Multi-parameter coordinated regulation system for electromechanical equipment of expressway

Abstract

The invention relates to the field of expressway electromechanical equipment, in particular to an expressway electromechanical equipment multi-parameter cooperative adjustment system, which realizes fine monitoring and display control of different road sections of an expressway through cooperative work of a video acquisition module, a video divider, a cooperative adjustment module, an adjustment module and an LED dimming control module. According to the system, the adaptive capacity to a complex environment is improved by utilizing a region segmentation type dynamic sensing technology, the data acquisition module is responsible for collecting exposure data of the video acquisition module and related information of the LED controller, the control module coordinates operation of all the modules, the video switching module outputs an exposure adjustment video image according to an instruction of the control module, and the control module controls the control module to operate. The cooperative adjustment module processes the brightness information, calculates the brightness variation and the specific value of the exposure adjustment video image, and feeds the information back to the video acquisition module to adjust the exposure, so that the intelligence and the response speed of the system are remarkably improved, and the operation safety and efficiency of the expressway are enhanced.

Description

Technical Field

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

[0002] Highway electromechanical equipment, as an important part of modern intelligent transportation systems, includes various devices such as video surveillance equipment, LED displays, signal lights, etc., which play an important role in ensuring traffic safety and improving traffic efficiency. With the continuous development of intelligent transportation systems, the types and quantities of highway electromechanical equipment have gradually increased, and the collaborative work among various devices has become increasingly important.

[0003] Existing highway electromechanical equipment usually works independently and lacks an effective collaborative mechanism. For example, camera equipment and LED display equipment are adjusted according to their own parameters respectively, without considering the mutual influence between devices. Under complex environmental conditions, such as scenes with large changes in light intensity, the camera equipment may have problems of overexposure or underexposure, while the LED display equipment may cause unclear information display due to improper brightness settings, affecting traffic safety and traffic efficiency.

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

[0005] Therefore, there is an urgent need for a system that can achieve multi-parameter collaborative adjustment of highway electromechanical equipment to improve the operation effect of equipment and energy utilization efficiency. Summary of the Invention

[0006] The object of the present invention is to provide a multi-parameter collaborative adjustment system for highway electromechanical equipment, which realizes refined perception and control of different road section environments by constructing a collaborative adjustment mechanism for video acquisition and LED display, and solves the problems of poor effect and high energy consumption caused by the independent operation of equipment in traditional systems.

[0007] The present invention proposes a multi-parameter collaborative adjustment system for highway electromechanical equipment, including:

[0008] A video acquisition module, configured to acquire multiple frames of original video images;

[0009] A video splitter, connected to the video acquisition module, configured 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, for calculating the brightness information corresponding to each of the multiple images, performing fusion processing on the brightness information, and generating an exposure adjustment instruction;

[0011] An adjustment module, connected to the collaborative adjustment module, for adjusting the exposure corresponding to different images 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, for receiving the multiple frames of original video images and feeding back an exposure adjustment value to the video acquisition module;

[0013] A data acquisition module for acquiring the exposure data of the video acquisition module and the LED drive current and LED current adjustment coefficient information of the LED controller;

[0014] A control module, respectively connected to the video acquisition module, the LED dimming control module, and the data acquisition module, for controlling the video acquisition module to perform image acquisition, and acquiring the exposure data of the video acquisition module and the LED drive current and LED current adjustment coefficient information of the LED controller through the data acquisition module;

[0015] A video switching module, connected to the control module, for outputting the exposure-adjusted video image under the control of the control module;

[0016] Wherein, the collaborative adjustment module is further configured to receive and store the LED drive current and the LED current adjustment coefficient information, after adjusting the exposure corresponding to different images of the exposure-adjusted video image, calculate the brightness change amount between the multiple frames of exposure-adjusted video images of the exposure-adjusted video image and the previous frame of exposure-adjusted video image and the brightness ratio between the exposure-adjusted video image and the multiple frames of original video images, and feed back the brightness change amount and the brightness ratio 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.

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

[0018] Preferably, the collaborative adjustment module transfers the exposure corresponding to different frames of the exposure adjustment video image to the collaborative data packet module, the collaborative data packet module transfers 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 amount to the display; the control module sends the exposure corresponding to different frames of the exposure adjustment video image to the display.

[0020] Preferably, it further includes a video display module; the control module inputs the exposure adjustment 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 drive current and the LED current adjustment coefficient; the video acquisition module acquires the multiple frames of original video images, calculates the average brightness value 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 adjustment video image.

[0023] Preferably, the video acquisition module adjusts the brightness corresponding to different frames by using the original video image; the adjusted image is the exposure adjustment video image; when the brightness ratio of the exposure adjustment video image to the multiple frames of original video images is 1±m, where m is a manually set constant; when the brightness ratio is less than 1, the video acquisition module adjusts the brightness corresponding to different frames by using the original video image; when the brightness ratio is greater than 1, the collaborative adjustment module adjusts the exposure amount corresponding to different frames of the exposure adjustment 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 each parameter and each parameter value in the collaborative parameter value list. When an abnormal parameter and parameter value 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, and generates control information to be sent to the control module; the model training module is used to train the collaborative control model according to the brightness change amount and the brightness ratio data, so that the collaborative control model makes a real-time prediction 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 according to the brightness change amount 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 further includes a video recognition unit, and the video recognition unit is used to recognize and analyze various images and convert the analysis results into parameters.

