Tunnel lamp intelligent control system

Abstract

The invention discloses a tunnel lamp intelligent control system, relates to the technical field of tunnel lamp control, and comprehensively improves the brightness adjustment precision and stability of the system in emergencies and peak traffic flows through six modules of data acquisition, light intensity test, brightness maintenance evaluation, accuracy analysis, accuracy division and dynamic adjustment. The light intensity and traffic flow changes are acquired through real-time data, the brightness response of an abnormal scene test control system is simulated to identify and evaluate the accuracy of the algorithm in emergencies, and the response parameters are predictively optimized through the dynamic adjustment module, so that the problems of misjudgment, instability, high energy consumption and the like of the existing algorithm under extreme conditions are solved, and meanwhile, the accuracy of the algorithm is improved. The adaptive capacity of the system to the abnormal environment is remarkably improved, stable, safe and energy-saving illumination in the tunnel is achieved, and the driving environment is more stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel lamp control, and in particular to an intelligent control system for tunnel lamps. Background Art

[0002] Intelligent control of tunnel lights refers to the automatic adjustment of the lighting system in the tunnel through intelligent technology to improve safety and achieve energy saving. The intelligent control system uses light sensors, traffic flow sensors and other equipment to monitor the light intensity, traffic flow and other data inside and outside the tunnel in real time, and automatically adjusts the brightness of the tunnel lights according to actual conditions. The intelligent control system can flexibly adjust the lighting brightness according to the changes in traffic flow in the tunnel, increasing the brightness when the traffic flow is large, and reducing the brightness when the traffic flow is low to save energy.

[0003] The prior art has the following deficiencies: Intelligent tunnel control systems may use complex algorithms to adjust brightness, but if the algorithm does not fully account for abnormal situations (such as sudden large-scale traffic or extreme weather conditions), the system may misjudge. In extreme cases, algorithm failure may cause the brightness to be adjusted irregularly between low, medium and high brightness, creating a confusing visual environment for the driver, making the driving experience extremely unstable and dangerous. In addition, if the algorithm cannot restore normal control, it may also cause the system to maintain extremely high brightness during off-peak hours, resulting in excessive energy consumption and system loss. Summary of the invention

[0004] The purpose of the present invention is to provide a tunnel lamp intelligent control system to solve the shortcomings of the background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent control system for tunnel lights, comprising a data acquisition module, a light intensity test module, a brightness maintenance evaluation module, an accuracy analysis module, an accuracy division module and a dynamic adjustment module; Data acquisition module: by installing high-precision light sensors and high-speed traffic flow sensors at the entrance and exit of the tunnel, real-time monitoring and collection of light intensity change data under multiple different weather conditions and traffic flow mutation data in several time periods, and generating corresponding data sets; Light intensity test module: Set test scenarios based on abnormal events in the tunnel, record the brightness response stability of the control system to various light intensity change rates, and evaluate the algorithm's real-time response capabilities under sudden light changes; Brightness maintenance evaluation module: Use traffic flow mutation data to simulate traffic flow surge scenarios, observe the brightness adjustment range of the control system, and evaluate the control system's ability to maintain brightness under continuous peak traffic; Accuracy analysis module: Comprehensively analyze the algorithm's real-time response capability to sudden light changes and the control system's ability to maintain brightness under sustained peak traffic, and determine the accuracy of the algorithm in adjusting tunnel light brightness in response to emergencies based on the analysis results; Accuracy classification module: divides the accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies into different levels, including accuracy adjustment, incomplete accuracy adjustment and inaccuracy adjustment, and performs corresponding processing; Dynamic adjustment module: For incomplete accuracy adjustment, the abnormal degree of accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results.

[0006] Preferably, in the light intensity test module, the brightness response time abnormality index is generated after analyzing the response time between the detection of the light intensity change and the start of the brightness adjustment by the algorithm in the control system. The brightness response time abnormality index is obtained by: Set the normal brightness response time T to follow the normal distribution , where: μ is the average value of the brightness response time, is the variance of the brightness response time. Based on historical data, the average and variance of the normal brightness response time are calculated to obtain μ and , used to construct the prior probability , the expression is: ; In the actual monitoring process, record the actual brightness response time of the system each time, denoted as ; Define the acquired brightness response time Under normal circumstances, fluctuations follow a normal distribution , so the current observation The likelihood function The calculation expression is: ; Based on Bayesian theorem, calculate the posterior probability that the brightness response time is within the normal range ,Right now: ; Where P(Normal) is the prior probability of the control system under normal conditions, and P(Tobs) is the marginal probability of the brightness response time under all possible conditions: ;in represents the observation probability under abnormal conditions, P(abnormal) is the prior probability of the control system under abnormal conditions; the posterior probability Converted into brightness response time abnormality index, the expression is: ; Where SQ is the brightness response time anomaly index.

