CN119150027A - Tunnel brightness control dynamic optimization method, system, terminal and medium - Google Patents
Tunnel brightness control dynamic optimization method, system, terminal and medium Download PDFInfo
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Abstract
The invention discloses a tunnel brightness control dynamic optimization method, a system, a terminal and a medium, which relate to the technical field of intelligent transportation and have the technical scheme that the state estimation is carried out on preprocessed multi-source state parameters by adopting a Kalman filtering method, the vehicle running estimation state which can represent the whole condition of a target tunnel can be accurately obtained, and then the vehicle running estimation state and an autoregressive distribution hysteresis model (ADL model) are combined to predict the vehicle flow and the vehicle speed, so that the whole change trend of single data can be accurately analyzed, the correlation characteristics among different data can be accurately represented, meanwhile, the solution of an illumination brightness target value is realized by considering a brightness optimization function constructed by the coordination of the vehicle flow and the vehicle speed, and the real-time change of the vehicle flow and the vehicle speed accuracy response can be realized from a global angle, thereby improving the reliability and timeliness of brightness adjustment.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a tunnel brightness control dynamic optimization method, a system, a terminal and a medium.
Background
In modern traffic systems, tunnels are used as key traffic nodes, and intelligent control of the lighting system plays a vital role in improving driving safety and reducing energy consumption. Conventional tunnel lighting systems typically employ a fixed space-time control strategy, which not only results in wasted energy, but also affects the driver's visual comfort and driving safety.
For this reason, in the prior art, it is described that intelligent control of tunnel illumination is achieved by taking into consideration factors such as vehicle speed, vehicle flow rate, and brightness, for example, short-time prediction is performed on data such as vehicle speed, vehicle flow rate, and brightness, and then the predicted vehicle speed, vehicle flow rate, and brightness values are input into a pre-constructed brightness compensation model, and tunnel brightness adjustment is achieved based on the brightness compensation result. In addition, the existing brightness compensation model generally realizes brightness compensation based on a single factor, for example, the brightness of the tunnel is regulated in a positive correlation mode when the traffic flow is increased, the correlation effect among various factors is ignored, and the real-time variable traffic flow, the speed and other multi-factor working conditions cannot be responded carefully.
Therefore, how to study and design a dynamic optimization method, system, terminal and medium for tunnel brightness control, which can overcome the above-mentioned drawbacks, is an urgent problem to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a tunnel brightness control dynamic optimization method, a system, a terminal and a medium, wherein the illumination brightness target value is solved by taking the brightness optimization function constructed by the coordination effect of the traffic flow and the vehicle speed into consideration, and the accuracy response of the traffic flow and the vehicle speed which change in real time can be realized from the global angle, so that the reliability and the timeliness of brightness adjustment are improved.
The technical aim of the invention is realized by the following technical scheme:
In a first aspect, a method for dynamically optimizing tunnel brightness control is provided, including the following steps:
Collecting multisource state parameters in a target tunnel, wherein the multisource state parameters comprise vehicle flow, vehicle speed, ambient light intensity and noise level;
Preprocessing the multisource state parameters, and carrying out state estimation on the preprocessed multisource state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state;
Inputting the vehicle running estimation state into an ADL model trained by considering time conditions and environmental conditions, and predicting to obtain predicted vehicle flow and predicted vehicle speed;
Inputting the predicted traffic flow and the predicted vehicle speed into a brightness optimization function constructed by considering the coordination effect of the traffic flow and the vehicle speed to obtain an illumination brightness target value;
and generating a corresponding real-time PWM signal according to the illumination brightness target value, and realizing brightness adjustment based on the real-time PWM signal.
Further, the process of preprocessing the multisource state parameter specifically includes:
Eliminating random errors and background noise in the multi-source state parameters through median filtering, mean filtering and/or wavelet transformation to obtain first parameters;
eliminating the influence of different dimensions and magnitudes in the first parameter through Z-score standardization or Min-Max standardization to obtain a second parameter;
And carrying out time alignment on the deviation of the sampling time in the second parameter by an interpolation method to obtain the preprocessed multi-source state parameter.