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

[0027] 1. Through the region segmentation type dynamic perception technology, the refined monitoring and display control of different sections of the highway are realized, and the adaptability of the system to complex environments is improved;

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

[0029] 3. Through the design of the adaptive exposure adjustment loop, the dynamic adaptive adjustment of the exposure parameters of the camera system is realized, and the quality of video images is improved;

[0030] 4. Through the two-way cooperation mechanism between the LED and the camera system, a composite system in which the LED and the camera system adjust and influence each other is constructed, and the problem of mutual interference between LED display and video monitoring in the traditional system is solved;

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

[0032] Figure 1 It is a schematic diagram of the overall structure of the multi-parameter cooperative adjustment system of the highway electromechanical equipment of the present invention;

[0033] Figure 2 It is a data interaction flow chart between the video acquisition module and the cooperative adjustment module in the present invention;

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

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

[0036] Figure 5 It is an internal structure diagram of the cooperative adjustment module in the present invention;

[0037] Figure 6This is the workflow diagram of the security detection module in the present invention. Detailed implementation manners

[0038] Please refer to the appendix Figure 1-6 Hereinafter, the detailed implementation manners of the present invention will be further described in detail with reference to the accompanying drawings.

[0039] As Figure 1 shown, the multi-parameter collaborative adjustment system for highway electromechanical equipment provided by the present invention includes a video acquisition module 1, a video splitter 2, a collaborative 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 collaborative data packet module 9, a display 10, a video display module 11, a video storage module, and a security detection module.

[0040] The video acquisition module 1 is used to acquire multiple frames of original video images. Preferably, the video acquisition module 1 adopts a high-dynamic-range industrial camera, supports regional exposure control, and the acquisition frequency can be set to 15 - 30 fps (frames per second) to meet the requirements of real-time monitoring. In the highway monitoring scenario, a higher frame rate can better capture the process of vehicles moving at high speed. Especially in sections with heavy traffic, a frame rate above 15 fps can effectively avoid image blurring and information loss. The resolution of the original video images acquired by the video acquisition module 1 is usually 1920×1080 or higher to ensure the clarity and detailed information of the images, facilitating the subsequent recognition 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 pictures, where different pictures correspond to different roadside LED modules. In an embodiment of the present invention, the video splitter 2 can split a complete highway monitoring picture into 3 - 8 sub-regions, such as the entrance area, the main line area, the exit area, etc., and each area corresponds to an LED display module on the roadside. Taking a certain highway toll station as an example, the monitoring picture 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 as to adjust the display content and brightness according to the actual situation of each area. The splitting method can be flexibly set according to the actual monitoring requirements, and can be either evenly split or unevenly split according to importance. The video splitter 2 adopts real-time image processing technology to ensure that the splitting process does not affect the real-time nature 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 pictures, perform fusion processing on the brightness information, and generate an exposure adjustment instruction. The collaborative adjustment module 3 adopts a multi-dimensional brightness information fusion algorithm, and the core of this algorithm is to perform weighted fusion on the brightness characteristics of different regions. Specifically, let the average brightness value of the i-th region be , the regional weight is , then the fused brightness index is calculated as follows: , where: is the fused brightness index, with the unit of brightness value (range 0 - 255); is the average brightness value of the i-th region, with the unit of brightness value (range 0 - 255); is the weight coefficient of the i-th region, dimensionless, with the value range of 0 - 1; is the total number of regions, that is, the number of regions after video segmentation; represents the summation operation from i = 1 to i = N. The weight coefficient can be dynamically adjusted according to factors such as regional importance and traffic flow. For example, in the ramp merging area of a highway, due to frequent vehicle lane changes and high safety risks, the weight of this area can be set to 0.3 - 0.5, while the weight of an ordinary straight section can be set to 0.1 - 0.2. In addition, in areas with drastic changes in lighting conditions such as tunnel entrances and exits, the weight coefficient should also be increased accordingly to ensure the monitoring quality of these key areas.

[0043] In addition, the collaborative adjustment module 3 also considers the time series characteristics and smooths the brightness data of continuous M frames (usually M = 5 - 10) to filter out instantaneous interference. In the highway scenario, fast-moving vehicles may cause instantaneous fluctuations in brightness. Especially when large vehicles pass through the monitoring area, the reflective surface may cause sudden changes in brightness. The time series smoothing process adopts a weighted moving average algorithm:

[0044] ,

[0045] where: is the smoothed brightness value at the current time t, with the unit of brightness value (range 0 - 255); is the fused brightness value at the current time t, with the unit of brightness value (range 0 - 255); is the smoothed brightness value at the previous time t - 1, with the unit of brightness value (range 0 - 255); is the smoothing coefficient, dimensionless, with the value range of 0 - 1; represents the current time; represents the previous time. The smoothing coefficient usually takes a value of 0.2 - 0.3. A smaller value can provide a smoother output but will increase the system response delay. In practical applications, The selection of the value needs to balance the smoothing effect and the response speed. For example, during the day-night transition (such as dusk or dawn), when the lighting conditions change relatively slowly, the value can be set to 0.2 to obtain a smoother brightness transition; while in scenarios such as sudden changes in traffic flow or at the entrance and exit of a tunnel, the value can be set to 0.3 to improve the system's response speed to environmental changes.

[0046] The adjustment module 4 is connected to the collaborative adjustment module 3 and is used to adjust the exposure corresponding to different picture areas of each frame of the multi-frame original video image according to the exposure adjustment instruction, and obtain the exposure-adjusted video image. The adjustment module 4 receives the exposure adjustment instruction generated by the collaborative adjustment module 3 and calculates the optimal exposure parameters for each segmented area. The exposure parameter adjustment follows the following rules: when the area brightness is lower than the target brightness, increase the exposure time or increase the exposure gain; when the area brightness is higher than the target brightness, reduce the exposure time or reduce the exposure gain. In different functional areas of the highway, the target brightness values may vary. For example, in the toll station area, since it is necessary to clearly identify the vehicle license plate, the target brightness is usually set in the range of 110 - 130 (based on the brightness range of 0 - 255); while in the normal driving area, the target brightness can be slightly lower, set in the range of 90 - 110, to reduce the burden on the monitoring system.

