Laser detection system based on traffic overload control equipment and detection method thereof

Abstract

The invention discloses a laser detection system based on traffic overload control equipment and a detection method thereof. Comprising a multi-wavelength laser detection module, a feedback adaptive laser power control module, a data fusion processing module, a vehicle load estimation module and a laser vision auxiliary calibration module. According to the system, through a multi-wavelength laser modulation and feedback control mechanism, accurate detection of the vehicle load under different environmental conditions is realized, and the signal separation degree in a complex traffic flow is remarkably improved. The system is combined with an adaptive weighting algorithm, multi-lane interference can be effectively isolated, the accuracy of single vehicle overload detection is improved, meanwhile, real-time image calibration is conducted on the bottom of a vehicle through a low-cost visual sensor, laser reflection errors are eliminated, and the detection precision of an edge area is ensured. Besides, through multi-dimensional data fusion and dynamic load estimation, overload detection speed and precision are optimized, detection delay caused by traffic jam or errors is reduced, and efficient and safe traffic management is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic monitoring and overload detection, and in particular to a laser detection system based on traffic overload control equipment and a detection method thereof. Background Art

[0002] At present, traffic overload detection systems are widely used in traffic control points such as highways and urban roads to monitor whether the passing vehicles exceed the prescribed load limit. These systems usually use mechanical sensing, pressure sensors, visual monitoring and other means to detect vehicle loads. Traditional overload detection methods mostly rely on ground weighing technology, or calculate the load by combining laser ranging with vehicle speed monitoring. However, these methods have obvious shortcomings in complex environments such as multi-lane highways.

[0003] First, in the existing technology, mechanical induction and pressure sensors are often affected by multiple factors such as vehicle type, road conditions and weather, resulting in low measurement accuracy. For example, in a multi-lane environment with high-density traffic, signal interference from other vehicles significantly reduces the monitoring accuracy of a single vehicle load. In addition, although traditional laser detection technology has high accuracy in static conditions, the attenuation problem of laser signals has not been effectively solved under dynamic, complex traffic flows and severe weather conditions (such as rain, snow, wetness, etc.), which directly affects the stability and reliability of the system.

[0004] To address these issues, the industry often uses redundant equipment (such as multiple cameras and pressure sensors) to improve accuracy, but this increases the complexity and maintenance costs of the system. At the same time, the laser reflection signal at the bottom of the vehicle is often difficult to capture accurately due to differences in vehicle speed, road conditions, and different vehicle shapes, resulting in low detection accuracy in edge areas. In addition, in order to reduce signal interference, a combination of multiple sensors is usually used. Although this can reduce interference, it is still difficult to effectively isolate signals and improve accuracy under complex road conditions. And in dealing with complex and changing traffic environments, especially high-density traffic and complex terrain, traditional methods cannot provide sufficient real-time and accuracy. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a laser detection system based on traffic overload control equipment and a detection method thereof in view of the defects existing in the above-mentioned prior art, so as to solve the problems raised in the above-mentioned background technology. In order to solve the above-mentioned technical problems, the technical scheme adopted by the present invention is as follows: A laser detection system based on traffic overload control equipment, comprising: a multi-wavelength laser detection module, used to emit a multi-wavelength modulated laser beam to the bottom of the vehicle and the road surface area, wherein the multi-wavelength modulated laser beam includes at least two laser beams with different wavelengths, and each wavelength is used to detect different vehicle structural features and road surface reflection characteristics; the multi-wavelength laser detection module is configured to synchronously switch laser beams of different wavelengths through a preset modulation frequency and time sequence, and perform differentiated detection on the bottom structure of the vehicle, the tire surface, the axle position and the road surface; a reflection signal receiving module, used to receive the return signal of the multi-wavelength laser beam after being reflected from the bottom of the vehicle and the road surface; the reflection signal receiving module includes a multi-band photoelectric detector, which can separate and mark the reflection signals from different sources according to the intensity and delay time of the received reflection signals of different wavelengths;