Further, the time condition is to divide the traffic flow into a plurality of traffic flow modes including early peak, late peak, daytime and night, and the environmental condition is to consider the influence of weather conditions on the traffic flow and the influence of the environmental light intensity on the illumination requirement.
Further, the brightness optimization function is a first function constructed by taking the vehicle speed as a main variable and taking the increment of the vehicle flow as a secondary variable, and the expression is as follows:
;
Wherein, Representing the time solved by the first functionA target value of illumination brightness at a time; representing a primary variable function in the first function; representing a minor variable function in the first function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a first reference vehicle flow; Representing constant entries in a first function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
Further, the brightness optimization function is a second function constructed by taking the traffic flow as a main variable and the increment of the vehicle speed as a secondary variable, and the expression is as follows:
;
Wherein, Representing the time solved by the second functionA target value of illumination brightness at a time; representing a minor variable function in the second function; Representing a primary variable function in the second function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; Representing a first reference vehicle speed; representing constant entries in the second function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
Further, the brightness optimization function is a third function constructed by taking the vehicle speed and the vehicle flow as the common leading action, and the expression is as follows:
;
Wherein, Representing the time solved by the third functionA target value of illumination brightness at a time; Representing a main variable function when a brightness optimization function is constructed by taking a vehicle speed as a main variable and taking an increment of a vehicle flow as a secondary variable; Representing a secondary variable function when a brightness optimization function is constructed by taking the vehicle speed as a primary variable and taking the increment of the vehicle flow as a secondary variable; representing a secondary variable function when a brightness optimization function is constructed by taking the traffic flow as a primary variable and the increment of the vehicle speed as a secondary variable; representing a main variable function when a brightness optimization function is constructed by taking traffic flow as a main variable and the increment of vehicle speed as a secondary variable; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a third reference vehicle speed; Representing a third reference vehicle flow; A constant value item when a brightness optimization function is constructed by taking the vehicle speed as a main variable and taking the increment of the vehicle flow as a secondary variable is shown; a constant value item when a brightness optimization function is constructed by taking the traffic flow as a main variable and the increment of the vehicle speed as a secondary variable is represented; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
Further, the generating process of the real-time PWM signal specifically includes:
establishing a basic brightness and a basic duty ratio of a PWM signal required by controlling the lighting equipment to reach the basic brightness;
determining an adjustment coefficient by the ratio of the illumination brightness target value to the base brightness;
And determining the duty ratio of the real-time PWM signal by the product of the basic duty ratio and the adjustment coefficient, and generating a corresponding real-time PWM signal.
In a second aspect, a tunnel brightness control dynamic optimization system is provided, where the system is configured to implement a tunnel brightness control dynamic optimization method according to any one of the first aspects, and the system includes:
The data acquisition module is used for acquiring multi-source state parameters in the target tunnel, wherein the multi-source state parameters comprise traffic flow, speed, ambient light intensity and noise level;
the state estimation module is used for preprocessing the multi-source state parameters, and carrying out state estimation on the preprocessed multi-source state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state;
the state prediction module is used for inputting the vehicle running estimation state into an ADL model trained by considering the time condition and the environmental condition, and predicting to obtain predicted vehicle flow and predicted vehicle speed;
The brightness solving module is used for inputting the predicted traffic flow and the predicted vehicle speed into a brightness optimizing function constructed by considering the coordination effect of the traffic flow and the vehicle speed to obtain an illumination brightness target value;
And the adjusting and optimizing module is used for generating a corresponding real-time PWM signal according to the illumination brightness target value and realizing brightness adjustment based on the real-time PWM signal.
In a third aspect, a computer terminal is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a dynamic optimization method for tunnel luminance control according to any one of the first aspects when executing the program.