[0047] The adjustment module 4 also implements a scene automatic recognition and strategy adaptation mechanism based on the brightness change amount (ΔL). The system sets two thresholds ΔL_low = 20 and ΔL_high = 50 for scene type judgment:

[0048] (1) When ΔL < ΔL_low, it is determined as a general scene (slow environmental light change), and the standard adjustment strategy is adopted: the upper limit of exposure parameter adjustment is 15% / frame of the current parameter; target brightness value: 110 - 130 in the toll station area, 90 - 110 in the normal driving area; smoothing coefficient α = 0.2, providing a smoother transition effect

[0049] (2) When ΔL_low ≤ ΔL ≤ ΔL_high, it is determined as a transition scene, and the progressive adjustment strategy is adopted: the upper limit of exposure parameter adjustment is (15% + 0.3×(ΔL - ΔL_low)) / frame of the current parameter; the target brightness is linearly adjusted according to ΔL; smoothing coefficient α = 0.2 + 0.1×(ΔL - ΔL_low) / (ΔL_high - ΔL_low)

[0050] (3)When ΔL > ΔL_high, it is determined as a special scenario (such as tunnel entrance / exit, strong light mutation 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 in the tunnel entrance area is 80 - 100, and the target brightness in the exit area is 120 - 140; the smoothing coefficient α = 0.3 to improve the system's response speed to environmental changes;

[0051] In addition, the system also conducts feature learning and classification on common road sections based on historical data. Through the analysis of the brightness change pattern of 30 consecutive frames, the system can identify specific road sections such as toll stations, tunnels, and ramps, and automatically load the preset optimized parameter configuration. For example, when the system detects that the vehicle is approaching the tunnel entrance, it will adjust the parameters 3 - 5 seconds in advance to cope with the upcoming drastic change in light.

[0052] The adjustment module 4 adopts a hierarchical adaptive gradient adjustment strategy, and dynamically adjusts the exposure parameter change limit according to the environmental light change rate:

[0053] Under standard environment: maintain a 15% adjustment limit to ensure smooth transition of the video image;

[0054] In the area of rapid light change: when the detected brightness change amount ΔL > 20, automatically increase the adjustment limit to a maximum of 30% / frame;

[0055] Pre - adaptation for special scenarios: For known light mutation areas such as tunnel entrances, the system pre - calculates the parameter change curve to achieve fast and accurate adjustment;

[0056] The optimized adaptive adjustment formula is:

[0057] Adjustment limit value = basic limit value (15%) + (ΔL - 5) / 50 × 15% (when ΔL > 5);

[0058] This adaptive adjustment mechanism significantly improves the system's response speed to light changes while maintaining visual comfort. It should be emphasized that controlling exposure during the acquisition stage is necessary for the following reasons:

[0059] Dynamic range protection: The camera sensor has a limited dynamic range. Correct exposure control can avoid irreversible loss of detail information, which cannot be restored in post - processing.

[0060] Requirement for real - time performance: The highway monitoring system needs to provide real - time video streams to operators and automatic recognition systems. Optimizing during the acquisition stage better meets the real - time performance requirements than post - processing.

[0061] System resource optimization: Performing real - time post - processing of brightness for multiple high - definition video streams requires a large amount of computing resources. Optimizing exposure during the acquisition stage is more efficient.

[0062] This optimized gradient adjustment strategy ensures that, without affecting the video quality, it can not only meet the requirements of human eye visual comfort but also quickly adapt to changes in environmental light.

[0063] The LED dimming control module 5 is connected to the video acquisition module 1 and the collaborative adjustment module 3, and is used to receive multiple frames of original video images and feedback the exposure adjustment value to the video acquisition module 1. The LED dimming control module 5 calculates the optimal LED brightness level based on the brightness information of the original video image and realizes precise brightness control through PWM (Pulse Width Modulation) technology. In the highway LED display system, the brightness level is usually divided into 10 - 16 levels and is dynamically adjusted according to the ambient light conditions. For example, in strong light environments (such as direct sunlight at noon), the LED brightness is set to levels 14 - 16 (equivalent to 6000 - 8000 cd / ㎡) to ensure that the information is still clearly visible in strong light; in normal lighting conditions (such as cloudy days or morning and evening), it is set to levels 8 - 12 (equivalent to 3000 - 5000 cd / ㎡); in low light environments (such as at night or in tunnels), it is set to levels 5 - 7 (equivalent to 1000 - 2500 cd / ㎡). These brightness level settings are based on a large amount of experimental data and research on driver visual perception, ensuring that the information displayed by the LED can be clearly recognized by the driver under various lighting conditions and avoiding glare and discomfort caused by excessive brightness.

[0064] The data acquisition module 6 is used to collect the exposure data of 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:

[0065] Real-time sampling of key parameters: Key parameters directly affecting image quality, such as exposure time and exposure gain, have their sampling frequency increased to 60 - 120 Hz, synchronized with the video frame rate, to ensure that parameter changes in each frame can be captured;

[0066] Sampling of LED control parameters: The sampling frequency of the LED drive current and LED current adjustment coefficient is increased to above 60 Hz to ensure synchronization with exposure changes;

[0067] Environmental state parameters: Parameters with relatively slow changes, such as ambient light intensity, can maintain a sampling frequency of 10 - 20 Hz to save system resources;

[0068] It should be clearly distinguished that the PWM modulation frequency of the LED and the parameter sampling frequency are two different concepts:

[0069] LED PWM modulation frequency: Maintained within the range of 2 - 20 kHz, which is the operating frequency of the LED drive circuit;

[0070] The LED parameter control frequency, i.e., the frequency for adjusting the LED drive current and the adjustment coefficient, needs to be increased to at least 60 Hz and synchronized with the video frame rate.

