CN-120722359-B - Mountain road trap active detection method and system based on acoustic wave reflection

CN120722359BCN 120722359 BCN120722359 BCN 120722359BCN-120722359-B

Abstract

The embodiment of the invention discloses a mountain road trap active detection method and system based on sound wave reflection, wherein the method comprises the following steps of collecting echo signals of multi-band detection sound waves reflected back after multipath propagation in a mountain road area to be detected through a microphone array, and evaluating and scoring the propagation paths according to a path reliability scoring mechanism; and if the trigger condition is not met, inputting the second characteristic data and the first characteristic data into a pre-trained machine learning model, and judging to obtain the trap type of the mountain area to be tested according to the machine learning model. The system has second-level response capability, greatly improves safety, realizes full-flow automation, reduces manual intervention, and greatly improves mountain road inspection efficiency.

Inventors

ZHU HONGWEI
YE JIANFENG
ZHAO JIANQIANG
Hong Erhu

Assignees

广州沛森园林景观设计有限公司

Dates

Publication Date: 20260505
Application Date: 20250717

Claims (10)

1. The mountain road trap active detection method based on acoustic wave reflection is characterized by comprising the following steps of: collecting echo signals reflected back after multi-band detection sound waves are subjected to multi-path propagation in a mountain road area to be detected through a microphone array, and obtaining propagation paths of the echo signals; Evaluating and scoring the propagation path according to a path credibility scoring mechanism, and judging whether echo signals corresponding to the propagation path when the evaluation score is lower than a credibility threshold value meet triggering conditions or not; if the trigger condition is judged to be met, judging that a trap exists in the mountain road area to be detected, and carrying out risk rapid early warning; If the triggering condition is judged not to be met, acquiring a propagation path when the evaluation score is greater than or equal to a credible threshold value, obtaining a credible propagation path, and extracting first characteristic data of echo signals corresponding to the credible propagation path; Identifying material data of a reflection point corresponding to the trusted propagation path, and encoding the material data to serve as second characteristic data; Inputting the second characteristic data and the first characteristic data into a pre-trained machine learning model, and judging and obtaining the trap category of the mountain road area to be tested according to the machine learning model, wherein the trap category comprises an instant trap, a potential trap and no trap.
2. The active detection method of mountain trap based on acoustic wave reflection according to claim 1, wherein the extracting the first characteristic data of the echo signal corresponding to the trusted propagation path comprises: And acquiring topographic map data of the mountain road area to be detected, correcting echo signals corresponding to the trusted propagation path according to the topographic map data, and extracting characteristic data of the corrected echo signals to obtain first characteristic data.
3. The active detection method of mountain path trap based on acoustic wave reflection according to claim 1, wherein the identifying the material data of the reflection point corresponding to the trusted propagation path comprises: Obtaining geological data of a mountain road area to be detected, and establishing an acoustic wave reflection database mapped with the geological data; acquiring echo data corresponding to the reflection points; And obtaining the material data of the reflection points according to the echo data and the acoustic wave reflection database.
4. The active detection method of mountain path trap based on acoustic wave reflection according to claim 1, wherein the identifying the material data of the reflection point corresponding to the trusted propagation path comprises: according to the time delay, frequency response and reflection intensity data of echo data corresponding to the obtained reflection point, inputting the time delay, frequency response and reflection intensity data into a pre-trained acoustic wave material classification model to obtain a first discrimination material of the reflection point; acquiring laser point cloud echo data corresponding to the reflection point, and acquiring a second discrimination material of the reflection point according to the laser point cloud echo data; And carrying out confidence fusion on the first discrimination material and the second discrimination material to obtain comprehensive discrimination materials, and taking the comprehensive discrimination materials as material data of reflection points.
5. The active detection method of mountain road traps based on acoustic wave reflection according to claim 1, wherein the inputting the second characteristic data and the first characteristic data into a pre-trained machine learning model, and judging the trap class of the mountain road region to be detected according to the machine learning model comprises: The machine learning model adopts a multi-layer perceptron MLP model, training data of the multi-layer perceptron MLP model adopts data marked with the combination of second characteristic data and first characteristic data and trap categories to be mapped as sample data, the second characteristic data and the first characteristic data are input into the pre-trained multi-layer perceptron MLP model, and the trap categories of the mountain road area to be tested are judged according to the multi-layer perceptron MLP model.
6. The active detection method of mountain road traps based on acoustic wave reflection according to claim 1, wherein said evaluating and scoring said propagation path according to a path credibility scoring mechanism comprises: And constructing a scoring model, wherein the evaluation indexes of the scoring model comprise a time delay consistency score, an integrity score, a smoothness score, a reflection point stability score, a material consistency score and an echo repeatability score, and summing the scores of all the evaluation indexes to obtain an evaluation score.
7. The method for actively detecting mountain road traps based on acoustic wave reflection according to claim 1, further comprising judging whether echo data corresponding to a trap area meets a risk threshold condition when the trap type is judged to be a potential trap; When judging that the risk threshold condition is met, acquiring each frame of echo data corresponding to the trap area in a set time period and the occurrence time corresponding to each frame of echo data; And inputting the time feature sequence formed by the echo data of each frame and the occurrence time thereof into a trained risk prediction model, obtaining a risk probability sequence of the trap region in a preset future time period according to the risk prediction model, and carrying out risk early warning according to the risk probability sequence.
8. The mountain road trap initiative detection system based on acoustic wave reflection is characterized by comprising: The acquisition unit is used for acquiring echo signals reflected by the multi-band detection sound waves after multipath propagation in the mountain area to be detected through the microphone array and acquiring propagation paths of the echo signals; The evaluation unit is used for evaluating and scoring the propagation path according to a path credibility scoring mechanism and judging whether echo signals corresponding to the propagation path when the evaluation score is lower than a credibility threshold value meet triggering conditions or not; the triggering unit is used for judging that the mountain road area to be detected has a trap if the triggering condition is judged to be met, and carrying out risk rapid early warning; the characteristic unit is used for acquiring a propagation path when the evaluation score is larger than or equal to a credible threshold value if the triggering condition is judged not to be met, obtaining a credible propagation path and extracting first characteristic data of echo signals corresponding to the credible propagation path; The identification unit is used for identifying the material data of the reflection point corresponding to the trusted propagation path, and encoding the material data to be used as second characteristic data; The judging unit is used for inputting the second characteristic data and the first characteristic data into a pre-trained machine learning model, judging and obtaining the trap category of the mountain road area to be tested according to the machine learning model, wherein the trap category comprises an instant trap, a potential trap and no trap.
9. An electronic device comprising a memory storing executable program code, a processor coupled to the memory, the processor invoking the executable program code stored in the memory for performing the active detection method of mountain road traps based on acoustic wave reflection of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that it stores a computer program, wherein the computer program causes a computer to execute the mountain road trap active detection method based on acoustic wave reflection as claimed in any one of claims 1 to 7.