In a fourth aspect, a computer readable medium is provided, on which a computer program is stored, the computer program being executable by a processor to implement a tunnel brightness control dynamic optimization method according to any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the tunnel brightness control dynamic optimization method provided by the invention, the state estimation is carried out on the preprocessed multi-source state parameters by adopting a Kalman filtering method, the vehicle running estimation state which can represent the overall condition of a target tunnel can be accurately obtained, and then the vehicle flow and the vehicle speed are predicted by combining the vehicle running estimation state and an autoregressive distribution hysteresis model (ADL model), so that the overall change trend of single data can be accurately analyzed, the association characteristics among different data can be accurately represented;
2. When the brightness optimization function is constructed, linear regression analysis is performed by taking one factor as a main variable and nonlinear regression analysis is performed on the error of the main variable by taking the other factor as a secondary variable based on historical sample data, so that the brightness optimization function considering the coordination effect of the traffic flow and the speed of the vehicle is obtained, and brightness adjustment under different change working conditions can be realized more accurately;
3. In the invention, when a brightness optimization function is constructed, in the nonlinear regression analysis process of errors of the main variables, the sum of brightness errors between each sample data and sample data corresponding to the secondary variables is minimized as an optimization target, and a secondary variable reference in each secondary variable function is solved, so that the independent variable value range in the analyzed secondary variable function is smaller, and more accurate and reliable secondary variable functions can be obtained;
4. The invention realizes the accurate dynamic control of the lighting system, remarkably improves the flexibility and accuracy of the system response, can continuously learn and adapt to the dynamic changes of traffic and environment, keeps the advancement and effectiveness of the system, ensures clear vision of a driver by maintaining reasonable lighting level, reduces visual fatigue and effectively reduces the risk of traffic accidents.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart in embodiment 1 of the present invention;
fig. 2 is a system block diagram in embodiment 2 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Embodiment 1A dynamic optimization method for tunnel brightness control, as shown in FIG. 1, comprises the following steps:
S1, collecting multi-source state parameters in a target tunnel, wherein the multi-source state parameters comprise vehicle flow, vehicle speed, ambient light intensity and noise level;
s2, preprocessing the multisource state parameters, and carrying out state estimation on the preprocessed multisource state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state;
S3, inputting the vehicle running estimation state into an ADL model trained by considering time conditions and environmental conditions, and predicting to obtain predicted vehicle flow and predicted vehicle speed;
s4, inputting the predicted traffic flow and the predicted vehicle speed into a brightness optimization function constructed by considering the coordination effect of the traffic flow and the vehicle speed to obtain an illumination brightness target value;
and S5, generating a corresponding real-time PWM signal according to the illumination brightness target value, and realizing brightness adjustment based on the real-time PWM signal. The PWM signal is a pulse width modulated signal.
In step S1, since the traffic flow and the vehicle speed are factors that have large fluctuation in a short time but have stable trend change in a long time and the tunnel has a long distribution range, when the traffic flow and the vehicle speed in the target tunnel are collected by adopting a limited sensor or an image collection end, the collected traffic flow and vehicle speed can only represent local conditions, and it is difficult to accurately represent global conditions.
For this purpose, the invention performs subsequent processing on the acquired data.
In step S2, the preprocessing of the multi-source state parameters comprises the steps of eliminating random errors and background noise in the multi-source state parameters through median filtering, mean filtering and/or wavelet transformation to obtain first parameters, eliminating the influences of different dimensions and magnitudes in the first parameters through Z-score standardization or Min-Max standardization to obtain second parameters, and carrying out time alignment on the deviation of sampling time in the second parameters through an interpolation method to obtain preprocessed multi-source state parameters.
It should be noted that, the present invention adopts the kalman filtering method to accurately estimate the state of the multi-source state parameter, but is not applied to the data prediction in the subsequent time.
In step S3, since one vehicle operation estimation state is a state vector composed of a plurality of states, in order to ensure the overall prediction trend of a single factor and the correlation influence among the factors in the following vehicle speed and vehicle flow prediction process, the present invention adopts an autoregressive distribution hysteresis model (ADL model) to predict the vehicle flow and the vehicle speed.
When the ADL model is built, the considered time condition is to divide the traffic flow into a plurality of traffic flow modes including early peak, late peak, daytime and night, and the considered environmental condition is to consider the influence of weather conditions on the traffic flow and the influence of the environmental light intensity on the illumination requirement.