[0007] Preferably, in the brightness maintenance evaluation module, the peak brightness fluctuation index is generated after analyzing the fluctuation of the brightness value under the peak flow rate, and the method for obtaining the peak brightness fluctuation index is: During the tunnel peak period, the time series data of brightness adjustment is collected to form a set of brightness value sequences L(t). The brightness time series data L(t) is converted to the frequency domain to obtain the frequency component, which is expressed as: ; Where: F(f) is the Fourier transform coefficient corresponding to frequency f, representing the signal strength at frequency f, N is the number of sampling points in the time series, L(t) is the brightness value at time t, is the kernel function of Fourier transform, i is the imaginary unit; Calculate the amplitude of each frequency component , which represents the intensity of brightness fluctuation at this frequency: ;in, is the real part of X(f), is the imaginary part of X(f). High-frequency component extraction: The spectrum is divided into low-frequency and high-frequency components, where the high-frequency components represent rapid fluctuations in brightness. Select frequencies above the frequency threshold. The high-frequency part of , calculates its corresponding amplitude value, and accumulates the amplitude value of the high-frequency part to form the high peak brightness fluctuation index, which is expressed as: in: is the sum of the amplitudes of the high frequency parts, is the total amplitude of the entire spectrum, and GH is the peak brightness fluctuation index.

[0008] Preferably, in the accuracy analysis module, the brightness response time anomaly index and the peak brightness fluctuation index are converted into feature vectors, and the feature vectors are used as inputs of the machine learning model. The machine learning model uses each set of feature vectors to predict the accuracy value label of the algorithm for adjusting the brightness of tunnel lights in emergencies as the prediction target, and takes minimizing the sum of the prediction errors of the accuracy value labels of all algorithms for adjusting the brightness of tunnel lights in emergencies as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The accuracy value of the algorithm for adjusting the brightness of tunnel lights in emergencies is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0009] Preferably, in the accuracy division module, the obtained accuracy value of the algorithm for adjusting the brightness of the tunnel lamp in an emergency is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the accuracy value of the algorithm for adjusting the brightness of the tunnel lamp in an emergency is compared with the first standard threshold and the second standard threshold respectively; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in the emergency is greater than the second standard threshold, it means that the algorithm has high accuracy in adjusting the brightness of the tunnel lights in the emergency, and a high accuracy adjustment signal is generated and classified into an accuracy adjustment category, indicating that the algorithm can accurately adjust the brightness without further adjustment; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the accuracy of the algorithm in adjusting the brightness of the tunnel lights in an emergency is moderate. At this time, a moderate accuracy adjustment signal is generated and classified as an incomplete accuracy adjustment category. The algorithm can partially respond to the sudden change but accurately. The algorithm parameters are recorded and optimized to improve future response capabilities; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is less than the first standard threshold, it means that the algorithm has low accuracy in adjusting the brightness of the tunnel lights in an emergency. At this time, a low-accuracy adjustment signal is generated and classified into an inaccuracy adjustment category. The algorithm does not respond accurately in the emergency, generates an alarm signal, and performs parameter tuning.

[0010] Preferably, in the dynamic adjustment module, for incomplete accuracy adjustment, the abnormal degree of the algorithm's accuracy in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results, specifically: Set a time period W, for incomplete accuracy adjustment, that is, the accuracy value of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, collect the accuracy values ​​of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, and construct a corresponding data set, and calculate the standard deviation of the data set as the abnormal index of the accuracy of the algorithm for adjusting the brightness of the tunnel lights in a fixed time period when responding to emergencies, marked as .

