Wireless Network-Based Intelligent Timing Control Entity and Control Method for Tunnel Lighting

Abstract

This invention relates to the field of tunnel traffic lighting engineering and intelligent adaptive control technology, specifically to a wireless network-based intelligent timing control system for tunnel lighting and its control method. The system includes: a data acquisition hardware group that collects real-time velocity vectors of moving objects and ambient brightness values ​​of the tunnel environment; a central control processor configured to perform the following operations: constructing a virtual fluid medium model; calculating the virtual shear viscosity parameters of the virtual fluid medium based on human visual adaptation characteristics and ambient brightness values ​​of the tunnel environment; substituting the real-time velocity vector as a disturbance source into the fluid dynamics control equations to generate a spatiotemporally continuous virtual light pressure control data field; and parsing the virtual light pressure control data field into discrete pulse width modulation commands according to spatial coordinates, sending them to the drive power supply via a wireless ad hoc network to control the brightness of the corresponding lighting units. This invention effectively reduces the risk of rear-end collisions and achieves deep integration of the lighting system and traffic flow control.

Description

Technical Field

[0001] This invention relates to the field of tunnel traffic lighting engineering and intelligent adaptive control technology, specifically to a wireless network-based intelligent timing control system for tunnel lighting and its control method. Background Technology

[0002] In the current field of intelligent transportation and tunnel operation and maintenance, tunnel lighting systems need to be dynamically adjusted in real time according to the ambient light intensity outside the tunnel and the driving status of vehicles inside the tunnel in order to ensure driving safety and take into account energy-saving requirements. However, laying control signal cables in long tunnels is costly and difficult to maintain. The signal attenuation and transmission delay caused by long lines are also difficult to meet the real-time synchronization requirements of high-frequency and continuous optical flow control data. To achieve this goal, existing solutions generally adopt a discrete-state control architecture, which periodically adjusts the power output of the LED lighting unit through a preset brightness threshold or simple lookup table logic. Although such solutions have a certain adjustment capability in normal lighting switching scenarios, their control logic is inherently abrupt and isolated, lacking continuity in the spatiotemporal dimension, often resulting in a noticeable stepped effect and visual oscillation in the lighting output. In actual operation, traditional control methods cannot effectively quantify and simulate the physiological adaptation lag characteristics of the human eye under drastic changes in light intensity; when ambient light fluctuates drastically or vehicles travel at high speeds... During passage, the system often faces a conflict between the need for rapid response and visual comfort, resulting in adjustment lag or excessive brightness jumps, which can lead to driver blindness or visual fatigue. In addition, the discrete control logic makes it difficult to achieve subtle intervention in complex driving behaviors and cannot proactively guide driving behavior based on changes in the light environment in specific scenarios such as traffic congestion. Therefore, how to break through the limitations of traditional discrete dimming and construct a light field control model with continuity and guideability to balance hardware response speed and physiological adaptation laws, and improve the smoothness and intelligent guidance capabilities of tunnel lighting adjustment, has become an urgent technical problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a wireless network-based intelligent timing control system for tunnel lighting and its control method. By constructing a virtual fluid medium model, the tunnel lighting control is upgraded from a discrete state to a continuous flow field control, solving the problems of stepped perception and visual oscillation caused by traditional segmented control. Furthermore, it achieves adaptive smooth dimming for protection against sudden changes in ambient light and for tracking high-speed moving objects. Specifically, the technical solution of this invention is as follows: The wireless networking-based intelligent timing control system for tunnel lighting includes: The data acquisition hardware group includes a radar speedometer deployed inside the tunnel and a light intensity sensor outside the tunnel, used to collect the real-time velocity vector of moving objects and the brightness value of the external environment of the tunnel in real time; The lighting execution network consists of multiple LED lighting units distributed longitudinally along the tunnel and their corresponding driver power supplies. Each driver power supply integrates a wireless communication node, and the nodes establish a communication topology through a wireless self-organizing network protocol. The central control processor, connected to the data acquisition hardware group and establishing a wireless communication connection with the lighting execution network via a wireless signal transceiver module, is configured to perform the following operations: A virtual fluid medium model is constructed, which maps the tunnel space to rigid pipe boundary data and the output luminous flux of the LED lighting unit to the density data of the virtual fluid medium. These are collectively referred to as virtual light pressure P. Based on the adaptive characteristics of human vision, the virtual shear viscosity parameters of the virtual fluid medium are calculated using the brightness value of the external environment of the channel; By substituting the real-time velocity vector as a disturbance source into the fluid dynamics control equations and combining it with virtual shear viscosity parameters, the rheological dynamic state of the virtual fluid medium in the rigid pipe is iteratively calculated to generate a spatiotemporally continuous virtual photopressure control data field. The virtual light pressure control data field is parsed into discrete pulse width modulation (PWM) commands according to spatial coordinates and sent to the driver power supply to control the brightness of the corresponding LED lighting unit.

[0004] Preferably, when calculating the virtual shear viscosity parameters, the central control processor performs the following logically rigorous calculation steps: Read the current ambient brightness value of the channel and the historical brightness value at the previous sampling time, and calculate the rate of change of ambient brightness. If the absolute value of the rate of change is greater than the preset mutation threshold, it is determined that an ambient light mutation has occurred. The virtual shear viscosity parameter is set to the first high viscosity value to force smooth the spatial gradient of the virtual light pressure control data field and suppress the violent fluctuation of lighting brightness. If the absolute value of the rate of change is less than or equal to the mutation threshold, then based on the logarithmic relationship of the Weber-Fechner law, the basic physiological relaxation time constant that is positively correlated with the current external ambient brightness value is calculated, and this constant is directly linearly mapped to the virtual shear viscosity parameter. The virtual shear viscosity parameter numerically characterizes the diffusion resistance of a virtual fluid medium when subjected to velocity vector perturbations, and is used to simulate the adaptive hysteresis of the human eye to changes in light intensity.

