CN116997057A - A tunnel lighting system and method based on fuzzy control rules - Google Patents

Abstract

The application discloses a tunnel lighting system and a method thereof based on a fuzzy control rule. The acquisition module is used for acquiring the information of the brightness, the traffic flow, the speed and the visibility of the tunnel inside and outside the tunnel. The collected data is uploaded to a sensor protocol analysis module through a signal transmission line to complete signal type conversion, the signal type conversion is input to an intelligent control algorithm module, a classification prediction result of the in-hole required brightness is obtained through a trained BP neural network model and is sent to a dimming controller, the in-hole brightness sensor detects whether the current brightness is consistent with the classification prediction brightness level, and the dimming controller sends a dimming instruction to adjust the LED lamp. The application adjusts the light in real time based on the changed parameters, ensures the safety of a driver and saves the electric energy.

Description

A tunnel lighting system and method based on fuzzy control rules

Technical field

The present invention relates to the technical field of tunnel lighting, and in particular to a tunnel lighting system and method based on fuzzy control rules.

Background technique

Tunnels must provide artificial lighting due to their semi-enclosed structural characteristics. As the number of tunnels continues to increase, the cost of tunnel lighting is also increasing. According to statistics, the annual cost of tunnel lighting in my country exceeds 5 billion yuan. Overall, the cost of tunnel lighting exceeds 5 billion yuan. Electric energy consumption exceeds 85% of total operational energy consumption. Traditional tunnel lighting control modes only determine the required brightness in the tunnel based on time or a single relevant parameter; some energy-saving control modes only consider energy saving, without considering safety and lamp service life. The cost of LED lamps and supporting drive circuits is relatively high. Frequent adjustment of the brightness of LED lamps only taking into account the reduction of electric energy will cause the LED lamps to heat up, which will never lead to a reduction in the service life of the lamps. The currently widely used time control mode and external brightness adaptive mode consume a large amount of power, causing energy waste.

Contents of the invention

The purpose of the present invention is to provide a tunnel lighting system based on fuzzy control rules.

The technical solution adopted by the present invention is:

A tunnel lighting system based on fuzzy control rules, which includes LED lamps arranged in each lighting section of the tunnel, a dimming controller, an acquisition module, an intelligent control algorithm module, a signal transmission line and a sensor protocol analysis module; the acquisition module is connected to signal transmission Lines and acquisition modules respectively complete the collection of brightness, traffic volume, vehicle speed and tunnel visibility information inside and outside the tunnel; the signal transmission line is connected to the sensor analysis module and connected to the intelligent algorithm adjustment module through the sensor analysis module. The signals collected by the acquisition module pass through the signal transmission line Sent to the sensor protocol analysis module, the sensor analysis module converts the signal type of the collected signal; the intelligent algorithm adjustment module is connected to the dimming controller; the intelligent algorithm adjustment module analyzes the data transmitted by the sensor analysis module based on fuzzy control rules to complete the needs in the cave Brightness level analysis; dimmer controller connects and controls LED lighting fixtures.

Further, the acquisition module includes a brightness detector, a microwave vehicle detector and a visibility detector. The brightness detector is used to detect the real-time brightness outside the tunnel and the actual brightness inside the tunnel; the microwave vehicle detector is installed at the entrance outside the tunnel for Detect the traffic flow and speed at the entrance of the tunnel; the visibility detector is installed at the entrance outside the tunnel to detect the visibility value in the tunnel.

Furthermore, a brightness detector used to detect the actual brightness in the tunnel is installed under the dome of each lighting section of the tunnel.

Furthermore, the sensor used to detect the real-time brightness outside the cave is installed at the starting point of the approach section 1.5m above the ground, facing 20° directly in the direction of the cave entrance.

Furthermore, the intelligent algorithm adjustment module uses BP neural network to complete the module design, and uses an optimization algorithm to optimize the weights and thresholds.

Further, the low-light controller receives instructions from the intelligent algorithm adjustment module and issues dimming instructions to the LED lighting fixtures to control the LED lighting fixtures.

