Traffic flow real-time monitoring system based on sound wave detection

Abstract

The utility model provides a traffic flow real-time monitoring system based on sound wave detection, and the system comprises a sound wave collection system which is used for collecting the audio of a driving vehicle; the data transmission system is used for transmitting the audio data acquired by the sound wave collection system to the noise reduction and analysis discrimination system in real time; and the noise reduction and analysis discrimination system is used for realizing vehicle identification, monitoring the traffic flow in real time and predicting the traffic flow. According to the utility model, through the sound wave detection technology, non-contact, all-weather and high-efficiency monitoring of the traffic flow is realized, and the cost and maintenance difficulty of a traditional monitoring method are reduced. Meanwhile, noise reduction and intelligent analysis technologies are combined, the accuracy and practicability of monitoring data are improved, more refined data support is provided for urban traffic management, traffic congestion can be relieved, and the urban traffic operation efficiency can be improved.

Description

Technical Field

[0001] The utility model belongs to the technical field of intelligent transportation systems, and in particular relates to a real-time traffic flow monitoring system based on sound wave detection. Background Art

[0002] With the accelerating pace of global urbanization, traffic congestion has become a major challenge hindering sustainable urban development. Traditional traffic flow monitoring methods, such as video surveillance and ground sensors, have met traffic management needs to a certain extent, but they have many limitations. Video surveillance is easily affected by lighting and weather conditions, significantly reducing its effectiveness at night or in inclement weather. Ground sensors must be embedded in the road surface, which not only incurs high installation and maintenance costs but is also prone to damage, impacting the road's service life. Furthermore, these traditional methods are often inefficient when processing large amounts of data, making them unable to meet the demands of real-time monitoring and rapid response.

[0003] In recent years, the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence has provided new solutions for traffic flow monitoring. Acoustic wave detection technology, as a non-contact, all-weather monitoring method, has gradually attracted attention. It uses acoustic wave signals generated by vehicles during driving, such as horns, engines, and tire-ground friction, as monitoring targets. Through signal processing and analysis, it enables real-time monitoring of traffic flow. However, how to effectively collect, transmit, and analyze these acoustic wave signals, accurately identify vehicle types and numbers, and use this data to predict traffic flow are currently pressing challenges in the application of acoustic wave detection technology in traffic flow monitoring. Utility Model Content

[0004] In order to solve the above problems, the utility model provides a real-time traffic flow monitoring system based on sound wave detection.

[0005] The technical solution of the utility model is as follows:

[0006] A real-time traffic flow monitoring system based on acoustic wave detection, comprising:

[0007] an acoustic wave collection system for collecting audio from moving vehicles;

[0008] A data transmission system for transmitting the audio data acquired by the sound wave collection system to the noise reduction and analysis and judgment system in real time;

[0009] The noise reduction and analysis and discrimination system is used to realize vehicle identification, real-time monitoring of vehicle flow and traffic flow prediction.

[0010] Furthermore, the acoustic wave collection system includes:

[0011] Microphone array, used to capture various sound wave signals generated during vehicle driving;

[0012] The preamplifier is used to amplify the weak sound wave signal captured by the microphone array and improve the signal-to-noise ratio;

[0013] Anti-wind noise device, used to reduce the interference of wind noise on sound wave detection;

[0014] Power Management System: Provides power to the microphone array and preamplifier.

[0015] Furthermore, the various types of sound wave signals include car horn sounds, engine running sounds, and tire-ground friction sounds.

[0016] Furthermore, the anti-wind noise device is specifically a wind direction shielding cover.

[0017] Furthermore, the data transmission system includes:

[0018] Wireless transmission module, used to transmit the data collected by the acoustic wave collection system to the back-end processing platform in real time;

[0019] Data encryption unit, used to encrypt transmitted data to ensure data security and privacy during transmission;

[0020] The breakpoint resume and automatic reconnection mechanism is used to automatically reconnect and continue data transmission in the event of network instability or connection interruption, ensuring data continuity and integrity.

[0021] The power supply and communication interface is used to provide power supply for the wireless transmission module and has a standard communication interface to facilitate connection and data exchange with other devices.

[0022] Furthermore, the noise reduction and analysis and discrimination system includes:

[0023] A digital signal processor is used to receive sound wave data from the wireless transmission system and perform noise reduction processing;

[0024] The feature extraction module is used to extract features from the noise-reduced sound wave signal and identify the sound waves generated by different vehicle types;

[0025] Traffic flow monitoring module, which monitors current traffic flow in real time and predicts future traffic flow based on the sound waves generated by vehicles;

[0026] The data analysis and visualization platform conducts further data analysis and visualization processing on the monitoring and prediction results to generate intuitive traffic flow monitoring reports and prediction charts.