[0011] Preferably, based on the predicted degree of abnormality , dynamically adjust the trigger threshold of the brightness response, the trigger threshold adjustment formula is: ;in: is the brightness response trigger threshold after dynamic adjustment, is the basic trigger threshold, i.e., the brightness response trigger standard under normal circumstances, and k is the adjustment coefficient, which is used to control the influence of the abnormal degree on the trigger threshold; according to the abnormal prediction value Dynamically adjust the rate of brightness change. The brightness adjustment rate formula is: ;in: is the adjusted brightness change rate is the basic brightness adjustment rate, i.e., the brightness adjustment speed under normal circumstances, and m is the rate adjustment coefficient, which controls the impact of the predicted abnormality on the adjustment rate.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention integrates data acquisition, light intensity testing, brightness maintenance evaluation, accuracy analysis, accuracy division and dynamic adjustment modules, so that the intelligent control system of tunnel lights can flexibly and accurately respond to changes in emergencies and peak traffic flow. With the help of high-precision sensors and simulated test scenarios, the system monitors and records changes in light intensity and traffic flow in real time, generates brightness response time anomaly index and peak brightness fluctuation index, and thus detects and evaluates the responsiveness of the algorithm under different environmental conditions. Through the accuracy analysis and division modules, the system can automatically identify the performance of the algorithm in emergencies, and classify and process according to preset thresholds to ensure appropriate brightness adjustment effects.

[0013] 2. The present invention uses a dynamic adjustment module to optimize the algorithm's brightness response trigger threshold and adjustment rate in real time for situations where the accuracy is not fully adjusted, thereby improving the system's response sensitivity and stability. This design not only effectively solves the problems of misjudgment, frequent adjustment, and excessive energy consumption of traditional algorithms under extreme conditions, but also significantly improves driving safety and energy efficiency in tunnels, ensuring a smooth lighting effect in various emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For examples, see Figure 1As shown, the tunnel lamp intelligent control system described in this embodiment includes a data acquisition module, a light intensity test module, a brightness maintenance evaluation module, an accuracy analysis module, an accuracy division module and a dynamic adjustment module; Data acquisition module: by installing high-precision light sensors and high-speed traffic flow sensors at the entrance and exit of the tunnel, real-time monitoring and collection of light intensity change data under multiple different weather conditions and traffic flow mutation data in several time periods, and generating corresponding data sets; Light intensity test module: Set test scenarios based on abnormal events in the tunnel, record the brightness response stability of the control system to various light intensity change rates, and evaluate the algorithm's real-time response capabilities under sudden light changes; Brightness maintenance evaluation module: Use traffic flow mutation data to simulate traffic flow surge scenarios, observe the brightness adjustment range of the control system, and evaluate the control system's ability to maintain brightness under continuous peak traffic; Accuracy analysis module: Comprehensively analyze the algorithm's real-time response capability to sudden light changes and the control system's ability to maintain brightness under sustained peak traffic, and determine the accuracy of the algorithm in adjusting tunnel light brightness in response to emergencies based on the analysis results; Accuracy classification module: divides the accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies into different levels, including accuracy adjustment, incomplete accuracy adjustment and inaccuracy adjustment, and performs corresponding processing; Dynamic adjustment module: For incomplete accuracy adjustment, the abnormal degree of accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results.

[0018] Among them, in the data acquisition module, select high-precision optical sensors suitable for the tunnel environment, which are required to be able to quickly and accurately detect changes in light intensity under different weather and lighting conditions. Select high-speed traffic flow sensors, which are required to accurately record parameters such as the number of vehicles per second and driving speed, and are suitable for scenarios with sudden surges in traffic volume. Install optical sensors at key locations at the entrance and exit of the tunnel to ensure that changes in light intensity inside and outside the tunnel can be fully monitored. Install the traffic flow sensor at the entrance of the tunnel (or at the exit if necessary) to ensure that traffic flow data can be collected in real time when vehicles enter and leave the tunnel.

[0019] Calibrate the light sensor and traffic flow sensor using a standard light source and known traffic flow data to ensure data accuracy. During the calibration process, record the sensor's response time and accuracy range to ensure that it can accurately capture rapidly changing data in actual use. Perform preliminary tests in different time periods and weather conditions (such as sunny, cloudy, rainy, and night) to check whether the sensor can continuously and stably collect data. Monitor the stability of data transmission to ensure that data can be reliably transmitted to the data acquisition module.

