simultaneously monitor conditions on different sections of the highway, promptly identifying safety hazards such as waterlogging, ice, or low visibility, and supporting traffic management decisions.

[0101] In the sixth embodiment of the present invention, the system further comprises a video storage module. The control module 7 controls the video display module 11 to display multiple frames of original video images, and controls the video storage module to store multiple frames of original video images.

[0102] The video storage module uses high-speed storage media, such as solid-state drives (SSDs) or RAID arrays, with a storage capacity typically ranging from 4TB to 8TB, capable of supporting 7-15 days of continuous high-definition video storage. In highway surveillance systems, the storage duration must be considered in light of accident investigation and data analysis needs. For example, statistics from a provincial highway network show that most accident investigations require 3-7 days of surveillance footage, so a storage capacity of 7-15 days is sufficient for most scenarios. Video compression utilizes H.265 encoding, which reduces storage requirements by 40% to 50% while maintaining video quality. Compared to traditional H.264 encoding, H.265 saves approximately 40% of storage space while maintaining the same image quality, offering significant cost advantages for large-scale surveillance systems. The video storage module supports circular storage, automatically overwriting the oldest video file when storage space is insufficient. It also supports marking and permanently saving important video clips to ensure that critical data is not overwritten. For example, in the event of a traffic accident or abnormal weather conditions, the system can automatically mark relevant video clips, or operators can manually mark them. These marked clips will be permanently stored or the retention period will be extended until the relevant investigation is completed.

[0103] In the seventh embodiment of the present invention, the collaborative adjustment module 3 calculates the LED brightness through the LED driving current and the LED current adjustment coefficient; the video acquisition module 1 captures multiple frames of original video images, calculates the average brightness of the original video images corresponding to different frames, and the video acquisition module 1 adjusts the brightness corresponding to different frames of each frame of the original video image to obtain an exposure-adjusted video image.

[0104] The LED brightness is calculated using the following formula:

[0105] ,

[0106] in: is the LED brightness value, the unit is cd / ㎡ (candela / square meter); is the LED driving current, in mA (milliampere); is the LED current regulation coefficient, dimensionless, ranging from 0.5 to 1.5; The proportionality coefficient is expressed in (cd / m²) / mA (candela per square meter per milliampere), and is usually set at 0.05-0.1. This is related to the performance parameters of the LED module and can be determined experimentally. For example, in a certain highway variable information sign system, the outdoor high-brightness LED module used has a brightness of approximately 1750-3500 cd / m2 when the drive current is 350 mA and the adjustment coefficient is 1.0. This brightness level ensures that the information is still clearly visible under sunny conditions, but under strong direct sunlight, the brightness may need to be further increased to enhance contrast. It is related to the performance parameters of the LED module and can be determined through experiments.

[0107] The video acquisition module 1 adjusts the brightness of the original video image based on the following principles: first, the average brightness of each area is calculated, and then the exposure parameters are adjusted according to the target brightness value. The formula for calculating the average brightness is as follows:

[0108] ,

[0109] in: is the average brightness of the area, the unit is brightness value (range 0-255); and are the height and width of the region image, in pixels; is the brightness value of the pixel at coordinate (i, j), in units of brightness value (range 0-255); Indicates finding the average value; Represents a two-dimensional summation, that is, the sum of the brightness values ​​of all pixels in the area. In highway monitoring systems, different target brightness values ​​can be set for different application scenarios. For example, in license plate recognition systems, to ensure clear and legible characters, the target brightness value is typically set to 120-150 (based on a brightness range of 0-255) during the day and 80-100 at night. These values ​​are determined based on a large amount of test data and can ensure image quality while avoiding overexposure or underexposure. In special weather conditions, such as rain, snow, fog, or haze, the target brightness value may need to be increased by 5% to 10% to compensate for the reduced contrast caused by atmospheric scattering.

