Traffic signal lamp intelligent dimming system based on illumination and temperature perception

Abstract

The invention discloses a traffic signal lamp intelligent dimming system based on illumination and temperature perception, and relates to the technical field of signal lamp dimming. Comprising a setting module for setting an illumination sensing module and a temperature sensing module, acquiring illumination intensity data based on the illumination sensing module to obtain illumination information items, and acquiring temperature data based on the temperature sensing module to obtain temperature information items; the storage module is used for creating a first brightness contrast library and a second brightness contrast library based on a data acquisition mode; according to the method, a dynamic brightness interval is generated by expanding a threshold value according to an analysis result of a double-contrast library, then a comprehensive brightness range is intelligently intercepted according to an interaction state, and a time-frequency coordination mechanism is synchronously introduced: an illumination / temperature grade is mapped into a flicker frequency and is bound with a judgment time item, and finally a brightness-frequency-time three-dimensional regulation and control cycle is formed. And signal visibility is enhanced through dynamic brightness contrast.

Description

Technical Field

[0001] This invention relates to the field of traffic light dimming technology, specifically to an intelligent traffic light dimming system based on light and temperature sensing. Background Technology

[0002] Intelligent dimming of traffic lights is a technology that uses sensors, control algorithms, and communication technology to automatically and dynamically adjust the brightness of traffic light sources in real time according to ambient lighting conditions such as daytime, nighttime, dusk, dawn, cloudy days, and foggy days. Its core objective is to achieve efficient energy use while ensuring clear visibility of traffic lights and traffic safety. Traditional traffic lights, especially early incandescent lamps or fixed-brightness LED lights, are usually set to a high fixed brightness to ensure clear visibility even under the worst daytime lighting conditions. This high brightness appears excessively bright or even glaring in dim environments such as nighttime, dusk, dawn, or cloudy days, resulting in a large amount of unnecessary energy consumption. Therefore, intelligent dimming of lights is necessary.

[0003] The traffic light with patent publication number CN105575145A, designed for adaptive smog environments, improves upon existing traffic lights by incorporating a visibility testing module and a dimming module. To ensure sufficient light intensity, ultra-high brightness LEDs are used, and these LEDs are densely distributed across the light panel. Different numbers and positions of LEDs are activated under varying conditions. The structure of the original traffic light is also improved to enhance heat dissipation, ensuring the high-density LED distribution functions correctly. The visibility tester is positioned on the cantilever of the traffic light, with the receiver aligned with the road direction. An extended, stepped lens cover enhances the accuracy of the visibility signal. After sensing the surrounding air visibility, the system automatically adjusts the light intensity of the traffic light, ensuring pedestrians and drivers can clearly receive accurate traffic signals and travel safely even in smoggy weather.

[0004] In the process of dimming signals, the above-mentioned and similar technical solutions rely on the interaction of multiple sensors and the fusion calculation of data captured by different sensors to determine the final adjustment result. However, in complex environments, the data captured by the sensors is numerous and varied. When the data is calculated based on the fusion algorithm, the final calculation result may not be the optimal solution for the current complex environment. This leads to inconsistencies in the driver's perception of traffic lights and can easily result in insufficient contrast adjustment of traffic lights. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent dimming system for traffic lights based on light and temperature sensing, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent dimming system for traffic lights based on light and temperature sensing, comprising: Setting Module: Sets up a light sensing module and a temperature sensing module. The light sensing module acquires light intensity data to obtain light information items, and the temperature sensing module acquires temperature data to obtain temperature information items. Storage module: Based on the data acquisition method, a first brightness comparison library and a second brightness comparison library are created. The first brightness comparison library stores the best comparison data between light intensity data and light brightness, and the second brightness comparison library stores the best comparison data between temperature data and light brightness. Analysis module: Based on the comparison relationship between the illumination information item and the temperature information item and the first brightness reference library and the second brightness reference library respectively, the module expands to obtain the first brightness range and the second brightness range, and obtains the comprehensive brightness data of the first brightness range and the second brightness range to obtain the comprehensive brightness range. Extension module: Perform state extension on the first brightness reference library and the second brightness reference library. The state extension includes the light flashing frequency, obtains the state reference item, sets the cycle time, and obtains the judgment time item. The dimming module uses a judgment time as the time cycle, a comprehensive brightness range as the brightness cycle, and a status comparison item as the flashing frequency cycle to perform a combined cyclical adjustment of the traffic light brightness. It combines illumination and temperature, performs independent calculations for illumination and temperature, and displays the independent calculation results as a combination, thus providing sufficient comparative adjustment for traffic lights in complex environments.