[0071] To solve the problem of synchronization between exposure changes and LED control, the system introduces an event-triggered adaptive control mechanism:

[0072] When it is detected that the exposure parameter change exceeds the preset threshold, the system automatically enters the high-frequency sampling mode, adopts a predictive control algorithm, calculates and adjusts the LED parameters in advance according to the exposure change trend, compensates for the control delay, establishes a parameter change buffer area to ensure that the rapidly changing parameters will not be lost due to the sampling interval. Through this multi-level sampling frequency design and event-triggered adaptive control mechanism, the system can ensure the real-time acquisition and control of key parameters while maintaining resource efficiency, effectively solving the problem that the LED current change cannot keep up with the exposure value change.

[0073] These optimization measures enable the system to respond more sensitively to light changes in the complex and changeable environment of the highway, ensure the synchronous optimization of video surveillance quality and LED display effect, and further improve the overall performance and adaptability of the system.

[0074] The selection of this sampling frequency is based on the typical time constant of the light change in the highway environment, which can not only meet the real-time requirements but also not generate too much redundant data. The types of data collected include exposure time (usually ranging from 1 / 10000 s to 1 / 30 s), exposure gain (usually ranging from 0 dB to 20 dB), LED drive current (usually ranging from 100 mA to 500 mA), and LED current adjustment coefficient (usually ranging from 0.5 to 1.5). These parameter ranges are determined based on the technical specifications of modern highway surveillance cameras and LED displays, and can cover various working conditions from extremely weak light to strong light.

[0075] The control module 7 is respectively connected to the video acquisition module 1, the LED dimming control module 5, and the data acquisition module 6, and is used to control the video acquisition module 1 to perform image acquisition, and collect the exposure data of the video acquisition module 1 and the LED drive current and LED current adjustment coefficient information of the LED controller through the data acquisition module 6. The control module 7, as the central control unit of the system, coordinates the work of each functional module to ensure the stable operation of the system. The control module 7 adopts a real-time operating system, and the response time is usually less than 10 ms to meet the real-time requirements of highway surveillance. In practical applications, vehicles on the highway may travel at a speed of 120 km / h (about 33.3 m / s). A response time of 10 ms means that the vehicle only moves about 33.3 cm, which is sufficient to ensure the rapid response of the system to emergencies and is of great significance for ensuring highway safety.

[0076] The video switching module 8 is connected to the control module 7 and is used to output an 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 a control instruction, or output both images simultaneously for comparison display. In the highway monitoring center, an operator can evaluate the adjustment effect of the system by switching different view modes. Especially in cases where the lighting conditions change drastically (such as when it suddenly changes from cloudy to sunny, or when a vehicle enters or exits a tunnel), the switching display function can help the operator quickly judge the improvement degree of the video quality. The response time of the switching operation is usually less than 50 ms to ensure the smoothness of the switching process and avoid image interruption or delay at critical moments.

[0077] The collaborative adjustment module 3 is also used to receive and store the LED drive current and LED current adjustment coefficient information. After adjusting the exposure corresponding to different frames of the exposure-adjusted video image, it calculates the brightness change amount 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 multiple frames of the original video image, and feeds back the brightness change amount and the brightness ratio to the video acquisition module 1.

[0078] The brightness change amount ( ) reflects the brightness dynamic characteristics between adjacent frames, and the calculation formula is as follows:

[0079] ,

[0080] Where: is the brightness change amount, with the unit of brightness value (range 0 - 255); is the brightness value of the i-th area in the current frame, with the unit of brightness value (range 0 - 255); is the brightness value of the i-th area in the previous frame, with the unit of brightness value (range 0 - 255); is the total number of areas; represents taking the average value; represents the summation operation from i = 1 to i = N; represents the absolute value of the difference in brightness values between the current frame and the previous frame. In highway monitoring applications, the brightness change amount is an important dynamic indicator. A relatively large brightness change amount (usually > 20, based on the brightness range of 0 - 255) indicates that the ambient light conditions change drastically, such as when the sunlight brightens and dims due to the rapid movement of clouds, or when a vehicle enters or exits a tunnel, etc., and the system needs to respond quickly; a relatively small brightness change amount (usually < 5) indicates that the ambient light conditions are relatively stable, and the system can maintain the current parameter settings. These thresholds are determined based on a large amount of actual monitoring data and have been verified in the monitoring systems of multiple highways.

[0081] The brightness ratio (R) reflects the effect after exposure adjustment, and the calculation formula is as follows: , Where: is the brightness ratio, dimensionless; is the brightness value of the i-th area after exposure adjustment, with the unit of brightness value (range 0 - 255); is the brightness value of the i-th area of the original image before adjustment, with the unit of brightness value (range 0 - 255); is the total number of areas; represents taking the average value; represents the summation operation from i = 1 to i = N; represents the ratio of the brightness after adjustment to the original brightness. Ideally, the value of R should be close to 1, indicating that the exposure adjustment is neither excessive nor insufficient. In practical applications, when the value of R is within the range of 1 ± m (m is a manually set constant, usually taking 0.1 - 0.2), the exposure adjustment effect is considered ideal. Taking a highway monitoring system as an example, empirical data shows that when the value of R is within the range of 0.8 - 1.2, the quality of the video image after exposure adjustment is the best, and vehicle details and road conditions can be clearly displayed; when the value of R is lower than 0.8, the image is too dark and details are likely to be missed; when the value of R is higher than 1.2, the image is too bright and overexposure is likely to occur. Therefore, the value of m is set to 0.2 based on the empirical value of the actual application effect.