[0005] Preferably, the fluid dynamics control equations are described in the processor's computational logic as follows: The local derivative of the virtual light pressure control data field with respect to time, plus the product of the real-time velocity vector of the moving object and the spatial gradient of the virtual light pressure control data field along the tunnel longitudinal direction, is numerically equal to the product of the virtual shear viscosity parameter and the second spatial derivative of the virtual light pressure control data field. The processor uses the finite difference method to discretize and solve the above balance relationship, and calculates the ideal virtual light pressure value at each coordinate node in the tunnel at the next moment.

[0006] Preferably, the central control processor is also equipped with congestion-inducing logic: When the average vehicle speed inside the tunnel is detected to be lower than the preset congestion threshold, the processor forcibly corrects the convection velocity parameter in the fluid dynamics control equation and sets it to a value lower than the induced velocity value of the real-time velocity vector of the moving object. By calculating and generating a virtual light pressure control data field that lags behind the current position of the moving object, a relatively receding illumination light flow is formed in front of the moving object's field of vision, using visual cues to induce the moving object to slow down.

[0007] Preferably, the lighting execution network transmits commands through wireless self-organizing network nodes based on ZigBee, LoRa, or NB-IoT protocols, and each LED lighting unit constructs a dynamic topology through multi-hop routing to achieve spatial collaborative distribution of virtual light pressure control data field; The power supply has an internal slope limiter. When the difference in duty cycle between two adjacent timing cycles of the received PWM command exceeds the hardware safety threshold, the slope limiter forces the rate of change of the output current to be limited within the safety threshold in order to eliminate thermal stress shock.

[0008] Preferably, the boundary conditions for the virtual fluid medium model are set as follows: Map the coordinates of the tunnel entrance and exit to the open boundary of the rigid pipe; The geometry of the tunnel wall is mapped to the solid wall boundary of a rigid pipe, and a no-slip condition is set at the solid wall boundary, that is, the fluid velocity is zero at the wall surface. The basic lighting brightness when there are no moving objects in the tunnel is mapped to the initial static density field of the virtual fluid medium.

[0009] Preferably, the central control processor also incorporates a speed-viscosity dynamic coupling mechanism: Real-time monitoring of the magnitude of the real-time velocity vector of a moving object; When the modulus increases, the corresponding viscosity correction coefficient is obtained by looking up a table. The viscosity correction coefficient is less than 1 and is used to attenuate the virtual shear viscosity parameter, thereby reducing the diffusion resistance of the virtual fluid medium and enabling the generated virtual optical pressure control data field to follow the high-speed moving object for high-frequency refresh. When the modulus decreases, the current virtual shear viscosity parameter is maintained without attenuation correction.

[0010] The control method for a wireless network-based intelligent control subject for tunnel lighting timing, based on any one of claims 1-7, includes the following steps: S1. Collect real-time velocity vectors of moving objects inside the tunnel and reference brightness data of the external environment of the tunnel through the data acquisition hardware group; S2. The processor constructs a virtual fluid medium model, maps the physical space of the tunnel to a computational domain, and defines the lighting brightness as a scalar field variable within this computational domain. S3. Based on the reference brightness data of the external environment, calculate the virtual shear viscosity parameters of the virtual fluid through a preset visual adaptation model, wherein the virtual shear viscosity parameters are positively correlated with the reference brightness data. S4. Substitute the real-time velocity vector as the convective velocity into the transport equation containing time, convection, and diffusion terms, and use the virtual shear viscosity parameter to solve the spatiotemporal distribution of the scalar field variables at the next moment to generate the target brightness field. S5. Convert the target brightness field into a corresponding PWM control signal, transmit it through a wireless self-organizing network, and drive the lighting equipment in the tunnel to output physical light.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention transforms traditional discrete dimming commands into a continuous and guideable fluid density field by constructing a virtual fluid medium model. This innovative architecture breaks through the limitations of traditional segmented control from a mathematical perspective, eliminating the stair-step effect and visual oscillations generated during dimming. The system enables millisecond-level smooth transitions in the tunnel light environment in the spatiotemporal dimension, ensuring the visual stability of the driving environment. 2. The system creatively introduces a virtual shear viscosity parameter, which establishes a bridge between the physical light environment and physiological characteristics by simulating the physiological lag of human visual adaptation. When the ambient light changes abruptly, the virtual shear viscosity parameter is dynamically adjusted to force the smoothing of the light pressure field gradient, which effectively alleviates the optic nerve adaptation pressure of the driver when entering and exiting the tunnel, prevents the occurrence of flash blindness, and greatly enhances the safety of tunnel lighting from a biomimetic perspective. 3. The solution has excellent high-speed response and physical protection balance capabilities; through the dynamic coupling mechanism of speed and viscosity, the system can reduce diffusion resistance when vehicles pass at high speeds, ensuring that the light pressure control field accurately and frequently follows the moving target; at the same time, in conjunction with the slope limiter on the hardware side, a dual defense line of software smoothing algorithm and physical hardware protection is constructed, which improves the following accuracy while eliminating the impact of thermal stress on the lamps. 4. The system breaks through the limitations of traditional lighting which can only be passively adjusted, and has an innovative function of actively guiding congestion. By correcting the parameters of the fluid equation, a relatively backward lighting flow is formed in front of the driver's field of vision, using visual cues to guide the driver to subconsciously slow down. This non-mandatory intervention can suppress aggressive driving behavior invisibly, effectively reduce the risk of rear-end collisions, and achieve a deep integration of the lighting system and traffic flow control. 5. This invention adopts wireless networking technology, abandoning the traditional signal bus wiring. By utilizing the self-organization and multi-hop characteristics of wireless nodes, it not only significantly reduces the engineering implementation cost, but also ensures the stable distribution and spatial coordination of the virtual optical pressure control data field in complex tunnel environments. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 Example 1: The wireless networking-based intelligent timing control system for tunnel lighting includes: The data acquisition hardware group includes a radar speedometer deployed inside the tunnel and a light intensity sensor outside the tunnel, used to collect the real-time velocity vector of moving objects and the brightness value of the external environment of the tunnel in real time; The lighting execution network consists of multiple LED lighting units distributed longitudinally along the tunnel and their corresponding driver power supplies. Each driver power supply integrates a wireless communication node, and the nodes establish a communication topology through a wireless self-organizing network protocol. The central control processor, connected to the data acquisition hardware group and establishing a wireless communication connection with the lighting execution network via a wireless signal transceiver module, is configured to perform the following operations: A virtual fluid medium model is constructed, mapping the tunnel space to rigid pipe boundary data, and mapping the output luminous flux of the LED lighting unit to the density data of the virtual fluid medium. This density data is uniformly represented as virtual light pressure in subsequent calculation logic and control field generation. ; Based on the adaptive characteristics of human vision, the virtual shear viscosity parameters of the virtual fluid medium are calculated using the brightness value of the external environment of the channel; By substituting the real-time velocity vector as a disturbance source into the fluid dynamics control equations and combining it with virtual shear viscosity parameters, the rheological dynamic state of the virtual fluid medium in the rigid pipe is iteratively calculated to generate a spatiotemporally continuous virtual photopressure control data field. The virtual light pressure control data field is parsed into discrete pulse width modulation (PWM) commands according to spatial coordinates and sent to the driver power supply to control the brightness of the corresponding LED lighting unit.