A tunnel lighting method based on fuzzy control rules, applied to the tunnel lighting system based on fuzzy control rules, includes the following steps:

Step 1: Obtain tunnel lighting related parameter data. Tunnel lighting related parameters include brightness inside and outside the tunnel, traffic volume, vehicle speed and tunnel visibility;

Step 2: Convert the tunnel lighting related parameter data to signal types;

Step 3: The converted lighting-related parameter data is input into the trained BP neural network model to obtain the classification prediction results of the required brightness in the cave based on fuzzy control rules, and is sent to the dimming controller;

Step 4: Obtain the current brightness detected by the brightness sensor in the tunnel, and determine whether the current brightness is consistent with the brightness level of the classification prediction result; if so, the dimming controller generates a dimming instruction to adjust the brightness and sends it to the LED lamp; otherwise, the dimming The controller generates dimming instructions that maintain the current level and sends them to the LED lamps;

Step 5: The lamp adjusts the brightness according to the dimming instruction or keeps it unchanged.

Furthermore, in step 3, the BP neural network is trained using a guided learning method; the optimization algorithm is used to optimize the weights and thresholds of the BP neural network.

The present invention adopts the above technical solution and designs a fuzzy controller for the required brightness inside the tunnel based on four real-time changing parameters: brightness outside the tunnel, traffic volume, vehicle speed and tunnel visibility. According to the demand brightness curve in the cave outputted by the fuzzy control rules, the level division of the demand brightness in the cave is completed, and the brightness classification control based on real-time changing parameters is carried out, which has better energy saving, safety and the effect of extending the service life of the lamps. The present invention uses BP neural network to classify and predict the sampled information about brightness outside the tunnel, traffic volume, vehicle speed and tunnel visibility, thereby ensuring the timeliness and stability of dimming without changing the sensor sampling frequency.

Description of the drawings

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments;

Figure 1 is a schematic diagram of the principle of a tunnel lighting system based on fuzzy control rules according to the present invention;

Figure 2 is a schematic flow chart of a tunnel lighting method based on fuzzy control rules according to the present invention;

Figure 3 is a schematic diagram of the intelligent algorithm design principle of the intelligent algorithm adjustment module;

Figure 4 is a schematic diagram of the layout of various tunnel sensors of the acquisition module.

Implementation

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

As shown in one of Figures 1 to 4, the present invention discloses a tunnel lighting system based on fuzzy control rules, which includes LED lamps, dimming controllers, acquisition modules, and intelligent control algorithm modules arranged in each lighting section of the tunnel. , signal transmission line and sensor protocol analysis module; the acquisition module is connected to the signal transmission line, and the acquisition module completes the collection of brightness, traffic flow, vehicle speed and tunnel visibility information inside and outside the tunnel respectively; the signal transmission line is connected to the sensor analysis module and is connected through the sensor analysis module Intelligent algorithm adjustment module, the signal collected by the acquisition module is sent to the sensor protocol analysis module through the signal transmission line, and the sensor analysis module converts the signal type of the collected signal; the intelligent algorithm adjustment module is connected to the dimming controller; the intelligent algorithm adjustment module is based on The fuzzy control rules analyze the data transmitted by the sensor analysis module to complete the analysis of the brightness level required in the cave; the dimming controller connects and controls the LED lighting fixtures.

Further, the acquisition module includes a brightness detector, a microwave vehicle detector and a visibility detector. The brightness detector is used to detect the real-time brightness outside the tunnel and the actual brightness inside the tunnel; the microwave vehicle detector is installed at the entrance outside the tunnel for Detect the traffic flow and speed at the entrance of the tunnel; the visibility detector is installed at the entrance outside the tunnel to detect the visibility value in the tunnel.

Furthermore, a brightness detector used to detect the actual brightness in the tunnel is installed under the dome of each lighting section of the tunnel.