[0027] Furthermore, the signal processor uses adaptive filtering or wavelet transform algorithm to effectively reduce noise on the sound wave data.

[0028] Technical effects of this utility model:

[0029] This new system uses acoustic wave detection technology to achieve non-contact, all-weather, and highly efficient monitoring of traffic flow, reducing the cost and maintenance complexity of traditional monitoring methods. Furthermore, by combining noise reduction with intelligent analysis technology, it improves the accuracy and practicality of monitoring data, providing more refined data support for urban traffic management, helping to alleviate traffic congestion and improve urban transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings illustrate various embodiments generally by way of example and not limitation, and together with the description and claims, serve to illustrate embodiments of the present invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive of the embodiments of the present apparatus or method.

[0031] Figure 1 Shown is a schematic diagram of the system framework of the present utility model. DETAILED DESCRIPTION

[0032] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] This application provides a real-time traffic flow monitoring system based on acoustic wave detection, which mainly includes an acoustic wave collection system, a data transmission system, and a noise reduction and analysis and judgment system, specifically including:

[0034] Implementation of the acoustic wave collection system

[0035] Microphone Array Layout and Selection

[0036] Based on the actual parameters of the monitored road (such as a width of 30 meters, a vehicle speed of 40 km / h, and four lanes in both directions), eight highly sensitive microphones were strategically positioned to form a linear array. The microphones were selected to meet the requirements of high sensitivity (-38 dB), low self-noise (16 dBA), and a wide frequency response range (20 Hz-20 kHz) to ensure the quality and accuracy of acoustic signal acquisition.

[0037] Preamplifier settings

[0038] The weak acoustic signals captured by the microphone array need to be amplified by a preamplifier. The amplification factor is set at 20dB to ensure that the subsequent processing system can receive a sufficiently strong signal. The preamplifier should have low noise (<1μV), high stability (temperature drift <0.01dB / °C), and strong anti-interference capabilities (>80dB SNR).

[0039] Installation of wind noise reduction device

[0040] A wind shield is installed around the microphone array to reduce wind noise interference with acoustic wave detection. The shield is made of lightweight aluminum alloy and features flow guides and vibration damping. Testing has shown that the wind shield can reduce wind noise by over 20dB at wind speeds of 10m / s.

[0041] Configuration of the power management system

[0042] A stable and reliable power management system should be implemented for components such as the microphone array, preamplifier, and wind noise suppression system. Solar power and battery backup solutions should be used to ensure continuous and stable system operation. The power management system should include overvoltage protection, overcurrent protection, and short-circuit protection.

[0043] Implementation of the data transmission system

[0044] Configuration of wireless transmission module

[0045] Wi-Fi 6 communication technology was selected, and a wireless transmission module was configured to enable real-time transmission of acoustic wave data to the back-end processing platform. The wireless transmission module should have high transmission rates (up to 9.6 Gbps), low latency (<1ms), and strong anti-interference capabilities (>90dBm anti-interference strength).

[0046] Implementation of Data Encryption Unit

[0047] Before data transmission, the sound wave data is encrypted by the data encryption unit. The AES-256 encryption algorithm is used to ensure data security and privacy during transmission. The data encryption unit should have key management and encryption algorithm update functions.

[0048] AES-256 encryption algorithm formula: The encryption process involves multiple rounds of nonlinear transformations and key expansion. The specific formula is relatively complex, but the core lies in encrypting data through multiple rounds of S-box replacement, row shift, column mixing, and round key addition operations.

[0049] Implementation of breakpoint resume and automatic reconnection mechanism

[0050] Designed to cope with unstable network or connection interruptions, the system automatically reconnects within 5 seconds after a network interruption and ensures data integrity and continuity.

[0051] Power supply and communication interface settings

[0052] It provides stable power supply for the wireless transmission module and adopts low power consumption design. It is equipped with a standard RJ45 communication interface to facilitate connection and data exchange with other devices.

[0053] Implementation of noise reduction and analysis and discrimination system

[0054] Configuration and algorithm application of digital signal processor

[0055] A high-performance digital signal processor (DSP) is used to receive the sound wave data from the wireless transmission system, and the adaptive filtering algorithm and wavelet transform algorithm are used to reduce the noise of the sound wave data.

[0056] Adaptive filtering algorithm formula:

[0057]

[0058] Among them, y(n) is the output signal, w i (n) is the filter weight, x(ni) is the delayed version of the input signal, and M is the filter length. By adjusting the weight w i (n), so that the error between the output signal y(n) and the expected signal is minimized.

[0059] Wavelet transform algorithm formula:

[0060]

[0061] Where Wf(a,b) is the wavelet transform coefficient, f(t) is the original signal, ψ(t) is the wavelet basis function, a is the scale parameter, and b is the displacement parameter. By selecting appropriate wavelet basis functions and parameters, multi-scale analysis of the acoustic signal is performed to achieve noise reduction.