[0020] Set the light sensor and vehicle flow sensor to high-frequency acquisition mode, collect light intensity changes once per second, and update the vehicle flow data according to the real-time data of vehicles passing through to capture subtle changes. Set the data collection cycle, such as 24 hours or a week of continuous collection, to ensure that different weather conditions (sunny, rainy, foggy) and traffic conditions (morning and evening peaks, non-peak hours, etc.) are covered. The collected light intensity change data and vehicle flow data are transmitted in real time to the database of the central control system. The database is backed up regularly to prevent data loss and ensure data integrity.

[0021] Monitor and record sudden changes in weather, such as sunny days suddenly turning into heavy rain, day and night alternation, foggy days with reduced visibility, etc. Mark the rate of change of light intensity during sudden weather events. For example, the rate of change of light intensity increases significantly during heavy rain, and use this data as a basis to evaluate the system's ability to cope with drastic changes in light intensity.

[0022] Record sudden changes in traffic flow, including surges or drops in traffic flow. For example, when a traffic accident causes a surge in diverted traffic flow, mark the time point, traffic increase, and duration of the event. Mark peak traffic and time periods with significant traffic fluctuations for subsequent algorithm testing and optimization.

[0023] The light intensity change data collected in different weather and time periods are integrated and stored by weather type (such as sunny, rainy, foggy, and night) and time period (morning peak, evening peak, and non-peak). The average light intensity change rate, maximum change value, and change trend are calculated for each case to form a complete light intensity change data set. The collected traffic flow data is classified by time period, such as morning peak, evening peak, off-peak period, and special cases such as sudden traffic changes (such as sudden increases and decreases). The average traffic flow, traffic peak, and volatility for each time period are calculated to form a detailed traffic flow change data set. The light intensity change data and traffic flow data are combined to generate a multi-dimensional data set for testing tunnel lighting control under different weather, time periods, and traffic flow conditions. Emergency events are marked separately to ensure that they can be used for simulation tests and subsequent algorithm accuracy evaluation.

[0024] Filter out the outliers caused by signal interference or sensor errors in data collection to ensure the accuracy and continuity of the data set. Randomly select some data for verification to ensure consistency with the actual environment and confirm the validity and reliability of the data set.

[0025] In the light intensity test module, based on the actual environment of the tunnel, common sudden light change conditions that affect driving vision are selected as test scenarios. Typical scenarios include: sudden rainstorm or dense fog: simulating the scene of sudden drop in light intensity. Rapid transition from sunny to cloudy: simulating the transition from bright to dim light. Day and night alternation or light contrast inside and outside the tunnel: simulating the situation of entering a dark area from a bright area.

[0026] According to the collected light intensity change data, set different rates of light change values. For example: Slow change: the light intensity changes slowly within 1 minute. Medium rate change: the light intensity gradually decreases or increases within a few seconds. Sudden rapid change: the light intensity decreases rapidly within 1 second to simulate sudden weather changes. Establish rate levels: divide the light intensity change rate into three levels: "slow speed", "medium speed" and "high speed" to facilitate accurate evaluation of different change rates.

[0027] The set light intensity rate change value is gradually input into the control system to simulate the real light mutation scene. At this time, the light sensor simulates the real light intensity change and transmits it to the control system, stimulating the system's brightness adjustment algorithm to respond.

[0028] The brightness response time abnormality index is generated by analyzing the response time of the algorithm in the control system from the time when the light intensity change is detected to the time when the brightness adjustment starts. The method for obtaining the brightness response time abnormality index is as follows: Set the normal brightness response time T to follow the normal distribution , where: μ is the average value of the brightness response time, is the variance of the brightness response time. Based on historical data, the average and variance of the normal brightness response time are calculated to obtain μ and , used to construct the prior probability , the expression is: ; In the actual monitoring process, record the actual brightness response time of the system each time, denoted as ; Define the acquired brightness response time Under normal circumstances, fluctuations follow a normal distribution , so the current observation The likelihood function The calculation expression is: ; Based on Bayes' theorem, calculate the posterior probability that the brightness response time belongs to the normal range, that is: ; Where P(Normal) is the prior probability of the control system under normal conditions, which can usually be set to 1 (assuming the normal state is the default state), and P(Tobs) is the marginal probability of the brightness response time under all possible conditions: ;in It represents the observation probability under abnormal conditions. It can generally be set to a fixed low probability value, or estimated based on abnormal historical data. P(abnormal) is the prior probability of the control system under abnormal conditions. Converted into brightness response time abnormality index, the expression is: ; Where SQ is the brightness response time anomaly index.