[0110] In the eighth embodiment of the present invention, the video acquisition module 1 uses the original video image to adjust the brightness corresponding to different pictures; the adjusted image is the exposure-adjusted video image; when the brightness ratio between the exposure-adjusted video image and the multiple frames of the original video image is 1±m, m is a manually set constant; when the brightness ratio is less than 1, the video acquisition module 1 uses the original video image to adjust the brightness corresponding to different pictures; when the brightness ratio is greater than 1, the collaborative adjustment module 3 adjusts the exposure corresponding to different pictures of the exposure-adjusted video image.

[0111] The manually set constant m is typically set to a value of 0.1-0.2, representing the system's allowable brightness deviation range. In practical applications of highway monitoring systems, this range is determined based on visual perception experiments and system performance testing. A value of m that is too small (e.g., <0.1) can cause the system to be overly sensitive, requiring frequent parameter adjustments and increasing the system burden. A value that is too large (e.g., >0.2) can reduce adjustment accuracy and affect image quality. When the brightness ratio is within the range of 0.8-1.2, the exposure adjustment is considered effective and the system maintains the current parameter settings. When the brightness ratio is less than 0.8, it indicates underexposure, and video acquisition module 1 increases the exposure parameters. When the brightness ratio is greater than 1.2, it indicates overexposure, and collaborative adjustment module 3 decreases the exposure parameters.

[0112] Exposure parameter adjustment uses the proportional-integral (PI) control algorithm, and the adjustment formula is as follows:

[0113] ,

[0114] in: The adjusted exposure parameter, the unit is related to the specific parameter (such as exposure time in seconds, exposure gain in dB); The current exposure parameter, the unit is same; is the brightness ratio, dimensionless; Indicates the deviation between the brightness ratio and the target value 1; is the proportionality coefficient, dimensionless, usually taking a value of 0.3-0.5; is the integration coefficient, the unit is 1 / second, usually the value is 0.05-0.1; Indicates the integral of the brightness ratio deviation, that is, the cumulative deviation, in seconds. The integral term is used to eliminate static errors and improve the steady-state accuracy of the system. In highway monitoring systems, the parameter selection of the PI control algorithm needs to balance the response speed and system stability. A value such as 0.5 provides faster response and is suitable for scenes with drastic changes in lighting conditions, such as tunnel entrances and exits; smaller A higher value (such as 0.3) provides a smoother adjustment process and is suitable for scenes with relatively stable lighting conditions, such as open roads. The choice of value affects the system's ability to correct continuous deviations. A value such as 0.1 eliminates static errors faster but may cause system overshoot; a smaller value Higher values ​​(such as 0.05) provide a smoother correction process, but the correction speed is slower.

[0115] In the ninth embodiment of the present invention, the collaborative adjustment module 3 includes a parameter detection module 31, a signal transmission module 32, an edge processing module 33 and a model training module 34; the parameter detection module 31 is used to read the collaborative parameter value list, and compare the parameters and parameter values ​​in the collaborative parameter value list. When abnormal parameters and parameter values ​​are detected, the corresponding abnormal information is sent to the signal transmission module 32; the edge processing module 33 receives the abnormal information of the signal transmission module 32, and generates control information after processing the abnormal information and sends it to the control module 7; the model training module 34 is used to train the collaborative control model according to the brightness change and brightness ratio data, so that the collaborative control model can make real-time predictions on the collaborative parameter value list.

[0116] The parameter detection module 31 monitors core system parameters, including brightness, exposure parameters, and LED parameters. Anomaly detection uses statistical methods to set normal parameter ranges and change rate thresholds. In highway monitoring systems, these thresholds are based on extensive historical data analysis and professional experience. For example, the normal brightness range is 30-220 (based on a 0-255 brightness range), with a brightness change rate threshold of 30 / second; the normal exposure time range is 1 / 10000 to 1 / 30 second, with a change rate threshold of 50% / second; and the normal LED drive current range is 100mA to 500mA, with a change rate threshold of 20% / second. The brightness range is set to take into account video quality requirements from darkness to bright sunlight; the brightness change rate threshold is based on typical ambient light fluctuations, such as cloud cover blocking sunlight or vehicle headlights; the exposure parameter range and change rate take into account the camera's technical specifications and stability requirements; and the LED parameter settings are based on the operating characteristics and lifespan of the LED module. When a parameter exceeds the normal range or the change rate exceeds the threshold, the system identifies an anomaly and generates an anomaly message.

