Intelligent street lamp system and brightness adjusting method based on FPGA and millimeter wave radar

Abstract

The application discloses a kind of intelligence street lamp system and brightness adjusting method based on FPGA and millimeter wave radar, it is related to street lamp lighting technical field, it includes millimeter wave radar, illumination sensor, ADC module, FPGA controller and LED street lamp, ADC sampling clock is generated by FPGA controller, reset variable, and constantly loop iteration is executed: the illumination intensity that illumination sensor real-time detection is acquired, according to preset illumination intensity threshold value determines flag bit Flag;According to the received millimeter wave radar trigger signal, the ADC echo data of radar transmission period is read, echo data is sampled and is handled FFT conversion, and frequency domain data is obtained, the amplitude-frequency data of continuous two frames of frequency domain data is obtained by cancellation processing;According to amplitude-frequency data, the attribute of radar detection target is judged, when detection target is dynamic target, according to flag bit Flag whether the street lamp is judged to be opened and is calculated according to frequency domain data to control the intelligent adjustment of street lamp brightness.

Description

Technical Field

[0001] This invention relates to the field of street lighting technology, and in particular to a smart street lighting system and brightness adjustment method based on FPGA and millimeter-wave radar. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Streetlights play a crucial role in urban lighting systems, and their operational efficiency and energy-saving effects directly impact urban energy consumption and residents' travel experience. Traditional streetlight systems generally employ a time-controlled switch mode, automatically turning streetlights on and off according to preset fixed times. However, this control method has significant drawbacks: time-controlled switches cannot obtain real-time information on the actual situation of pedestrians and vehicles within the illuminated area, resulting in streets remaining brightly lit even late at night when there are no people or vehicles, causing a large amount of ineffective energy consumption and poor energy-saving effects. Furthermore, they cannot flexibly adjust the lighting status according to dynamic changes in vehicle and pedestrian traffic, causing inconvenience to residents and resulting in a poor user experience.

[0004] To address the shortcomings of traditional street light control, current smart street light systems are incorporating cameras as sensing devices. These cameras utilize visual detection technology and image processing algorithms to detect and identify key objects such as pedestrians and vehicles, thereby enabling dynamic adjustment of lighting. However, in practical applications, these vision-based smart street light systems are highly susceptible to environmental factors. In severe weather conditions such as heavy fog, smog, rain, snow, and dust, suspended particles and moisture in the air severely impede light transmission, significantly reducing the camera's detection distance and accuracy. The captured images become blurry and unclear, making it impossible to accurately identify target objects. Consequently, the street light system loses its effective adjustment basis, making it difficult to guarantee lighting needs and travel safety during inclement weather.

[0005] Considering the advantage of microwave or millimeter-wave radar in stable operation under adverse weather conditions, smart street light control systems based on radar detection have been proposed. However, due to the clutter in radar detection signals, the target detection accuracy of the system is low, and there is a lack of effective target differentiation methods, resulting in a high false positive and false negative rate. In addition, existing dimming strategies simply adjust the brightness of street lights based on the presence or absence of nearby people / vehicles, lacking a dynamic control mechanism based on actual dynamic road scenarios, resulting in poor stability and adaptability, and easily causing energy waste. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a smart street light system and brightness adjustment method based on FPGA and millimeter-wave radar. Using millimeter-wave radar as an environmental target perception sensor and FPGA (Field Programmable Gate Array) as the core processor, the system overcomes the limitations of existing time-controlled street lights (poor energy efficiency), vision-based brightness adjustment (high susceptibility to severe weather), and radar-based brightness adjustment (high misjudgment rate and poor stability). This enables all-weather target detection and intelligent brightness adjustment for urban street lights.

[0007] In a first aspect, the present invention provides a smart street light system based on FPGA and millimeter-wave radar.

[0008] A smart street light system based on FPGA and millimeter-wave radar includes millimeter-wave radar, light sensor, ADC module, FPGA controller and LED street light;

[0009] The millimeter-wave radar outputs I / Q quadrature signals and a trigger signal. The I / Q quadrature signals are transmitted to the input of the ADC module via a cable. The trigger signal and the light intensity detected by the light sensor are transmitted to the analog input of the FPGA controller. The output of the ADC module is connected to the I / O port of the FPGA controller, and the FPGA controller generates the ADC sampling clock and sends it to the ADC module. The FPGA controller is connected to the LED street light through the I / O port.

[0010] The FPGA controller is used to receive the millimeter-wave radar trigger signal, the radar echo signal sampled by the ADC module, and the detected light intensity, and adjusts the brightness of the LED street light by calculation.