[0029] The larger the brightness response time anomaly index is, the more the current brightness response time deviates significantly from the normal range, reflecting the poor real-time response capability of the algorithm under sudden light changes. A high anomaly index indicates that the system fails to adjust the brightness in time, which may cause the driver to experience an uncomfortable visual environment under sudden light conditions, increasing traffic safety risks. Therefore, a higher anomaly index indicates that the algorithm is insufficient in dealing with sudden light changes and needs further optimization and adjustment.

[0030] On the contrary, the smaller the brightness response time abnormality index is, the more consistent the current brightness response time is with the normal range, indicating that the algorithm has a strong real-time response capability under sudden light changes. A low abnormality index means that the system can quickly and accurately adjust the lighting brightness to ensure the safety and comfort of the driver under different lighting conditions. At this point, the system's intelligent control capability has been effectively verified, and it can maintain good lighting effects in sudden situations, thereby enhancing traffic safety.

[0031] In the brightness maintenance evaluation module, first, obtain historical traffic flow data, including normal traffic flow and sudden traffic flow. This data should cover traffic flow changes in different time periods and weather conditions. Based on the historical data, build multiple traffic flow surge scenarios to evaluate the control system. These scenarios should consider different sudden conditions, such as traffic accidents, special events, or weather changes.

[0032] Determine the start time, duration, and traffic volume increase for each simulation scenario. Set thresholds to distinguish between peak and normal traffic conditions. Input traffic volume mutation data into the control system and start real-time monitoring and adjustment. The system should automatically adjust the lighting brightness in the tunnel based on the input traffic data. Observe in real time the brightness adjustment range of the control system in the case of a surge in traffic volume, and record the specific brightness value of each adjustment.

[0033] After analyzing the fluctuation of brightness value under peak flow, the peak brightness fluctuation index is generated. The method for obtaining the peak brightness fluctuation index is: During the tunnel peak period, the time series data of brightness adjustment is collected to form a set of brightness value sequences L(t). If there is an obvious trend item in the brightness data (such as continuous increase or decrease), the trend can be removed by difference or filtering methods to make the data more concentrated on the fluctuation component. The brightness time series data L(t) is converted to the frequency domain to obtain the frequency component, which is expressed as: ; Where: F(f) is the Fourier transform coefficient (complex form) corresponding to frequency f, representing the signal strength at frequency f, N is the number of sampling points in the time series, L(t) is the brightness value at time t, is the kernel function of Fourier transform, i is the imaginary unit; Calculate the magnitude (amplitude) of each frequency component , which represents the intensity of brightness fluctuation at this frequency: ;in, is the real part of X(f), is the imaginary part of X(f). High-frequency component extraction: The spectrum is usually divided into low-frequency and high-frequency components, where the high-frequency components represent rapid fluctuations in brightness. Select a frequency threshold above The high-frequency part is calculated to calculate the corresponding amplitude value. The frequency threshold can be set according to the requirements of brightness fluctuation, such as the number of changes per second. The amplitude of the high-frequency part is accumulated to form a high peak brightness fluctuation index, which is expressed as: in: is the high frequency part (frequency above the threshold ) is the sum of the amplitudes of . is the total amplitude of the entire spectrum, and GH is the peak brightness fluctuation index.

[0034] The larger the peak brightness fluctuation index, the more dramatic the brightness fluctuation of the control system during peak traffic, and the more difficult it is to maintain the brightness level smoothly. This means that when the control system handles the brightness requirements of continuous peak traffic, frequent and excessive brightness adjustments may occur, resulting in unstable brightness. For drivers, such frequent brightness fluctuations may cause visual fatigue or interference, increasing driving risks. Therefore, a higher fluctuation index indicates that the system has a weaker ability to maintain brightness, and further optimization may be needed to improve the smoothness and consistency of brightness adjustment.

[0035] On the contrary, the smaller the peak brightness fluctuation index, the more stable the system can maintain the brightness level under peak traffic, with smaller adjustment amplitude and frequency. This means that the control system can quickly adapt and stably provide consistent lighting when facing continuous peak traffic, reducing unnecessary fluctuations. A low fluctuation index usually means that the control system has good brightness maintenance capabilities and can provide a stable and comfortable lighting environment during peak hours, which helps to improve the safety and comfort of tunnel driving.