[0007] Furthermore, the light sensing module includes a quantum dot spectral sensor, and the method for acquiring light information items includes: Set up at least two sets of quantum dot spectral sensors, assign labels to the quantum dot spectral sensors, and obtain the sensor label items; Based on the sensor label, viewpoint information and band information are set, where viewpoint information is the light field scanning viewpoint and band information is the light field scanning band. Based on the perspective information, the sensor labels are arranged in a fixed order, and the average value of the light intensity data is obtained based on the sensor labels to obtain the light information items.

[0008] Furthermore, the temperature sensing module includes a non-contact thermal topology measurement assembly. The method for acquiring temperature information items includes: the non-contact thermal topology measurement assembly includes a micron-wave infrared thermal imager, a terahertz wave scanner, and a thin-film sensing device; the hot spot distribution of the target signal lamp substrate is acquired based on the micron-wave infrared thermal imager to obtain a first temperature item; the air convection state inside the target signal lamp housing is acquired based on the terahertz wave scanner to obtain a second temperature item; the temperature gradient of the heat sink fins is acquired based on the thin-film sensing device to obtain a third temperature item; and the first temperature item, the second temperature item, and the third temperature item are combined to obtain the temperature information item.

[0009] Furthermore, the method for creating the first brightness reference library includes: Create a dynamic spectral sensing library, including multi-band energy weights, glare compensation matrices, and human eye response models; Based on multi-band energy weighting, at least two key bands are divided, and an independent brightness mapping table is established. Based on the glare compensation matrix, the glare compensation coefficient matrix under different solar altitude angles is stored. Based on the human eye response model, the photometric curve function is integrated to dynamically calculate the actual visual equivalent brightness. The data calculation and storage results of the dynamic spectral perception library are combined to obtain the first brightness comparison library.

[0010] Furthermore, the method for creating the second brightness reference library includes: Create a thermodynamic lifetime model library, including substrate thermal resistance mapping data, optical decay prediction matrix, and phase change cooling strategy; Based on the substrate thermal resistance mapping data, the relationship curves between thermal resistance and brightness of different heat sink materials are stored. A three-dimensional lookup table of temperature, working time and brightness decay is established based on the light decay prediction matrix. The latent heat absorption efficiency of phase change materials at different temperatures is recorded based on the phase change cooling strategy. The second brightness reference library is obtained by combining the substrate thermal resistance mapping data, the light decay prediction matrix and the phase change cooling strategy.

[0011] Furthermore, the method for obtaining the first brightness range and the second brightness range includes: Based on the comparison results of the illumination information item and the temperature information item with the first brightness reference library and the second brightness reference library respectively, the first brightness value and the second brightness value are obtained respectively; An expansion threshold is set, which is a percentage increase or decrease value. A first brightness range is obtained based on the combination of the expansion threshold and a first brightness value. A second brightness range is obtained based on the combination of the expansion threshold and a second brightness value.

[0012] Furthermore, the method for obtaining the comprehensive brightness range includes: Determine the interaction data between the first brightness range and the second brightness range. When the first brightness range interacts with the second brightness range, obtain the overlapping area of ​​the interaction as the comprehensive brightness range. When the first brightness range and the second brightness range do not interact, a truncation threshold is set. The truncation threshold is a fixed range value. Based on the combination result of the truncation threshold and the first brightness range and the second brightness range, the first range truncation item and the second range truncation item are obtained. The comprehensive brightness range is obtained by combining the first range truncation item and the second range truncation item.