[0006] The feedback adaptive laser power control module is connected to the multi-wavelength laser detection module and is used to dynamically adjust the emission power of the multi-wavelength laser beam according to the different wavelength laser reflection intensities received by the reflection signal receiving module. The dynamic adjustment of the laser power is comprehensively calculated based on the real-time ambient light intensity, vehicle type and vehicle speed. The calculation formula is: ,in, is the laser emission power, is the real-time ambient light intensity, is the current speed of the vehicle, is the laser wavelength currently used, function It is a nonlinear function obtained by experimental fitting, which is used to ensure the stability of the reflected signal intensity under different environmental conditions; A data fusion processing module is used to fuse the multi-wavelength reflection signal data received by the reflection signal receiving module with the vehicle dynamic data to generate multi-dimensional signal features; wherein the vehicle dynamic data includes but is not limited to vehicle speed, axle position, and vehicle type, and the data fusion processing module includes: a noise suppression unit, which is used to filter out random noise in the reflection signal based on a Kalman filter to suppress signal fluctuations caused by environmental interference and vehicle vibration; a feature extraction unit, which is used to extract the vehicle's axle load, tire characteristics, and vehicle dynamic load distribution based on the reflection characteristics of different components at the bottom of the vehicle; an adaptive weighted fusion unit, which is used to perform weighted fusion on the reflection data of each wavelength based on the reliability weights of different wavelength signals in the multi-wavelength reflection signal, wherein the weights The calculation basis is: ,in, The wavelength The signal strength under is the sum of the signal intensities at all wavelengths, and the influence of different wavelength reflection signals on the final vehicle load detection results is determined through normalization processing; The vehicle load dynamic estimation module is connected to the data fusion processing module and is used to estimate the real-time load state of the vehicle based on the multi-dimensional signal characteristics after weighted fusion, wherein the single-axle load of the vehicle The calculation formula is: ,in, is the reflection delay time of the laser beam, is the distance between the bottom of the vehicle and the road surface, is the weight of the reflected signal at each wavelength, function Represents an axle load estimation model obtained by nonlinear fitting; a laser vision-assisted calibration module is used to calibrate the edge conditions of the reflection signal during the laser detection process, and the laser vision-assisted calibration module includes: a visual sensor unit, used to capture real-time images of the bottom of the vehicle; an image laser data alignment unit, used to perform edge correction on the laser detection result according to the geometric features of the bottom of the vehicle and the spatial position of the reflection signal; a data consistency verification unit, used to compare the calibrated data with the original laser reflection data for consistency, and when the verification result exceeds the set error threshold, trigger the feedback adjustment mechanism to readjust the laser emission power and signal processing parameters; a data storage and output module, used to store all detection data, vehicle load status and environmental data, and upload the detection results to the traffic management system or the overload control control center, wherein the data storage and output module is configured to automatically and synchronously upload data at a predetermined time interval and send an alarm signal when an overload condition is detected.

[0007] As a further solution of the present invention, the emission wavelength range of the multi-wavelength laser detection module includes an infrared band of 850nm-1550nm and a visible light band of 400nm-700nm, wherein different bands are used to respectively detect the reflection characteristics of metal parts and non-metal parts at the bottom of the vehicle.

[0008] As a further solution of the present invention, the feedback adaptive laser power control module also includes an ambient light intensity sensor and a vehicle speed sensor. The ambient light intensity sensor is used to measure the external light intensity, and the vehicle speed sensor is used to monitor the vehicle's driving speed in real time, and dynamically adjust the laser emission power based on the data of the two.

[0009] As a further solution of the present invention, the noise suppression unit in the data fusion processing module uses a combination algorithm of a Kalman filter and an adaptive mean filter, wherein the Kalman filter is used to dynamically eliminate high-frequency noise caused by vehicle vibration, and the adaptive mean filter is used to smooth low-frequency noise caused by environmental interference.

[0010] As a further solution of the present invention, the weight coefficient of the adaptive weighted fusion unit is It is also determined based on the stability score of each wavelength in the historical vehicle type data, where the stability score is calculated through multiple past test results, and the formula is: ,in, The wavelength Next The vehicle load value detected for the first time, is the deviation from the actual load value, The number of historical detections.

[0011] As a further solution of the present invention, the nonlinear fitting model for estimating the single-axle load of the vehicle in the vehicle load dynamic estimation module is obtained by training using a machine learning method, wherein the training data includes vehicle type, number of axles, tire contact area, vehicle speed and road conditions, and the estimation formula is determined by a fitting model based on support vector regression.

[0012] As a further solution of the present invention, the image laser data alignment unit of the laser vision-assisted calibration module performs edge detection and shape feature matching on the bottom image of the vehicle to achieve spatial position alignment with the laser detection result, ensuring that the reflection signal deviation in the edge area of ​​the vehicle is controlled within 1 mm.

[0013] As a further solution of the present invention, the predetermined time interval of the data storage and output module is dynamically adjusted according to the traffic flow. When the traffic flow is high, the predetermined time interval is automatically shortened to less than 5 seconds. When the traffic flow is low, the time interval can be extended to 30 seconds.

[0014] As a further solution of the present invention, the system also includes a remote monitoring module that communicates with the traffic management system. The remote monitoring module is used to monitor the equipment status and detection results of all detection points in real time, and automatically issue a maintenance request when an abnormality occurs in the detection equipment.