[0015] This embodiment provides a wireless network-based intelligent timing control system for tunnel lighting; The data acquisition hardware group aims to construct a perception interface between the physical world and the digital twin world. In this embodiment, the data acquisition hardware group includes radar speed detectors, such as millimeter-wave radars, deployed at preset intervals (e.g., 50-100 meters) within the tunnel, used to collect real-time speed vectors of moving objects, such as vehicles. In addition, light intensity sensors deployed at the tunnel entrance and in the buffer zone outside the tunnel are used to collect the ambient brightness values ​​of the external environment in real time. If multiple moving objects exist simultaneously within the tunnel, the real-time velocity vectors of each object are weighted and superimposed according to their spatial positions to construct a multi-source disturbance field as the input to the fluid dynamics control equations. The lighting execution network, as the output terminal of the physical light environment, consists of multiple LED lighting units distributed along the longitudinal direction of the tunnel and their corresponding driving power supplies in this embodiment. These LED lighting units are discretely distributed point light sources in physical space, but under the control logic of this invention, they will be regarded as discrete sampling points of a continuous optical flow field. The central control processor is the core computing unit of this system, and it is connected to the aforementioned hardware via a wireless communication link. The central control processor is configured to execute a novel cross-space mapping operation, specifically including the following logical steps: Construct a virtual fluid medium model: Virtual fluid medium model: refers to a mathematical framework introduced to solve the temporal mismatch between discrete dimming commands and continuous visual adaptation; its role is to transform the lighting control problem into a fluid dynamics problem, and its origin is the idea of ​​simplified Navier-Stokes equations; In this step, the processor maps the tunnel space to rigid pipe boundary data, i.e., the container for fluid flow, and maps the output luminous flux of the LED lighting unit to density data of the virtual fluid medium, i.e., the thickness or concentration of the fluid; in this invention, this is defined as virtual light pressure. By introducing a unified virtual light pressure variable, the originally isolated and abrupt LED brightness value is transformed into a fluid density field with continuity and guideability. The physical essence of virtual light pressure P is a scalar mapping of the output brightness of the lighting system. Its numerical distribution follows the continuity requirements of fluid dynamics, thereby realizing the stepless transition of dimming commands in the spatiotemporal dimension. Parameters calculated based on the human eye's visual adaptation characteristics: The processor utilizes the ambient brightness value of the channel. Calculate the virtual shear viscosity parameters of the virtual fluid medium. This parameter serves as a bridge connecting the physical optical environment and physiological visual characteristics, determining the viscosity of the photofluid flowing within the pipe. Iterative calculation of rheological dynamic state: The processor will use real-time velocity vector Substituting the external disturbance source into the fluid dynamics control equations, combined with virtual shear viscosity parameters The process iteratively calculates the rheological dynamics of a virtual fluid medium within a rigid pipe. This process is no longer a simple lookup and adjustment of light, but rather a simulation of the dynamic equilibrium process of a fluid with a specific viscosity under the impact of a vehicle as particles, thereby generating a spatiotemporally continuous virtual photopressure control data field. ; Parsed as PWM instructions: The processor will control the virtual photopressure data field By spatial coordinates The commands are parsed into discrete pulse width modulation (PWM) instructions and sent to the driver power supply to control the brightness of the corresponding LED lighting units. The parsing process uses a linear mapping and limiting algorithm. in, This represents the virtual light pressure value at the coordinates. This is the preset maximum redundant optical pressure nominal value of the system. For hardware-supported PWM resolutions, such as 255 or 1024; through this mapping formula, the continuous optical flow field amplitude is linearly converted into a hardware-executable duty cycle value. This embodiment upgrades tunnel lighting control from traditional discrete state control to continuous flow field control. This scheme utilizes the natural smoothness and continuity of the fluid model to mathematically eliminate the stepped feeling and visual oscillation caused by traditional segmented control. The system can automatically balance the contradiction between rapid response to fluid disturbance and visual comfort corresponding to fluid viscous damping, achieving millisecond-level adaptive smooth dimming.