[0036] Accuracy analysis module: Comprehensively analyze the algorithm's real-time response capabilities to sudden light changes and the control system's ability to maintain brightness under continuous peak traffic, and determine the accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies based on the analysis results.

[0037] The brightness response time anomaly index and the peak brightness fluctuation index are converted into feature vectors, and the feature vectors are used as the input of the machine learning model. The machine learning model uses each set of feature vectors to predict the accuracy value label of the algorithm for adjusting the brightness of tunnel lights in emergencies as the prediction target, and takes minimizing the sum of the prediction errors of the accuracy value labels of all algorithms for adjusting the brightness of tunnel lights in emergencies as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The accuracy value of the algorithm for adjusting the brightness of tunnel lights in emergencies is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0038] The method for obtaining the accuracy value of the algorithm for adjusting the brightness of the tunnel light in an emergency is as follows: from the first eigenvector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, SQ is the brightness response time anomaly index, GH is the peak brightness fluctuation index, The accuracy value of the algorithm in adjusting the brightness of tunnel lights in emergencies.

[0039] Accuracy classification module: The accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies is divided into different levels, namely, accuracy adjustment, incomplete accuracy adjustment and inaccuracy adjustment, and corresponding processing is performed.

[0040] The obtained accuracy value of the algorithm for adjusting the brightness of the tunnel light in an emergency is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the accuracy value of the algorithm for adjusting the brightness of the tunnel light in an emergency is compared with the first standard threshold and the second standard threshold respectively; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is greater than the second standard threshold, it means that the algorithm has high accuracy in adjusting the brightness of the tunnel lights in an emergency, and a high-accuracy adjustment signal is generated and classified into an accuracy adjustment category, indicating that the algorithm can accurately adjust the brightness and the system does not need further adjustment; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the accuracy of the algorithm in adjusting the brightness of the tunnel lights in an emergency is moderate. At this time, a moderate accuracy adjustment signal is generated and classified as an incomplete accuracy adjustment category. The algorithm can partially respond to the sudden change, but it is not accurate enough. The system can record and optimize the algorithm parameters to improve future response capabilities. If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is less than the first standard threshold, it means that the algorithm has low accuracy in adjusting the brightness of the tunnel lights in an emergency. At this time, a low-accuracy adjustment signal is generated and classified as an inaccurate adjustment category. The algorithm does not respond accurately enough in an emergency. The system should generate an alarm signal and perform necessary parameter tuning, or enable a backup algorithm to ensure safety.

[0041] Dynamic adjustment module: For incomplete accuracy adjustment, the abnormal degree of accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results.

[0042] Set a time period W, for incomplete accuracy adjustment, that is, the accuracy value of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, collect the accuracy values ​​of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, and construct a corresponding data set, and calculate the standard deviation of the data set as the abnormal index of the accuracy of the algorithm for adjusting the brightness of the tunnel lights in a fixed time period when responding to emergencies, marked as .

[0043] According to the predicted abnormality , dynamically adjust the trigger threshold of the brightness response to improve the system's response sensitivity or reduce unnecessary frequent adjustments. The trigger threshold adjustment formula is: ;in: is the brightness response trigger threshold after dynamic adjustment, is the basic trigger threshold, i.e. the brightness response trigger standard under normal circumstances, and k is the adjustment coefficient, which is used to control the impact of abnormality on the trigger threshold, usually set through experiments or historical data. When it is high, the system predicts a large degree of abnormality. To prevent instability caused by frequent brightness adjustments, the trigger threshold can be increased. , so that the system is not easily affected by small fluctuations; if If it is lower, the trigger threshold is close to the basic value, ensuring normal response sensitivity.

[0044] Based on the abnormal prediction value Dynamically adjust the rate of brightness change so that the system can adapt to brightness changes in emergencies more quickly or more smoothly. The brightness adjustment rate formula is: ;in: The brightness change rate after adjustment determines the speed of brightness adjustment. is the basic brightness adjustment rate, i.e. the brightness adjustment speed under normal circumstances, and m is the rate adjustment coefficient, which controls the impact of the predicted abnormality on the adjustment rate and is set based on historical data. When it is high, it indicates that the light changes caused by the sudden event are more drastic, and the brightness adjustment rate is will be higher than the base rate, allowing the system to respond more quickly to light changes; when When the brightness is low, the brightness adjustment rate is kept at the basic rate to ensure the stability of the adjustment.