[0015] A detection method based on a laser detection system of traffic overload control equipment is characterized in that it includes the following steps: S1, emitting a multi-wavelength modulated laser beam to the bottom and road surface of a moving vehicle through the multi-wavelength laser detection module to capture reflection signals at different wavelengths; S2, receiving the reflection signal through the reflection signal receiving module, and preliminarily separating the reflection data of different wavelengths; S3, dynamically adjusting the laser emission power according to the real-time ambient light intensity and vehicle speed through the feedback adaptive laser power control module; S4, performing noise suppression, feature extraction and weighted fusion on the received reflection signal through the data fusion processing module to obtain multi-dimensional signal characteristics; S5, calculating the single-axle load of the vehicle according to the signal characteristics and vehicle dynamic data after weighted fusion through the vehicle load dynamic estimation module; S6, calibrating the laser detection results of the edge area through the laser vision-assisted calibration module; S7, storing the estimated vehicle load data in the data storage and output module, and judging whether to trigger the overload alarm signal according to the detection result.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Through multi-band data fusion processing, laser signals of different wavelengths are combined with multi-dimensional data such as vehicle speed and axle position, and an adaptive weighted algorithm is used for dynamic adjustment, which effectively isolates the signal interference from multi-lane vehicles, improves the signal separation, and thus improves the accuracy of single vehicle overload detection. In a multi-lane parallel environment, the interference separation of the system is significantly improved, ensuring that the load detection of each vehicle is not interfered by other vehicles, and significantly enhancing the detection accuracy and system reliability in the complex situation of multi-lane parallel. And solves the error problem that may occur in the bottom edge area of ​​the vehicle with traditional laser detection. Through low-cost visual sensors, real-time image acquisition of the bottom of the vehicle is carried out, and precise alignment and calibration is carried out in combination with the spatial position of the laser signal, eliminating the accuracy loss caused by uneven laser reflection, which is conducive to ensuring that the detection results remain highly accurate in complex traffic flow or edge detection areas, so that the system can provide stable overload detection results under any conditions. Especially in the environment of complex vehicles (such as trailers, trucks, etc.) and high-density traffic flow, the system can work accurately on vehicles of different heights and structures. 2. The dynamic load estimation module combines laser signals with vehicle dynamic data (such as speed, axle position, etc.), and uses an adaptive algorithm to dynamically fuse multi-dimensional signals, effectively improving the accuracy and efficiency of real-time load estimation. Especially when high-density traffic flows or multi-axle heavy vehicles pass through, the system can accurately estimate the load status of the vehicle in real time, so as to make overload detection judgments more quickly. This technology can significantly improve the speed and accuracy of overload detection without adding any additional hardware costs, ensuring efficient processing of traffic flows without affecting the completion of overload monitoring tasks, and further reducing traffic congestion caused by detection errors or delays in traditional methods. The multi-dimensional signal fusion and feedback control system fully considers the efficiency of the system while ensuring the accuracy of overload detection. By adaptively adjusting the laser power and automatically optimizing the detection mode, the system can maintain a fast response during peak traffic hours, ensuring high-precision detection without adding unnecessary traffic congestion and delays. Unlike traditional overload control equipment, this solution can adjust the detection strategy according to the traffic volume and road conditions, avoiding redundant detection and over-reliance on equipment, greatly improving the work efficiency and traffic capacity in the overload control process, so that vehicles can complete overload detection without stopping, thereby improving the smoothness and management efficiency of the entire traffic flow. 3. Through the multi-wavelength modulated laser detection system, combined with feedback adaptive laser power control, the detection accuracy problem caused by the inability of traditional single-wavelength laser detection to adapt to complex environments (such as different weather, light, slippery roads, etc.) is successfully solved. The system monitors the reflected signal intensity in real time and automatically adjusts the laser emission power according to environmental changes to reduce signal attenuation caused by environmental factors such as weather and light.This innovative integration of multi-wavelength laser signals and real-time environmental adjustment mechanisms has improved overload detection accuracy in complex environments, reducing the system's detection error to less than 3% in harsh environments such as rain, snow, and wet conditions, which is much lower than the 10% error of existing technologies. This combined technology not only enhances environmental adaptability and signal stability, but also improves the accuracy and robustness of the overall system, solving the problem of insufficient detection accuracy in dynamic and complex traffic environments.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a system architecture diagram of the present invention.

[0020] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 and Figure 2In an embodiment of the present invention, a laser detection system based on traffic overload control equipment includes: a multi-wavelength laser detection module, which is used to emit a multi-wavelength modulated laser beam to the bottom of the vehicle and the road area, wherein the multi-wavelength modulated laser beam includes at least two laser beams with different wavelengths, and each wavelength is used to detect different vehicle structural features and road reflection characteristics; the multi-wavelength laser detection module is configured to synchronously switch laser beams of different wavelengths through a preset modulation frequency and time sequence, and perform differentiated detection on the bottom structure of the vehicle, the tire surface, the axle position and the road surface; a reflection signal receiving module, which is used to receive the return signal of the multi-wavelength laser beam after being reflected from the bottom of the vehicle and the road surface; the reflection signal receiving module includes a multi-band photoelectric detector, which can separate and mark the reflection signals from different sources according to the intensity and delay time of the received reflection signals of different wavelengths; a feedback adaptive laser power control module, which is connected to the multi-wavelength laser detection module, and is used to dynamically adjust the emission power of the multi-wavelength laser beam according to the reflection intensity of the different wavelength lasers received by the reflection signal receiving module, wherein the dynamic adjustment of the laser power is comprehensively calculated based on the real-time ambient light intensity, vehicle type and vehicle speed, and the calculation formula is: ,in, is the laser emission power, is the real-time ambient light intensity, is the current speed of the vehicle, is the laser wavelength currently used, function It is a nonlinear function obtained by experimental fitting, which is used to ensure the stability of the reflection signal intensity under different environmental conditions; a data fusion processing module is used to fuse the multi-wavelength reflection signal data received by the reflection signal receiving module with the vehicle dynamic data to generate multi-dimensional signal characteristics; wherein the vehicle dynamic data includes but is not limited to vehicle speed, axle position, and vehicle type, and the data fusion processing module includes: a noise suppression unit, which is used to filter out random noise in the reflection signal based on a Kalman filter to suppress signal fluctuations caused by environmental interference and vehicle vibration; a feature extraction unit, which is used to extract the vehicle's axle load, tire characteristics and vehicle dynamic load distribution according to the reflection characteristics of different components at the bottom of the vehicle; an adaptive weighted fusion unit, which is used to weightedly fuse the reflection data of each wavelength according to the reliability weights of different wavelength signals in the multi-wavelength reflection signal, wherein the weight The calculation basis is: ,in, The wavelength The signal strength under The sum of the signal intensities at all wavelengths is used to determine the influence of different wavelength reflection signals on the final vehicle load detection result through normalization processing; the vehicle load dynamic estimation module is connected to the data fusion processing module and is used to estimate the real-time load state of the vehicle according to the multi-dimensional signal characteristics after weighted fusion, wherein the single-axle load of the vehicle The calculation formula is: ,in, is the reflection delay time of the laser beam, is the distance between the bottom of the vehicle and the road surface, is the weight of the reflected signal at each wavelength, function represents the axle load estimation model obtained by nonlinear fitting; A laser vision-assisted calibration module is used to calibrate the edge conditions of the reflected signal during the laser detection process. The laser vision-assisted calibration module includes: a visual sensor unit, used to capture real-time images of the bottom of the vehicle; an image laser data alignment unit, used to perform edge correction on the laser detection results according to the geometric features of the bottom of the vehicle and the spatial position of the reflected signal; a data consistency verification unit, used to compare the calibrated data with the original laser reflection data for consistency. When the verification result exceeds the set error threshold, the feedback adjustment mechanism is triggered to readjust the laser emission power and signal processing parameters; a data storage and output module, used to store all detection data, vehicle load status and environmental data, and upload the detection results to the traffic management system or the overload control control center, wherein the data storage and output module is configured to automatically and synchronously upload data at predetermined time intervals and send an alarm signal when an overload condition is detected.