[0013] Furthermore, the method for obtaining the determination time item includes: A combined grading system was used to classify the first and second brightness reference libraries to obtain first-level items and second-level items. The grading reference time was set based on the first-level items and the second-level items, respectively. Based on the correspondence between the illumination information item and the temperature information item and the first level item and the second level item, the first judgment level and the second judgment level are obtained, and the first control time and the second judgment control time are obtained. Based on the first control time and the second judgment control time, the switching time is set to obtain the judgment time item.

[0014] Furthermore, the method for obtaining the state comparison item includes: The first brightness comparison library also stores the best comparison data between light intensity data and light flicker frequency, and the second brightness comparison library also stores the best comparison data between different temperatures and light flicker frequency. Using illumination information and temperature information as reference data, the light flicker frequency corresponding to the illumination information and the light flicker frequency corresponding to the temperature information are obtained respectively to obtain a first frequency item and a second frequency item. The first frequency item and the second frequency item are combined to obtain a state reference item.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This intelligent dimming system for traffic lights, based on illumination and temperature sensing, generates dynamic brightness ranges by expanding thresholds through analysis of dual comparison libraries. It then intelligently extracts the comprehensive brightness range based on the interaction status and simultaneously introduces a time-frequency coordination mechanism: mapping illumination / temperature levels to flicker frequencies and binding them to the judgment time item, ultimately forming a three-dimensional control cycle of brightness-frequency-time. This enhances signal visibility through dynamic brightness comparison and reduces LED junction temperature through temperature-driven brightness segmentation, extending device lifespan.

[0016] Meanwhile, the quantum dot spectral sensor array, combined with a dynamic weighted fusion algorithm, effectively filters out interference such as direct sunlight and advertising glare. The sensor covers multiple wavelengths from 380 to 1050 nm and, with a 120° wide-angle scan, significantly improves the accuracy of illumination data. At the same time, the non-contact thermal topology monitoring system integrates micron-wave infrared thermal imaging, terahertz convection scanning, and PT1000 thin-film sensing to construct a three-dimensional temperature field model. The two modules independently acquire data, resisting environmental noise from both the spectral purity and thermodynamic state of the light source, ensuring the reliability of the basic data for illumination and temperature information, and fundamentally solving the problem of misjudgment by traditional sensors in complex scenarios. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of the process for obtaining the first temperature item, the second temperature item, and the third temperature item of the present invention. Figure 3 This is a schematic diagram of the process for obtaining the first brightness range and the second brightness range of the present invention; Figure 4 This is a schematic diagram of the interaction structure between the first brightness range and the second brightness range of the present invention; Figure 5 This is a schematic diagram of the non-interactive structure of the first brightness range and the second brightness range of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In modern traffic management systems, intelligent dimming technology for traffic lights plays a crucial role. Its goal is to dynamically adjust the brightness of traffic lights based on changes in ambient light, ensuring drivers can clearly and accurately identify traffic signals under various lighting conditions, thereby guaranteeing traffic safety and efficiency. However, currently widely used traffic light dimming algorithms typically rely on the collaborative work of multiple sensors in complex and changing environments. These sensors capture information such as ambient light intensity and weather conditions. The algorithm fuses and calculates the data collected by these sensors to ultimately determine the brightness adjustment scheme for the traffic lights. Ideally, this multi-sensor fusion approach can improve the accuracy and adaptability of dimming. However, in practical applications, complex environments often introduce a large amount of noise data to the sensors, causing the calculation results of the fusion algorithm to deviate from the optimal solution. Specifically, interference factors in complex environments include, but are not limited to, direct sunlight, reflections from tall buildings, glare from billboards, and interference from vehicle lights. These interference factors can make the data captured by the sensors complex and inaccurate. For example, direct sunlight may cause the light sensor to become oversensitive, while reflections from tall buildings may cause misjudgments of light intensity. When a large amount of such interference data floods into the fusion algorithm, the algorithm's calculation results will be severely affected. The technical solution provided in this application, through the analysis results of a dual-reference library, generates a dynamic brightness range by expanding the threshold, and then intelligently extracts the comprehensive brightness range based on the interaction state. Simultaneously, a time-frequency coordination mechanism is introduced: the illumination / temperature level is mapped to the flicker frequency and bound to the judgment time term, ultimately forming a three-dimensional control cycle of brightness-frequency-time. This enhances signal visibility through dynamic brightness comparison and extends device lifespan by reducing LED junction temperature through temperature-driven brightness segmentation. Figure 1 As shown, it includes a setting module, a storage module, an analysis module, an expansion module, and a dimming module.

