Method and system for controlling intelligent navigation lamp strip in tunnel

Abstract

The invention discloses a method and a system for controlling an intelligent navigation lamp strip in a tunnel. The method comprises the following steps: acquiring vehicle information in the tunnel; the vehicle information is analyzed, and the driving state of the vehicle in the tunnel is obtained; and regulating and controlling the intelligent navigation lamp strip according to the driving state of the vehicle. A multi-sensor fusion technology and a state classification model are adopted, real-time detection and dynamic light control of the traffic flow state are achieved, key parameters such as the vehicle speed and the vehicle flow can be rapidly obtained through a real-time data acquisition and analysis technology, the light color and intensity are adjusted in real time through the model, the response time of the system is remarkably shortened, and the system reliability is improved. And the operation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control, and particularly relates to a control method and system for intelligent navigation light strips inside a tunnel. Background Art

[0002] Although the existing tunnel traffic management systems have made certain developments in terms of lighting control and traffic status monitoring, there are still many deficiencies. Traditional tunnel lights mostly adopt a fixed-intensity lighting method and cannot dynamically adjust the brightness or color according to the actual traffic flow. This not only causes energy waste during low traffic flow but also fails to provide sufficient visibility and safety during high traffic flow or congestion. In addition, the current monitoring systems usually rely on a single sensor (such as a camera or a geomagnetic coil) to collect data, and these sensors are easily affected by environmental conditions, such as insufficient light or bad weather, resulting in a decrease in the accuracy of the monitoring data. Existing systems also mostly use the fixed-threshold method to judge the traffic status, which has poor flexibility and is difficult to cope with complex and changeable traffic flow conditions. It may even lead to misjudgment, reducing the practicality of the lighting adjustment. Moreover, the sudden changes during the lighting adjustment process may also cause interference to the driver's vision and psychology, increasing the driving risk. Therefore, there is an urgent need for a new control method based on multi-sensor data collection, intelligent traffic status prediction and classification, and dynamic lighting control. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a control method and system for intelligent navigation light strips inside a tunnel, which can overcome the limitations of the traditional fixed-threshold method and significantly improve the monitoring accuracy and adaptability.

[0004] The present invention provides a control method for intelligent navigation light strips inside a tunnel, including:

[0005] Collecting vehicle information inside the tunnel;

[0006] Analyzing the vehicle information to obtain the driving state of the vehicles inside the tunnel;

[0007] Adjusting the intelligent navigation light strips according to the driving state of the vehicles.

[0008] Optionally, collecting vehicle information inside the tunnel includes:

[0009] Measuring the traffic flow inside the tunnel by using a geomagnetic induction coil, measuring the driving speed of the vehicles inside the tunnel by using a laser speed sensor, and collecting the traffic operation state inside the tunnel in real time by using a camera.

[0010] Optionally, analyzing the vehicle information to obtain the driving state of the vehicles inside the tunnel includes:

[0011] Obtaining the traffic indicators inside the tunnel;

[0012] Analyze the vehicle information using the traffic indicators to obtain an analysis result;

[0013] Obtain the driving state of the vehicles inside the tunnel according to the analysis result.

[0014] Optionally, the traffic indicators include: traffic flow, average vehicle speed, headway, traffic density, and road occupancy rate.

[0015] Optionally, the method for obtaining the traffic indicators includes:

[0016] The method for obtaining the traffic flow is:

[0017] Q = N / T

[0018] The method for obtaining the average vehicle speed is:

[0019]

[0020] The method for obtaining the headway is:

[0021]

[0022] The method for obtaining the traffic density is:

[0023]

[0024] The method for obtaining the road occupancy rate is:

[0025]

[0026] where Q is the traffic flow, N is the total number of vehicles within a unit time, T is the acquisition time length, V avg is the average vehicle speed inside the tunnel, V i is the speed of each vehicle, t i+1 -t i represents the passing time difference between two vehicles, C represents the designed traffic volume, and P is the road occupancy rate.