[0045] In this embodiment, the data acquisition module installs high-precision sensors at the entrance and exit of the tunnel to monitor and collect light intensity changes and traffic flow mutation data in real time to generate a data set; the light intensity test module sets abnormal event test scenarios to record the brightness response of the system at different light intensity change rates to evaluate the real-time response capability of the algorithm; the brightness maintenance evaluation module simulates a surge in traffic flow to evaluate the brightness stability of the system under continuous peaks; the accuracy analysis module comprehensively analyzes the performance of the algorithm under sudden light changes and peak traffic to determine the accuracy of adjusting the brightness; the accuracy classification module divides the accuracy into three categories: high, medium and low, and takes corresponding measures; the dynamic adjustment module dynamically adjusts the trigger threshold and adjustment rate of the brightness response based on the abnormal situation prediction with medium accuracy to optimize the sensitivity and stability of the system and ensure reliable lighting support under various emergency conditions.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0048] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An intelligent control system for tunnel lights, characterized in that: It includes data acquisition module, light intensity test module, brightness maintenance evaluation module, accuracy analysis module, accuracy division module and dynamic adjustment module; Data acquisition module: by installing high-precision light sensors and high-speed traffic flow sensors at the entrance and exit of the tunnel, real-time monitoring and collection of light intensity change data under multiple different weather conditions and traffic flow mutation data in several time periods, and generating corresponding data sets; Light intensity test module: Set test scenarios based on abnormal events in the tunnel, record the brightness response stability of the control system to various light intensity change rates, and evaluate the algorithm's real-time response capabilities under sudden light changes; Brightness maintenance evaluation module: Use traffic flow mutation data to simulate traffic flow surge scenarios, observe the brightness adjustment range of the control system, and evaluate the control system's ability to maintain brightness under continuous peak traffic; Accuracy analysis module: Comprehensively analyze the algorithm's real-time response capability to sudden light changes and the control system's ability to maintain brightness under sustained peak traffic, and determine the accuracy of the algorithm in adjusting tunnel light brightness in response to emergencies based on the analysis results; Accuracy classification module: divides the accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies into different levels, including accuracy adjustment, incomplete accuracy adjustment and inaccuracy adjustment, and performs corresponding processing; Dynamic adjustment module: For incomplete accuracy adjustment, the abnormal degree of accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results.

2. The intelligent control system for tunnel lights according to claim 1, characterized in that: In the light intensity test module, the response time between the detection of light intensity change and the start of brightness adjustment in the control system algorithm is analyzed to generate a brightness response time abnormality index. The brightness response time abnormality index is obtained as follows: Set the normal brightness response time T to follow the normal distribution , where: μ is the average value of the brightness response time, is the variance of the brightness response time. Based on historical data, the average and variance of the normal brightness response time are calculated to obtain μ and , used to construct the prior probability , the expression is: ; In the actual monitoring process, record the actual brightness response time of the system each time, denoted as ; Define the acquired brightness response time Under normal circumstances, fluctuations follow a normal distribution , so the current observation The likelihood function The calculation expression is: ; Based on Bayesian theorem, calculate the posterior probability that the brightness response time is within the normal range ,Right now: ; Where P(Normal) is the prior probability of the control system under normal conditions, and P(Tobs) is the marginal probability of the brightness response time under all possible conditions: ;in represents the observation probability under abnormal conditions, P(abnormal) is the prior probability of the control system under abnormal conditions; the posterior probability Converted into brightness response time abnormality index, the expression is: ; Where SQ is the brightness response time anomaly index.