Description

Mountain road trap active detection method and system based on acoustic wave reflection Technical Field The embodiment of the invention relates to the technical field of road detection, in particular to a mountain road trap active detection method and system based on sound wave reflection, electronic equipment and a storage medium. Background At present, in the field of mountain road safety monitoring, a visual image recognition method is often adopted, a camera or an unmanned aerial vehicle is used for aerial image recognition, ground cracks and collapse areas are recognized, and then the images are automatically judged through image segmentation, edge detection and deep learning. However, visual image recognition methods have certain limitations, such as exposure to light, large influence of weather conditions, and difficulty in recognizing potential traps below the surface. At present, acoustic wave detection is used for detecting highway traps such as pits, collapse, holes and the like, but is mainly focused on structural health monitoring, concrete defect detection, pavement disease identification and the like, but mountain road traps have some remarkable differences from highway traps in detection, such as great differences in terrain conditions, environmental noise, surface materials, trap types, signal attenuation modes and the like. Therefore, the existing sound wave highway detection method does not have trap recognition capability under complex terrains, cannot be suitable for unstructured complex mountain road terrains, and still belongs to the technical blank of active intelligent detection under mountain road environments. Disclosure of Invention In order to overcome the defects of the prior art, the embodiment of the invention aims to provide a mountain road trap active detection method, a mountain road trap active detection system and electronic equipment based on acoustic wave reflection, so that all-weather and all-terrain detection applicability is improved, trap detection coverage rate can be improved by using acoustic wave reflection in different angle directions, and more accurate, real-time and intelligent mountain road trap detection capability is realized. In order to solve the above problems, a first aspect of the embodiments of the present invention discloses an active detection method for mountain traps based on acoustic wave reflection, which includes the following steps: collecting echo signals reflected back after multi-band detection sound waves are subjected to multi-path propagation in a mountain road area to be detected through a microphone array, and obtaining propagation paths of the echo signals; Evaluating and scoring the propagation path according to a path credibility scoring mechanism, and judging whether echo signals corresponding to the propagation path when the evaluation score is lower than a credibility threshold value meet triggering conditions or not; if the trigger condition is judged to be met, judging that a trap exists in the mountain road area to be detected, and carrying out risk rapid early warning; If the triggering condition is judged not to be met, acquiring a propagation path when the evaluation score is greater than or equal to a credible threshold value, obtaining a credible propagation path, and extracting first characteristic data of echo signals corresponding to the credible propagation path; Identifying material data of a reflection point corresponding to the trusted propagation path, and encoding the material data to serve as second characteristic data; Inputting the second characteristic data and the first characteristic data into a pre-trained machine learning model, and judging and obtaining the trap category of the mountain road area to be tested according to the machine learning model, wherein the trap category comprises an instant trap, a potential trap and no trap. Preferably, the extracting the first characteristic data of the echo signal corresponding to the trusted propagation path includes: And acquiring topographic map data of the mountain road area to be detected, correcting echo signals corresponding to the trusted propagation path according to the topographic map data, and extracting characteristic data of the corrected echo signals to obtain first characteristic data. Preferably, the identifying the material data of the reflection point corresponding to the trusted propagation path includes: Obtaining geological data of a mountain road area to be detected, and establishing an acoustic wave reflection database mapped with the geological data; acquiring echo data corresponding to the reflection points; And obtaining the material data of the reflection points according to the echo data and the acoustic wave reflection database. Preferably, the identifying the material data of the reflection point corresponding to the trusted propagation path includes: according to the time delay, frequency response and reflection intensity data