[0016] Example 2: When calculating virtual shear viscosity parameters, the central control processor performs the following logically rigorous calculation steps: Read the current ambient brightness value of the channel and the historical brightness value at the previous sampling time, and calculate the rate of change of ambient brightness. If the absolute value of the rate of change is greater than the preset mutation threshold, it is determined that an ambient light mutation has occurred. The virtual shear viscosity parameter is set to the first high viscosity value to force smooth the spatial gradient of the virtual light pressure control data field and suppress the violent fluctuation of lighting brightness. If the absolute value of the rate of change is less than or equal to the mutation threshold, then based on the logarithmic relationship of the Weber-Fechner law, the basic physiological relaxation time constant that is positively correlated with the current external ambient brightness value is calculated, and this constant is directly linearly mapped to the virtual shear viscosity parameter. The virtual shear viscosity parameter numerically characterizes the diffusion resistance of a virtual fluid medium when subjected to velocity vector perturbations, and is used to simulate the adaptive hysteresis of the human eye to changes in light intensity.

[0017] This embodiment further defines the specific logic of the central control processor in calculating the virtual shear viscosity parameter, which is the key to realizing biomimetic control; Virtual shear viscosity parameters The purpose is to simulate the physiological adaptation lag of the human pupil under different light environments; in this embodiment, the central control processor executes the following logically rigorous calculation steps: Ambient light mutation monitoring: The processor reads the current ambient brightness value of the channel. and the historical brightness value at the previous sampling time Calculate the rate of change of external ambient brightness ; Mutation response strategy: Forced smoothing If the rate of change If the absolute value of the change is greater than a preset change threshold, which is set according to the maximum brightness change rate that the human eye can tolerate, for example, a sudden change in ambient light is determined to occur when clouds suddenly block the sun and cause a sudden change in illuminance. The change threshold is set according to the maximum brightness change rate that the human eye can tolerate, and is preferably 50% of the brightness of the previous sampling period. At this time, the processor sets the virtual shear viscosity parameter to the first high viscosity value. Its value range is set to to ; Technical motivation: High viscosity means that the fluid has extremely high flow resistance. Even if there is a violent disturbance from the outside, the fluid morphology, i.e. the light distribution, can only change slowly. This physically forces the spatial gradient of the virtual light pressure control data field to be smoothed, thereby suppressing violent fluctuations in lighting brightness and preventing drivers from experiencing blindness. Steady-state adaptive strategy: Modified Weber-Fechner law, if the rate of change If the absolute value is less than or equal to the mutation threshold, it indicates that the environmental change is stable. At this time, the processor calculates the basic physiological relaxation time constant, which is positively correlated with the current external environmental brightness value, based on the logarithmic relationship of the Weber-Fechner Law. Unit: seconds; To ensure the mathematical rigor of logarithmic operations, the processor introduces a unit brightness reference value. The value is The external brightness is normalized, and the calculation formula is revised as follows: in, : Measured ambient brightness value of the channel; Unit reference luminance, used to eliminate physical dimensions and ensure The independent variable of the function is a dimensionless pure number; : Proportional coefficient, which is related to the chemical reaction rate of the human optic nerve; The fundamental constant represents the minimum adaptation delay in a completely dark environment; The value is a preset, extremely small positive number to prevent abnormal logarithmic operations; where, the constant is... The value of must satisfy: When approaching zero, the time constant The calculation result is always greater than zero, in order to conform to the physical meaning of physiological visual adaptation; To ensure dimensional consistency in the fluid dynamics equations, the processor incorporates the tunnel design flow velocity. As a reference value, the time constant Convert to virtual shear viscosity parameters The conversion formula is: Dimensional verification: Units are , Units are ;but The unit is This corresponds to the standard dimension of kinematic viscosity in fluid mechanics. Complete consistency ensures that the physical meaning of the diffusion term in the subsequent differential equation holds true; This embodiment creatively introduces the physical quantity of visual viscosity, realizing a mathematical model of the physiological characteristics of the human eye, which is fast in light adaptation, slow in dark adaptation, and inability to withstand drastic changes. By dynamically adjusting the viscosity, the system provides a transition that is comfortable for the human eye when the ambient light is stable, and provides a shock absorber-like protection when the ambient light changes abruptly, effectively reducing visual discomfort.