[0027] Optionally, analyzing the vehicle information using the traffic indicators to obtain the analysis result includes:

[0028] Based on the traffic indicators, use the LSTM time series model to obtain the analysis result, where the LSTM time series model is used to predict the traffic flow change trend in the short term in real-time operation.

[0029] Optionally, regulating the intelligent navigation light strip according to the driving state of the vehicle includes:

[0030] When the driving state is in a smooth state, the intelligent navigation light strip is controlled to be in a low-brightness silent mode;

[0031] When the driving state is in a slow-moving state, the intelligent navigation light strip is controlled to be in a medium-brightness breathing mode;

[0032] When the driving state is in a congested state, the intelligent navigation light strip is controlled to be in a high-brightness flashing mode.

[0033] Adjusting the intelligent navigation light strip according to the driving state of the vehicle further includes: sending a warning message to the road management center through wireless communication technology, and notifying the networked vehicles passing through the tunnel at the same time, so that the driver can adjust the driving strategy in advance.

[0034] The present invention also provides a control system for an intelligent navigation light strip inside a tunnel, including: a data acquisition module, a data processing module, a central control module, and a lighting control and debugging module;

[0035] The data acquisition module is used to collect vehicle data and the vehicle traffic flow state;

[0036] The data processing module is used to extract the effective information of the vehicle data and format it into a unified digital signal;

[0037] The central control module is used to divide the traffic flow state according to the processed vehicle data;

[0038] The lighting control and debugging module is used to debug the lighting according to the division result.

[0039] Compared with the prior art, the present invention has the following advantages and technical effects:

[0040] The present invention adopts multi-sensor fusion technology (such as geomagnetic coils, laser speed sensors) and a state classification model to realize real-time detection of traffic flow state and dynamic lighting control. The real-time data acquisition and analysis technology can quickly obtain key parameters such as vehicle speed and traffic flow, and the lighting color and intensity are adjusted in real time through the model, significantly shortening the system response time and improving the operation efficiency.

[0041] The present invention introduces a dynamic adjustment model and a smooth transition mechanism into the lighting control logic. The dynamic adjustment model intelligently matches the lighting color and intensity based on the traffic state, and at the same time, the smooth transition mechanism avoids the interference caused by frequent lighting switching to the driver, improving the comfort and acceptability of lighting guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings forming a part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0043] Figure 1 It is a flowchart of the method for controlling the intelligent navigation light strip inside the tunnel according to an embodiment of the present invention. Detailed implementation manners

[0044] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will describe this application in detail with reference to the drawings and in combination with the embodiments.

[0045] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0046] With the acceleration of the urbanization process and the complexity of the traffic network, tunnels have become an indispensable part of the modern traffic system. However, the unique light environment and traffic density changes inside the tunnel often lead to driver perception delay and safety hazards. Especially during peak hours or in case of emergencies, the traditional static lighting system is difficult to quickly adapt to the changes in the traffic flow state and cannot provide timely and effective driving guidance. Based on this, this system provides intuitive visual cues for drivers by dynamically adjusting the light color and mode, thereby improving the traffic mobility inside the tunnel and enhancing driving safety and comfort.

[0047] The present invention can adjust the color of the light strip in real time according to the traffic flow state inside the tunnel, intuitively indicating the current traffic condition to the driver. The system collects data such as vehicle speed and traffic flow density through a sensor network deployed inside the tunnel, uses machine learning to determine the traffic flow state (such as smooth, slow, congested), and corresponds different states with green, yellow, and red lights respectively. The light strip adopts a segmented control method, supports regional dynamic adjustment and multi-zone linkage, and switches to the emergency mode in case of emergencies, further enhancing the traffic safety and management efficiency of the tunnel.

[0048] This system has the characteristics of fast dynamic response, intuitive information transmission, low maintenance cost, etc., providing an intelligent and automated solution for tunnel traffic management.

[0049] To achieve the above object, this embodiment proposes a method for controlling the intelligent navigation light strip inside the tunnel, as Figure 1 shown, specifically including the following steps:

[0050] Collect vehicle information inside the tunnel;

[0051] Analyze the vehicle information to obtain the driving state of the vehicles inside the tunnel;

[0052] Regulate the intelligent navigation light strip according to the driving state of the vehicles.