[0011] A further technical solution is that the trigger signal output by the millimeter-wave radar outputs a high level during the radar's pulse signal transmission period and a low level during the idle period;

[0012] The FPGA controller generates a system clock through its internal phase-locked loop IP core, and then divides the system clock to generate an ADC sampling clock.

[0013] Secondly, the present invention provides a method for adjusting the brightness of smart streetlights based on FPGA and millimeter-wave radar.

[0014] A method for adjusting the brightness of smart streetlights based on FPGA and millimeter-wave radar is disclosed. This method, implemented using the aforementioned smart streetlight system, generates an ADC sampling clock via an FPGA controller, resets variables, and iteratively executes the following steps:

[0015] The light intensity detected in real time by the light sensor is obtained, and the flag bit is determined according to the preset light intensity threshold.

[0016] The ADC echo data of the radar transmission period is read based on the received millimeter-wave radar trigger signal. The echo data is sampled and transformed by FFT to obtain frequency domain data. The amplitude frequency data is obtained after canceling two consecutive frames of frequency domain data.

[0017] The radar determines the attributes of the detected target based on the amplitude-frequency data. When the detected target is a moving target, it determines whether to turn on the streetlights based on the flag and performs calculations based on the frequency domain data to control the intelligent adjustment of the streetlight brightness.

[0018] A further technical solution involves determining a flag bit (Flag) based on the real-time light intensity detected by the light sensor and a preset light intensity threshold, including:

[0019] When the light intensity is lower than the light intensity threshold T1, the flag bit Flag is set to 1;

[0020] When the light intensity is greater than the light intensity threshold T2, the flag bit Flag is set to zero;

[0021] When the light intensity is between the light intensity thresholds T1 and T2, the value of the flag bit remains unchanged.

[0022] A further technical solution involves the FPGA controller reading the ADC echo data from the radar transmission period based on the received millimeter-wave radar trigger signal, and performing sampling processing on the echo data, including:

[0023] The ADC module acquires a data point at the rising edge of the sampling clock provided by the FPGA and continuously acquires radar echo data according to the clock timing.

[0024] The FPGA controller reads the trigger signal provided by the millimeter-wave radar from the analog port in real time. When the trigger signal is high, it starts receiving and buffering the radar echo data collected by the ADC module; conversely, when the trigger signal is low, it stops receiving the data collected by the ADC module.

[0025] In the data receiving buffer of the FPGA controller, the first and last data points of the buffered data are deleted, and the middle data is extracted to obtain the sampled echo data.

[0026] A further technical solution utilizes the dual-port storage of frequency domain data within the FPGA controller, and performs cancellation processing on two consecutive frames of frequency domain data to obtain amplitude-frequency data, including:

[0027] The frequency domain data of the previous frame is cached using the internal dual-port RAM of the FPGA controller.

[0028] To remove stationary targets, subtract the real part I and the imaginary part Q of the frequency domain data of the current frame from those of the previous frame, as shown below:

[0029] ;

[0030] in, k For the number of data points, n The frame number for storing data;

[0031] Modulo operation is performed on the real and imaginary parts of the frequency domain data after two consecutive frames of cancellation to obtain the amplitude-frequency data, which is represented as:

[0032] ;

[0033] in, This represents the real part of the signal after frequency domain data cancellation. This represents the imaginary part after cancellation of frequency domain data. The amplitude after canceling the frequency domain data.

[0034] Further technical solutions involve determining the attributes of radar-detected targets based on amplitude-frequency data, including:

[0035] According to the n Frame Amplitude Frequency Data Calculate dynamic threshold ;in, These are weighting coefficients. N The number of data points for the FFT transform;

[0036] In the n Frame Amplitude Frequency Data In this process, a judgment is made based on three consecutive data points according to a dynamic threshold:

[0037] If the judgment condition is met, the same judgment is performed on the next two consecutive frames of amplitude-frequency data. If the judgment condition is met, the radar detects the target as a moving target.

[0038] Conversely, if any frame of amplitude-frequency data does not meet the judgment condition, the radar-detected target is judged to be a stationary target.

[0039] A further technical solution involves judging three consecutive data points based on a dynamic threshold, with the judgment condition being:

[0040] , ;

[0041] Where N is the number of FFT transform points, This is a dynamic threshold.