[0117] The signal transmission module 32 uses a priority queue to manage exception information, prioritizing high-priority exceptions (such as safety-related parameter anomalies). In highway monitoring systems, different types of anomalies can have varying degrees of impact on system operation and traffic safety. For example, a sudden decrease in LED display brightness, potentially preventing the driver from timely identifying the information, is a high-priority anomaly; whereas a slight fluctuation in video image brightness is a low-priority anomaly. Priorities are set based on the potential impact of an anomaly on traffic safety, ensuring that the system prioritizes the most critical issues. Signal transmission utilizes an encrypted transmission protocol to ensure secure and reliable data transmission and prevent malicious tampering or interference.

[0118] The edge processing module 33 classifies and processes abnormal information: for minor abnormalities, a parameter fine-tuning strategy is adopted; for moderate abnormalities, a parameter reset is performed; for severe abnormalities, a safety mode is activated, and the system is switched to a preset safety parameter configuration. In highway monitoring systems, the design of the abnormality handling strategy must take into account both system stability and traffic safety. For example, when abnormal fluctuations in LED brightness are detected, minor abnormalities (fluctuations <10%) can be resolved by fine-tuning the drive current; moderate abnormalities (fluctuations 10% to 30%) may require resetting the LED control parameters; severe abnormalities (fluctuations >30% or persistent abnormalities) may require activating a backup display mode or alerting the management center. The response time of edge processing is typically less than 100ms to ensure that the system can quickly respond to abnormal situations. Especially in high-speed, high-risk environments such as highways, rapid response is crucial to preventing accidents.

[0119] The model training module 34 uses an incremental learning algorithm to periodically analyze historical data (typically every 24 hours) and update the collaborative control model parameters. The advantage of incremental learning is that it continuously absorbs new data while retaining the experience of the existing model. This makes it suitable for applications such as highways, where environmental conditions can fluctuate. For example, the system can gradually improve its adaptability to environmental changes by learning optimal parameter configurations for different seasons and weather conditions. Model training is performed during periods of low system load, typically late at night when traffic is light, to ensure that the system's real-time control capabilities are not affected. After the trained model parameters have passed model verification, they gradually replace the existing model parameters to ensure a smooth and controllable model update process. For example, a gradual weight change strategy can be implemented, with the new model weight starting at 10% and increasing by 10% daily until the model update is complete within 10 days. This incremental update strategy effectively avoids the risks associated with sudden changes in the model.

[0120] In the tenth embodiment of the present invention, the system also includes a safety detection module; the safety detection module is used to analyze various parameter values ​​and various images, and perform safety detection based on the brightness change and the brightness ratio. When a safety abnormality is detected, the safety detection module generates a prompt message and a stop message and sends it to the control module 7; the safety detection module also includes a video recognition unit, which is used to identify and analyze various images, and convert the analysis results into parameters.

[0121] The safety detection module monitors the operational safety of the system, including parameter safety and functional safety. Parameter safety testing focuses on whether parameters are within a safe range, such as whether the LED brightness exceeds the safety limit (usually 5000cd / ㎡) and whether exposure parameters are within a reasonable range. In a highway environment, excessive LED brightness may cause dazzle to the driver, affecting safe driving, especially at night or in dimly lit conditions; while excessively low brightness may prevent information from being recognized in a timely manner. The safety limit of 5000cd / ㎡ is based on human eye comfort research and traffic safety standards. It can ensure information visibility while avoiding visual discomfort to the driver. Functional safety testing focuses on whether system functions are operating normally, such as whether video acquisition is continuous and whether brightness adjustment is effective. For example, if a video signal interruption or severe delay is detected, it may indicate a fault in the monitoring system, which requires timely processing to avoid monitoring blind spots.

