CN-122002669-A - Intelligent illumination control method and system based on tunnel partition brightness gradient gradual change

CN122002669ACN 122002669 ACN122002669 ACN 122002669ACN-122002669-A

Abstract

The application relates to the technical field of self-adaptive control, and discloses an intelligent illumination control method and system based on tunnel partition brightness gradient gradual change. The method comprises the steps of acquiring a train distance observation value and a train speed observation value by a millimeter wave radar sensor and an infrared correlation sensor, obtaining a position state quantity and a speed state quantity by filtering through a weighted sliding window, comparing the position state quantity with five illumination section boundary coordinates to obtain a section number, substituting the speed state quantity into an accumulated luminous flux integration type according to the section number to obtain a physiological integration state variable, reversely calculating a target brightness level by taking the difference between the physiological integration state variable and a target threshold value as an integration gap, and converting the target brightness level into PWM duty ratio to drive an LED lamp array to output a zone gradient gradual change brightness. The application improves the self-adaptive response precision of tunnel illumination control to the individual vision physiological differences of different train speeds and different drivers.

Inventors

WANG ZHAOLIANG
LI YUEBIN
LIU MENG
SUN BO
QIN NI
SHI MENGQI

Assignees

神州高铁轨道交通运营管理有限公司

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. An intelligent illumination control method based on gradient of tunnel partition brightness is characterized by comprising the following steps: Step S1, acquiring a distance observation value and a speed observation value of a train by a millimeter wave radar sensor and an infrared correlation sensor, and obtaining a position state quantity and a speed state quantity after filtering by a weighted sliding window; step S2, comparing the position state quantity with preset boundary coordinates of five illumination sections one by one to obtain a section number; S3, calling a corresponding control law according to the section number, substituting the speed state quantity into an accumulated luminous flux integral type to obtain a physiological integral state variable, and reversely calculating to obtain a target brightness level by taking the difference between the physiological integral state variable and a target threshold value as an integral gap and combining the speed state quantity; And S4, converting the target brightness level into a PWM duty ratio, and driving the LED lamp arrays corresponding to the illumination sections to output the zone gradient and gradual brightness.
2. The intelligent control method for lighting based on gradient gradation of tunnel partition brightness according to claim 1, wherein the step S1 comprises: Acquiring a distance observation value, a speed observation value and a signal intensity value of a train by a millimeter wave radar sensor at 20Hz updating frequency, acquiring a breaking time difference of a passing section of the train by an infrared correlation sensor, and acquiring a section correction speed value based on the ratio of the breaking time difference to the distance between the infrared correlation sensors; Substituting the distance observation value and the speed observation value into a weighted sliding window with the length of N=10 respectively, taking a quartile range IQR as a criterion, and removing the distance observation value and the speed observation value which deviate from the median by more than 1.5 xIQR from the sliding window to obtain an effective distance observation set and an effective speed observation set; Normalizing and weighting each element in the effective distance observation set and the effective speed observation set based on the signal intensity value, and summing products of the normalized weights and corresponding element values to respectively obtain a distance filtering value and a speed filtering value; And comparing the distance filtering value with a pre-calibrated coordinate of the infrared correlation sensor, covering the position state quantity of the distance filtering value with the pre-calibrated coordinate, and weighting and fusing the speed filtering value and the section correction speed value by taking 0.7 and 0.3 as fusion weights to obtain the speed state quantity.
3. The intelligent control method for lighting based on gradient of tunnel partition according to claim 1, wherein in the step S2, the position state quantity is compared with preset boundary coordinates of five lighting segments one by one to obtain segment numbers, and the method comprises the following steps: Taking the tail end section of a station platform in the uplink direction of the tunnel as a coordinate origin, taking the starting end section of a station platform in the station B as a termination coordinate, and dividing the tunnel and an inlet/outlet area into a near section, an inlet transition section, a middle traveling section, an outlet transition section and a far section in sequence to obtain boundary coordinates of the five illumination sections; And converting the position state quantity into a tunnel coordinate system to obtain a train head absolute coordinate, and comparing the train head absolute coordinate with the boundary coordinates of the five illumination sections one by one to obtain the section number.