[0020] Setting Module: Sets up the light sensing module and the temperature sensing module. The light sensing module acquires light intensity data to obtain light information items, and the temperature sensing module acquires temperature data to obtain temperature information items.

[0021] It should be noted that the illumination sensing module includes quantum dot spectral sensors. The method for obtaining illumination information items includes: setting at least two sets of quantum dot spectral sensors, labeling the quantum dot spectral sensors to obtain sensor label items; based on the sensor label items, setting viewing angle information and band information, where the viewing angle information is the light field scanning viewing angle and the band information is the light field scanning band; based on the viewing angle information, arranging the sensor label items in a fixed order, and obtaining the average value of the illumination intensity data based on the sensor label items, thereby obtaining the illumination information items.

[0022] In the specific implementation process, six sets of quantum dot spectral sensors are set up and labeled as No. 1 to No. 6, resulting in sensor labeling items. Viewing angle information and wavelength information are also set, with the viewing angle being 120° and the wavelength range being 380nm-1050nm. The sensor labeling items are arranged in a fixed order, forming a honeycomb-shaped microlens array. The acquisition process is as follows: graph TB A [Original optical signal] --> B [Lens beam splitter] B --> C [Visible light channel] B --> D [Near Infrared Channel] C --> E [Polarization State Analysis] D --> F [Spectral Energy Distribution] E & F --> G [Dynamic Weight Fusion] G --> H [Output light intensity Lux value + Spectral quality index SQI] This leads to the acquisition of lighting information.

[0023] It is important to note that, such as Figure 2 As shown, the temperature sensing module includes a non-contact thermal topology measurement assembly. The method for acquiring temperature information items includes: the non-contact thermal topology measurement assembly includes a micron-wave infrared thermal imager, a terahertz wave scanner, and a thin-film sensing device; the hot spot distribution of the target signal lamp substrate is acquired based on the micron-wave infrared thermal imager to obtain a first temperature item; the air convection state inside the target signal lamp housing is acquired based on the terahertz wave scanner to obtain a second temperature item; the temperature gradient of the heat sink fins is acquired based on the thin-film sensing device to obtain a third temperature item; and the first, second, and third temperature items are combined to obtain the temperature information item.

[0024] Specifically, the spatial resolution of the micron-wave infrared thermal image is 0.5 mm², the spatial resolution of the terahertz wave scanner is 3 cm³, and the thin-film sensing device is a PT1000 thin-film sensor with a spatial resolution of ±0.1℃. The overall temperature information of the target signal light is obtained through the micron-wave infrared thermal image, the terahertz wave scanner, and the thin-film sensing device, and the temperature information item is obtained.

[0025] Storage module: Based on the data acquisition method, create a first brightness reference library and a second brightness reference library.

[0026] It should be noted that the first brightness comparison library stores the best comparison data between light intensity data and light brightness, while the second brightness comparison library stores the best comparison data between temperature data and light brightness.

[0027] It should be noted that the method for creating the first brightness reference library includes: creating a dynamic spectral sensing library, including multi-band energy weights, glare compensation matrices, and human eye response models; based on the multi-band energy weights, dividing at least two key bands and establishing independent brightness mapping tables; based on the glare compensation matrix, storing glare compensation coefficient matrices under different solar altitude angles; based on the human eye response model, integrating photopic curve functions to dynamically calculate the actual visual equivalent brightness; and combining the data calculation and storage results from the dynamic spectral sensing library to obtain the first brightness reference library.