In step S4, the luminance optimization function may be implemented according to standard sample data, where one sample data includes the vehicle speed, the vehicle flow rate, and the luminance of the illumination plane at the corresponding vehicle speed and vehicle flow rate. The invention adopts a sample set formed by collecting a large number of samples, firstly takes one factor as a main variable to carry out linear regression analysis, and then takes the other factor as a secondary variable to carry out nonlinear regression analysis on the error of the main variable, thereby obtaining a brightness optimization function considering the coordination effect of the traffic flow and the speed of the vehicle, and being capable of more accurately realizing brightness adjustment under different change working conditions.
Before the brightness optimization function is constructed, the reference quantity is required to be determined according to each sample data in the sample set, specifically, when the brightness optimization function is constructed, the sum of brightness errors between each sample data and sample data corresponding to the secondary variable is minimized as an optimization target in the nonlinear regression analysis process of errors of the main variable, the secondary variable reference in each secondary variable function is solved, the independent variable value range in the obtained secondary variable function is smaller, and the obtaining of a more accurate and reliable secondary variable function is facilitated.
As a first alternative embodiment, the brightness optimization function is a first function constructed by taking the vehicle speed as a main variable and taking the increment of the vehicle flow as a secondary variable, and the expression is as follows:
;
Wherein, Representing the time solved by the first functionA target value of illumination brightness at a time; representing a primary variable function in the first function; representing a minor variable function in the first function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a first reference vehicle flow; Representing constant entries in a first function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
As a second alternative embodiment, the brightness optimization function is a second function constructed by taking the vehicle flow as a main variable and the increment of the vehicle speed as a secondary variable, and the expression is:
;
Wherein, Representing the time solved by the second functionA target value of illumination brightness at a time; representing a minor variable function in the second function; Representing a primary variable function in the second function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; Representing a first reference vehicle speed; representing constant entries in the second function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
The two construction methods take a factor as a dominant effect, and the tunnel brightness mutation is easy to cause in special situations such as emergency braking of a vehicle, so that the invention combines the two construction methods to reconstruct the brightness optimization function.
As a third alternative embodiment, in the third construction method, unlike the first and second construction methods, the reference vehicle speed or the reference vehicle flow rate is selected differently, and after the third reference vehicle speed and the third reference vehicle flow rate are simultaneously and optimally solved, the construction is performed by the first and second construction methods again, and finally the average value is calculated.
For example, the luminance optimization function is a third function constructed by taking the vehicle speed and the vehicle flow as the co-dominant effect, and the expression is:
;
Wherein, Representing the time solved by the third functionA target value of illumination brightness at a time; Representing a main variable function when a brightness optimization function is constructed by taking a vehicle speed as a main variable and taking an increment of a vehicle flow as a secondary variable; Representing a secondary variable function when a brightness optimization function is constructed by taking the vehicle speed as a primary variable and taking the increment of the vehicle flow as a secondary variable; representing a secondary variable function when a brightness optimization function is constructed by taking the traffic flow as a primary variable and the increment of the vehicle speed as a secondary variable; representing a main variable function when a brightness optimization function is constructed by taking traffic flow as a main variable and the increment of vehicle speed as a secondary variable; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a third reference vehicle speed; Representing a third reference vehicle flow; A constant value item when a brightness optimization function is constructed by taking the vehicle speed as a main variable and taking the increment of the vehicle flow as a secondary variable is shown; a constant value item when a brightness optimization function is constructed by taking the traffic flow as a main variable and the increment of the vehicle speed as a secondary variable is represented; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
In step S5, the real-time PWM signal generation process specifically includes the steps of establishing a basic brightness and a basic duty ratio of the PWM signal required by controlling the lighting equipment to reach the basic brightness, determining an adjustment coefficient according to the ratio of a lighting brightness target value to the basic brightness, determining the duty ratio of the real-time PWM signal according to the product of the basic duty ratio and the adjustment coefficient, and generating a corresponding real-time PWM signal.
Embodiment 2a dynamic optimization system for tunnel luminance control is used to implement a dynamic optimization method for tunnel luminance control as described in embodiment 1, and as shown in fig. 2, the system includes a data acquisition module, a state estimation module, a state prediction module, a luminance solving module, and an adjustment optimization module.
