CN-122028275-A - Tunnel illumination self-adaptive regulation and control system and method based on vehicle-road cooperation

CN122028275ACN 122028275 ACN122028275 ACN 122028275ACN-122028275-A

Abstract

The invention discloses a tunnel illumination self-adaptive regulation and control system and a tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation, which relate to the technical field of intelligent tunnels and comprise the steps of collecting real-time vehicle-road cooperation data, carrying out space-time alignment and anomaly filtering on the vehicle-road cooperation data to generate a pretreatment data set, previewing a dimming strategy in a digital twin environment according to future vehicle distribution density and light environment demand parameters, optimizing and generating a gradient brightness instruction set, inputting a pretrained generation countermeasure network model, simulating interference signals and dynamically filtering noise, outputting a purified dimming instruction stream, sending a dimming control instruction to tunnel lamps through a wireless networking, adjusting the brightness and the color temperature of the lamps, and executing feedback update of the digital twin body and generation of the countermeasure network model parameters. According to the invention, through a vehicle-road cooperative data space-time alignment and anomaly filtering mechanism, the space-time deviation and noise interference of the vehicle-road cooperative data are eliminated, and the confidence of the preprocessed data is improved.

Inventors

XU JUNLI
Deng Shenao
Dang Yatao
FENG QIANG
MENG XIANWEI
TAI NANA
WANG QUANQUAN
WANG XIANTAO

Assignees

海南耀电科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260226

Claims (10)