[0028] Specifically, the multi-band energy weighting divides the spectrum into five key bands: 450nm blue, 520nm green, 620nm red, 850nm IR, and 940nm IR-Cut. An independent brightness mapping table is established to address the issue of red light penetration in foggy weather. A glare compensation coefficient matrix is ​​stored for different solar altitude angles to eliminate strong backlight interference during sunrise and sunset. The photometric curve function includes the CIE 1931 photometric curve + V(λ) function, which conforms to the physiological perception characteristics of the human eye.

[0029] It should be noted that the method for creating the second brightness reference library includes: creating a thermodynamic lifetime model library, including substrate thermal resistance mapping data, light decay prediction matrix, and phase change cooling strategy; storing the thermal resistance and brightness relationship curves of different heat sink materials based on substrate thermal resistance mapping data; establishing a three-dimensional lookup table of temperature, operating time, and brightness decay based on the light decay prediction matrix; recording the latent heat absorption efficiency of phase change materials at different temperatures based on the phase change cooling strategy; and combining the substrate thermal resistance mapping data, light decay prediction matrix, and phase change cooling strategy to obtain the second brightness reference library.

[0030] Specifically, the mathematical model for the relationship between thermal resistance and brightness for different heat sink materials is: R_th(ja) = (Tj - Ta) / P_diss, where R_th(ja) is the thermal resistance from the junction to the environment, in °C / W, representing the efficiency of heat transfer from the LED chip to the environment; Tj is the LED junction temperature, the core temperature that directly determines the device's lifespan; Ta is the ambient temperature; and P_diss is the electrical power actually converted into heat energy by the LED during operation. The mathematical model for the three-dimensional lookup table of temperature, operating time, and brightness decay is: L_decay = 1 - e^(-0.0012T·t), where L_decay is the brightness decay rate, indicating that high temperature accelerates phosphor carbonization; t represents the cumulative operating time, with 0.0012 defaulting to the decay coefficient; and T is the LED junction temperature, representing the core operating temperature of the LED chip. The mathematical model for the latent heat absorption efficiency of phase change materials at different temperatures is: Q = m·Cp·ΔT + m·α·L_fusion, where Q is the latent heat absorption efficiency of the phase change material at different temperatures. α represents the total heat absorption, L_fusion represents the latent heat of phase change, which is the latent heat absorbed or released per unit mass of material during phase change, m represents the mass of the phase change material, Cp represents the specific heat capacity, and ΔT represents the temperature range.

[0031] Analysis module: Based on the comparison relationship between the illumination information item and the temperature information item and the first brightness reference library and the second brightness reference library respectively, the module expands to obtain the first brightness range and the second brightness range, and obtains the comprehensive brightness data of the first brightness range and the second brightness range to obtain the comprehensive brightness range.

[0032] It is important to note that, such as Figure 3 As shown, the method for obtaining the first brightness range and the second brightness range includes: obtaining the first brightness value and the second brightness value based on the comparison results of the illumination information item and the temperature information item with the first brightness reference library and the second brightness reference library, respectively; setting an expansion threshold, the expansion threshold being an increase or decrease percentage value; obtaining the first brightness range based on the combination of the expansion threshold and the first brightness value; and obtaining the second brightness range based on the combination of the expansion threshold and the second brightness value.

[0033] Specifically, the expansion threshold is set to ±5%. The first brightness value and the second brightness value are obtained based on the comparison results of the illumination information item and the temperature information item with the first brightness reference library and the second brightness reference library, respectively. When the first brightness value is 80% of the reference brightness and the second brightness value is 70% of the reference brightness, the first brightness range is 75%-85% and the second brightness range is 65%-75% according to the set expansion threshold.

[0034] It is important to note that, such as Figures 4-5The method for obtaining the comprehensive brightness range includes: determining the interaction data between the first brightness range and the second brightness range; when the first brightness range interacts with the second brightness range, obtaining the overlapping area as the comprehensive brightness range; when the first brightness range does not interact with the second brightness range, setting a truncation threshold, which is a fixed range value; based on the combination result of the truncation threshold and the first brightness range and the second brightness range, obtaining the first range truncation item and the second range truncation item; and combining the first range truncation item and the second range truncation item to obtain the comprehensive brightness range.