The system comprises a data acquisition module, a state estimation module, a state prediction module, a brightness solving module and an adjustment optimization module, wherein the data acquisition module is used for acquiring multi-source state parameters in a target tunnel, the multi-source state parameters comprise traffic flow, speed, ambient light intensity and noise level, the state estimation module is used for preprocessing the multi-source state parameters and carrying out state estimation on the preprocessed multi-source state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state, the state prediction module is used for inputting the vehicle running estimation state into an ADL model trained by considering time conditions and ambient conditions to obtain predicted traffic flow and predicted speed, the brightness solving module is used for inputting the predicted traffic flow and the predicted speed into a brightness optimization function constructed by considering traffic flow and speed coordination to obtain an illumination brightness target value, and the adjustment optimization module is used for generating a corresponding real-time PWM signal according to the illumination brightness target value and realizing brightness adjustment based on the real-time PWM signal.
The invention also discloses a computer terminal which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the dynamic optimization method for tunnel brightness control according to the embodiment 1 when executing the program.
The present invention also describes a computer-readable medium having a computer program stored thereon, the computer program being executable by a processor to implement a tunnel luminance control dynamic optimization method as described in embodiment 1.
The invention adopts a Kalman filtering method to carry out state estimation on the preprocessed multi-source state parameters, can accurately obtain the vehicle operation estimation state capable of representing the overall condition of a target tunnel, and then combines the vehicle operation estimation state and an autoregressive distribution hysteresis model (ADL model) to carry out vehicle flow and vehicle speed prediction, thereby accurately analyzing the overall change trend of single data, accurately representing the association characteristic among different data, simultaneously realizing the solution of the illumination brightness target value by considering the brightness optimization function constructed by the coordination effect of the vehicle flow and the vehicle speed, and realizing the accurate response of the vehicle flow and the vehicle speed which change in real time from the global angle, thereby improving the reliability and timeliness of brightness adjustment.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The tunnel brightness control dynamic optimization method is characterized by comprising the following steps of:
Collecting multisource state parameters in a target tunnel, wherein the multisource state parameters comprise vehicle flow, vehicle speed, ambient light intensity and noise level;
Preprocessing the multisource state parameters, and carrying out state estimation on the preprocessed multisource state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state;
Inputting the vehicle running estimation state into an ADL model trained by considering time conditions and environmental conditions, and predicting to obtain predicted vehicle flow and predicted vehicle speed;
Inputting the predicted traffic flow and the predicted vehicle speed into a brightness optimization function constructed by considering the coordination effect of the traffic flow and the vehicle speed to obtain an illumination brightness target value;
and generating a corresponding real-time PWM signal according to the illumination brightness target value, and realizing brightness adjustment based on the real-time PWM signal.
2. The method for dynamically optimizing tunnel brightness control according to claim 1, wherein the preprocessing of the multi-source state parameter comprises the following steps:
Eliminating random errors and background noise in the multi-source state parameters through median filtering, mean filtering and/or wavelet transformation to obtain first parameters;
eliminating the influence of different dimensions and magnitudes in the first parameter through Z-score standardization or Min-Max standardization to obtain a second parameter;
And carrying out time alignment on the deviation of the sampling time in the second parameter by an interpolation method to obtain the preprocessed multi-source state parameter.
3. The method according to claim 1, wherein the time condition is to divide traffic into a plurality of traffic patterns including early peak, late peak, daytime and nighttime, and the environmental condition is to consider the influence of weather conditions on traffic and the influence of environmental light intensity on lighting requirements.