[0042] A further technical solution involves determining whether to turn on the streetlights based on a flag and calculating based on frequency domain data to intelligently adjust the streetlight brightness, including:

[0043] When the flag value is 0, the street light is turned off;

[0044] Conversely, when the flag value is 1 and a moving target is detected, the streetlights are turned on, and intelligent adjustment of the streetlight brightness is controlled based on frequency domain data, including:

[0045] The amplitude-frequency data of the millimeter-wave radar is converted into range image data. The values ​​of the distance between all moving targets and the streetlights are traversed, the minimum distance is compared, and the pulse width duty cycle (PWM) signal of the streetlight is dynamically generated based on the minimum distance.

[0046] Initialize the variable CNT. Based on the crystal oscillator clock signal of the FPGA controller, increment the variable CNT by 1 on each rising edge of the clock. Continuously perform the increment operation. When the variable CNT is greater than the set value, the FPGA controller controls the I / O port to output a PWM signal, and the variable CNT is reset to zero.

[0047] The brightness of the streetlights is intelligently adjusted based on the PWM signal and its duty cycle.

[0048] A further technical solution involves converting the amplitude-frequency data of millimeter-wave radar into range image data, using the following conversion formula:

[0049] ;

[0050] in, The period of the radar frequency modulation signal. B is the difference frequency, C is the radar signal bandwidth, and D is the speed of light.

[0051] The pulse width modulation (PWM) signal for the streetlight is dynamically generated based on the minimum distance d, and its mapping relationship is as follows:

[0052] .

[0053] The above one or more technical solutions have the following beneficial effects:

[0054] 1. This invention proposes a smart street light system and brightness adjustment method based on FPGA and millimeter-wave radar. It utilizes millimeter-wave radar as an environmental target perception sensor and FPGA as the core processor. By integrating the dynamic perception capability of millimeter-wave radar with the real-time data processing technology of FPGA, and employing a method combining single-frame multi-point threshold detection with multi-frame temporal decision-making, the detection rate of moving targets is effectively improved, and the occurrence of false positives and false negatives is reduced. Simultaneously, based on the real-time detection of moving targets around the street light by the millimeter-wave radar, PWM technology is used to adjust the driving current of the LED street light, achieving segmented intelligent brightness control. This avoids ineffective lighting of street lights during periods without people or vehicles, significantly reducing power consumption and effectively saving energy. Ultimately, it enables all-weather target detection and intelligent brightness adjustment of urban street lights.

[0055] 2. The smart street light system and method provided by this invention uses millimeter-wave radar as the target perception device. Millimeter-wave radar has strong anti-interference capabilities and is not affected by severe weather such as heavy fog, smog, rain, snow, and dust. It can realize all-weather vehicle and pedestrian target detection and intelligent brightness adjustment, making up for the shortcomings of traditional smart street lights based on visual sensors that cannot work normally in severe weather, and ensuring lighting needs and travel safety under various weather conditions.

[0056] 3. In terms of system hardware design, this invention generates a system clock and performs frequency division processing through the internal phase-locked loop IP core of the FPGA, providing a precise sampling clock for the ADC module and ensuring the accuracy and integrity of radar echo data acquisition. During data processing, inter-frame cancellation processing is used to remove static clutter, further improving the signal-to-noise ratio of the target signal and providing strong support for the accuracy of target detection.

[0057] 4. The brightness adjustment method of the present invention combines the dual factors of ambient light intensity and target distance. It can not only determine whether to turn on the street light based on the lighting conditions, but also dynamically adjust the brightness according to the distance between the target and the street light. It not only meets the lighting needs in different environments, but also minimizes energy consumption. It has good practicality and economy, and is applicable to various types of urban street lights. It has broad application prospects in the construction of new smart cities.

[0058] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0059] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0060] Figure 1This is a schematic diagram of the structure of a smart street light system based on FPGA and millimeter-wave radar in an embodiment of the present invention;

[0061] Figure 2 This is a timing diagram of the sampling clock of the ADC module in an embodiment of the present invention;

[0062] Figure 3 This is a flowchart of a smart street light brightness intelligent adjustment method based on FPGA and millimeter-wave radar in an embodiment of the present invention.

[0063] Figure 4 This is a schematic diagram of millimeter-wave radar frequency domain data in an embodiment of the present invention; wherein, ( a (a) is the original time-domain data of the millimeter-wave radar acquired by the FPGA; (b) is the frequency-domain data of the millimeter-wave radar acquired by the FPGA and processed by FFT.

[0064] Figure 5 This is a data curve diagram after the FPGA performs cancellation processing on two adjacent frames of frequency domain data in an embodiment of the present invention;

[0065] Figure 6 This is a schematic diagram of moving target judgment and feature points in the amplitude-frequency curves of three consecutive frames in an embodiment of the present invention.