[0082] The video acquisition module 1 adjusts the exposure according to the exposure adjustment value feedback by the LED dimming control module 5. Specifically, when receiving the exposure adjustment instruction, the video acquisition module 1 adjusts the exposure parameters of each area according to the parameters set in the instruction. The adjustment of the exposure parameters follows the principle of smooth transition to avoid image quality fluctuations caused by sudden parameter changes. In the highway monitoring system, the adjustment process is usually completed within 3 - 5 frames to ensure the continuity of the visual effect. For example, in the scenario where a vehicle enters a tunnel during the day, the video acquisition module needs to quickly increase the exposure parameters to adapt to the sudden weakening of the light. However, if the adjustment is too sudden, it will cause the video image to flicker. By smoothly transitioning within 3 - 5 frames (about 0.1 - 0.3 seconds, based on a frame rate of 15fps), visual discomfort can be avoided while ensuring the response speed.

[0083] In the second embodiment of the present invention, the system further includes a collaborative data packet module 9. As described above, the collaborative adjustment module 3 sends the brightness change amount between the multi-frame exposure adjustment video images of the exposure adjustment video image and the previous frame of exposure adjustment video image to the collaborative data packet module 9, and the collaborative data packet module 9 sends the brightness change amount to the control module 7, and the control module 7 stores the brightness change amount.

[0084] The collaborative data packet module 9 adopts a standardized data packet format, including fields such as timestamp, data type, data value, and check information. The total length of the data packet is usually 64 - 128 bytes to adapt to the transmission requirements of different types of data. In the network environment of the highway monitoring system, the choice of data packet size needs to balance 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 need to transmit more information. The collaborative data packet module 9 supports data compression function. When transmitting a large amount of data, the data compression rate can be set to 50% - 70% to reduce the network transmission burden. In a large-scale highway network monitoring system, the data volume generated by multiple monitoring points may reach dozens of MB per second. Data compression can effectively reduce the network bandwidth requirement and improve the scalability of the system.

[0085] The control module 7 stores the received brightness change amount data in the internal cache. The storage period is usually 1 hour, and the storage interval is 5 seconds, that is, about 720 data points are stored per hour. This storage strategy is set based on the typical time constant of the highway lighting condition change, which can not only capture important change trends but also not generate too much redundant data. These data will be used for system performance evaluation and parameter optimization to support the self-adjustment and long-term evolution of the system. For example, by analyzing the brightness change patterns at different times of the day, the system can predictably adjust parameters, such as optimizing the configuration in advance before sunrise and sunset to cope with the drastic changes in lighting conditions.

[0086] In the third embodiment of the present invention, the collaborative adjustment module 3 transfers the exposure corresponding to different frames of the exposure adjustment video image to the collaborative data packet module 9. The collaborative data packet module 9 transfers the exposure to the control module 7, and the control module 7 stores the exposure.

[0087] The exposure information includes exposure time, exposure gain, and white balance parameters of each region, etc. These parameters are encapsulated in JSON format for easy data parsing and storage.

[0088] In the highway monitoring system, there may be significant differences in exposure parameters in different regions. For example, due to the large lighting difference between the tunnel entrance and exit areas, the exposure time may vary by 5 - 10 times. Taking a mountain highway tunnel as an example, at noon on a sunny day, the exposure time of the external area of the tunnel is usually 0.001 - 0.002 seconds, while the exposure time of the internal area of the tunnel may need 0.0(1 - 0.02 seconds. By storing the independent exposure parameters of each region, the system can more precisely control the image quality and ensure clear monitoring images under different lighting conditions.

[0089] The control module 7 stores this exposure information in the system database with a storage period of 24 hours to support the day-night cycle learning and parameter optimization of the system. The 24-hour cycle is selected based on the periodicity of natural light changes, which can completely record the parameter changes at different times of the day and provide a data basis for the long-term optimization of the system. By analyzing the historical exposure parameter data, the system can identify the optimal parameter configuration mode and automatically apply it under similar conditions, reducing the need for manual intervention.

[0090] In the fourth embodiment of the present invention, the system further includes a display 10. The collaborative data packet module 9 sends the brightness change amount to the display 10; the control module 7 sends the exposures corresponding to different frames of the exposure adjustment video image to the display 10.

[0091] The display 10 generally uses a high-resolution LCD display screen with a resolution of not less than 1920×1080, supports multi-window display, and can simultaneously display information such as the original video image, the exposure adjustment video image, the brightness change amount curve, and the exposure parameters of each region. In the highway monitoring center, operators need to monitor the video images and system parameters of multiple regions simultaneously. The high-resolution display screen can provide richer visual information, facilitating problem identification and decision-making. The display interface adopts an ergonomic design to ensure that operators can intuitively monitor the system operation status and adjustment effects. For example, color coding is used to display the brightness change amount, with green indicating a stable state ( <5), yellow indicating a medium change (5≤ ≤20), and red indicating a drastic change ( >20), which is convenient for operators to quickly identify abnormal conditions.

[0092] The brightness change amount data is displayed in the form of a line graph, with the horizontal axis representing time and the vertical axis representing the brightness change amount value. The display time range can be adjusted between 1 minute and 1 hour. In highway monitoring applications, different time scales are suitable for different analysis requirements. For example, a 1-minute time scale is suitable for observing short-term brightness fluctuations, such as when a vehicle passes by or a cloud blocks the sun; while a 1-hour time scale is suitable for observing long-term trends, such as the brightness change during sunrise and sunset. The exposure parameters are displayed in the form of a table or a dashboard, facilitating operators to monitor the exposure settings of each region in real time. For important road sections (such as accident-prone areas, areas affected by bad weather, etc.), special marks can be set to remind operators to pay key attention.

[0093] In the fifth embodiment of the present invention, the system further includes a video display module 11. The control module 7 inputs the exposure adjustment video image output by the video switching module 8 into the video display module 11 for display.