4. The dynamic optimization method for tunnel brightness control according to claim 1, wherein the brightness optimization function is a first function constructed by taking a vehicle speed as a main variable and an increment of a vehicle flow as a secondary variable, and the expression is as follows:
;
Wherein, Representing the time solved by the first functionA target value of illumination brightness at a time; representing a primary variable function in the first function; representing a minor variable function in the first function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a first reference vehicle flow; Representing constant entries in a first function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
5. The dynamic optimization method for tunnel brightness control according to claim 1, wherein the brightness optimization function is a second function constructed by taking traffic flow as a main variable and an increment of vehicle speed as a secondary variable, and the expression is:
;
Wherein, Representing the time solved by the second functionA target value of illumination brightness at a time; representing a minor variable function in the second function; Representing a primary variable function in the second function; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; Representing a first reference vehicle speed; representing constant entries in the second function; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
6. The dynamic optimization method for tunnel luminance control according to claim 1, wherein the luminance optimization function is a third function constructed by taking a vehicle speed and a vehicle flow as a co-dominant effect, and the expression is:
;
Wherein, Representing the time solved by the third functionA target value of illumination brightness at a time; Representing a main variable function when a brightness optimization function is constructed by taking a vehicle speed as a main variable and taking an increment of a vehicle flow as a secondary variable; Representing a secondary variable function when a brightness optimization function is constructed by taking the vehicle speed as a primary variable and taking the increment of the vehicle flow as a secondary variable; representing a secondary variable function when a brightness optimization function is constructed by taking the traffic flow as a primary variable and the increment of the vehicle speed as a secondary variable; representing a main variable function when a brightness optimization function is constructed by taking traffic flow as a main variable and the increment of vehicle speed as a secondary variable; Representation of Predicting the traffic flow at the moment; Representation of Predicting the speed of the vehicle at the moment; representing a third reference vehicle speed; Representing a third reference vehicle flow; A constant value item when a brightness optimization function is constructed by taking the vehicle speed as a main variable and taking the increment of the vehicle flow as a secondary variable is shown; a constant value item when a brightness optimization function is constructed by taking the traffic flow as a main variable and the increment of the vehicle speed as a secondary variable is represented; Representing a sample set of constructed luminance optimization functions in size Is a matrix representation of (2); Representing the total number of samples in the sample set; representing the number of the sample set taking the traffic flow as a variable; Representing the number of the sample sets taking the vehicle speed as a variable; indicating the vehicle speed as Traffic flow rateIllumination brightness of the corresponding sample; indicating the vehicle speed as The traffic flow isIllumination brightness of the corresponding sample.
7. The method for dynamically optimizing tunnel luminance control according to claim 1, wherein the generating process of the real-time PWM signal specifically comprises:
establishing a basic brightness and a basic duty ratio of a PWM signal required by controlling the lighting equipment to reach the basic brightness;
determining an adjustment coefficient by the ratio of the illumination brightness target value to the base brightness;
And determining the duty ratio of the real-time PWM signal by the product of the basic duty ratio and the adjustment coefficient, and generating a corresponding real-time PWM signal.
8. A tunnel luminance control dynamic optimization system for implementing a tunnel luminance control dynamic optimization method according to any one of claims 1 to 7, comprising:
The data acquisition module is used for acquiring multi-source state parameters in the target tunnel, wherein the multi-source state parameters comprise traffic flow, speed, ambient light intensity and noise level;
the state estimation module is used for preprocessing the multi-source state parameters, and carrying out state estimation on the preprocessed multi-source state parameters by adopting a Kalman filtering method to obtain a vehicle running estimation state;
the state prediction module is used for inputting the vehicle running estimation state into an ADL model trained by considering the time condition and the environmental condition, and predicting to obtain predicted vehicle flow and predicted vehicle speed;
The brightness solving module is used for inputting the predicted traffic flow and the predicted vehicle speed into a brightness optimizing function constructed by considering the coordination effect of the traffic flow and the vehicle speed to obtain an illumination brightness target value;
And the adjusting and optimizing module is used for generating a corresponding real-time PWM signal according to the illumination brightness target value and realizing brightness adjustment based on the real-time PWM signal.
9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a tunnel brightness control dynamic optimization method according to any one of claims 1-7 when executing the program.
