CN-122028278-A - Tunnel illumination brightness self-adaptive control method based on wireless networking

CN122028278ACN 122028278 ACN122028278 ACN 122028278ACN-122028278-A

Abstract

The invention relates to the technical field of intelligent traffic and tunnel illumination control, in particular to a tunnel illumination brightness self-adaptive control method based on wireless networking, which comprises the steps of data acquisition mapping, distribution model construction, parameter multicasting and edge execution, wherein a system acquires vehicle data and signal to noise ratio by using radar to construct a dynamic brightness distribution model taking a vehicle as a center, the system is characterized in that a distributed architecture is adopted, a gateway only multicasts global feature packets containing position, speed and diffusion form parameters, each single lamp controller autonomously analyzes a target duty ratio by combining local coordinates and completes illumination electrical parameter self-adaptive adjustment.

Inventors

ZENG FANZHENG
FANG YAO

Assignees

浙江永通科技发展有限公司

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (8)

1. The tunnel illumination brightness self-adaptive control method based on wireless networking is characterized by being applied to a tunnel illumination system comprising an edge computing gateway and a plurality of single-lamp controllers, and comprises the following steps of: Step S1, data acquisition and parameter mapping, wherein the edge computing gateway acquires real-time position coordinates and running speed of a vehicle in a tunnel through a radar sensor and monitors a signal-to-noise ratio value of a wireless communication network in real time; S2, constructing a distribution model, wherein the edge computing gateway constructs a one-dimensional numerical distribution model by taking the real-time position coordinates as the center, and calculates a dynamic brightness distribution data model covering a tunnel section by utilizing an unsteady diffusion algorithm and combining with the diffusion form parameters, wherein the unsteady diffusion algorithm is a space mapping algorithm for dynamically adjusting the shape parameters of a distribution function by utilizing time-varying signal-to-noise ratio parameters as diffusion coefficients in a discrete time domain; step S3, parameter multicasting, wherein the edge computing gateway broadcasts a distribution characteristic data packet containing the real-time position coordinates, the running speed and the diffusion form parameters to all single-lamp controllers in a tunnel through a multicasting channel of a wireless communication protocol based on a pre-constructed wireless illumination control subnet logic; And S4, executing at the edge end, wherein each single lamp controller receives the distributed characteristic data packet, substitutes a preset brightness analysis function to calculate a target duty ratio at the current moment by combining with a physical installation coordinate pre-stored by the single lamp controller, and generates a PWM signal to drive an LED power supply to output current according to the target duty ratio so as to finish the self-adaptive adjustment of the lighting electrical parameters based on the networking state.
2. The method according to claim 1, wherein in the step S1, the conversion of the snr value into a diffusion morphological parameter follows the following logic: establishing a negative correlation function relation between the diffusion morphological parameters and the signal-to-noise ratio value; When the signal-to-noise ratio value is lower than a preset network quality threshold, outputting a diffusion morphological parameter with a larger value, so that the effective illumination coverage of the dynamic brightness distribution data model in space is enlarged to compensate the communication time delay in a low signal-to-noise ratio environment; and when the signal-to-noise ratio value is higher than or equal to the preset network quality threshold value, outputting a diffusion morphological parameter with a smaller value, so that the dynamic brightness distribution data model presents convergence distribution in space to match the real-time position of the vehicle.
3. The method according to claim 1, wherein in the step S2, the numerical calculation logic of the dynamic brightness distribution data model is: Defining the change rate of the amplitude of the one-dimensional numerical distribution model along with the time and the space distance; taking the diffusion morphological parameters as distribution scale parameters in the change rate calculation; The calculation result needs to be satisfied that the brightness value is in nonlinear attenuation along with the increase of the space distance from the real-time position coordinate, and the attenuation rate is inversely proportional to the diffusion morphological parameter, so that when the diffusion morphological parameter is increased due to poor communication quality, a pre-illumination area extending forward in the vehicle travelling direction is automatically generated.
4. The method according to claim 1, wherein in the step S3, the multicast channel broadcast through the wireless communication protocol specifically includes: the edge computing gateway adopts ZigBee or LoRaWAN protocol to configure unified multicast group ID; Packaging the real-time position coordinates, the running speed and the diffusion form parameters into a control frame with a fixed byte length; the control frame is transmitted at a fixed broadcast period and no specific single-lamp controller address is specified, so that the load occupancy of the wireless channel does not change with an increase in the number of single-lamp controllers.
5. The method according to claim 1, wherein in the step S4, the single lamp controller performs a calculation step including a data packet loss self-healing logic: step S41, judging whether a new distribution characteristic data packet is successfully received in the current control period; Step S42, if the receiving is successful, updating a local cache by using the new real-time position coordinates, and calculating a target duty ratio by using the brightness analysis function; And S43, if the vehicle is not successfully received, reading the vehicle position coordinate, the running speed and the diffusion form parameter cached in the previous control period, estimating the predicted position coordinate of the vehicle at the current moment by using a linear extrapolation algorithm, substituting the predicted position coordinate and the cached diffusion form parameter into the brightness analysis function to calculate the target duty ratio until a new data packet is received or the overtime reset is achieved.
6. The method according to claim 1, wherein in the step S4, the calculation logic of the luminance analysis function is: Acquiring an absolute distance difference value between the physical installation coordinates of the single-lamp controller and the real-time position coordinates; Processing the absolute distance difference value by using a Gaussian distribution function or an exponential decay function, and introducing a standard deviation or a decay rate of the diffusion morphological parameter adjustment function; and multiplying the normalized value output by the function by a preset maximum brightness value to obtain the reference value of the target duty ratio.
7. The method for adaptively controlling the illumination brightness of a tunnel based on wireless networking according to claim 1, further comprising a step of smoothing clipping the PWM signal after said step S4: calculating the absolute value of the difference between the target duty ratio generated at the current moment and the duty ratio output in the last control period; judging whether the absolute value of the difference is larger than a preset visual flicker threshold; If so, taking the duty ratio output in the previous control period as a reference, and increasing or decreasing according to the maximum variation allowed by the visual flicker threshold value to generate a corrected PWM signal; if not, generating PWM signals directly by utilizing the target duty ratio.
8. The wireless networking-based tunnel lighting brightness adaptive control method of claim 1, wherein the negative correlation mapping logic is embodied as: The diffusion form parameter is equal to a normalized value of a preset reference constant divided by the signal to noise ratio value, or the diffusion form parameter is equal to a product of a preset maximum diffusion form parameter minus the signal to noise ratio value and a weighting coefficient with a length dimension; through the calculation, the network congestion degree is ensured to be directly mapped into the spatial redundancy of the illumination area.