1. A tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation is characterized by comprising the following steps of, Collecting real-time vehicle-road cooperative data, and performing space-time alignment and anomaly filtering on the vehicle-road cooperative data to generate a preprocessing data set; Constructing a lightweight vehicle digital twin body based on the preprocessing data set, extracting vehicle track characteristics and a visual adaptation curve, and predicting future vehicle distribution density and light environment demand parameters through a long-term and short-term memory network; According to future vehicle distribution density and light environment demand parameters, previewing a dimming strategy in a digital twin environment, optimizing to generate a gradient brightness instruction set, inputting a pretrained generation countermeasure network model, simulating an interference signal, dynamically filtering noise, and outputting a purified dimming instruction stream; dynamically encrypting and verifying the integrity of the purified dimming command stream to generate a tamper-proof dimming control command; and sending a dimming control instruction to the tunnel lamp through wireless networking, adjusting the brightness and the color temperature of the lamp, and executing feedback to update the digital twin body and generate the parameters of the countermeasure network model.
2. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation as set forth in claim 1, wherein the vehicle-road cooperation data comprises vehicle speed data, vehicle position data, vehicle light state data, light intensity data of the environment inside and outside the tunnel and wireless channel state information data.
3. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation as set forth in claim 2, wherein the generation of the pretreatment data set comprises the following specific steps, Adding a time stamp to perform hardware-level synchronization according to the light intensity data of the environment inside and outside the tunnel and the wireless channel state information data in the vehicle-road cooperative data, and generating time synchronization data; Converting the vehicle position data to a tunnel local coordinate system through affine transformation, and fusing inertial navigation and UWB positioning to update the vehicle position when the GPS signal fails to work, so as to generate space alignment data; performing cubic spline interpolation on asynchronously acquired ambient light intensity, vehicle speed, vehicle position and vehicle light state data to unify time sequence frequency, and generating space-time alignment data; detecting abnormal data in the space-time alignment data by improving an isolated forest algorithm, and carrying out abnormal filtering by combining with the signal-to-noise ratio of a wireless channel to generate filtering data; and integrating the filtered data according to a time window to generate a space-time correlation matrix, interpolating the missing values by adopting a space-time K nearest neighbor algorithm, and outputting a preprocessed data set.
4. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation of claim 3, wherein the predicted future vehicle distribution density and light environment demand parameters comprise the following specific steps, Constructing a space-time diagram structure according to the vehicle speed, the vehicle position and the light intensity data of the environment inside and outside the tunnel in the preprocessing data set; inputting the space-time diagram structure into a pre-trained space-time diagram convolution network to extract vehicle track characteristics and output node characteristic vectors; Inputting the node characteristic vector into a pre-trained long-short-period memory network to generate a track embedded vector, and dynamically integrating historical light intensity data of the vehicle by adopting a self-adaptive light field function to generate a visual adaptation curve; And inputting a pre-trained multi-agent depth deterministic strategy gradient model based on the track embedded vector and the visual adaptation curve, and predicting future vehicle distribution density and light environment demand parameters.
5. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation of claim 4, wherein the output of the purified dimming instruction stream comprises the following specific steps of, Mapping the vehicle distribution density into a quantum probability amplitude through quantum state coding, and generating an initial brightness instruction set by combining the light environment demand parameters; inputting an initial brightness instruction set into a pre-trained quantum convolution generation countermeasure network, generating a noise-containing dimming instruction by fusing quantum convolution kernel entanglement transformation and a classical long-short-term memory network by a generator, and recognizing a noise mode by a discriminator through a space-time attention mechanism to generate a denoising instruction; constructing a dynamic tuning differential equation through a noise-containing dimming instruction, defining the brightness change rate as the sum of a light demand gradient driving term and a non-smooth dissipation term, and solving an optimal brightness field by adopting a spectrum method discretization equation; And applying controllable quantum noise to the optimal brightness field to simulate wireless channel interference, generating a discriminator reconstruction denoising instruction of an countermeasure network through quantum convolution, and outputting a purified dimming instruction stream.
6. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation of claim 5, wherein the generation of the tamper-proof dimming control command comprises the following specific steps of, Generating an encryption key through a chaos-quantum dynamic key based on the purified dimming instruction stream, the vehicle distribution density and the wireless channel signal-to-noise ratio; dividing the purified dimming instruction stream into blocks according to time windows by using an encryption key, dynamically encrypting each block by using a quantum tensor entanglement function, and injecting Gaussian noise confusion to generate an encryption block; calculating a quantum hash value of the encryption block based on the encryption block, constructing a quantum Meckel tree, generating Meckel root hash, writing the Meckel root hash into a block chain main chain, and simultaneously storing the quantum hash values of the encryption block and the encryption block into distributed side chains; and randomly selecting a designated encryption block from the distributed side chains, comparing the quantum hash value of the encryption block with the blockchain main chain record, judging that the encryption block is tampered if the deviation exceeds a preset deviation threshold value, and generating a tamper-resistant dimming control instruction.
7. The tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation of claim 6, wherein the steps of updating digital twin and generating countermeasure network model parameters are as follows, Acquiring wireless channel impulse response, constructing a time-varying channel matrix, performing power normalization processing, and generating a channel state matrix; Based on the tamper-proof dimming control instruction and the channel state matrix, generating an encryption instruction through quantum key distribution, and transmitting the encryption instruction to the tunnel lamp; analyzing the encryption instruction into brightness and color temperature parameters, driving the lamp to adjust light, and synchronously collecting the light intensity data of the lamp and the environment in the tunnel as feedback data; Performing digital twins and generating an countermeasure network model parameter update at the edge node based on the feedback data.
8. The tunnel illumination self-adaptive regulation and control system based on the vehicle-road cooperation is based on the tunnel illumination self-adaptive regulation and control method based on the vehicle-road cooperation, which is characterized by comprising a data processing module, a twin modeling module, a strategy optimization module, an instruction encryption module and a networking feedback module; the data processing module is used for collecting real-time vehicle-road cooperative data, carrying out space-time alignment and anomaly filtering on the vehicle-road cooperative data, and generating a preprocessing data set; the twin modeling module is used for constructing a lightweight vehicle digital twin body based on the preprocessing data set, extracting vehicle track characteristics and visual adaptation curves, and predicting future vehicle distribution density and light environment demand parameters through a long-term and short-term memory network; The strategy optimization module is used for previewing a dimming strategy in a digital twin environment according to future vehicle distribution density and light environment demand parameters, optimizing and generating a gradient brightness instruction set, inputting a pretrained generation countermeasure network model, simulating an interference signal, dynamically filtering noise, and outputting a purified dimming instruction stream; The instruction encryption module is used for dynamically encrypting and verifying the integrity of the purified dimming instruction stream to generate a tamper-resistant dimming control instruction; The networking feedback module is used for sending a dimming control instruction to the tunnel lamp through wireless networking, adjusting the brightness and the color temperature of the lamp, and executing feedback to update the digital twin body and generate the parameters of the countermeasure network model.
9. The computer equipment comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is characterized in that the processor realizes the steps of the tunnel illumination self-adaptive regulation and control method based on the vehicle-road cooperation according to any one of claims 1-7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, is characterized in that the computer program, when being executed by a processor, implements the steps of the tunnel illumination adaptive control method based on vehicle-road cooperation as set forth in any one of claims 1 to 7.