[0053] Specifically, based on the comprehensive collection of multi-dimensional data such as vehicle speed and traffic flow, the present invention combines the LSTM time series model to accurately predict and classify the traffic state, overcoming the limitations of the traditional fixed threshold method, and significantly improving the accuracy and adaptability of monitoring. Through the intelligent lighting control model, the system can dynamically adjust the lighting brightness and color according to the real-time traffic state, optimize the tunnel lighting effect, and at the same time introduce a smooth transition mechanism to avoid the interference caused by frequent lighting switching to drivers, improving the driving experience. More importantly, this solution has the characteristics of energy conservation and environmental protection. By dynamically adjusting the lighting intensity, it effectively reduces energy consumption, providing an efficient, intelligent, and environmentally friendly solution for modern tunnel management.

[0054] Furthermore, the vehicle information collected inside the tunnel includes:

[0055] The traffic flow inside the tunnel is measured by using geomagnetic induction coils, the driving speed of vehicles inside the tunnel is measured by using laser speed sensors, and the real-time traffic operation state inside the tunnel is collected by using cameras.

[0056] Specifically, this module uses traffic flow and speed sensors and high-definition cameras to monitor the traffic flow and vehicle driving speed inside the tunnel in real time. The traffic flow in the tunnel is measured by using geomagnetic induction coils. The geomagnetic coils are buried under the tunnel pavement and use the principle of electromagnetic induction to sense the magnetic field changes caused by the passing of vehicles, generate pulse signals and record the number of vehicles, thereby realizing the real-time monitoring of traffic flow. Cooperating with the ground sensor detector and the data processing module, the traffic flow can be efficiently counted. The driving speed of vehicles inside the tunnel is measured by using laser speed sensors. The sensors use the laser Doppler effect and the time difference method to calculate the vehicle speed by monitoring the frequency shift when the vehicle passes through the laser beam or by the time difference between two points. The sensors can be installed on the top or side wall of the tunnel to collect vehicle speed data in a non-contact manner in real time, and classify the vehicle speed state through data processing. Combining with high-definition cameras to measure the traffic environment inside the tunnel in real time provides reliable vehicle data support for the tunnel intelligent navigation light belt system.

[0057] Furthermore, analyzing the vehicle information to obtain the driving state of vehicles inside the tunnel includes:

[0058] Obtaining the traffic indicators inside the tunnel;

[0059] Analyzing the vehicle information by using the traffic indicators to obtain the analysis results;

[0060] According to the analysis results, obtaining the driving state of vehicles inside the tunnel.

[0061] Furthermore, the traffic indicators include: traffic flow, average vehicle speed, headway, traffic density, and road occupancy rate.

[0062] Specifically, the traffic flow reflects the traffic density per unit time, the vehicle speed reveals the driving efficiency of the vehicle, and the headway and road occupancy indicate the traffic mobility and congestion level.

[0063] Furthermore, the methods for obtaining traffic indicators include:

[0064] The method for obtaining the traffic flow is:

[0065] Q = N / T

[0066] The method for obtaining the average vehicle speed is:

[0067]

[0068] The method for obtaining the headway is:

[0069]

[0070] The method for obtaining the traffic density is:

[0071]

[0072] The method for obtaining the road occupancy is:

[0073]

[0074] Where Q is the traffic flow, N is the total number of vehicles per unit time, T is the collection time length, V avg is the average vehicle speed inside the inner lane, V i is the speed of each vehicle, t i+1 -t i represents the passing time difference between two vehicles, C represents the designed traffic volume, and P is the road occupancy.

[0075] Furthermore, using the traffic indicators to analyze vehicle information, the analysis results obtained include:

[0076] Based on the traffic indicators, using the LSTM time series method to predict and analyze the traffic operation state, and obtaining the internal traffic state of the speed adjustment in the short term in the future. Among them, the analysis results include: smooth state, slow-moving state, and congested state.