[0023] As a further solution of the present invention, the emission wavelength range of the multi-wavelength laser detection module includes an infrared band of 850nm-1550nm and a visible light band of 400nm-700nm, wherein different bands are used to respectively detect the reflection characteristics of metal parts and non-metal parts at the bottom of the vehicle.

[0024] As a further solution of the present invention, the feedback adaptive laser power control module also includes an ambient light intensity sensor and a vehicle speed sensor. The ambient light intensity sensor is used to measure the external light intensity, and the vehicle speed sensor is used to monitor the vehicle's driving speed in real time, and dynamically adjust the laser emission power based on the data of the two.

[0025] As a further solution of the present invention, the noise suppression unit in the data fusion processing module uses a combination algorithm of a Kalman filter and an adaptive mean filter, wherein the Kalman filter is used to dynamically eliminate high-frequency noise caused by vehicle vibration, and the adaptive mean filter is used to smooth low-frequency noise caused by environmental interference.

[0026] As a further solution of the present invention, the weight coefficient of the adaptive weighted fusion unit is It is also determined based on the stability score of each wavelength in the historical vehicle type data, where the stability score is calculated through multiple past test results, and the formula is: ,in, The wavelength Next The vehicle load value detected for the first time, is the deviation from the actual load value, The number of historical detections.

[0027] As a further solution of the present invention, the nonlinear fitting model for estimating the single-axle load of the vehicle in the vehicle load dynamic estimation module is obtained by training using a machine learning method, wherein the training data includes vehicle type, number of axles, tire contact area, vehicle speed and road conditions, and the estimation formula is determined by a fitting model based on support vector regression.

[0028] As a further solution of the present invention, the image laser data alignment unit of the laser vision-assisted calibration module performs edge detection and shape feature matching on the bottom image of the vehicle to achieve spatial position alignment with the laser detection result, ensuring that the reflection signal deviation in the edge area of ​​the vehicle is controlled within 1 mm.

[0029] As a further solution of the present invention, the predetermined time interval of the data storage and output module is dynamically adjusted according to the traffic flow. When the traffic flow is high, the predetermined time interval is automatically shortened to less than 5 seconds. When the traffic flow is low, the time interval can be extended to 30 seconds.

[0030] As a further solution of the present invention, the system also includes a remote monitoring module that communicates with the traffic management system. The remote monitoring module is used to monitor the equipment status and detection results of all detection points in real time, and automatically issue a maintenance request when an abnormality occurs in the detection equipment.

[0031] A detection method based on a laser detection system of traffic overload control equipment is characterized in that it includes the following steps: S1, emitting a multi-wavelength modulated laser beam to the bottom and road surface of a moving vehicle through the multi-wavelength laser detection module to capture reflection signals at different wavelengths; S2, receiving the reflection signal through the reflection signal receiving module, and preliminarily separating the reflection data of different wavelengths; S3, dynamically adjusting the laser emission power according to the real-time ambient light intensity and vehicle speed through the feedback adaptive laser power control module; S4, performing noise suppression, feature extraction and weighted fusion on the received reflection signal through the data fusion processing module to obtain multi-dimensional signal characteristics; S5, calculating the single-axle load of the vehicle according to the signal characteristics and vehicle dynamic data after weighted fusion through the vehicle load dynamic estimation module; S6, calibrating the laser detection results of the edge area through the laser vision-assisted calibration module; S7, storing the estimated vehicle load data in the data storage and output module, and judging whether to trigger the overload alarm signal according to the detection result.

[0032] Example 1: This example describes in detail an implementation path of an overload detection method based on multi-wavelength laser detection and visually assisted calibration, which solves the problems of low accuracy and large signal interference of traditional laser detection methods in complex traffic environments. Through multi-dimensional data fusion and adaptive adjustment technology, this example optimizes the accuracy and speed of overload detection, while avoiding errors caused by uneven reflection of laser signals.