[0122] When a safety anomaly is detected, the safety detection module generates different types of prompt information and control instructions based on the level of the anomaly: for minor anomalies, a warning message is generated, but it does not affect the operation of the system; for moderate anomalies, a warning message is generated and a parameter reset is triggered; for serious anomalies, an alarm message is generated and a stop message is sent, and the system switches to a safe mode or an emergency shutdown. In highway monitoring systems, the classification of anomaly levels is usually based on the potential impact on traffic safety. For example, occasional flashing of the LED display is a minor anomaly, and the system can continue to operate but a warning must be recorded; incorrect or blurred content on the LED display is a moderate anomaly, and the display parameters need to be reset and the effect verified; complete failure of the LED display or display of seriously misleading information is a serious anomaly, and it is necessary to immediately switch to the backup display mode or turn off the display, and send an emergency alarm to the management center.

[0123] The video recognition unit uses computer vision technology to identify specific scenes and events from video images, such as changes in traffic flow, weather conditions, and lighting conditions. In highway monitoring applications, video recognition technology can significantly enhance the system's intelligence. For example, by identifying waterlogging or ice on the road, the system can automatically adjust the LED display content to remind the driver to slow down. By identifying fog and haze, the system can adjust camera parameters to enhance image contrast. By identifying changes in traffic density, the system can predict potential congestion and adjust information release strategies in advance. Recognition results are converted into numerical parameters for intelligent control decisions. For example, when rain or snow is detected, LED brightness and video exposure parameters are automatically adjusted to meet the monitoring and information display requirements in adverse weather conditions. Specifically, on rainy days, LED brightness can be increased by 10% to 15% to compensate for the loss of brightness caused by rain scattering. On snowy days, video image contrast can be increased by 15% to 20% to enhance the distinction between snow and vehicles on the road.

[0124] The detailed description of the above embodiments will clearly enable those skilled in the art to understand the technical solutions and implementations of the present invention. By establishing a coordinated adjustment mechanism for video capture and LED display, the present invention achieves intelligent and refined control of highway electromechanical equipment, improving equipment operation while optimizing energy efficiency, providing important support for the intelligent construction of highways.

[0125] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A multi-parameter coordinated adjustment system for electromechanical equipment on highways, characterized by: include: Video acquisition module, used to acquire multiple frames of original video images; A video splitter, connected to the video acquisition module, is used to split the multiple frames of original video images into multiple pictures, where different pictures correspond to different roadside LED modules; a collaborative adjustment module, connected to the video splitter, configured to calculate brightness information corresponding to each of the plurality of images, perform fusion processing on the brightness information, and generate an exposure adjustment instruction; an adjustment module connected to the collaborative adjustment module, configured to adjust the exposure corresponding to different frames of each frame of the multiple frames of original video images according to the exposure adjustment instruction to obtain an exposure-adjusted video image; an LED dimming control module, connected to the video acquisition module and the collaborative adjustment module, configured to receive the multiple frames of original video images and feed back exposure adjustment values ​​to the video acquisition module; a data acquisition module for acquiring exposure data from the video acquisition module and information about the LED drive current and LED current adjustment coefficient of the LED controller; a control module connected to the video acquisition module, the LED dimming control module, and the data acquisition module, respectively, for controlling the video acquisition module to perform image acquisition and acquiring exposure data from the video acquisition module and information about the LED drive current and LED current adjustment coefficient of the LED controller through the data acquisition module; a video switching module connected to the control module for outputting the exposure-adjusted video image under the control of the control module; wherein the collaborative adjustment module is further configured to receive and store the LED drive current and LED current adjustment coefficient information, and after adjusting the exposure corresponding to different frames of the exposure-adjusted video image, calculate the brightness change between multiple frames of the exposure-adjusted video image and the previous frame of the exposure-adjusted video image, and the brightness ratio between the exposure-adjusted video image and the multiple frames of the original video image, and feed the brightness change and the brightness ratio back to the video acquisition module; the video acquisition module adjusts the exposure according to the exposure adjustment value fed back by the LED dimming control module; The collaborative adjustment module includes a parameter detection module, a signal transmission module, an edge processing module and a model training module; the parameter detection module is used to read the collaborative parameter value list and compare the parameters and parameter values ​​in the collaborative parameter value list. When abnormal parameters and parameter values ​​are detected, the corresponding abnormal information is sent to the signal transmission module; the edge processing module receives the abnormal information from the signal transmission module, processes the abnormal information, generates control information, and sends it to the control module; the model training module is used to train the collaborative control model based on the brightness change and brightness ratio data, so that the collaborative control model can make real-time predictions on the collaborative parameter value list; The system further includes a safety detection module; the safety detection module is used to analyze various parameter values ​​and various images, and perform safety detection based on the brightness change and the brightness ratio. When a safety anomaly is detected, the safety detection module generates a prompt message and a stop message and sends them to the control module; the safety detection module also includes a video recognition unit, which is used to identify and analyze various images and convert the analysis results into parameters; The adjustment module implements an automatic scene recognition and policy adaptation mechanism based on the brightness change ΔL. The system sets two thresholds ΔL_low = 20 and ΔL_high = 50 for scene type judgment: (1) When ΔL < ΔL_low, it is determined to be a general scene, and the ambient light changes slowly. The standard adjustment strategy is adopted: the upper limit of the exposure parameter adjustment is 15% / frame of the current parameter; the target brightness value is 110-130 in the toll station area and 90-110 in the ordinary driving area; the smoothing coefficient α = 0.2, providing a smoother transition effect (2) When ΔL_low≤ΔL≤ΔL_high, it is determined to be a transition scene and a gradual adjustment strategy is adopted: the upper limit of the exposure parameter adjustment is (15%+0.3×(ΔL-ΔL_low)) / frame of the current parameter; the target brightness is linearly adjusted according to ΔL; the smoothing coefficient α=0.2+0.1×(ΔL-ΔL_low) / (ΔL_high-ΔL_low) (3) When ΔL>ΔL_high, it is determined to be a special scene, which is a tunnel entrance or tunnel exit or a strong light sudden change area. A fast response strategy is adopted: the upper limit of exposure parameter adjustment is 30% / frame of the current parameter; the target brightness of the tunnel entrance area is 80-100, and the target brightness of the exit area is 120-140; the smoothing coefficient α=0.3, which improves the system's response speed to environmental changes.

2. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 1 is characterized in that: It also includes a collaborative data packet module; the collaborative adjustment module sends the brightness change of multiple frames of exposure-adjusted video images and the previous frame of exposure-adjusted video image to the collaborative data packet module, the collaborative data packet module sends the brightness change to the control module, and the control module stores the brightness change.

3. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 2 is characterized in that: The collaborative adjustment module transmits the exposures corresponding to different frames of the exposure-adjusted video image to the collaborative data packet module, the collaborative data packet module transmits the exposures to the control module, and the control module stores the exposures.

4. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 2 is characterized in that: It also includes a display; the collaborative data packet module sends the brightness change to the display; and the control module sends the exposure corresponding to different frames of the exposure-adjusted video image to the display.

5. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 1 is characterized in that: It also includes a video display module; the control module inputs the exposure-adjusted video image output by the video switching module into the video display module for display.

6. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 5 is characterized in that: It also includes a video storage module; the control module controls the video display module to display the multiple frames of original video images, and controls the video storage module to store the multiple frames of original video images.

7. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 1 is characterized in that: The collaborative adjustment module calculates the LED brightness through the LED driving current and the LED current adjustment coefficient; the video acquisition module collects the multiple frames of original video images, calculates the brightness average of the original video images corresponding to different frames, and the video acquisition module adjusts the brightness corresponding to different frames of each frame of the original video image to obtain the exposure-adjusted video image.

8. The highway electromechanical equipment multi-parameter coordinated adjustment system according to claim 1 is characterized in that: The video acquisition module uses the original video image to adjust the brightness corresponding to different pictures; the adjusted image is the exposure-adjusted video image; when the brightness ratio between the exposure-adjusted video image and the multiple frames of original video images is 1±m, m is a manually set constant; when the brightness ratio is less than 1, the video acquisition module uses the original video image to adjust the brightness corresponding to different pictures; when the brightness ratio is greater than 1, the collaborative adjustment module adjusts the exposure corresponding to different pictures of the exposure-adjusted video image.

Images