[0066] The components include: 1. Millimeter-wave radar; 2. ADC module; 3. FPGA controller; 4. LED street light; and 5. Light sensor. Detailed Implementation

[0067] It should be noted that the following detailed descriptions are exemplary and are intended only to describe specific embodiments and to provide further explanation of the invention, and are not intended to limit the scope of exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0068] Example 1

[0069] This embodiment provides a smart street light system based on FPGA and millimeter-wave radar, such as Figure 1As shown, the system includes a millimeter-wave radar 1, a light sensor 5, an ADC module 2, an FPGA controller 3, and an LED street light 4. The millimeter-wave radar outputs an I / Q quadrature signal and a synchronous trigger signal (hereinafter referred to as the trigger signal). The I / Q quadrature signal is transmitted to the input of the ADC module via a cable. The trigger signal and the light intensity detected by the light sensor are transmitted to the analog input of the FPGA controller. The output of the ADC module is connected to the I / O port of the FPGA controller, and the FPGA controller generates the ADC sampling clock and sends it to the ADC module. The FPGA controller is connected to the LED street light via its I / O port.

[0070] In this embodiment, the FPGA controller receives the millimeter-wave radar trigger signal, the radar echo signal sampled by the ADC module, and the detected light intensity. The system then calculates and adjusts the brightness of the LED streetlights based on the received signals. Through the above system design, a complete closed loop of "sensing-conversion-processing-execution" can be formed, enabling all-weather target detection and intelligent brightness adjustment of urban streetlights.

[0071] As an alternative implementation, the ADC module (i.e., analog-to-digital conversion module) uses AD9280, the FPGA controller uses XC7Z020, the millimeter-wave radar uses 24GHz linear frequency modulated continuous wave radar, and the light sensor uses GY-30 module.

[0072] Furthermore, the trigger signal output by the millimeter-wave radar is high during the radar's pulse signal transmission period and low during idle periods. This design allows the FPGA to read ADC data only during the radar's effective operating hours, avoiding the acquisition of invalid data during idle periods and reducing data redundancy and wasted FPGA computing power.

[0073] In addition, the sampling clock of the ADC module is provided by the FPGA controller. The FPGA controller generates the system clock through the internal phase-locked loop IP core, and generates the ADC sampling clock after frequency division of the system clock. In this way, the clock frequency can be flexibly adjusted through the FPGA to adapt to the sampling requirements of different radar models and ensure the stability of the clock signal.

[0074] like Figure 2 As shown, as an optional implementation, the FPGA outputs a 24.15MHz clock (clk_out) through its internal phase-locked loop IP core based on a 50MHz crystal oscillator signal. The 24.15MHz clock is then divided into ten parts to generate a 2.415MHz sampling clock (ad_clk) as the ADC driving clock, thereby achieving accurate sampling of 256 data points within one radar signal cycle of 106us.

[0075] Example 2

[0076] This embodiment provides a method for adjusting the brightness of smart streetlights based on FPGA and millimeter-wave radar. This method is implemented based on the aforementioned smart streetlight system. It uses an FPGA controller to generate an ADC sampling clock, reset variables, and continuously iterate through the following steps:

[0077] The light intensity detected in real time by the light sensor is obtained, and the flag bit is determined according to the preset light intensity threshold.

[0078] The ADC echo data of the radar transmission period is read based on the received millimeter-wave radar trigger signal. The echo data is sampled and transformed by FFT to obtain frequency domain data. The amplitude frequency data is obtained after canceling two consecutive frames of frequency domain data.

[0079] The radar determines the attributes of the detected target based on the amplitude-frequency data. When the detected target is a moving target, it determines whether to turn on the streetlights based on the flag and performs calculations based on the frequency domain data to control the intelligent adjustment of the streetlight brightness.

[0080] The following content provides a more detailed description of the smart street light brightness adjustment method based on FPGA and millimeter-wave radar proposed in this embodiment.

[0081] like Figure 3 As shown, the intelligent street light brightness adjustment method specifically includes the following steps:

[0082] Step S1: The FPGA generates the sampling clock for the ADC module, such as... Figure 2 As shown.

[0083] Step S2: The FPGA reads the data from the light sensor and determines the flag bit (Flag) based on the real-time light intensity detected by the light sensor and the preset light intensity threshold, including:

[0084] When the light intensity is lower than the light intensity threshold T1, the flag bit Flag is set to 1;

[0085] When the light intensity is greater than the light intensity threshold T2, the flag bit Flag is set to zero;

[0086] When the light intensity is between the light intensity thresholds T1 and T2, the value of the flag bit remains unchanged.