[0018] Example 3: The fluid dynamics governing equations are described in the processor's computational logic as the following equilibrium relationship: The local derivative of the virtual light pressure control data field with respect to time, plus the product of the real-time velocity vector of the moving object and the spatial gradient of the virtual light pressure control data field along the tunnel longitudinal direction, is numerically equal to the product of the virtual shear viscosity parameter and the second spatial derivative of the virtual light pressure control data field. The balance relationship also includes an elastic recovery source term, which is numerically equal to the product of the optical flow field recovery coefficient and the difference between the virtual light pressure at the current coordinate node and the nominal brightness deviation of the tunnel basic lighting. In order to suppress high-frequency noise and offset truncation error in the numerical calculation process, the balance relationship can optionally introduce a fourth-order numerical diffusion term based on a small stability coefficient when discretizing the solution. The processor employs an explicit forward difference scheme to discretize and iteratively solve the transport equations. The specific difference operators are as follows: in, Indicates the first The sampling time, the first The light pressure value of each spatial node The sampling period is The spatial step size for the distribution of LED units; and These represent the longitudinal distances along the tunnel. Each node has one and two adjacent computational nodes with spatial steps; Indicates the first The nominal luminance value of the tunnel basic lighting corresponding to each spatial node. This is a preset infinitesimal stability coefficient used to offset the fourth-order truncation error; to ensure the numerical stability of the calculation results, the system constraint time step must satisfy the Courant-Friedrichs-Lewy,CFL condition, i.e. ; This embodiment specifically describes the core algorithm driving the entire system, namely the fluid dynamics control equations; Fluid dynamics governing equations: Their purpose is to describe the evolution of photofluids in the spatiotemporal dimension. In this embodiment, to ensure strict uniformity of the physical dimensions of each term in the equation—that is, each term represents the rate of change of light intensity—the equation is described in the processor's computational logic as the following equilibrium relationship: a one-dimensional transport equation with a source term recovery mechanism: Parameter definition and dimensional analysis, unsteady-state term The local derivative of the virtual photopressure control data field with respect to time; its dimension is light intensity. ; Convection term Velocity dimension Light intensity with light pressure gradient dimension Multiply, the result is in the dimension of light intensity. ; diffusion term Virtual shear viscosity parameters dimension Dimensional light intensity with second derivative of light pressure Multiply, the result is in the dimension of light intensity. ; Elastic recovery source item , The inherent basic lighting requirements of tunnels, such as the static maintenance brightness specified in national standards, are measured in luminous intensity. ; The Optical Flow Field Restoration Coefficient (ORC) is the physical representation of the rate at which a system deviates from its fundamental state, and its dimensions are: The overall dimension of this item is light intensity. To prevent issues arising from the spatial step size in numerical calculations. The excessively large truncation error introduces an additional numerical diffusion term when the processor solves for the discrete difference operator. High-frequency noise suppression is performed, among which This is a preset micro-stability coefficient; The dimensions are The value is usually higher than that of the virtual shear viscosity parameter. The difference is 2-3 orders of magnitude smaller, which is used to filter out high-frequency numerical oscillations without affecting the physical reality; Optical flow field restitution coefficient The value range is set to In practical applications, The static adaptation time of the human eye when transitioning from a bright environment to a dark environment Inversely proportional, that is This ensures that the tunnel lighting can smoothly return to the basic static lighting level within 3-5 seconds when there is no moving object disturbance, avoiding power waste caused by long delays; Solution logic: The physical motivation for introducing the elastic recovery source term is that when there are no vehicles, i.e. And without diffusion effects, the equation degenerates into This ensures that the light intensity automatically converges and stabilizes at the level of the base lighting over time. To prevent integral divergence in numerical computation, the processor continues to use the finite difference method (FDM) to discretize and solve the above equilibrium relationship. During the solution process, the processor introduces the numerical diffusion term and the elastic recovery source term to force the virtual photopressure field to converge in the time domain, thus avoiding the problem caused by vehicle speed. The divergence of numerical integrals caused by drastic fluctuations; The equation cleverly utilizes the convection-diffusion mechanism in fluid mechanics. The convection term ensures that the light can respond quickly to the vehicle speed to meet safety requirements, while the diffusion term uses the virtual shear viscosity parameter to force the smoothing of the second derivative of the illumination to meet comfort requirements. This control method based on the analytical solution of the partial differential equation mathematically guarantees the continuous differentiability of the output light curve and avoids hard shear caused by logical judgment.

[0019] Example 4: The central control processor is also equipped with congestion guidance logic: When the average vehicle speed inside the tunnel is detected to be lower than the preset congestion threshold, the processor forcibly corrects the convection velocity parameter in the fluid dynamics control equation and sets it to a value lower than the induced velocity value of the real-time velocity vector of the moving object. By calculating and generating a virtual light pressure control data field that lags behind the current position of the moving object, a relatively receding illumination light flow is formed in front of the moving object's field of vision, using visual cues to induce the moving object to slow down.

[0020] This embodiment describes a congestion induction logic based on subconscious influence; The congestion guidance logic aims to proactively intervene in driving behavior by utilizing changes in the lighting environment; the central control processor is equipped with the following logic: Congestion assessment: The system monitors the average vehicle speed in the tunnel in real time. When the average vehicle speed is lower than the preset congestion threshold, such as 40 km / h, indicating that congestion is about to occur, the guidance mode is activated. Flow rate correction: The processor forces a correction to the convection velocity parameters in the fluid dynamics control equations; at this point, the real-time velocity vector of the moving object is no longer used directly. Instead, it is set as the induced velocity value. And satisfy For example, it can be set to 0.8 times the current vehicle speed; Optical flow hysteresis generation: With the above correction, the virtual light pressure control data field generated by the equation will propagate in space more slowly than the actual speed of the vehicle; this results in a relatively receding illumination light stream in front of the moving object's field of vision, that is, the light band moves slightly slower than the vehicle speed. This is a counterintuitive application of the emergent effect; based on the principle of visual flow, when a driver perceives that the speed of the surrounding light flow relative to themselves is slowing down, they will have a subconscious illusion that they are driving too fast, thus subconsciously releasing the accelerator to slow down; this method does not rely on mandatory red lights or signs, but uses virtual traffic guidance waves to suppress aggressive driving invisibly and alleviate the risk of rear-end collisions in congestion; when the guidance mode is activated, the amplitude of the virtual light pressure fluctuation generated by the guidance speed is limited to within 20% of the basic lighting brightness, ensuring that visual cues are provided without interfering with driving safety.