[0035] Specifically, when the first brightness value is 80% of the reference brightness and the second brightness value is 75% of the reference brightness, the first brightness range is 75%-85% and the second brightness range is 70%-80% according to the set expansion threshold. The first and second brightness ranges interact, and the overlapping area is taken as the comprehensive brightness range, i.e., the comprehensive brightness range is 75%-80%. Conversely, when the first brightness value is 90% of the reference brightness and the second brightness value is 60% of the reference brightness, the first brightness range is 85%-95% and the second brightness range is 55%-65% according to the set expansion threshold. The first and second brightness ranges do not interact. A truncation threshold is set, which is a fixed range value of 10%, and is evenly distributed between the first and second brightness ranges. Based on the combination of the truncation threshold and the first and second brightness ranges, the first range truncation item is 80%-85%, and the second range truncation item is 60%-65%, thus obtaining the comprehensive brightness range.

[0036] Extension module: Performs state extension on the first brightness reference library and the second brightness reference library. The state extension includes the light flashing frequency, obtains the state reference item, sets the cycle time, and obtains the judgment time item.

[0037] It should be noted that the method for obtaining the determination time item includes: performing a combined level division on the first brightness reference library and the second brightness reference library to obtain the first level item and the second level item; setting the level reference time based on the first level item and the second level item respectively; obtaining the first determination level and the second determination level based on the correspondence between the illumination information item and the temperature information item and the first level item and the second level item, and obtaining the first reference time and the second determination reference time; setting the switching time based on the first reference time and the second determination reference time to obtain the determination time item.

[0038] Specifically, a combined classification of the first and second brightness reference libraries is performed to obtain first and second level items. The first level items are: low intensity, medium intensity, and high intensity, corresponding to light brightness of 0-40%, 40%-70%, and 70%-100% of the reference brightness, respectively. The second level items are: low temperature, medium temperature, and high temperature, corresponding to light brightness of 0-40%, 40%-70%, and 70%-100% of the reference brightness, respectively. The level comparison time is set based on the first and second level items, with comparison times of 15s, 10s, and 5s. Based on the correspondence between the light intensity information item and the temperature information item and the first and second level items, the first judgment level and the second judgment level are obtained, along with the first comparison time and the second judgment comparison time. A switching time is set based on the first comparison time and the second judgment comparison time to obtain the judgment time item. For example, under medium intensity light intensity and low temperature data, the corresponding first comparison time and the second judgment comparison time are 10s and 15s, respectively, and the switching time is 25s.

[0039] It should be noted that the method for obtaining the state reference item includes: the first brightness reference library also stores the best reference data between light intensity data and light flicker frequency, and the second brightness reference library also stores the best reference data between different temperature data and light flicker frequency; using the light information item and temperature information item as reference data, the light flicker frequency corresponding to the light information item and the light flicker frequency corresponding to the temperature information item are obtained respectively to obtain the first frequency item and the second frequency item, and the first frequency item and the second frequency item are combined to obtain the state reference item.

[0040] Specifically, the optimal comparison data between light intensity data and light flicker frequency stored in the first brightness comparison library is as follows: low intensity corresponds to a light flicker frequency of 1Hz, medium intensity corresponds to a light flicker frequency of 1.5Hz, and high intensity corresponds to a light flicker frequency of 2Hz. The optimal comparison data between temperature data and light flicker frequency stored in the second brightness comparison library is as follows: low temperature corresponds to a light flicker frequency of 1Hz, medium temperature corresponds to a light flicker frequency of 1.5Hz, and high temperature corresponds to a light flicker frequency of 2Hz. At this time, the light flicker frequency corresponding to the light information item and the light flicker frequency corresponding to the temperature information item are obtained respectively to obtain the first frequency item and the second frequency item. The first frequency item and the second frequency item are combined to obtain the state comparison item.