Description

Tunnel illumination brightness self-adaptive control method based on wireless networking Technical Field The invention relates to the technical field of intelligent traffic and tunnel illumination control, in particular to a tunnel illumination brightness self-adaptive control method based on wireless networking. Background With the continuous upgrade of intelligent traffic infrastructure, tunnel lighting systems are gradually evolving towards wireless network-based Internet of things architecture to meet the requirements of flexible deployment and intelligent management and control, however, tunnels are used as typical semi-closed limited spaces, and the special physical environment of the tunnels makes the wireless communication quality extremely easy to be interfered and fluctuated; The conventional point-to-point or centralized communication mode is adopted in the prior tunnel illumination control technology, and a control center needs to independently send instructions to each single-lamp controller; under the scene of high-density deployment of extra-long tunnels or lamps, the communication mechanism can lead the load occupancy rate of a wireless channel to be linearly increased along with the number of the lamps, so that serious channel congestion, data accumulation and control delay are easily caused; in addition, the prior art generally lacks self-adaptive compensation logic for network physical layer quality, when the signal-to-noise ratio of wireless signals in a tunnel is reduced or packet loss occurs, illumination equipment cannot adjust a coverage strategy according to a communication state, so that illumination response is delayed to a vehicle driving position, an illumination dark area or a visual blind area is extremely easy to form, and driving safety is seriously threatened; Therefore, how to reduce the channel resource occupation of a large-scale lighting node, and ensure the real-time performance and visual continuity of lighting control in an environment with unstable communication quality becomes a problem to be solved in the field. Disclosure of Invention In order to solve the technical problems, the invention provides a tunnel illumination brightness self-adaptive control method based on wireless networking, and specifically, the technical scheme of the invention comprises the following steps: an edge computing gateway and a tunnel lighting system of a plurality of single lamp controllers, the method comprising the steps of: Step S1, data acquisition and parameter mapping, wherein the edge computing gateway acquires real-time position coordinates and running speed of a vehicle in a tunnel through a radar sensor and monitors a signal-to-noise ratio value of a wireless communication network in real time; S2, constructing a distribution model, wherein the edge computing gateway constructs a one-dimensional numerical distribution model by taking the real-time position coordinates as the center, and calculates a dynamic brightness distribution data model covering a tunnel section by utilizing an unsteady diffusion algorithm and combining with the diffusion form parameters, wherein the unsteady diffusion algorithm is a space mapping algorithm for dynamically adjusting the shape parameters of a distribution function by utilizing time-varying signal-to-noise ratio parameters as diffusion coefficients in a discrete time domain; step S3, parameter multicasting, wherein the edge computing gateway broadcasts a distribution characteristic data packet containing the real-time position coordinates, the running speed and the diffusion form parameters to all single-lamp controllers in a tunnel through a multicasting channel of a wireless communication protocol based on a pre-constructed wireless illumination control subnet logic; And S4, executing at the edge end, wherein each single lamp controller receives the distributed characteristic data packet, substitutes a preset brightness analysis function to calculate a target duty ratio at the current moment by combining with a physical installation coordinate pre-stored by the single lamp controller, and generates a PWM signal to drive an LED power supply to output current according to the target duty ratio so as to finish the self-adaptive adjustment of the lighting electrical parameters based on the networking state. Preferably, said converting said signal-to-noise ratio value to a diffusion morphological parameter follows the logic: establishing a negative correlation function relation between the diffusion morphological parameters and the signal-to-noise ratio value; When the signal-to-noise ratio value is lower than a preset network quality threshold, outputting a diffusion morphological parameter with a larger value, so that the effective illumination coverage of the dynamic brightness distribution data model in space is enlarged to compensate the communication time delay in a low signal-to-noise ratio environment; and when the signal-to-noise ra