[0021] Example 5: The lighting execution network transmits commands through wireless self-organizing network nodes based on ZigBee, LoRa, or NB-IoT protocols. Each LED lighting unit constructs a dynamic topology through multi-hop routing to achieve spatial collaborative distribution of virtual light pressure control data field. The power supply has an internal slope limiter. When the difference in duty cycle between two adjacent timing cycles of the received PWM command exceeds the hardware safety threshold, the slope limiter forces the rate of change of the output current to be limited within the safety threshold in order to eliminate thermal stress shock.

[0022] This embodiment relates to the communication and hardware protection mechanisms of the lighting execution network; In this embodiment, the lighting execution network receives instructions from the central processing unit through a wireless communication module integrated into the driving power supply. This method utilizes the multi-hop characteristics of wireless self-organizing networks, eliminating the need to lay additional signal transmission cables and fundamentally solving the problems of difficult wiring and high maintenance costs in tunnel engineering. The slope limiter serves to create a hardware-level defense; in this embodiment, the slope limiter (SlopeLimiter) is integrated into the internal hardware circuitry of the drive power supply; its operating logic is as follows: When the duty cycle difference between two adjacent timing cycles of the received PWM command When the hardware safety threshold is exceeded, which is set based on the thermal expansion coefficient of the LED chip, the slope limiter ignores the instruction requirements and forcibly limits the rate of change of the output current to within the safety threshold. Although the CPU's fluid algorithm has ensured the smoothness of instructions, the slope limiter, as a physical firewall, can prevent extreme jump instructions caused by communication errors or system failures from damaging the lamps. This mechanism eliminates the thermal stress impact caused by frequent large-span step signals in LED lamps, and it is calculated that it can theoretically extend the life of the power supply and lamp chips by about 25%.

[0023] Example 6: The boundary conditions for the virtual fluid medium model are set as follows: Map the coordinates of the tunnel entrance and exit to the open boundary of the rigid pipe; The geometric dimensions of the tunnel inner wall are mapped to the solid wall boundary of the rigid pipe, and no slip condition is set at the physical topology level to anchor the position of the lamp. At the mathematical solution level, it is equivalent to the light pressure gradient constraint at both ends of the longitudinal one-dimensional computational domain. The basic lighting brightness when there are no moving objects in the tunnel is mapped to the initial static density field of the virtual fluid medium.

[0024] This embodiment details the boundary conditions of the virtual fluid medium model, which is a necessary prerequisite for solving partial differential equations using the finite difference method. The boundary conditions for the virtual fluid medium model are set as follows: Open Boundary: Mapping the coordinates of the tunnel entrance and exit to the open boundaries of the rigid pipe means that the virtual fluid light flux can freely enter and exit these two ports, simulating the physical phenomenon of natural light entering or exiting the tunnel, ensuring that the solution of the equation at the endpoints does not undergo numerical reflection or oscillation. No-slipCondition: Since the computational domain is defined as a one-dimensional space along the tunnel's longitudinal direction, the boundary conditions are set to first-order Neumann boundaries: to implement the above physical constraints in numerical computation, the processor sets the spatial gradient of the light pressure to zero at the tunnel entrance and exit nodes, i.e. This setting ensures that the virtual light flow has physical continuity as it passes through both ends of the tunnel, preventing light oscillations caused by numerical reflections. Technical motivation: This corresponds to the fact that in actual physical scenarios, the positions of the lamps installed on the tunnel wall are fixed, and the light intensity distribution must be based on the physical position of the lamps to prevent the calculated light pressure field from drifting out of the physical tunnel range; Initial conditions: The basic lighting brightness when there are no moving objects in the tunnel, such as the minimum sustained brightness set according to national standards, is mapped to the initial static density field of the virtual fluid medium; this serves as the zero-point state of the equation iteration. ; Reasonable boundary condition setting not only ensures the convergence and stability of the numerical solution of the fluid dynamics equation, but also ensures that the calculated optical flow is always constrained within the physical space of the tunnel, so that the virtual calculation results are precisely aligned with the actual physical environment.

[0025] Example 7: The central control processor also introduces a speed-viscosity dynamic coupling mechanism: Real-time monitoring of the magnitude of the real-time velocity vector of a moving object; When the modulus increases, the corresponding viscosity correction coefficient is obtained by looking up a table. The viscosity correction coefficient is less than 1 and is used to attenuate the virtual shear viscosity parameter, thereby reducing the diffusion resistance of the virtual fluid medium and enabling the generated virtual optical pressure control data field to follow the high-speed moving object for high-frequency refresh. When the modulus decreases, the current virtual shear viscosity parameter is maintained without attenuation correction.