[0094] The video display module 11 uses a professional-grade monitor, supporting multi-format video inputs, including HDMI, SDI, and IP streams, etc. In a highway monitoring center, different types of video sources may adopt different transmission protocols, and multi-format support can ensure system compatibility and scalability. The display screen size is usually 21 - 27 inches, with a resolution of not less than 1920×1080, and the color accuracy reaches 8 bits or higher to ensure the detail and color reproduction accuracy of video images. High color accuracy is of great significance for identifying specific situations on highways, such as road surface water accumulation, oil stains, or icing, etc. These situations usually show subtle color and texture changes, and high-quality display devices are required to accurately present them. The video display module 11 supports multi-screen split display, which can display video images of multiple areas simultaneously, facilitating operators to conduct comparative analysis. For example, under adverse weather conditions, operators can monitor the conditions of different sections of the highway simultaneously, promptly discover safety hazards such as water accumulation, icing, or low visibility, and provide support for traffic management decisions.

[0095] In the sixth embodiment of the present invention, the system further includes 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.

[0096] The video storage module uses high-speed storage media, such as solid-state drives (SSDs) or RAID disk arrays, with a storage capacity usually of 4TB - 8TB, and can support continuous high-definition video storage for 7 - 15 days. In a highway monitoring system, the selection of storage duration needs to consider the requirements of accident investigation and data analysis. Taking a provincial highway network as an example, statistical data shows that most accident investigations require retrieving 3 - 7 days of surveillance videos, so a storage capacity of 7 - 15 days can meet the vast majority of application scenarios. Video compression uses H.265 encoding, which can reduce the storage requirement by 40% - 50% while ensuring video quality. Compared with traditional H.264 encoding, H.265 can save approximately 40% of storage space at the same picture quality, having a significant cost advantage for large-scale monitoring systems. The video storage module supports the cyclic storage function. When the storage space is insufficient, it automatically overwrites the earliest video files. At the same time, it supports the marking and permanent preservation function of important video segments to ensure that key data will not be overwritten. For example, in the event of a traffic accident or abnormal weather conditions, the system can automatically mark relevant video segments, or be manually marked by operators, and these marked segments will be permanently preserved or have their preservation period extended until the relevant investigation is completed.

[0097] In the seventh embodiment of the present invention, the cooperative adjustment module 3 calculates the LED brightness through the LED drive current and the LED current adjustment coefficient; the video acquisition module 1 acquires multiple frames of original video images, calculates the average brightness of the original video images corresponding to different pictures, and the video acquisition module 1 adjusts the brightness corresponding to different pictures of each frame of the original video image to obtain the exposure-adjusted video image.

[0098] The LED brightness is calculated using the following formula:

[0099] ,

[0100] Where: is the LED brightness value, with the unit of cd / ㎡ (candela per square meter); is the LED drive current, with the unit of mA (milliampere); is the LED current adjustment coefficient, dimensionless, and the value range is 0.5 - 1.5; is the proportionality coefficient, with the unit of (cd / m²) / mA (candela per square meter per milliampere), and usually takes a value of 0.05 - 0.1. The proportionality coefficient is related to the performance parameters of the LED module and can be determined through experiments. For example, in a variable message sign system on a certain highway, an outdoor high-brightness LED module is used. When the drive current is 350 mA and the adjustment coefficient is 1.0, the LED brightness is approximately 1750 - 3500 cd / ㎡. This brightness level can still ensure clear visibility of information in sunny environments, but in the case of direct strong light, the brightness may need to be further increased to enhance the contrast. The proportionality coefficient is related to the performance parameters of the LED module and can be determined through experiments.

[0101] The adjustment of the brightness of the original video image by the video acquisition module 1 is based on the following principle: First, calculate the average brightness of each area, and then adjust the exposure parameters according to the target brightness value. The formula for calculating the average brightness is as follows:

[0102] ,

[0103] Where: is the average area brightness, with the unit of brightness value (range 0 - 255); and are the height and width of the area image respectively, with the unit of pixel; is the brightness value of the pixel at the coordinate (i,j), with the unit of brightness value (range 0 - 255); represents taking the average value; It represents a two-dimensional summation, that is, the sum of the brightness values of all pixel points in the region. In the highway monitoring system, different target brightness values can be set according to different application scenarios. For example, in the license plate recognition system, to ensure that the characters are clearly distinguishable, the daytime target brightness value is usually set to 120 - 150 (based on the brightness range of 0 - 255), and at night it is 80 - 100. These values are determined based on a large amount of test data, which can ensure the image quality while avoiding overexposure or underexposure. Under special weather conditions, such as rainy, snowy or foggy weather, the target brightness value may need to be increased by 5% - 10% to compensate for the reduced contrast caused by atmospheric scattering.

[0104] In the eighth embodiment of the present invention, the video acquisition module 1 adjusts the brightness corresponding to different pictures using the original video image; the adjusted image is the exposure-adjusted video image; when the brightness ratio of the exposure-adjusted video image to multiple frames of the original video image is 1 ± m, where m is a manually set constant; when the brightness ratio is less than 1, the video acquisition module 1 adjusts the brightness corresponding to different pictures using the original video image; when the brightness ratio is greater than 1, the collaborative adjustment module 3 adjusts the exposure amount corresponding to different pictures of the exposure-adjusted video image.

[0105] The value of the manually set constant m is usually 0.1 - 0.2, indicating the allowable brightness deviation range of the system. In the actual application of the highway monitoring system, this range value is determined based on visual perception experiments and system performance tests. If the m value is too small (e.g., <0.1), the system will be too sensitive, frequently adjust parameters, and increase the system burden; if the m value is too large (e.g., >0.2), the adjustment accuracy will be reduced, affecting the image quality. When the brightness ratio is within the range of 0.8 - 1.2, it is considered that the exposure adjustment effect is good, and the system maintains the current parameter settings; when the brightness ratio is less than 0.8, it indicates underexposure, and the video acquisition module 1 increases the exposure parameter; when the brightness ratio is greater than 1.2, it indicates overexposure, and the collaborative adjustment module 3 reduces the exposure parameter.