[0041] The dimming module uses a combination of time-based adjustment, a comprehensive brightness range-based brightness cycle, and a status reference-based flashing frequency cycle to perform cyclical adjustment of the signal light brightness.

[0042] It is important to note that by combining illumination and temperature, and performing independent calculations for each, and then displaying the results as a combined summary, sufficient comparative adjustment can be provided for traffic light regulation in complex environments.

[0043] In the specific implementation process, when the first brightness range and the second brightness range are obtained as 55%-65% and 25%-35% respectively, the resulting comprehensive range items are 55%-60% and 30%-35%. At this point, the first and second level items are determined as medium intensity and low temperature, respectively. Under medium intensity light intensity and low temperature data, the corresponding first and second judgment control times are 10s and 15s, respectively. The switching time is then 25s. Simultaneously, the corresponding first and second frequency items are 1.5Hz and 1Hz, respectively, with an average frequency of 1.2. At 5Hz, the judgment time item is used as the time cycle, that is, 25 seconds as one cycle. The comprehensive brightness range is used as the brightness cycle, that is, 55%-60% and 30%-35%. The status comparison item is used as the flashing frequency cycle, that is, 1.25Hz as the flashing frequency. The combined cyclical adjustment of the traffic light brightness is carried out. That is, within 25 seconds, the transition between 55%-60% and 30%-35% of the light brightness is completed with a flashing frequency of 1.25Hz. Then, the illumination and temperature are calculated independently and the independent calculation results are displayed as a combination, thus providing sufficient comparative adjustment for traffic lights in complex environments.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A smart dimming system for traffic lights based on light and temperature sensing, including: Setting Module: Sets up a light sensing module and a temperature sensing module. The light sensing module acquires light intensity data to obtain light information items, and the temperature sensing module acquires temperature data to obtain temperature information items. Its characteristic is that it further includes: Storage module: Based on the data acquisition method, a first brightness comparison library and a second brightness comparison library are created. The first brightness comparison library stores the best comparison data between light intensity data and light brightness, and the second brightness comparison library stores the best comparison data between temperature data and light brightness. Analysis module: Based on the comparison relationship between the illumination information item and the temperature information item and the first brightness reference library and the second brightness reference library respectively, the module expands to obtain the first brightness range and the second brightness range, and obtains the comprehensive brightness data of the first brightness range and the second brightness range to obtain the comprehensive brightness range. Extension module: Perform state extension on the first brightness reference library and the second brightness reference library. The state extension includes the light flashing frequency, obtains the state reference item, sets the cycle time, and obtains the judgment time item. The dimming module uses a judgment time as the time cycle, a comprehensive brightness range as the brightness cycle, and a status comparison item as the flashing frequency cycle to perform a combined cyclical adjustment of the traffic light brightness. It combines illumination and temperature, performs independent calculations for illumination and temperature, and displays the independent calculation results as a combination, thus providing sufficient comparative adjustment for traffic lights in complex environments.

2. The intelligent dimming system for traffic lights based on illumination and temperature sensing according to claim 1, characterized in that: The illumination sensing module includes a quantum dot spectral sensor, and the method for acquiring illumination information items includes: Set up at least two sets of quantum dot spectral sensors, assign labels to the quantum dot spectral sensors, and obtain the sensor label items; Based on the sensor label, viewpoint information and band information are set, where viewpoint information is the light field scanning viewpoint and band information is the light field scanning band. Based on the perspective information, the sensor labels are arranged in a fixed order, and the average value of the light intensity data is obtained based on the sensor labels to obtain the light information items.

3. The intelligent dimming system for traffic lights based on illumination and temperature sensing according to claim 1, characterized in that: The temperature sensing module includes a non-contact thermal topology measurement assembly. The method for acquiring temperature information items includes: the non-contact thermal topology measurement assembly includes a micron-wave infrared thermal imager, a terahertz wave scanner, and a thin-film sensing device; the hot spot distribution of the target signal lamp substrate is acquired based on the micron-wave infrared thermal imager to obtain a first temperature item; the air convection state inside the target signal lamp housing is acquired based on the terahertz wave scanner to obtain a second temperature item; the temperature gradient of the heat sink fins is acquired based on the thin-film sensing device to obtain a third temperature item; and the first temperature item, the second temperature item, and the third temperature item are combined to obtain the temperature information item.