[0026] This embodiment introduces a speed-viscosity dynamic coupling mechanism in the central control processor to solve the response delay problem in high-speed scenarios; The velocity-viscosity dynamic coupling mechanism aims to correct the limitations of a single viscosity model under high-speed conditions; the specific execution steps are as follows: Velocity modulus monitoring: The processor monitors the real-time velocity vector of moving objects. modulus , i.e., vehicle speed scalar; Viscosity decay correction, high-speed scenarios: When the modulus When a viscosity is increased, the processor obtains the corresponding viscosity correction factor using a pre-stored lookup table. The mapping relationship corresponding to the lookup table method is as follows: when the real-time speed hour, ;when hour, ;when hour, The value is fixed at 0.8; through this piecewise linear correction, the viscosity of the virtual fluid decreases as the vehicle speed increases, thereby improving the system's dynamic tracking response speed to high-speed targets; This coefficient satisfies Furthermore, it is negatively correlated with the velocity modulus; the processor uses this coefficient to determine the basic virtual shear viscosity parameters. Perform attenuation correction, i.e. ; Technical motivation: When the vehicle speed is extremely high, if the fluid viscosity is too high, the calculated light pressure field will change too slowly and cannot keep up with the vehicle's movement, resulting in a trailing phenomenon; by reducing viscosity to reduce diffusion resistance, the generated virtual light pressure control data field can be refreshed at a higher frequency and flow more violently. Maintaining the mechanism, low-speed scenarios: When the modulus When the speed is reduced or in the low-speed range, the current virtual shear viscosity parameter is kept unchanged without attenuation correction in order to prioritize visual smoothness and comfort. This mechanism creatively solves the technical contradiction that the faster the vehicle speed, the more sensitive the human eye is to changes in light intensity, but traditional control becomes increasingly lagging because smoothing algorithms usually introduce delays. By actively reducing the viscosity of the photofluid at high speeds, the system can significantly improve its dynamic tracking ability of high-speed moving objects while maintaining basic smoothness.

[0027] Example 8: Please see Figure 2 A control method for a wireless network-based intelligent control system for tunnel lighting timing, based on any one of embodiments 1-7, includes the following steps: S1. Collect real-time velocity vectors of moving objects inside the tunnel and reference brightness data of the external environment of the tunnel through the data acquisition hardware group; S2. The processor constructs a virtual fluid medium model, maps the physical space of the tunnel to a computational domain, and defines the lighting brightness as a scalar field variable within this computational domain. S3. Based on the reference brightness data of the external environment, calculate the virtual shear viscosity parameters of the virtual fluid through a preset visual adaptation model, wherein the virtual shear viscosity parameters are positively correlated with the reference brightness data. S4. Substitute the real-time velocity vector as the convective velocity into the transport equation containing time, convection, and diffusion terms, and use the virtual shear viscosity parameter to solve the spatiotemporal distribution of the scalar field variables at the next moment to generate the target brightness field. S5. Convert the target brightness field into a corresponding PWM control signal, transmit it through a wireless self-organizing network, and drive the lighting equipment in the tunnel to output physical light.

[0028] This embodiment provides a control method based on the aforementioned control entity, which connects the hardware and algorithm logic into a complete execution flow; including the following steps: S1. Data Acquisition: Real-time velocity vectors of moving objects within the tunnel are acquired using radar and light intensity meters in the data acquisition hardware group. Reference brightness data of the tunnel's external environment ; S2. Spatial Mapping: The processor constructs a virtual fluid medium model, mapping the physical space of the tunnel into a computational domain, and defines the illumination brightness as a scalar field variable within this computational domain, namely, light pressure. ; S3, Viscosity Calculation: Based on reference brightness data of the external environment. By using a pre-defined visual adaptation model, combined with the Weber-Fechner law and catastrophe protection logic, the virtual shear viscosity parameters of the virtual fluid are calculated. In the non-mutation state, the virtual shear viscosity parameter is positively correlated with the baseline brightness data. The brighter the external environment, the smaller the pupil, and the faster the adaptation. The viscosity can be adjusted appropriately. The darker the external environment, the larger the pupil, and the slower the adaptation. The viscosity needs to be increased. S4. Solving the equation: Solve the real-time velocity vector... Substituting the convective velocity into the transport equation, which includes time, convection, and diffusion terms (i.e., the aforementioned fluid dynamics governing equations), and utilizing the virtual shear viscosity parameter... Solve for the spatiotemporal distribution of the scalar field variables at the next moment to generate the target brightness field; S5, Physical Drive: Converts the target brightness field into a corresponding PWM control signal, transmits it through a wireless self-organizing network, and drives the lighting equipment in the tunnel to output physical light. The method involves a series of interconnected steps, forming a closed-loop perception-mapping-computation-execution system. Its core value lies in simplifying the complex, multi-variable coupled tunnel lighting control problem into a standardized fluid dynamics problem, thereby utilizing mature mathematical physics tools to achieve intelligent, adaptive, and human-centered lighting control.

[0029] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wireless networking-based intelligent timing control system for tunnel lighting, characterized in that: include: The data acquisition hardware group includes a radar speedometer deployed inside the tunnel and a light intensity sensor outside the tunnel, used to collect the real-time velocity vector of moving objects and the brightness value of the external environment of the tunnel in real time; The lighting execution network consists of multiple LED lighting units distributed longitudinally along the tunnel and their corresponding driver power supplies. Each driver power supply integrates a wireless communication node, and the nodes establish a communication topology through a wireless self-organizing network protocol. The central control processor, connected to the data acquisition hardware group and establishing a wireless communication connection with the lighting execution network via a wireless signal transceiver module, is configured to perform the following operations: A virtual fluid medium model is constructed, which maps the tunnel space to rigid pipe boundary data and the output luminous flux of the LED lighting unit to the density data of the virtual fluid medium. These are collectively referred to as virtual light pressure P. Based on the adaptive characteristics of human vision, the virtual shear viscosity parameters of the virtual fluid medium are calculated using the brightness value of the external environment of the channel; By substituting the real-time velocity vector as a disturbance source into the fluid dynamics control equations and combining it with virtual shear viscosity parameters, the rheological dynamic state of the virtual fluid medium in the rigid pipe is iteratively calculated to generate a spatiotemporally continuous virtual photopressure control data field. The virtual light pressure control data field is parsed into discrete pulse width modulation (PWM) commands according to spatial coordinates and sent to the driver power supply to control the brightness of the corresponding LED lighting unit.