[0077] Furthermore, according to the driving state of the vehicle, the regulation of the intelligent navigation light strip includes:

[0078] When the driving state is in the smooth state, then control the intelligent navigation light strip to be in the low-brightness silent mode;

[0079] When the driving state is in the slow-moving state, then control the intelligent navigation light strip to be in the medium-brightness breathing mode;

[0080] When the driving state is a congested state, the intelligent navigation light strip is controlled to be in a high-brightness flashing mode.

[0081] Specifically, the internal traffic flow state of the tunnel is output through the above detection and analysis. Using a wireless communication module (such as ZigBee or 5G), control instructions are sent to the light controller to adjust the light state in real time. According to the traffic flow state, the color, brightness, and mode of the light are adjusted. For example: Unobstructed state: green, low brightness, static mode. Normal traffic flow: yellow, medium brightness, breathing mode. Congested state: red, high brightness, flashing mode. The current light state is fed back through the light controller to verify whether it is consistent with the set value. If a deviation is detected (such as the light color or brightness does not match), the control instruction is resent. The PWM (Pulse Width Modulation) technology is used to control the light brightness, and a redundant design is introduced in the tunnel intelligent light regulation system. By installing multiple groups of sensors and a dual control system, a dual-path communication network is established, and a dual power supply and UPS system are configured so that when a key component fails, it automatically switches to the backup system to ensure the continuous and reliable intelligent adjustment of the light color and brightness. Emergency sensors are built in to monitor emergencies in real time. Once an emergency is detected, the system automatically switches to the emergency mode, enables high-brightness stable lighting, and guides vehicles and personnel to evacuate quickly and safely through flashing or color changes. At the same time, the system will immediately notify the road management department of the abnormal state, and the road management department can adjust the light brightness in the tunnel, change the lane indication, publish information on the variable message sign (VMS), or notify the traffic patrol personnel to go to the scene for handling.

[0082] The present invention also provides a control system for the intelligent navigation light strip inside the tunnel, including: a data acquisition module, a data processing module, a central control module, and a light control and debugging module;

[0083] The data acquisition module is used to collect vehicle data and the vehicle traffic flow state;

[0084] The data processing module is used to extract the valid information of the vehicle data and format it into a unified digital signal;

[0085] The central control module is used to divide the traffic flow state according to the processed vehicle data;

[0086] The light control and debugging model is used to debug the light according to the division result.

[0087] Specifically, a high-precision camera: used to record the internal traffic operation state of the tunnel in real time.

[0088] The traffic flow and vehicle speed sensor: collects the traffic flow and vehicle speed data inside the tunnel and transmits them to the central control system.

[0089] Data acquisition module: Responsible for receiving and preliminarily processing data from high-precision cameras and sensors, and preparing the data for transmission to the central control system.

[0090] Central control system: By receiving the traffic flow and vehicle speed data transmitted by the sensors, analyzing these data, and predicting and classifying the operating state of the traffic flow according to the LSTM time series model, it is divided into three traffic flow operating states: congestion, slow, and smooth.

[0091] Light control module: Adjust the navigation light color according to the traffic flow operating state divided by the central control system, and provide the driver with information on the internal traffic state of the tunnel.

[0092] The high-precision camera and traffic flow and vehicle speed sensors are connected to the data acquisition module through data lines. The high-definition camera real-time collects the traffic flow state of the vehicles inside the tunnel, and the traffic flow and vehicle speed sensors collect the data of the vehicles driving inside the tunnel.

[0093] When obtaining the traffic flow state information through the sensors, after extracting the effective information, it is formatted into a unified digital signal. Through optical fiber transmission, the data is transmitted to the central control system.

[0094] The central control system receives and integrates sensor data such as traffic flow, vehicle speed, and headway. After denoising and feature extraction, it uses the LSTM time series model to predict and analyze the traffic state. The system classifies the traffic state into categories such as smooth, slow, and congested according to the set threshold or model output, and at the same time reduces the impact of frequent state switching to ensure the stability and accuracy of the classification results.