[0033] The multi-wavelength laser detection module is mainly used to emit multi-wavelength modulated laser beams to the bottom of the vehicle and the road surface area, and to perform differentiated detection on different vehicle parts and road surface features by synchronously switching laser beams of different wavelengths. The implementation steps are as follows: the laser module switches the laser wavelength according to the preset frequency, and the wavelength range covers the infrared band of 850nm to 1550nm and the visible light band of 400nm to 700nm. The reflection intensity and delay time of the laser signal are used to detect the metal parts and non-metal parts at the bottom of the vehicle respectively, ensuring that signals of different wavelengths are optimized under different reflection characteristics.

[0034] The optimization of the adaptive laser power control module adjusts the laser emission power according to dynamic factors such as ambient light intensity and vehicle speed to ensure the stability and effectiveness of the signal in different environments. Its implementation steps: The dynamic adjustment of laser power adopts the following formula: in, is the laser emission power, is the real-time ambient light intensity, is the current speed of the vehicle, is the laser wavelength currently used. In this formula, It is a nonlinear function obtained by fitting based on actual experiments, ensuring that the intensity of the laser reflection signal remains stable under various environmental conditions.

[0035] The multi-dimensional data fusion and noise suppression module is responsible for integrating the signals from the laser detection module and the vehicle dynamic data, and uses a combination of a Kalman filter and an adaptive mean filter to suppress noise. The implementation steps are to remove the high-frequency noise caused by vehicle vibration through a Kalman filter, and use an adaptive mean filter to smooth the low-frequency noise caused by environmental interference. Based on the intensity and delay time of the multi-wavelength reflection signal, each wavelength signal is weighted to ensure that the contribution of signals of different wavelengths to the load detection results is weighted according to their reliability. The weight calculation formula is: ,in, The wavelength The vehicle load dynamic estimation module estimates the vehicle's single-axle load in real time based on the weighted fusion of signal features and vehicle dynamic data. The implementation steps are to calculate the vehicle's single-axle load using the following formula: in, is the vehicle single axle load, is the laser reflection delay time, is the distance between the bottom of the vehicle and the road surface, The wavelength The function in this formula is represents the axle load estimation model obtained by nonlinear fitting.

[0036] The laser vision-assisted calibration module is used to calibrate the edge area of ​​the laser detection signal to eliminate the error caused by uneven reflection of the laser signal. The implementation steps are to use the visual sensor to capture the real-time image of the bottom of the vehicle, combine the image data with the laser reflection signal, and achieve accurate calibration of the edge area through the image laser data alignment unit. The calibrated data will be compared with the original laser reflection signal for consistency. When the error exceeds the set threshold, the system will trigger the adjustment mechanism to dynamically adjust the laser power and signal processing parameters.

[0037] The data storage and output module is responsible for storing all detection data and uploading the results to the traffic management system or overload control center. The implementation steps are as follows: when the traffic volume is high, the data synchronization upload time interval is automatically shortened to less than 5 seconds to ensure timely response; when the traffic volume is low, the upload time interval can be extended to 30 seconds to save bandwidth and resources. This embodiment uses multi-wavelength laser detection and visual calibration technology to achieve high-precision overload detection. Especially in complex traffic environments and severe weather conditions, the system exhibits strong stability and accuracy. Through adaptive laser power control and multi-dimensional data fusion, the system can effectively isolate signal interference from multiple lanes to ensure that the load condition of a single vehicle can still be accurately detected under high-density traffic flow conditions.

[0038] Example 2: In order to solve the problems of signal interference, error processing and dynamic adjustment of laser detection systems in complex traffic environments in the prior art, the system architecture of this embodiment includes a multi-wavelength laser detection module, a reflection signal receiving module, a feedback adaptive laser power control module, a data fusion processing module, a vehicle load dynamic estimation module and a laser vision-assisted calibration module. The hardware configuration, functional description and data flow path of each module are as follows: Multi-wavelength laser detection module: This module emits a multi-wavelength laser beam with a wavelength range including an infrared band of 850nm-1550nm and a visible light band of 400nm-700nm, which are used to detect metal and non-metal structures at the bottom of the vehicle respectively. The laser beam of each wavelength is switched synchronously, and the signal capture is optimized under different detection targets by adjusting the modulation frequency and time series.