[0087] This embodiment uses a dual-threshold approach instead of a single-threshold approach to determine the Flag state. When the light intensity is between T1 and T2, the current value of the Flag is maintained. This dual-threshold mechanism can avoid frequent switching or brightness changes of streetlights caused by small fluctuations in light intensity (such as brief changes in brightness caused by cloud cover), which can reduce the switching losses of LED lights, extend the life of LED streetlights, and reduce energy waste. Moreover, the Flag state is clearly defined: Flag=1 means insufficient light and lighting is needed, and Flag=0 means sufficient light and no lighting is needed. By setting this binary state, subsequent brightness adjustment decisions can be made more directly without complex light intensity quantification analysis, shortening the time from light detection to brightness response, improving system real-time performance, and reducing FPGA computing power consumption.

[0088] As an alternative implementation method, in this embodiment, T1=100 and T2=500.

[0089] Step S3: The FPGA reads the echo data collected by the ADC during the millimeter-wave radar signal transmission period and performs sampling processing, such as... Figure 4 As shown in (a).

[0090] Specifically, step S3 is as follows:

[0091] The ADC module acquires a data point at the rising edge of the sampling clock provided by the FPGA and continuously acquires radar echo data according to the clock timing.

[0092] The FPGA controller reads the trigger signal provided by the millimeter-wave radar from the analog port in real time. When the trigger signal is high, it starts receiving and buffering the radar echo data collected by the ADC module; conversely, when the trigger signal is low, it stops receiving the data collected by the ADC module.

[0093] In the data receiving buffer of the FPGA controller, the first and last data points of the buffered data are deleted, and the middle data is extracted to obtain the sampled echo data.

[0094] Through the aforementioned timing synchronization mechanism, it can be ensured that the collected echo data can truly reflect the distance relationship between the radar and the target, such as the correspondence between the difference frequency and the distance. The removal of the first and last data can eliminate noise interference in the early stage of sampling, making the frequency domain data obtained by subsequent FFT transformation clearer. Sampling processing can reduce the amount of data per frame, reduce the computational load of the FPGA performing FFT transformation, shorten the processing time of a single frame of data, and enable the system to respond to dynamic changes of the target more quickly, such as timely brightening when a pedestrian approaches rapidly.

[0095] Step S4: The sampled echo signal is fed into the FFT IP core, and the radar time-domain data is converted into frequency-domain data through FFT transformation, such as... Figure 4 As shown in (b).

[0096] Step S5: Utilize the dual-port storage of frequency domain data within the FPGA, and perform cancellation processing on two consecutive frames of frequency domain data to obtain amplitude-frequency data.

[0097] like Figure 5 As shown, step S5 specifically involves:

[0098] First, the frequency domain data of the previous frame is cached using the dual-port RAM inside the FPGA.

[0099] Secondly, the real part I and the imaginary part Q of the frequency domain data of the current frame and the previous frame are subtracted to remove stationary targets, as shown below:

[0100] ;

[0101] Where k is the number of data points and n is the frame number for storing data.

[0102] By using the above inter-frame cancellation processing, the difference between the stable frequency domain characteristics of static targets (such as trees and buildings) and the changing frequency domain characteristics of moving targets is utilized to accurately remove static clutter, retain moving target signals, avoid static clutter being misjudged as moving targets, and reduce the probability of false triggering of "lights still on when there is no target".

[0103] Then, modulo operations are performed on the real and imaginary parts of the frequency domain data after two consecutive frames of cancellation to obtain the amplitude-frequency data:

[0104] ;

[0105] in, This represents the real part of the signal after frequency domain data cancellation. This represents the imaginary part after cancellation of frequency domain data. The amplitude after canceling the frequency domain data.

[0106] By calculating the amplitude-frequency data, subsequent dynamic threshold judgment can be made more direct and easier to implement. For example, if three consecutive points exceed the threshold, there is no need to process the phase information of complex signals, which reduces the difficulty of FPGA algorithm design and shortens the development cycle.

[0107] Step S6: Use amplitude and frequency data to determine the target attribute. If the target is a static target, repeat step S3; otherwise, if the target is a moving target, proceed to the next step S7.

[0108] Specifically, in step S6, the attributes of the radar-detected target are determined based on the amplitude-frequency data, including:

[0109] Based on the amplitude frequency data of the nth frame Calculate dynamic threshold ;in, Here, are the weighting coefficients, and N is the number of data points for the FFT transform. As an alternative implementation method, The value ranges from 1.1 to 1.5, and N is 256. This dynamic threshold setting adapts to different environmental noise levels, ensuring accurate identification of moving targets in both noisy urban roads (e.g., with many vehicles and pedestrians) and quiet suburban roads (e.g., with few targets and low noise).