Description

Tunnel illumination self-adaptive regulation and control system and method based on vehicle-road cooperation Technical Field The invention relates to the technical field of intelligent tunnels, in particular to a tunnel illumination self-adaptive regulation and control system and method based on vehicle-road cooperation. Background The vehicle-road cooperation realizes real-time data interaction between the vehicle and road infrastructure through V2X communication, edge calculation and Internet of things technology, and provides basic support for intelligent traffic management. In the tunnel illumination field, dynamic dimming gradually replaces a traditional fixed brightness mode, and preliminary self-adaptive regulation and control are realized through a light intensity sensor and traffic flow detection. Digital twinning is also applied to traffic scenes for constructing virtual traffic environments and simulating vehicle behavior. In the prior art, the generation of the countermeasure network model is applied to traffic flow prediction and signal optimization, but the multi-source data fusion and anti-interference capability under a complex tunnel scene are still limited. The existing tunnel dimming depends on static rules or single sensor data, and it is difficult to effectively fuse vehicle-road collaborative multi-mode data. Because of the space-time asynchronism of the cooperative data of the vehicle and the interference of wireless transmission noise, the prediction error of the distribution density of the vehicle is large, and the adaptability of the light environment demand parameters and the actual driving vision is poor. The dimming instruction generation process lacks of dynamic modeling of channel interference, is easily influenced by malicious attack or signal distortion, and causes illumination abrupt change and energy waste. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides a tunnel illumination self-adaptive regulation and control method based on vehicle-road coordination, which solves the problems of low tunnel illumination dimming precision and poor anti-interference capability caused by inconsistent vehicle-road coordination data time and space and noise interference. In order to solve the technical problems, the invention provides the following technical scheme: In a first aspect, the invention provides a tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation, which comprises the steps of collecting real-time vehicle-road cooperation data, performing space-time alignment and anomaly filtering on the vehicle-road cooperation data, and generating a pretreatment data set; Constructing a lightweight vehicle digital twin body based on the preprocessing data set, extracting vehicle track characteristics and a visual adaptation curve, and predicting future vehicle distribution density and light environment demand parameters through a long-term and short-term memory network; According to future vehicle distribution density and light environment demand parameters, previewing a dimming strategy in a digital twin environment, optimizing to generate a gradient brightness instruction set, inputting a pretrained generation countermeasure network model, simulating an interference signal, dynamically filtering noise, and outputting a purified dimming instruction stream; dynamically encrypting and verifying the integrity of the purified dimming command stream to generate a tamper-proof dimming control command; and sending a dimming control instruction to the tunnel lamp through wireless networking, adjusting the brightness and the color temperature of the lamp, and executing feedback to update the digital twin body and generate the parameters of the countermeasure network model. The tunnel illumination self-adaptive regulation and control method based on the vehicle-road coordination is characterized in that the vehicle-road coordination data comprise vehicle speed data, vehicle position data, vehicle light state data, light intensity data of the environment inside and outside the tunnel and wireless channel state information data. As an optimal scheme of the tunnel illumination self-adaptive regulation and control method based on vehicle-road cooperation, the method comprises the following specific steps of, Adding a time stamp to perform hardware-level synchronization according to the light intensity data of the environment inside and outside the tunnel and the wireless channel state information data in the vehicle-road cooperative data, and generating time synchronization data; Converting the vehicle position data to a tunnel local coordinate system through affine transformation, and fusing inertial navigation and UWB positioning to update the vehicle position when the GPS signal fails to work, so as to generate space alignment data; performing cu