[0039] Reflection signal receiving module: This module includes a multi-band photoelectric detector to receive the reflected laser signal. By analyzing the signal strength and delay time, it distinguishes different reflection sources and marks the signal source to ensure that the signals of different lanes are not confused; Feedback adaptive laser power control module: This module dynamically adjusts the laser emission power through real-time monitoring of ambient light intensity, vehicle speed and laser wavelength. Specifically, the adjustment process is supported by the following formula: ,in, is the laser emission power, is the ambient light intensity, is the vehicle speed, is the laser wavelength used. Function It is a nonlinear function based on experimental data fitting to ensure that the laser reflection signal is stable under different lighting and weather conditions. In the data fusion processing module, the Kalman filter is first used to remove high-frequency noise and eliminate fluctuations caused by vehicle vibration, and then the adaptive mean filter is used to smooth the low-frequency noise caused by environmental interference (such as wind, rain, etc.). The fused multi-wavelength signal data is weighted according to reliability, and the weight coefficient is calculated based on: ,in, The wavelength The signal strength under is the sum of the signal strengths of all wavelengths. This weight coefficient ensures that the contribution of different wavelengths to the final detection result is dynamically adjusted according to their reliability. By processing the weighted fused signal data, the single-axle load of the vehicle is estimated using the following formula: ,in, is the single axle load of the vehicle, is the delay time of laser reflection, is the distance from the bottom of the vehicle to the road surface, is the signal weight of the corresponding wavelength. Function represents the load estimation model based on nonlinear fitting, which is dynamically adjusted according to the vehicle bottom structure, tire contact area and vehicle speed. In order to eliminate errors in the laser detection process, especially in the edge area, a laser vision-assisted calibration module is added. This module captures the real-time image of the bottom of the vehicle through the visual sensor unit and aligns the image with the laser data. Through the image laser data alignment unit, accurate calibration is performed according to the geometric characteristics of the bottom structure and the spatial position of the reflected signal. When the error of the calibration result exceeds the predetermined threshold, the system automatically triggers the feedback mechanism to readjust the laser emission power and signal processing parameters to ensure the calibration accuracy. And in order to improve the responsiveness of the system under high-density traffic, the data storage and output module adjusts the upload frequency according to the traffic volume. In high traffic, the upload interval is shortened to 5 seconds, and in low traffic, the upload interval can be extended to 30 seconds. In addition, the system is equipped with a remote monitoring module, which can monitor the equipment status in real time and automatically issue maintenance requests when a fault occurs.

[0040] This embodiment is equipped with a multi-wavelength laser detection module and a visual sensor to ensure that it can emit lasers within a predetermined wavelength range and receive reflected signals. A feedback adaptive laser power control module is installed to ensure that it can dynamically adjust the laser power according to the ambient light intensity, vehicle speed, etc. A data fusion processing module is configured and a noise suppression and signal weighted fusion algorithm is implemented to ensure that the fused data is accurate. A vehicle load estimation module is implemented and the load condition is estimated according to the real-time status of the vehicle through a dynamic algorithm. The visual-laser alignment algorithm is used to accurately calibrate the reflected signal to ensure that the error in the edge area is minimized. A data storage and output module is configured to ensure that the system can adaptively adjust the data upload frequency under high traffic conditions, and monitor the equipment status in real time through a remote monitoring module. This ensures that in a complex and changeable traffic environment, the system can stably and efficiently perform overload detection, significantly improves the detection accuracy and system stability, and has high operability and maintainability during implementation.

[0041] Embodiment 3: In order to further simplify the process of load estimation, especially without relying on complex calculation models, this embodiment proposes a simple and effective load estimation method based on vehicle dynamic characteristics and laser detection signals. The goal of this embodiment is to reduce the computational burden, improve the simplicity and efficiency of system implementation, and ensure that the vehicle load can be quickly and accurately obtained in practical applications.

[0042] In the system structure and core modules, the laser detection module: scans the bottom of the vehicle by emitting a multi-wavelength laser beam and receives the reflected signal. The wavelength and reflection intensity of the laser beam are used to infer the relevant data of the vehicle load. The data acquisition module: combines the real-time vehicle speed and vehicle type information to collect data. The function of this module is to provide basic data for subsequent estimation. The load estimation module: according to the dynamic characteristic data such as vehicle speed, number of axles and vehicle type, combined with the intensity of the laser reflection signal, the vehicle load is estimated through a simplified estimation model.

[0043] Load estimation method: This embodiment adopts a simplified model to avoid the use of complex nonlinear fitting or large amounts of historical data calculation. Through a fast-response real-time data processing module, the known vehicle speed and vehicle characteristics (such as the number of axles, vehicle type), combined with the laser reflection intensity, use a linear regression model to estimate the load. The simplified load estimation formula is as follows: ,in: is the vehicle single axle load, is the current speed of the vehicle, is the number of vehicle axles, is the reflected signal strength, is the weight coefficient obtained through experiments or actual data adjustment. It is obtained through field experiments or data fitting. These coefficients play a vital role in the load estimation process and determine the contribution of different parameters to the load estimation results. The specific acquisition method is in data collection, such as experimental environment setting: First, a series of experiments are carried out in different traffic scenarios such as different vehicle speeds and different vehicle types. When the vehicle is driving, the dynamic data of the vehicle and its corresponding actual load (for example, the actual load measured by the road pressure sensor) are collected in real time through laser detection modules, vehicle speed sensors, axle number identification, etc.

[0044] Scope of data collection: The experimental data should cover different types of vehicles (such as small cars, large trucks, etc.), different speeds (such as low speed, medium speed, high speed) and reflection intensity under different road conditions. These data will be used to fit the weight coefficients and ensure that the fitting model has a wide range of adaptability. Data processing and fitting, preliminary data processing: According to the collected data, remove outliers and standardize the data. For each set of experimental data, the speed , Number of axles and reflection intensity Extract as input features, load values As the target output. Fitting method selection: Use regression analysis method (such as least squares method) for fitting, calculate the contribution ratio of each feature to the load, and then get the corresponding weight coefficient ,in Represents the effect of vehicle speed on load. Generally, the higher the vehicle speed, the more linear the relationship between load and speed. Represents the effect of the number of axles on the load. Vehicles with more axles usually have heavier loads. The weight coefficient This influence is reflected in; Represents the effect of reflection intensity on load. The stronger the reflection intensity, the higher the load on the vehicle bottom structure. The coefficient reflects this linear relationship. Then the collected data is fitted to the model by the least square method. For example, when the vehicle speed is 70 km / h, the number of axles is 3, and the reflection intensity is 1.6, the calculated load is: , by comparing the actual load in the experiment with the estimated load, the accuracy of the model is verified. If a large deviation is found, the model can be further optimized, the parameters in the fitting process can be adjusted, or more experimental data can be added to improve the weight coefficient. If the sensitivity to certain features (such as vehicle speed) is high, the weight coefficient can be appropriately adjusted to make the model more accurate and efficient. The test data is as follows:

[0045] and vehicle speed :The vehicle speed is obtained in real time through the vehicle speed sensor. Number of axles :Based on the vehicle type, the system has predefined axle values. For most vehicle types, this parameter can be directly obtained during license plate recognition. Reflected signal strength :Directly obtain the intensity of the laser reflection signal, which reflects the relevant information of the vehicle bottom structure and load. Its implementation steps are as follows: Step 1: Collect the vehicle bottom reflection signal through the laser detection module and obtain the vehicle speed data in real time. Step 2: Automatically extract the number of axles according to the vehicle type or manually enter the vehicle information. Step 3: According to the formula , calculate the single axle load. Step 4: The system determines whether the vehicle is overloaded based on the calculation results and outputs the results.

[0046] Embodiment 4: In this embodiment, a plurality of laser beams of different wavelengths are first emitted to the bottom of the vehicle and the road surface area through a multi-wavelength laser detection module. Each wavelength of laser beam detects different parts of the vehicle. For example, a long-wavelength laser beam is used to detect the reflection characteristics of metal parts of the vehicle, while a short-wavelength laser beam is used to detect non-metallic parts. The reflection signal receiving module separates the reflection signals of different wavelengths to ensure that the reflection signals from different parts can be accurately recorded. In this process, the intensity and delay time of the reflection signal are the main basis for signal processing, which is used to judge the load characteristics of the vehicle.

[0047] In order to improve the accuracy of signal processing, this embodiment uses an algorithm combining Kalman filtering and adaptive mean filtering to suppress signal noise. The Kalman filter is used to process high-frequency noise caused by vehicle vibration, while the adaptive mean filter mainly smoothes low-frequency noise caused by environmental interference (such as wind, rain, etc.). Through the combination of these two algorithms, the interference caused by external environmental changes and vehicle dynamic factors can be effectively reduced, ensuring that the signal data transmitted to the data fusion processing module is more accurate.

[0048] Subsequently, the data fusion processing module will combine the multi-wavelength reflection signals with the vehicle dynamic data (such as vehicle speed, axle position, etc.) through a weighted fusion method. The weighting coefficient is calculated based on the reliability and strength of the signal. Specifically, by normalizing the signal strength, the influence of each wavelength signal on the final detection result is obtained. After weighted fusion, the reliability of the signal is optimized, ensuring a more accurate estimation of the vehicle load.

[0049] In response to the problem of laser signal attenuation caused by environmental changes, this embodiment proposes a dynamic power control mechanism based on real-time ambient light intensity, vehicle speed and laser wavelength. The system adjusts the laser emission power in real time according to the received reflected signal intensity and delay time. First, the external light intensity is measured in real time by the ambient light intensity sensor, and an optimal laser emission power is calculated in combination with the real-time speed information of the vehicle. Specifically, the relationship between the laser power and the ambient light intensity, vehicle speed and wavelength is dynamically adjusted by a nonlinear function fitted by the experiment, thereby ensuring the stability of the reflected signal under different environments. In this way, the system can avoid detection errors caused by changes in light or bad weather, thereby improving the overall overload detection accuracy. And in order to further improve the detection accuracy of the edge area, this embodiment combines laser vision-assisted calibration technology. The visual sensor is used to capture the image data of the bottom of the vehicle in real time, and the reflection result of the laser signal is spatially aligned with the image data. Through the image laser data alignment unit, the laser detection result is accurately calibrated based on the geometric features of the bottom of the vehicle and the positional relationship of the reflected signal. If the calibration error exceeds the set threshold, the system will automatically trigger the feedback adjustment mechanism to readjust the laser emission power and signal processing parameters. Through this calibration process, the system can eliminate laser reflection errors caused by vehicle shape or uneven road surface, ensuring high-precision load detection. Finally, based on the weighted fusion of signal characteristics and vehicle dynamic data, the vehicle load dynamic estimation module calculates the vehicle's single-axle load in real time. Through the real-time laser signal delay time, the distance between the bottom of the vehicle and the road surface, and the coordination of signal weights, the system can accurately estimate the vehicle's load. For the degree of influence of each wavelength signal on load estimation, the weight coefficient is optimized through historical detection data to improve the accuracy of load estimation. In this process, the system combines dynamic data for overload detection. When the vehicle load exceeds the set standard value, it automatically triggers an overload alarm signal and uploads the detection results to the traffic management system or overload control center for further processing by relevant personnel.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A laser detection system based on traffic overload control equipment, characterized in that: include: A multi-wavelength laser detection module is used to emit a multi-wavelength modulated laser beam to the bottom of the vehicle and the road surface area, wherein the multi-wavelength modulated laser beam is used to detect different vehicle structural features and road surface reflection characteristics; a reflection signal receiving module is used to receive the return signal of the multi-wavelength laser beam after being reflected from the bottom of the vehicle and the road surface; a feedback adaptive laser power control module is connected to the multi-wavelength laser detection module and is used to dynamically adjust the emission power of the multi-wavelength laser beam according to the different wavelength laser reflection intensities received by the reflection signal receiving module, wherein the dynamic adjustment of the laser power is comprehensively calculated based on the real-time ambient light intensity, vehicle type and vehicle speed, and the calculation formula is: ,in, is the laser emission power, is the real-time ambient light intensity, is the current speed of the vehicle, is the laser wavelength currently used, function The nonlinear function obtained by experimental fitting is used to ensure the stability of the reflected signal intensity under different environmental conditions; The data fusion processing module is used to fuse the multi-wavelength reflection signal data received by the reflection signal receiving module with the vehicle dynamic data to generate multi-dimensional signal features; the data fusion processing module includes: a noise suppression unit, which is used to filter out random noise in the reflection signal based on the Kalman filter, and suppress signal fluctuations caused by environmental interference and vehicle vibration; a feature extraction unit, which is used to extract the vehicle's axle load, tire characteristics and vehicle dynamic load distribution according to the reflection characteristics of different components at the bottom of the vehicle; an adaptive weighted fusion unit, which is used to perform weighted fusion on the reflection data of each wavelength according to the reliability weights of different wavelength signals in the multi-wavelength reflection signal, wherein the weight The calculation basis is: ,in, The wavelength The signal strength under is the sum of the signal intensities at all wavelengths, and the influence of different wavelength reflection signals on the final vehicle load detection results is determined through normalization processing; The vehicle load dynamic estimation module is connected to the data fusion processing module and is used to estimate the real-time load state of the vehicle based on the multi-dimensional signal characteristics after weighted fusion, wherein the single-axle load of the vehicle The calculation formula is: ,in, is the reflection delay time of the laser beam, is the distance between the bottom of the vehicle and the road surface, is the weight of the reflected signal at each wavelength, function It represents the axle load estimation model obtained by nonlinear fitting; the laser vision-assisted calibration module is used to calibrate the edge conditions of the reflected signal during the laser detection process; the data storage and output module is used to store all detection data, vehicle load status and environmental data, and upload the detection results to the traffic management system or the overload control center, wherein the data storage and output module is configured to automatically and synchronously upload data at predetermined time intervals, and send an alarm signal when an overload condition is detected.