[0110] Amplitude frequency data in the nth frame In the process, three consecutive data points are judged based on a dynamic threshold. If the judgment condition is met, the same judgment is performed on the next two consecutive frames of amplitude-frequency data. If both judgment conditions are met, the radar detects a moving target. Otherwise, if any frame of amplitude-frequency data does not meet the judgment condition, the radar detects a stationary target.

[0111] That is, in the nth frame amplitude frequency data In this process, a dynamic threshold is used to judge three consecutive data points, and the judgment condition is as follows:

[0112] , ;

[0113] Where N is the number of FFT transform points, This is a dynamic threshold.

[0114] If the above conditions are not met, repeat step S3; if the above conditions are met, proceed to the next step: for the Xth consecutive received data... n+1 (k), X n+2 (k) Perform the above judgment operation on the amplitude frequency of the two frames. When X n (k), X n+1 (k), X n+2 (k) If all three frames of data meet the above conditions, it is judged as a moving target.

[0115] The above-mentioned three-frame continuous decision mechanism can effectively eliminate instantaneous interference and avoid frequent adjustments of streetlights due to "false targets" (such as falling leaves). It balances energy saving and lighting stability. Moreover, the combination of multi-frame decision and dynamic threshold can keep the target detection performance of the system stable in different scenarios (such as rainy days, foggy days, night, early morning, etc.) without relying on manual parameter adjustment, thus reducing system operation and maintenance costs.

[0116] Step S7: When a moving target appears, the Flag value mentioned in step S2 is judged in real time. If the Flag is 1, the next step S8 is executed; otherwise, step S9 is executed.

[0117] Specifically, when the Flag value is 0, the streetlights are turned off; conversely, when the Flag value is 1 and a moving target is detected, the streetlights are turned on, and the brightness is intelligently adjusted based on frequency domain data. That is, the system first determines whether lighting is needed based on the Flag. When there is sufficient light (Flag=0), the streetlights are turned off to avoid ineffective daytime lighting; when there is insufficient light (Flag=1), the PWM is calculated based on the target distance to adjust the brightness. This hierarchical decision-making avoids unnecessary calculations. For example, when Flag=0, there is no need to process distance data, which improves the FPGA response speed and reduces power consumption.

[0118] Step S8: Based on the real-time detection of target information around the streetlights by millimeter-wave radar, intelligently adjust the brightness of the streetlights.

[0119] Specifically, the steps include:

[0120] First, the amplitude-frequency data of the millimeter-wave radar is converted into range image data. The conversion formula is as follows:

[0121] ;

[0122] in, The period of the radar frequency modulation signal. B is the difference frequency, C is the radar signal bandwidth, and D is the speed of light.

[0123] Secondly, when Flag=1, iterate through the distance values ​​of all moving targets from the streetlight, compare them to find the minimum distance d, and dynamically generate the pulse width duty cycle (PWM) signal for the streetlight based on the value of d. The mapping relationship is as follows:

[0124] ;

[0125] When Flag=0, the PWM value is set to 0, and the street light turns off.

[0126] Next, the variable CNT is initialized. Based on the FPGA's crystal oscillator clock signal, CNT is incremented by 1 on each rising edge of the clock. This step is continuously executed in a loop. When CNT ≥ the set value of 500000, the FPGA controls the I / O port to output a PWM signal, and CNT is reset to zero.

[0127] Finally, the LED streetlights intelligently adjust their brightness based on the PWM duty cycle.

[0128] The above scheme uses CNT counting and reset to stably generate PWM signals, ensuring smooth brightness adjustment without flickering and protecting human eye health.

[0129] In other words, based on the linear frequency modulated continuous wave of millimeter-wave radar, an accurate conversion formula from amplitude-frequency data to distance data is derived. This allows for the accurate calculation of the distance between the target and the streetlight, providing a precise distance basis for brightness adjustment and avoiding improper brightness adjustment due to distance calculation errors. Furthermore, the brightness is dynamically adjusted according to the minimum target distance to avoid being too bright or too dim, thus improving the travel experience for pedestrians and vehicles. Moreover, during the brightness adjustment process, the PWM duty cycle mapping relationship is clearly defined, and the interval adjustment not only meets the lighting needs of different distances but also avoids brightness fluctuations caused by frequent fine-tuning, balancing comfort and stability.