Furthermore, the sensor used to detect the real-time brightness outside the cave is installed at the starting point of the approach section 1.5m above the ground, facing 20° directly in the direction of the cave entrance.

Furthermore, the intelligent algorithm adjustment module uses BP neural network to complete the module design, and uses an optimization algorithm to optimize the weights and thresholds.

Further, the low-light controller receives instructions from the intelligent algorithm adjustment module and issues dimming instructions to the LED lighting fixtures to control the LED lighting fixtures.

A tunnel lighting method based on fuzzy control rules, applied to the tunnel lighting system based on fuzzy control rules, includes the following steps:

Step 1: Obtain tunnel lighting related parameter data. Tunnel lighting related parameters include brightness inside and outside the tunnel, traffic volume, vehicle speed and tunnel visibility;

Step 2: Convert the tunnel lighting related parameter data to signal types;

Step 3: The converted lighting-related parameter data is input into the trained BP neural network model to obtain the classification prediction results of the required brightness in the cave based on fuzzy control rules, and is sent to the dimming controller;

Step 4: Obtain the current brightness detected by the brightness sensor in the tunnel, and determine whether the current brightness is consistent with the brightness level of the classification prediction result; if so, the dimming controller generates a dimming instruction to adjust the brightness and sends it to the LED lamp; otherwise, the dimming The controller generates dimming instructions that maintain the current level and sends them to the LED lamps;

Step 5: The lamp adjusts the brightness according to the dimming instruction or keeps it unchanged.

Furthermore, in step 3, the BP neural network is trained using a guided learning method; the optimization algorithm is used to optimize the weights and thresholds of the BP neural network.

Specifically, the present invention selects four parameters closely related to tunnel lighting to complete the design of fuzzy control rules, providing guidance for the output of required brightness in the tunnel under different traffic conditions. In order to extend the service life of LED lamps, the required brightness in the tunnel is reasonably graded based on the brightness curve of the tunnel lighting output by fuzzy rules, and the dimming frequency of LED lamps is greatly reduced. The BP neural network uses a guided learning method when training, so the BP neural network is used to learn the division of brightness levels in the cave based on fuzzy control rules, and complete the design of the classification prediction model. In view of the fact that the weights and thresholds of the BP neural network have a great impact on the classification prediction accuracy, the (particle swarm) optimization algorithm was used to optimize the weights and thresholds of the BP neural network, and the design of the demand brightness classification prediction model in the cave was completed. The model analysis The information collected in real time is used to classify and predict the required brightness in the cave. If the classification prediction result is consistent with the previous sampling period, the LED lamp will not adjust the brightness, otherwise it will be adjusted to the dimming level predicted by the classification.

The present invention adopts the above technical solution, and the collection module collects the brightness, traffic volume, vehicle speed and tunnel visibility information inside and outside the tunnel. The collected data is uploaded to the control server through the signal transmission line, and then the sensor protocol analysis module completes the conversion of the signal type. After the signal type conversion is completed, the data is input to the intelligent control algorithm module. The trained BP neural network model completes the classification prediction of the required brightness in the cave. The classification prediction results are sent to the dimming controller. The brightness sensor in the cave detects the current brightness. Whether the brightness is consistent with the predicted brightness level of the classification, the dimming controller issues a dimming instruction, and the LED lamp adjusts the brightness according to the instruction or remains unchanged. The patented invention can adjust light in real time based on changing parameters, ensuring the safety of the driver and saving electricity at the same time. The graded dimming strategy can reduce the frequency of dimming, improve the stability of lighting, and also extend the life of LED lamps. service life.

To sum up, the present invention designs a fuzzy controller for indoor demand brightness based on four real-time changing parameters: brightness outside the tunnel, traffic volume, vehicle speed and tunnel visibility. According to the demand brightness curve in the cave outputted by the fuzzy control rules, the level division of the demand brightness in the cave is completed, and the brightness classification control based on real-time changing parameters is carried out, which has better energy saving, safety and the effect of extending the service life of the lamps. The present invention uses BP neural network to classify and predict the sampled information about brightness outside the tunnel, traffic volume, vehicle speed and tunnel visibility, thereby ensuring the timeliness and stability of dimming without changing the sensor sampling frequency.

Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. The embodiments and features in the embodiments in this application may be combined with each other without conflict. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application but rather to represent selected embodiments of the present application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

Claims (8)

1. A tunnel lighting system based on fuzzy control rules, characterized in that: it includes LED lamps arranged in each lighting section of the tunnel, a dimming controller, a collection module, an intelligent control algorithm module, a signal transmission line and a sensor protocol analysis module ; The acquisition module is connected to the signal transmission line, and the acquisition module completes the collection of brightness, traffic flow, vehicle speed and tunnel visibility information inside and outside the tunnel respectively; the signal transmission line is connected to the sensor analysis module and connected to the intelligent algorithm adjustment module through the sensor analysis module, and the acquisition module collects The signal is sent to the sensor protocol analysis module through the signal transmission line. The sensor analysis module converts the signal type of the collected signal; the intelligent algorithm adjustment module is connected to the dimming controller; the intelligent algorithm adjustment module analyzes the data transmitted by the sensor analysis module based on fuzzy control rules. Data is used to complete the analysis of the brightness level required in the cave; the dimming controller connects and controls the LED lighting fixtures. 2. A tunnel lighting system based on fuzzy control rules according to claim 1, characterized in that: the acquisition module includes a brightness detector, a microwave vehicle detector and a visibility detector, and the brightness detector is used to detect real-time brightness outside the tunnel. and the actual brightness inside the tunnel; the microwave vehicle detector is installed at the entrance outside the tunnel to detect the traffic flow and speed at the tunnel entrance; the visibility detector is installed at the entrance outside the tunnel to detect the visibility inside the tunnel value. 3. A tunnel lighting system based on fuzzy control rules according to claim 2, characterized in that: a brightness detector used to detect the actual brightness in the tunnel is installed under the dome of each lighting section of the tunnel. 4. A tunnel lighting system based on fuzzy control rules according to claim 2, characterized in that: the sensor used to detect the real-time brightness outside the tunnel is installed at a height of 1.5m from the ground near the starting point of the section, facing the entrance of the tunnel. direction 20°. 5. A tunnel lighting system based on fuzzy control rules according to claim 1, characterized in that: the intelligent algorithm adjustment module uses BP neural network to complete the design of the module, and uses an optimization algorithm to optimize weights and thresholds. 6. A tunnel lighting system based on fuzzy control rules according to claim 1, characterized in that: the low-light controller receives instructions from the intelligent algorithm adjustment module and issues dimming instructions to the LED lighting fixtures to control the LED lighting fixtures. 7. A tunnel lighting method based on fuzzy control rules, applied to a tunnel lighting system based on fuzzy control rules according to one of claims 1 to 6, characterized in that: it includes the following steps: Step 1: Obtain tunnel lighting related parameter data. Tunnel lighting related parameters include brightness inside and outside the tunnel, traffic volume, vehicle speed and tunnel visibility; Step 2: Convert the tunnel lighting related parameter data to signal types; Step 3: The converted lighting-related parameter data is input into the trained BP neural network model to obtain the classification prediction results of the required brightness in the cave based on fuzzy control rules, and is sent to the dimming controller; Step 4: Obtain the current brightness detected by the brightness sensor in the tunnel, and determine whether the current brightness is consistent with the brightness level of the classification prediction result; if so, the dimming controller generates a dimming instruction to adjust the brightness and sends it to the LED lamp; otherwise, the dimming The controller generates dimming instructions that maintain the current level and sends them to the LED lamps; Step 5: The lamp adjusts the brightness according to the dimming instruction or keeps it unchanged. 8. A tunnel lighting method based on fuzzy control rules according to claim 7, characterized in that: in step 3, the BP neural network adopts a guided learning method for training; an optimization algorithm is used to calculate the BP neural network weights and Thresholds are optimized.