4. The intelligent dimming system for traffic lights based on light and temperature sensing according to claim 1, characterized in that: The method for creating the first brightness reference library includes: Create a dynamic spectral sensing library, including multi-band energy weights, glare compensation matrices, and human eye response models; Based on multi-band energy weighting, at least two key bands are divided, and an independent brightness mapping table is established. Based on the glare compensation matrix, the glare compensation coefficient matrix under different solar altitude angles is stored. Based on the human eye response model, the photometric curve function is integrated to dynamically calculate the actual visual equivalent brightness. The data calculation and storage results of the dynamic spectral perception library are combined to obtain the first brightness comparison library.

5. The intelligent dimming system for traffic lights based on illumination and temperature sensing according to claim 1, characterized in that: The method for creating the second brightness reference library includes: Create a thermodynamic lifetime model library, including substrate thermal resistance mapping data, optical decay prediction matrix, and phase change cooling strategy; Based on the substrate thermal resistance mapping data, the relationship curves between thermal resistance and brightness of different heat sink materials are stored. A three-dimensional lookup table of temperature, working time and brightness decay is established based on the light decay prediction matrix. The latent heat absorption efficiency of phase change materials at different temperatures is recorded based on the phase change cooling strategy. The second brightness reference library is obtained by combining the substrate thermal resistance mapping data, the light decay prediction matrix and the phase change cooling strategy.

6. The intelligent dimming system for traffic lights based on light and temperature sensing according to claim 1, characterized in that: The methods for obtaining the first brightness range and the second brightness range include: Based on the comparison results of the illumination information item and the temperature information item with the first brightness reference library and the second brightness reference library respectively, the first brightness value and the second brightness value are obtained respectively; An expansion threshold is set, which is a percentage increase or decrease value. A first brightness range is obtained based on the combination of the expansion threshold and a first brightness value. A second brightness range is obtained based on the combination of the expansion threshold and a second brightness value.

7. The intelligent dimming system for traffic lights based on light and temperature sensing according to claim 1, characterized in that: The method for obtaining the comprehensive brightness range includes: Determine the interaction data between the first brightness range and the second brightness range. When the first brightness range interacts with the second brightness range, obtain the overlapping area of ​​the interaction as the comprehensive brightness range. When the first brightness range and the second brightness range do not interact, a truncation threshold is set. The truncation threshold is a fixed range value. Based on the combination result of the truncation threshold and the first brightness range and the second brightness range, the first range truncation item and the second range truncation item are obtained. The comprehensive brightness range is obtained by combining the first range truncation item and the second range truncation item.

8. The intelligent dimming system for traffic lights based on light and temperature sensing according to claim 1, characterized in that: The method for obtaining the determination time item includes: A combined grading system was used to classify the first and second brightness reference libraries to obtain first-level items and second-level items. The grading reference time was set based on the first-level items and the second-level items, respectively. Based on the correspondence between the illumination information item and the temperature information item and the first level item and the second level item, the first judgment level and the second judgment level are obtained, and the first control time and the second judgment control time are obtained. Based on the first control time and the second judgment control time, the switching time is set to obtain the judgment time item.

9. The intelligent dimming system for traffic lights based on illumination and temperature sensing according to claim 1, characterized in that: The method for obtaining the state comparison item includes: The first brightness comparison library also stores the best comparison data between light intensity data and light flicker frequency, and the second brightness comparison library also stores the best comparison data between different temperatures and light flicker frequency. Using illumination information and temperature information as reference data, the light flicker frequency corresponding to the illumination information and the light flicker frequency corresponding to the temperature information are obtained respectively to obtain a first frequency item and a second frequency item. The first frequency item and the second frequency item are combined to obtain a state reference item.

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