[0130] Step S9: Repeat steps S2 to S8.

[0131] The smart street light system and brightness adjustment method based on FPGA and millimeter-wave radar provided in this embodiment, through FPGA hardware architecture optimization and algorithm co-design, and through frequency division cascaded sampling, a two-level decision mechanism, and intelligent brightness adjustment strategy, forms a closed-loop solution of "high-precision sampling - anti-interference processing - intelligent decision - dynamic dimming," which can play a role in data collection and energy saving in new smart cities. This method is applicable to various types of street lights and has broad application prospects and economic benefits. Moreover, compared with the existing scheme of using millimeter-wave radar to monitor traffic and pedestrian flow on the road and jointly detect changes in illuminance for energy-saving dimming, this embodiment effectively distinguishes between moving and stationary targets through local signal processing, and uses the distance between people / vehicles and street lights as the basis for adjusting the street light brightness dynamically. This achieves better lighting control and energy-saving effects, and effectively avoids the drawbacks of traditional host computer remote control, such as complex system deployment, high implementation cost, and high adjustment delay.

[0132] The steps involved in the above embodiment two correspond to those in embodiment one. For specific implementation details, please refer to the relevant description section of embodiment one.

[0133] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0134] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention has been described in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the present invention.

Claims

1. A smart street light system based on FPGA and millimeter-wave radar, characterized in that, This includes millimeter-wave radar, light sensors, ADC modules, FPGA controllers, and LED streetlights; The millimeter-wave radar outputs I / Q quadrature signals and a trigger signal. The I / Q quadrature signals are transmitted to the input of the ADC module via a cable. The trigger signal and the light intensity detected by the light sensor are transmitted to the analog input of the FPGA controller. The output of the ADC module is connected to the I / O port of the FPGA controller, and the FPGA controller generates the ADC sampling clock and sends it to the ADC module. The FPGA controller is connected to the LED street light through the I / O port. The FPGA controller is used to receive the millimeter-wave radar trigger signal, the radar echo signal sampled by the ADC module, and the detected light intensity, and adjusts the brightness of the LED street light by calculation. The frequency domain data is stored in the dual-port RAM of the FPGA controller. After cancelling two consecutive frames of frequency domain data, amplitude-frequency data is obtained. This includes: caching the previous frame's frequency domain data in the dual-port RAM of the FPGA controller; and subtracting the real part I and imaginary part Q of the current frame's frequency domain data from the previous frame's data to remove static targets, as shown below: Where k is the number of data points and n is the frame number storing the data; after two consecutive frames cancel each other out, the real and imaginary parts of the frequency domain data are modulo-divided to obtain the amplitude-frequency data, which is represented as: ;in, This represents the real part of the signal after frequency domain data cancellation. This represents the imaginary part after cancellation of frequency domain data. The amplitude after frequency domain data cancellation; Determining the attributes of radar-detected targets based on amplitude-frequency data includes: based on the amplitude-frequency data of the nth frame. Calculate dynamic threshold ;in, The weighting coefficients are N, where N is the number of data points in the FFT transform; the amplitude-frequency data in the nth frame... In this process, the three consecutive data points are judged based on the dynamic threshold. If the judgment condition is met, the same judgment is performed on the next two consecutive frames of amplitude-frequency data. If both judgment conditions are met, the radar detects the target as a moving target. Conversely, if any frame of amplitude-frequency data does not meet the judgment condition, the radar-detected target is judged to be a stationary target.

2. The smart street light system based on FPGA and millimeter-wave radar as described in claim 1, characterized in that, The trigger signal output by the millimeter-wave radar is high during the radar's pulse signal transmission period and low during the idle period. The FPGA controller generates a system clock through its internal phase-locked loop IP core, and then divides the system clock to generate an ADC sampling clock.