[0106] The exposure parameter adjustment adopts the proportional-integral (PI) control algorithm, and the adjustment formula is as follows:

[0107] ,

[0108] Where: is the adjusted exposure parameter, and the unit is related to the specific parameter (for example, the unit of exposure time is seconds, and the unit of exposure gain is dB); is the current exposure parameter, and the unit is the same as ; is the brightness ratio, dimensionless; represents the deviation between the brightness ratio and the target value 1; is the proportionality coefficient, dimensionless, and usually takes a value of 0.3 - 0.5; is the integral coefficient, with the unit of 1 / second, and usually takes values from 0.05 to 0.1; represents the integral of the brightness ratio deviation, that is, the cumulative deviation, with the unit of second. The integral term is used to eliminate the static error and improve the steady-state accuracy of the system. In the highway monitoring system, the parameter selection of the PI control algorithm needs to balance the response speed and system stability. A larger value (such as 0.5) can provide a faster response speed and is suitable for scenarios with drastic changes in lighting conditions, such as tunnel entrances and exits; a smaller value (such as 0.3) provides a smoother adjustment process and is suitable for scenarios with relatively stable lighting conditions, such as open sections. The selection of the value affects the system's ability to correct continuous deviations. A larger value (such as 0.1) can eliminate the static error faster, but may cause system overshoot; a smaller value (such as 0.05) provides a smoother correction process, but the correction speed is slower.

[0109] In the ninth embodiment of the present invention, the cooperative 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 list of cooperative parameter values and compare each parameter and its value in the list of cooperative parameter values. When abnormal parameters and values are detected, the corresponding abnormal information is sent to the signal transmission module 32; the edge processing module 33 receives the abnormal information from the signal transmission module 32, processes the abnormal information, and generates control information to be sent to the control module 7; the model training module 34 is used to train the cooperative control model based on the brightness change amount and brightness ratio data, so that the cooperative control model makes real-time predictions on the list of cooperative parameter values.

[0110] The parameter detection module 31 monitors the core parameters of the system, including brightness values, exposure parameters, LED parameters, etc. Abnormality detection uses statistical methods, setting normal parameter ranges and change rate thresholds. In the highway monitoring system, the setting of these thresholds is based on a large amount of historical data analysis and professional experience. For example, the normal range of brightness value is 30 - 220 (based on the brightness range of 0 - 255), and the change rate threshold of brightness is 30 / second; the normal range of exposure time is from 1 / 10000 second to 1 / 30 second, and the change rate threshold is 50% / second; the normal range of LED drive current is 100mA to 500mA, and the change rate threshold is 20% / second. The setting of the brightness value range takes into account the video quality requirements from night to strong light conditions; the brightness change rate threshold is based on the typical speed of ambient light change, such as when clouds block sunlight or vehicle lights sweep across, etc.; the exposure parameter range and change rate take into account the technical specifications and stability requirements of the camera; the setting of LED parameters is based on the working characteristics and lifespan considerations of the LED module. When the parameter exceeds the normal range or the change rate exceeds the threshold, the system determines it as an abnormality and generates an abnormality message.

[0111] The signal transmission module 32 uses a priority queue to manage abnormality messages, and high-priority abnormalities (such as abnormalities related to safety parameters) are processed first. In the highway monitoring system, different types of abnormalities have different impacts on the system operation and traffic safety. For example, a sudden decrease in the LED display brightness may cause information to not be recognized by the driver in time, which belongs to a high-priority abnormality; while a slight fluctuation in the video image brightness belongs to a low-priority abnormality. The priority setting is based on the potential impact of the abnormality on traffic safety, ensuring that the system can process the most critical issues first. The signal transmission uses an encrypted transmission protocol to ensure the security and reliability of data transmission, preventing malicious tampering or interference.

[0112] The edge processing module 33 classifies and processes the abnormality messages: for minor abnormalities, it adopts a parameter fine-tuning strategy; for moderate abnormalities, it resets the parameters; for severe abnormalities, it activates the safety mode and switches the system to a preset safe parameter configuration. In the highway monitoring system, the design of the abnormality handling strategy needs to take into account both system stability and traffic safety. For example, when detecting abnormal fluctuations in the LED brightness, for minor abnormalities (fluctuation amplitude < 10%), it can be solved by fine-tuning the drive current; for moderate abnormalities (fluctuation amplitude 10% - 30%), it may be necessary to reset the LED control parameters; for severe abnormalities (fluctuation amplitude > 30% or continuous abnormality), it may be necessary to activate the backup display mode or alarm the management center. The response time of edge processing is usually less than 100ms to ensure that the system can quickly respond to abnormal situations. Especially in an environment like a highway where the vehicle speed is fast and the risk is high, a quick response is crucial for accident prevention.

[0113] The model training module 34 adopts an incremental learning algorithm to analyze historical data regularly (usually every 24 hours) and update the parameters of the cooperative control model. The advantage of incremental learning is that it can continuously absorb the information of new data while retaining the experience of the original model, which is suitable for application scenarios with variable environmental conditions such as highways. For example, the system can gradually improve its adaptability to environmental changes by learning the optimal parameter configurations under different seasons and weather conditions. The model training process is carried out when the system load is low, usually during the late-night period with low traffic volume, without affecting the real-time control function of the system. After the trained model parameters pass the model verification, they gradually replace the existing model parameters to ensure a stable and controllable model update process. For example, a weight gradual change strategy can be adopted. The weight of the new model starts from 10% and increases by 10% every day, and the model update is completed within 10 days. This progressive update strategy can effectively avoid the risks brought by model mutations.

[0114] In the tenth embodiment of the present invention, 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 amount 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 7; the safety detection module further includes a video recognition unit, and the video recognition unit is used to recognize and analyze various images and convert the analysis results into parameters.