[0095] According to the driving state of the traffic flow, the system divides the traffic state into three categories: smooth, slow, and congested, and corresponds to the light colors. Through dynamic adjustment, it real-time detects the traffic flow operating state and generates a control signal, and transmits the signal to the lighting device to adjust the color. It is adjusted to green, yellow, and red respectively for the three states of smooth, slow, and congested. At the same time, the system adopts a smooth transition and feedback mechanism to ensure stable and efficient lighting adjustment, meeting the requirements of driving safety and energy conservation and environmental protection inside the tunnel.

[0096] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. The intelligent navigation light strip control method inside a tunnel is characterized in that, Including: Collect vehicle information inside the tunnel; Analyze the vehicle information to obtain the driving state of vehicles inside the tunnel; Regulate the intelligent navigation light strip according to the driving state of the vehicle.

2. The intelligent navigation light strip control method inside the tunnel according to claim 1, wherein Collecting vehicle information inside the tunnel includes: Measuring the traffic flow inside the tunnel using a geomagnetic induction coil, measuring the driving speed of vehicles inside the tunnel using a laser speed sensor, and collecting the traffic operation state inside the tunnel in real time using a camera.

3. The intelligent navigation light strip control method inside a tunnel according to claim 2, characterized in that, Analyzing the vehicle information to obtain the driving state of vehicles inside the tunnel includes: Obtaining the traffic indicators inside the tunnel; Analyzing the vehicle information using the traffic indicators to obtain an analysis result; Obtaining the driving state of vehicles inside the tunnel according to the analysis result.

4. The method for controlling an intelligent navigation light strip inside a tunnel according to claim 3, wherein The traffic indicators include: traffic flow, average vehicle speed, vehicle interval time, traffic density, and road occupancy rate.

5. The intelligent navigation light strip control method inside a tunnel according to claim 4, characterized in that The method for obtaining the traffic indicators includes: The method for obtaining the traffic flow is: Q = N / T The method for obtaining the average vehicle speed is: The method for obtaining the vehicle interval time is: The method for obtaining the traffic density is: The method for obtaining the road occupancy rate is: Among them, Q is the traffic flow, N is the total number of vehicles within a unit time, T is the collection time length, V avg is the average speed inside the inner lane, V i is the speed of each vehicle, t i+1 -t i represents the passing time difference between two vehicles, C represents the designed traffic volume, and P is the road occupancy rate.

6. The intelligent navigation light strip control method inside the tunnel according to claim 3, wherein, Analyzing the vehicle information using the traffic indicators to obtain an analysis result includes: Based on the traffic indicators, using an LSTM time series model to obtain an analysis result, where the LSTM time series model is used to predict the traffic flow change trend in the short term in real time.

7. The intelligent navigation light strip control method inside the tunnel according to claim 6, characterized in that, Regulating the intelligent navigation light strip according to the driving state of the vehicle includes: When the driving state is a smooth state, control the intelligent navigation light strip to be in a low-brightness silent mode; When the driving state is a slow-moving state, control the intelligent navigation light strip to be in a medium-brightness breathing mode; When the driving state is a congested state, control the intelligent navigation light strip to be in a high-brightness flashing mode.

8. The intelligent navigation light strip control method inside the tunnel according to claim 7, characterized in that, Regulating the intelligent navigation light strip according to the driving state of the vehicle further includes: sending a warning message to the road management center through wireless communication technology, and notifying the networked vehicles passing through the tunnel at the same time, so that the driver can adjust the driving strategy in advance.

9. The control system of the intelligent navigation light strip inside the tunnel is characterized in that, Including: A data collection module, a data processing module, a central control module, and a lighting control and debugging module; The data collection module is used to collect vehicle data and the vehicle traffic flow state; The data processing module is used to extract the valid information of the vehicle data and format it into a unified digital signal; The central control module is used to divide the traffic flow state according to the processed vehicle data; The lighting control and debugging module is used to debug the lighting according to the division result.

Images