3. A method for adjusting the brightness of smart streetlights based on FPGA and millimeter-wave radar, characterized in that, This method is implemented based on the smart street light system described in any one of claims 1-2. It generates an ADC sampling clock through an FPGA controller, resets variables, and continuously iterates through the following steps: The light intensity detected in real time by the light sensor is obtained, and the flag bit is determined according to the preset light intensity threshold. The ADC echo data of the radar transmission period is read based on the received millimeter-wave radar trigger signal. The echo data is sampled and transformed by FFT to obtain frequency domain data. The amplitude frequency data is obtained after canceling two consecutive frames of frequency domain data. By utilizing the dual-port storage of frequency domain data within the FPGA controller, amplitude-frequency data is obtained after cancelling two consecutive frames of frequency domain data, including: The frequency domain data of the previous frame is cached using the internal dual-port RAM of the FPGA controller. To remove stationary targets, subtract the real part I and the imaginary part Q of the frequency domain data of the current frame from those of the previous frame, as shown below: ； Where k is the number of data points and n is the frame number for storing data; Modulo operation is performed on the real and imaginary parts of the frequency domain data after two consecutive frames of cancellation to obtain the amplitude-frequency data, which is represented as: ； in, This represents the real part of the signal after frequency domain data cancellation. This represents the imaginary part after cancellation of frequency domain data. The amplitude after frequency domain data cancellation; The radar determines the attributes of the detected target based on the amplitude-frequency data. When the detected target is a moving target, it determines whether to turn on the streetlights based on the flag and performs calculations based on the frequency domain data to control the intelligent adjustment of the streetlight brightness. Determining the attributes of radar-detected targets based on amplitude-frequency data includes: Based on the amplitude frequency data of the nth frame Calculate dynamic threshold ;in, The weighting coefficients are N, where N is the number of data points for the FFT transformation. Amplitude frequency data in the nth frame In this process, a judgment is made based on three consecutive data points according to a dynamic threshold: If the judgment condition is met, the same judgment is performed on the next two consecutive frames of amplitude-frequency data. If the judgment condition is met, the radar detects the target as a moving target. Conversely, if any frame of amplitude-frequency data does not meet the judgment condition, the radar-detected target is judged to be a stationary target.

4. The smart street light brightness adjustment method based on FPGA and millimeter-wave radar as described in claim 3, characterized in that, Based on the real-time light intensity detected by the light sensor and the preset light intensity threshold, a flag is determined, including: When the light intensity is lower than the light intensity threshold T1, the flag bit Flag is set to 1; When the light intensity is greater than the light intensity threshold T2, the flag bit Flag is set to zero; When the light intensity is between the light intensity thresholds T1 and T2, the value of the flag bit remains unchanged.

5. The smart street light brightness adjustment method based on FPGA and millimeter-wave radar as described in claim 3, characterized in that, The FPGA controller reads the ADC echo data during the radar transmission period based on the received millimeter-wave radar trigger signal, and performs sampling processing on the echo data, including: The ADC module acquires a data point at the rising edge of the sampling clock provided by the FPGA and continuously acquires radar echo data according to the clock timing. The FPGA controller reads the trigger signal provided by the millimeter-wave radar from the analog port in real time. When the trigger signal is high, it starts receiving and buffering the radar echo data collected by the ADC module; conversely, when the trigger signal is low, it stops receiving the data collected by the ADC module. In the data receiving buffer of the FPGA controller, the first and last data points of the buffered data are deleted, and the middle data is extracted to obtain the sampled echo data.

6. The smart street light brightness adjustment method based on FPGA and millimeter-wave radar as described in claim 3, characterized in that, The dynamic threshold is used to judge three consecutive data points, and the judgment condition is: ， ； Where N is the number of FFT transform points, This is a dynamic threshold.

7. The smart street light brightness adjustment method based on FPGA and millimeter-wave radar as described in claim 3, characterized in that, The system uses a flag to determine whether to turn on the streetlights and performs calculations based on frequency domain data to intelligently adjust the streetlight brightness, including: When the flag value is 0, the street light is turned off; Conversely, when the flag value is 1 and a moving target is detected, the streetlights are turned on, and intelligent adjustment of the streetlight brightness is controlled based on frequency domain data, including: The amplitude-frequency data of the millimeter-wave radar is converted into range image data. The values ​​of the distances of all moving targets to the streetlights are iterated, the minimum distance is compared, and the pulse width duty cycle (PWM) signal of the streetlight is dynamically generated based on the value of the minimum distance. Initialize the variable CNT, and increment CNT by 1 on each rising edge of the FPGA controller's crystal oscillator clock. Continuously perform the increment operation. When the variable CNT is greater than the set value, the FPGA controller controls the I / O port to output a PWM signal, and the variable CNT is reset to zero. The brightness of the streetlights is intelligently adjusted based on the PWM duty cycle.

8. The method for adjusting the brightness of smart streetlights based on FPGA and millimeter-wave radar as described in claim 7, characterized in that, The formula for converting millimeter-wave radar amplitude-frequency data into range image data is as follows: ； in, The period of the radar frequency modulation signal. B is the difference frequency, C is the radar signal bandwidth, and D is the speed of light. The pulse width modulation (PWM) signal for the streetlight is dynamically generated based on the minimum distance d, and its mapping relationship is as follows: 。

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