[0115] The safety detection module monitors the operation safety of the system, including two aspects: parameter safety and functional safety. Parameter safety detection mainly focuses on whether the parameters are within the safe range, such as whether the LED brightness exceeds the safety limit value (usually 5000 cd / ㎡), and whether the exposure parameters are within a reasonable range, etc. In the highway environment, too high LED brightness may cause glare to drivers and affect safe driving, especially at night or in low-light conditions; while too low brightness may cause information to not be recognized in time. The safety limit value of 5000 cd / ㎡ is determined based on human eye comfort research and traffic safety standards, which can ensure the visibility of information while avoiding visual discomfort to drivers. Functional safety detection mainly focuses on whether the system functions are operating normally, such as whether video acquisition is continuous and whether brightness adjustment is effective, etc. For example, if a video signal interruption or serious delay is detected, it may indicate a fault in the monitoring system and needs to be processed in time to avoid monitoring blind spots.

[0116] When a security exception is detected, the security detection module generates different types of prompt messages and control instructions according to the exception level: for minor exceptions, warning messages are generated without affecting the system operation; for moderate exceptions, warning messages are generated and parameter reset is triggered; for severe exceptions, alarm messages are generated and stop messages are sent, and the system switches to the security mode or emergency shutdown. In the highway monitoring system, the classification of exception levels is usually based on the potential impact on traffic safety. For example, occasional flashing of the LED display belongs to a minor exception, and the system can continue to operate but needs to record the warning; incorrect or blurred LED display content belongs to a moderate exception, and the display parameters need to be reset and the effect verified; complete failure of the LED display or display of seriously misleading information belongs to a severe exception, and it is necessary to immediately switch to the standby display mode or turn off the display and send an emergency alarm to the management center.

[0117] The video recognition unit uses computer vision technology and can recognize specific scenes and events from video images, such as traffic flow changes, weather changes, lighting condition changes, etc. In highway monitoring applications, video recognition technology can greatly improve the intelligent level of the system. For example, by recognizing water accumulation or icing on the road surface, the system can automatically adjust the LED display content to remind the driver to slow down; by recognizing foggy weather, the system can adjust the camera parameters to enhance the image contrast; by recognizing changes in traffic flow density, the system can predict possible congestion and adjust the information release strategy in advance. The recognition results are converted into numerical parameters for the intelligent control decision-making of the system. For example, when rain or snow weather is recognized, the LED brightness and video exposure parameters are automatically adjusted to meet the monitoring and information display requirements under bad weather conditions. Specifically, in rainy days, the LED brightness can be increased by 10% - 15% to compensate for the brightness loss caused by rain scattering; in snowy days, the contrast of the video image can be increased by 15% - 20% to enhance the distinguishability between the road surface snow and vehicles.

[0118] Through the detailed description of the above embodiments, those skilled in the art can clearly understand the technical solution and implementation method of the present invention. The present invention realizes the intelligent and refined control of highway electromechanical equipment by constructing a collaborative adjustment mechanism for video acquisition and LED display, improves the operation effect of the equipment while optimizing the energy utilization efficiency, and provides important support for the intelligent construction of highways.

[0119] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Highway electromechanical equipment multi-parameter collaborative regulation system, characterized in that Comprising: A video acquisition module, configured to acquire multiple frames of original video images; A video splitter, connected to the video acquisition module, configured 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 the brightness information corresponding to each of the multiple pictures, 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 pictures 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 feedback an exposure adjustment value to the video acquisition module; A data acquisition module, configured to acquire the exposure data of the video acquisition module and the LED drive current and LED current adjustment coefficient information of the LED controller; A control module, respectively connected to the video acquisition module, the LED dimming control module, and the data acquisition module, configured to control the video acquisition module to perform image acquisition, and acquire the exposure data of the video acquisition module and the LED drive current and LED current adjustment coefficient information of the LED controller through the data acquisition module; A video switching module, connected to the control module, configured to output 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 the LED current adjustment coefficient information, after adjusting the exposure corresponding to different pictures of the exposure-adjusted video image, calculate the brightness change amount between the multiple frames of exposure-adjusted video images of the exposure-adjusted video image and the previous frame of exposure-adjusted video image and the brightness ratio between the exposure-adjusted video image and the multiple frames of original video images, and feedback the brightness change amount and the brightness ratio to the video acquisition module; The video acquisition module adjusts the exposure according to the exposure adjustment value feedback by the LED dimming control module.

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

3. The multi-parameter collaborative regulation system for highway electromechanical equipment according to claim 2, wherein, The collaborative adjustment module transfers the exposure corresponding to different pictures of the exposure-adjusted video image to the collaborative data packet module, the collaborative data packet module transfers the exposure to the control module, and the control module stores the exposure.

4. The multi-parameter collaborative regulation system for highway electromechanical equipment according to claim 2, wherein It further includes a display; the collaborative data packet module sends the brightness change amount to the display; the control module sends the exposure corresponding to different pictures of the exposure-adjusted video image to the display.

5. The multi-parameter collaborative regulation system for highway electromechanical equipment according to claim 1, wherein 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.

6. The multi-parameter collaborative regulation system for highway electromechanical equipment according to claim 5, wherein, 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.

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

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

9. The multi-parameter collaborative adjustment system for highway electromechanical equipment according to claim 1, wherein 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 each parameter and each parameter value in the collaborative parameter value list. When an abnormal parameter and parameter value 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, and generates control information to be sent to the control module; the model training module is used to train the collaborative control model according to the brightness change amount and the brightness ratio data, so that the collaborative control model makes a real-time prediction on the collaborative parameter value list.

10. The multi-parameter collaborative adjustment system for highway electromechanical equipment according to claim 1, characterized in that 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 according to the brightness change amount and the brightness ratio. When a safety abnormality is detected, the safety detection module generates a prompt message and a stop message to be sent to the control module; the safety detection module further includes a video recognition unit, and the video recognition unit is used to recognize and analyze various images and convert the analysis results into parameters.

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