4. The intelligent control method for lighting based on gradient gradation of tunnel partition brightness according to claim 3, wherein the step S3 calls a corresponding control law according to the segment number, substitutes the speed state quantity into the cumulative luminous flux integral type to obtain a physiological integral state variable, and includes: When the section number corresponds to the approaching section, resetting the physiological integral state variable, and starting a new round of integral calculation; And multiplying the product of the actual output brightness value of the lamp and the negative exponent power of the speed state quantity by a sampling period, and adding the product with the physiological integral state variable at the last sampling time to obtain the physiological integral state variable at the current sampling time, wherein the exponent coefficient in the negative exponent power of the speed state quantity takes a value of 0.6, and the sampling period takes a value of 50 milliseconds.
5. The intelligent control method for lighting based on tunnel partition luminance gradient gradation according to claim 4, wherein in the step S3, the difference between the physiological integral state variable and the target threshold is taken as an integral gap, comprising: And the target accumulated luminous flux threshold value of visual dark adaptation of a driver is differenced with the physiological integral state variable at the current sampling moment to obtain the integral gap, wherein the target accumulated luminous flux threshold value takes a dark adaptation target value when the section number corresponds to an entrance transition section, and takes a bright adaptation target value when the section number corresponds to an exit transition section, and the bright adaptation target value is smaller than the dark adaptation target value.
6. The intelligent control method for lighting based on gradient of tunnel partition according to claim 5, wherein in step S3, the target brightness level is calculated reversely by combining the speed state quantity, and the method comprises the steps of: dividing the product of the integral gap and one point six power of the speed state quantity by the residual running distance of the current illumination section to obtain a target brightness value, wherein the residual running distance is determined by the difference between the absolute coordinates of the head of the train and the termination boundary coordinates of the current illumination section; multiplying the difference ratio of the target brightness value to the rated maximum brightness and the lowest standby brightness of the lamp by ten, rounding and rounding to obtain the target brightness level; and when the section number corresponds to the middle-section driving section, the target brightness level is fixedly output into three stages.
7. The intelligent control method for lighting based on the gradient gradation of the tunnel partition according to claim 1, wherein the step S4 of converting the target brightness level into the PWM duty ratio, driving the LED lamp array of the corresponding lighting section to output the gradient gradation brightness comprises: Obtaining a PWM duty ratio based on the linear mapping relation between the target brightness level and the maximum duty ratio and the minimum standby duty ratio, wherein the PWM dimming frequency is 2000 Hz, the maximum duty ratio is 95%, and the minimum standby duty ratio is 8%; When the target brightness levels of the uplink train and the downlink train cover the same lamp set at the same time, the larger target brightness level in the uplink train and the downlink train is taken as the final output control level of the corresponding lamp set, and the LED lamp array is driven to output the zonal gradient gradual brightness.
8. An intelligent lighting control system based on gradient gradation of tunnel partition brightness, which is used for realizing the intelligent lighting control method based on gradient gradation of tunnel partition brightness according to any one of claims 1-7, and comprises: the acquisition module is used for acquiring a distance observation value and a speed observation value of the train by the millimeter wave radar sensor and the infrared correlation sensor, and obtaining a position state quantity and a speed state quantity after filtering by a weighted sliding window; The comparison module is used for comparing the position state quantity with preset boundary coordinates of five illumination sections one by one to obtain a section number; The estimation module is used for calling a corresponding control law according to the section number, substituting the speed state quantity into the accumulated luminous flux integration type to obtain a physiological integration state variable, and reversely estimating to obtain a target brightness level by taking the difference between the physiological integration state variable and a target threshold value as an integration gap and combining the speed state quantity; And the driving module is used for converting the target brightness level into a PWM duty ratio and driving the LED lamp arrays corresponding to the illumination sections to output the zone gradient and gradual brightness.
9. An intelligent lighting control device based on gradient gradation of brightness of a tunnel partition, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the intelligent lighting control method based on gradient gradation of brightness of a tunnel partition according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, causes the processor to perform the intelligent control method for illumination based on tunnel partition luminance gradient gradation according to any one of claims 1 to 7.