[0062] Implementation of feature extraction module

[0063] Feature extraction is performed on the noise-reduced sound wave signal, and the specific sound wave patterns generated by different vehicle types are identified through time domain analysis (such as short-time energy, zero-crossing rate, etc.) and frequency domain analysis (such as FFT transformation, power spectral density, etc.) methods.

[0064] After feature extraction, the support vector machine (SVM) algorithm is used for classification and recognition. SVM algorithm formula:

[0065]

[0066] Among them, f(x) is the classification function, αi is the Lagrange multiplier, K(x i,x) is the kernel function (such as linear kernel, polynomial kernel or radial basis kernel), xi is the support vector, and b is the bias term. By training the SVM model, accurate recognition of different vehicle types can be achieved.

[0067] Establishment of traffic flow monitoring module

[0068] Based on the output of the feature extraction module, a traffic flow monitoring module is established. A deep learning model (such as a long short-term memory network (LSTM)) is used to predict traffic flow. By training the LSTM model, historical traffic data is used to predict future traffic flow.

[0069] Building a data analysis and visualization platform

[0070] A data analysis and visualization platform was built using a B / S architecture. The platform offers functions such as data query, report generation, and chart presentation. It also features data storage, backup, and recovery, using RAID disk array technology to ensure data security and integrity.

[0071] Overall system workflow

[0072] The sound wave collection system captures the sound wave signals generated during vehicle driving and amplifies them through a preamplifier.

[0073] The anti-wind noise device reduces the interference of wind noise and ensures the accuracy of the sound wave signal.

[0074] The data transmission system transmits the sound wave data to the back-end processing platform in real time through the wireless transmission module, and performs data encryption to ensure data security.

[0075] The noise reduction and analysis and discrimination system receives the transmitted sound wave data, performs noise reduction processing through a digital signal processor, and improves the signal quality.

[0076] The feature extraction module extracts characteristic sound waves of different vehicle types from the noise-reduced signal and provides input data for the traffic flow monitoring module.

[0077] The traffic flow monitoring module monitors the current traffic flow in real time and predicts the future based on characteristic sound waves, and outputs the results to the data analysis and visualization platform.

[0078] The data analysis and visualization platform further analyzes and processes the monitoring and prediction results, generating intuitive reports and charts to provide support for traffic management decisions.

[0079] The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field within the technical scope disclosed by the present invention can make equivalent replacements or changes based on the technical solution and utility model concept of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A real-time traffic flow monitoring system based on acoustic wave detection, characterized in that: include: an acoustic wave collection system for collecting audio from moving vehicles; A data transmission system, used to transmit the audio data acquired by the sound wave collection system to the noise reduction and analysis and judgment system in real time; The noise reduction and analysis and discrimination system is used to realize vehicle identification, real-time monitoring of vehicle flow and traffic flow prediction.

2. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 1 is characterized in that: The acoustic wave collection system comprises: Microphone array, used to capture various sound wave signals generated during vehicle driving; The preamplifier is used to amplify the weak sound wave signal captured by the microphone array and improve the signal-to-noise ratio; Anti-wind noise device, used to reduce the interference of wind noise on sound wave detection; Power Management System: Provides power to the microphone array and preamplifier.

3. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 2 is characterized in that: The various types of sound wave signals include car horn sounds, engine running sounds, and tire-ground friction sounds.

4. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 2 is characterized in that: The anti-wind noise device is specifically a wind direction shielding cover.

5. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 1 is characterized in that: The data transmission system comprises: Wireless transmission module, used to transmit the data collected by the acoustic wave collection system to the back-end processing platform in real time; Data encryption unit, used to encrypt transmitted data to ensure data security and privacy during transmission; Breakpoint resume and automatic reconnection mechanism, used to automatically reconnect and continue data transmission in the event of network instability or connection interruption, ensuring data continuity and integrity; The power supply and communication interface is used to provide power supply for the wireless transmission module and has a standard communication interface to facilitate connection and data exchange with other devices.

6. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 1 is characterized in that: The noise reduction and analysis and discrimination system includes: A digital signal processor is used to receive sound wave data from the wireless transmission system and perform noise reduction processing; The feature extraction module is used to extract features from the noise-reduced sound wave signal and identify the sound waves generated by different vehicle types; Traffic flow monitoring module, which monitors current traffic flow in real time and predicts future traffic flow based on the sound waves generated by vehicles; The data analysis and visualization platform conducts further data analysis and visualization processing on the monitoring and prediction results to generate intuitive traffic flow monitoring reports and prediction charts.

7. The real-time traffic flow monitoring system based on acoustic wave detection according to claim 6 is characterized in that: The digital signal processor uses adaptive filtering or wavelet transform algorithm to effectively reduce noise on the sound wave data.

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