2. The intelligent timing control system for tunnel lighting based on wireless networking as described in claim 1, characterized in that, When calculating virtual shear viscosity parameters, the central control processor performs the following logically rigorous calculation steps: Read the current ambient brightness value of the channel and the historical brightness value at the previous sampling time, and calculate the rate of change of ambient brightness. If the absolute value of the rate of change is greater than the preset mutation threshold, it is determined that an ambient light mutation has occurred. The virtual shear viscosity parameter is set to the first high viscosity value to force smooth the spatial gradient of the virtual light pressure control data field and suppress the violent fluctuation of lighting brightness. If the absolute value of the rate of change is less than or equal to the mutation threshold, the basic physiological relaxation time constant that is positively correlated with the current external ambient brightness value is calculated based on the logarithmic relationship of the Weber-Fechner law, and this constant is directly linearly mapped to the virtual shear viscosity parameter. The virtual shear viscosity parameter numerically characterizes the diffusion resistance of a virtual fluid medium when subjected to velocity vector perturbations, and is used to simulate the adaptive hysteresis of the human eye to changes in light intensity.

3. The intelligent timing control body for tunnel lighting based on wireless networking as described in claim 1, characterized in that, The fluid dynamics governing equations are described in the processor's computational logic as follows: The local derivative of the virtual light pressure control data field with respect to time, plus the product of the real-time velocity vector of the moving object and the spatial gradient of the virtual light pressure control data field along the tunnel longitudinal direction, is numerically equal to the product of the virtual shear viscosity parameter and the second spatial derivative of the virtual light pressure control data field. The processor uses the finite difference method to discretize and solve the above balance relationship, and calculates the ideal virtual light pressure value at each coordinate node in the tunnel at the next moment.

4. The intelligent timing control body for tunnel lighting based on wireless networking as described in claim 1, characterized in that, The central control processor is also equipped with congestion guidance logic: When the average vehicle speed inside the tunnel is detected to be lower than the preset congestion threshold, the processor forcibly corrects the convection velocity parameter in the fluid dynamics control equation and sets it to a value lower than the induced velocity value of the real-time velocity vector of the moving object. By calculating and generating a virtual light pressure control data field that lags behind the current position of the moving object, a relatively receding illumination light flow is formed in front of the moving object's field of vision, using visual cues to induce the moving object to slow down.

5. The intelligent timing control body for tunnel lighting based on wireless networking according to claim 1, characterized in that, The lighting execution network transmits commands through wireless self-organizing network nodes based on ZigBee, LoRa, or NB-IoT protocols. Each LED lighting unit constructs a dynamic topology through multi-hop routing to achieve spatial collaborative distribution of virtual light pressure control data field. The power supply has an internal slope limiter. When the difference in duty cycle between two adjacent timing cycles of the received PWM command exceeds the hardware safety threshold, the slope limiter forces the rate of change of the output current to be limited within the safety threshold in order to eliminate thermal stress shock.

6. The intelligent timing control body for tunnel lighting based on wireless networking according to claim 1, characterized in that, The boundary conditions for the virtual fluid medium model are set as follows: Map the coordinates of the tunnel entrance and exit to the open boundary of the rigid pipe; The geometry of the tunnel wall is mapped to the solid wall boundary of a rigid pipe, and a no-slip condition is set at the solid wall boundary, that is, the fluid velocity is zero at the wall surface. The basic lighting brightness when there are no moving objects in the tunnel is mapped to the initial static density field of the virtual fluid medium.

7. The intelligent timing control body for tunnel lighting based on wireless networking according to claim 3, characterized in that, The central control processor also introduces a speed-viscosity dynamic coupling mechanism: Real-time monitoring of the magnitude of the real-time velocity vector of a moving object; When the modulus increases, the corresponding viscosity correction coefficient is obtained by looking up a table. The viscosity correction coefficient is less than 1 and is used to attenuate the virtual shear viscosity parameter, thereby reducing the diffusion resistance of the virtual fluid medium and enabling the generated virtual optical pressure control data field to follow the high-speed moving object for high-frequency refresh. When the modulus decreases, the current virtual shear viscosity parameter is maintained without attenuation correction.

8. A control method for a wireless network-based intelligent control system for tunnel lighting timing, based on the wireless network-based intelligent control system for tunnel lighting timing as described in any one of claims 1-7, characterized in that, Includes the following steps: S1. Collect real-time velocity vectors of moving objects inside the tunnel and reference brightness data of the external environment of the tunnel through the data acquisition hardware group; S2. The processor constructs a virtual fluid medium model, maps the physical space of the tunnel to a computational domain, and defines the lighting brightness as a scalar field variable within this computational domain. S3. Based on the reference brightness data of the external environment, calculate the virtual shear viscosity parameters of the virtual fluid through a preset visual adaptation model, wherein the virtual shear viscosity parameters are positively correlated with the reference brightness data. S4. Substitute the real-time velocity vector as the convective velocity into the transport equation containing time, convection, and diffusion terms, and use the virtual shear viscosity parameter to solve the spatiotemporal distribution of the scalar field variables at the next moment to generate the target brightness field. S5. Convert the target brightness field into a corresponding PWM control signal, transmit it through a wireless self-organizing network, and drive the lighting equipment in the tunnel to output physical light.

Images