Description

Intelligent illumination control method and system based on tunnel partition brightness gradient gradual change Technical Field The application relates to the technical field of self-adaptive control, in particular to an intelligent illumination control method and system based on tunnel partition brightness gradient gradual change. Background The urban subway and high-speed railway tunnel lighting system adopts a fixed brightness control mode for a long time, namely constant full-power lighting output is maintained in the whole tunnel range, and the brightness of the lamp is not adjusted along with the change of the position or the running state of the train. The control mode drives the LED lamp array to continuously work at a preset fixed brightness level, has simple control logic, but cannot dynamically adjust the illumination output of each section according to the real-time position of the train, so that the whole tunnel can still maintain full-power operation when the train is not present, and a large amount of illumination energy sources are wasted. In the prior art, a zonal illumination control scheme based on train position triggering has appeared, and basic zonal dimming control is realized by arranging sensors along a tunnel to detect the moment that a train reaches a specific section and looking up a table by taking the train space position as an index to output a corresponding brightness level. However, the scheme has the following outstanding defects that firstly, the brightness level takes the space position of a train as the only driving variable, the physiological essence of human eye vision adaptation is neglected to be the integral accumulation process of time-light quantity rather than the space displacement process, so that the train outputs the same brightness level when passing through the same space position at different speeds, the vision adaptation is seriously insufficient in a high-speed scene, secondly, the brightness switching between adjacent sections is triggered by taking fixed space coordinates as a boundary, the brightness control quantity is subjected to step mutation at the boundary of the sections, short blind area impact is caused to the vision of a driver, thirdly, the system control parameter is fixed after being set at one time in the debugging stage, the difference of vision physiological thresholds among individuals of different drivers cannot be adapted, and the dynamic fluctuation of the threshold value of the same driver in different fatigue states cannot be responded. In the defects, the visual adaptation compensation of the existing scheme in a high-speed driving scene always has systematic deficiency through the driving brightness control of the space position of the train. In order to make up for the defect, a new problem is generated by introducing a physiological integral state variable, namely if a physiological integral target threshold still adopts a group unified default value, visual physiological differences among different drivers cannot be included in control law calculation, and part of integral notch reverse estimation results of the drivers continuously deviate from actual visual adaptation requirements, and furthermore, if the target threshold is subjected to individual calibration, a calibration mechanism needs to rely on online iteration of historical driving data, so that the target threshold can be quickly converged to an individual optimal value within limited driving times, and parameter divergence caused by single driving abnormal data is avoided. Disclosure of Invention The application provides an intelligent control method and system for illumination based on gradient of tunnel partition, which solve the problems of serious insufficient visual adaptation and abrupt change of zone boundary brightness caused by static mapping of brightness level at a train space position in the conventional tunnel illumination control, and improve the self-adaptive response precision of tunnel illumination control to individual visual physiological differences of different train speeds and different drivers. In a first aspect, the present application provides an intelligent control method for illumination based on gradient of tunnel partition, where the intelligent control method for illumination based on gradient of tunnel partition includes: Step S1, acquiring a distance observation value and a speed observation value of a train by a millimeter wave radar sensor and an infrared correlation sensor, and obtaining a position state quantity and a speed state quantity after filtering by a weighted sliding window; step S2, comparing the position state quantity with preset boundary coordinates of five illumination sections one by one to obtain a section number; S3, calling a corresponding control law according to the section number, substituting the speed state quantity into an accumulated luminous flux integral type to obtain a physiological integral state v