3. The intelligent control system for tunnel lights according to claim 2, characterized in that: In the brightness maintenance evaluation module, the peak brightness fluctuation index is generated after analyzing the fluctuation of the brightness value under the peak flow rate. The method for obtaining the peak brightness fluctuation index is: During the tunnel peak period, the time series data of brightness adjustment is collected to form a set of brightness value sequences L(t). The brightness time series data L(t) is converted to the frequency domain to obtain the frequency component, which is expressed as: ; Where: F(f) is the Fourier transform coefficient corresponding to frequency f, representing the signal strength at frequency f, N is the number of sampling points in the time series, L(t) is the brightness value at time t, is the kernel function of Fourier transform, i is the imaginary unit; Calculate the amplitude of each frequency component , which represents the intensity of brightness fluctuation at this frequency: ;in, is the real part of X(f), is the imaginary part of X(f). High-frequency component extraction: The spectrum is divided into low-frequency and high-frequency components, where the high-frequency components represent rapid fluctuations in brightness. Select frequencies above the frequency threshold. The high-frequency part of , calculates its corresponding amplitude value, and accumulates the amplitude value of the high-frequency part to form the high peak brightness fluctuation index, which is expressed as: in: is the sum of the amplitudes of the high frequency parts, is the total amplitude of the entire spectrum, and GH is the peak brightness fluctuation index.

4. The intelligent control system for tunnel lights according to claim 3, characterized in that: In the accuracy analysis module, the brightness response time anomaly index and the peak brightness fluctuation index are converted into feature vectors, and the feature vectors are used as the input of the machine learning model. The machine learning model uses each set of feature vectors to predict the accuracy value label of the algorithm for adjusting the brightness of tunnel lights in emergencies as the prediction target, and minimizes the sum of the prediction errors of the accuracy value labels of all algorithms for adjusting the brightness of tunnel lights in emergencies as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The accuracy value of the algorithm for adjusting the brightness of tunnel lights in emergencies is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. The intelligent control system for tunnel lights according to claim 4, characterized in that: In the accuracy division module, the obtained accuracy value of the algorithm for adjusting the brightness of the tunnel light in an emergency is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the accuracy value of the algorithm for adjusting the brightness of the tunnel light in an emergency is compared with the first standard threshold and the second standard threshold respectively; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in the emergency is greater than the second standard threshold, it means that the algorithm has high accuracy in adjusting the brightness of the tunnel lights in the emergency, and a high accuracy adjustment signal is generated and classified into an accuracy adjustment category, indicating that the algorithm can accurately adjust the brightness without further adjustment; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the accuracy of the algorithm in adjusting the brightness of the tunnel lights in an emergency is moderate. At this time, a moderate accuracy adjustment signal is generated and classified as an incomplete accuracy adjustment category. The algorithm can partially respond to the sudden change but accurately. The algorithm parameters are recorded and optimized to improve future response capabilities; If the accuracy value of the algorithm in adjusting the brightness of the tunnel lights in an emergency is less than the first standard threshold, it means that the algorithm has low accuracy in adjusting the brightness of the tunnel lights in an emergency. At this time, a low-accuracy adjustment signal is generated and classified into an inaccuracy adjustment category. The algorithm does not respond accurately in the emergency, generates an alarm signal, and performs parameter tuning.

6. The intelligent control system for tunnel lights according to claim 1, characterized in that: In the dynamic adjustment module, for incomplete accuracy adjustment, the abnormal degree of accuracy of the algorithm in adjusting the brightness of tunnel lights in response to emergencies within a fixed time period is predicted, and the trigger threshold and adjustment rate of the brightness response in the algorithm are dynamically adjusted according to the prediction results, specifically: Set a time period W, for incomplete accuracy adjustment, that is, the accuracy value of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, collect the accuracy values ​​of the algorithm for adjusting the brightness of the tunnel lights in an emergency generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, and construct a corresponding data set, and calculate the standard deviation of the data set as the abnormal index of the accuracy of the algorithm for adjusting the brightness of the tunnel lights in a fixed time period when responding to emergencies, marked as .

7. The intelligent control system for tunnel lights according to claim 6, characterized in that: According to the predicted abnormality , dynamically adjust the trigger threshold of the brightness response, the trigger threshold adjustment formula is: ;in: is the brightness response trigger threshold after dynamic adjustment, is the basic trigger threshold, i.e., the brightness response trigger standard under normal circumstances, and k is the adjustment coefficient, which is used to control the influence of the abnormal degree on the trigger threshold; according to the abnormal prediction value Dynamically adjust the rate of brightness change. The brightness adjustment rate formula is: ;in: is the adjusted brightness change rate is the basic brightness adjustment rate, i.e., the brightness adjustment speed under normal circumstances, and m is the rate adjustment coefficient, which controls the impact of the predicted abnormality on the adjustment rate.

Images