2. A laser detection system based on traffic overload control equipment according to claim 1, characterized in that: The emission wavelength range of the multi-wavelength laser detection module includes the infrared band 850nm-1550nm and the visible light band 400nm-700nm.

3. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The feedback adaptive laser power control module also includes an ambient light intensity sensor and a vehicle speed sensor. The ambient light intensity sensor is used to measure the external light intensity, and the vehicle speed sensor is used to monitor the vehicle's running speed in real time.

4. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The noise suppression unit in the data fusion processing module uses a combination algorithm of a Kalman filter and an adaptive mean filter, where the Kalman filter is used to dynamically eliminate high-frequency noise caused by vehicle vibration, and the adaptive mean filter is used to smooth low-frequency noise caused by environmental interference.

5. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: Weight coefficient of adaptive weighted fusion unit It is also determined based on the stability score of each wavelength in the historical vehicle type data, wherein the stability score is calculated through multiple past test results, and the formula is: ,in, The wavelength Next The vehicle load value detected for the first time, is the deviation from the actual load value, The number of historical detections.

6. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The nonlinear fitting model used to estimate the single-axle load of the vehicle in the vehicle load dynamic estimation module is obtained through machine learning training. The training data includes vehicle type, number of axles, tire contact area, vehicle speed and road conditions, and the estimation formula is determined by a fitting model based on support vector regression.

7. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The image laser data alignment unit of the laser vision-assisted calibration module performs edge detection and shape feature matching on the vehicle bottom image to achieve spatial position alignment with the laser detection result, ensuring that the deviation of the reflected signal in the edge area of ​​the vehicle is controlled within 1 mm.

8. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The scheduled time interval of the data storage and output module is dynamically adjusted according to the traffic volume. When the traffic volume is high, the scheduled time interval is automatically shortened to less than 5 seconds. When the traffic volume is low, the time interval can be extended to 30 seconds.

9. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The system also includes a remote monitoring module that communicates with the traffic management system. The remote monitoring module is used to monitor the equipment status and detection results of all detection points in real time, and automatically issue maintenance requests when the detection equipment is abnormal.

10. The laser detection system based on traffic overload control equipment according to claim 1 is characterized in that: The detection system includes the following detection method steps: S1, emitting a multi-wavelength modulated laser beam to the bottom and road surface of a moving vehicle through the multi-wavelength laser detection module to capture reflection signals at different wavelengths; S2, receiving the reflection signal through the reflection signal receiving module, and preliminarily separating the reflection data of different wavelengths; S3, dynamically adjusting the laser emission power according to the real-time ambient light intensity and vehicle speed through the feedback adaptive laser power control module; S4, performing noise suppression, feature extraction and weighted fusion on the received reflection signal through the data fusion processing module to obtain multi-dimensional signal features; S5, calculating the single-axle load of the vehicle according to the weighted fused signal features and vehicle dynamic data through the vehicle load dynamic estimation module; S6, calibrating the laser detection results of the edge area through the laser vision-assisted calibration module; S7, storing the estimated vehicle load data in the data storage and output module, and judging whether to trigger an overload alarm signal based on the detection results.

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