10. A computer readable medium having stored thereon a computer program, wherein the computer program is executed by a processor to implement a tunnel brightness control dynamic optimization method according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119399964A (en) * | 2025-01-06 | 2025-02-07 | 中交综合规划设计院有限公司 | A tunnel lighting intelligent control method and system |
| CN120583562A (en) * | 2025-07-31 | 2025-09-02 | 深圳市诺龙技术股份有限公司 | Road DC lighting control method based on multi-source sensor fusion control |
| CN120583562B (en) * | 2025-07-31 | 2025-10-10 | 深圳市诺龙技术股份有限公司 | Road DC lighting control method based on multi-source sensor fusion control |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646372A (en) * | 2013-12-10 | 2014-03-19 | 交通运输部公路科学研究所 | Expressway operation integration optimization and evaluation method |
| JP2018024363A (en) * | 2016-08-11 | 2018-02-15 | 株式会社デンソー | Vehicle display control device and vehicle driving assist system |
| US20180096595A1 (en) * | 2016-10-04 | 2018-04-05 | Street Simplified, LLC | Traffic Control Systems and Methods |
| CN107909247A (en) * | 2017-10-26 | 2018-04-13 | 东南大学 | A kind of city macroscopic view Road Traffic Safety Factors analysis method based on spatial level Bayesian model |
| CN108898178A (en) * | 2018-06-27 | 2018-11-27 | 江苏大学 | A kind of human driver's bend track modeling method |
| US20190311616A1 (en) * | 2018-04-10 | 2019-10-10 | Cavh Llc | Connected and automated vehicle systems and methods for the entire roadway network |
| CN212588551U (en) * | 2020-06-22 | 2021-02-23 | 深圳华智测控技术有限公司 | Tunnel lighting control device and tunnel lighting system |
| US20220299619A1 (en) * | 2015-07-17 | 2022-09-22 | Yuqian HU | Method, apparatus, and system for wireless sensing based on linkwise motion statistics |
| CN118609368A (en) * | 2024-07-02 | 2024-09-06 | 山东金宇信息科技集团有限公司 | A method, device and medium for intelligent control of tunnel traffic based on holographic perception |
-
2024
- 2024-11-11 CN CN202411598035.XA patent/CN119150027B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646372A (en) * | 2013-12-10 | 2014-03-19 | 交通运输部公路科学研究所 | Expressway operation integration optimization and evaluation method |
| US20220299619A1 (en) * | 2015-07-17 | 2022-09-22 | Yuqian HU | Method, apparatus, and system for wireless sensing based on linkwise motion statistics |
| JP2018024363A (en) * | 2016-08-11 | 2018-02-15 | 株式会社デンソー | Vehicle display control device and vehicle driving assist system |
| US20180096595A1 (en) * | 2016-10-04 | 2018-04-05 | Street Simplified, LLC | Traffic Control Systems and Methods |
| CN107909247A (en) * | 2017-10-26 | 2018-04-13 | 东南大学 | A kind of city macroscopic view Road Traffic Safety Factors analysis method based on spatial level Bayesian model |
| US20190311616A1 (en) * | 2018-04-10 | 2019-10-10 | Cavh Llc | Connected and automated vehicle systems and methods for the entire roadway network |
| CN108898178A (en) * | 2018-06-27 | 2018-11-27 | 江苏大学 | A kind of human driver's bend track modeling method |
| CN212588551U (en) * | 2020-06-22 | 2021-02-23 | 深圳华智测控技术有限公司 | Tunnel lighting control device and tunnel lighting system |
| CN118609368A (en) * | 2024-07-02 | 2024-09-06 | 山东金宇信息科技集团有限公司 | A method, device and medium for intelligent control of tunnel traffic based on holographic perception |
Non-Patent Citations (2)
| Title |
|---|
| PARISE G等: "Structured distribution of electric power systems:The example of a roadway tunnel architecture", 《IEEE TRANS.IND》, vol. 46, no. 5, 19 October 2010 (2010-10-19), pages 2099 - 2105, XP011313887 * |
| 杨云等: "隧道多源模糊自适应智能照明网络设计与实现", 《地下空间与工程学报》, vol. 19, 18 December 2023 (2023-12-18), pages 955 - 962 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119399964A (en) * | 2025-01-06 | 2025-02-07 | 中交综合规划设计院有限公司 | A tunnel lighting intelligent control method and system |
| CN120583562A (en) * | 2025-07-31 | 2025-09-02 | 深圳市诺龙技术股份有限公司 | Road DC lighting control method based on multi-source sensor fusion control |
| CN120583562B (en) * | 2025-07-31 | 2025-10-10 | 深圳市诺龙技术股份有限公司 | Road DC lighting control method based on multi-source sensor fusion control |
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