A method, device and medium for monitoring slippery state of tunnel pavement

Abstract

The embodiment of the present application discloses a method, device and medium for monitoring the slippery state of a tunnel pavement, which belongs to the technical field of tunnel pavement monitoring and solves the problem of low accuracy in identifying the slippery state of a tunnel pavement. Based on the position of the sound wave beam on the tunnel pavement, the tunnel pavement is divided into multiple sub-areas; the sound wave beam is converted into a sound wave image to obtain sound wave images corresponding to the multiple sub-areas; the sound wave images corresponding to the multiple sub-areas in the same time period are spliced ​​to obtain a tunnel pavement sound wave image; the multiple sound wave images corresponding to the multiple sub-areas in the continuous time period are arranged to obtain a road surface slippery state change diagram corresponding to each sub-area; based on the changes in the sound waves of the images corresponding to the tunnel pavement sound wave image and the road surface slippery state change diagram, an initial prediction is made on the duration of the slippery state of the road surface in the tunnel; the initial prediction result is adjusted based on the determined environmental coefficient to determine the final prediction result.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel pavement monitoring, and in particular to a method, device and medium for monitoring the slippery state of a tunnel pavement. Background Art

[0002] The relationship between weather and traffic is complex and far-reaching. Whenever bad weather strikes, public travel safety and traffic efficiency are often greatly challenged. This challenge is particularly significant in tunnels. Bad weather conditions, especially ice and snow, will directly reduce the traffic efficiency of vehicles in tunnels and even increase the possibility of traffic accidents. This instability is undoubtedly a huge hidden danger to the safe and smooth operation of tunnel traffic.

[0003] Among the existing technical means, although many systems and methods have been used to improve the safety and efficiency of tunnel traffic, most of them focus on the image recognition of the geographical environment around the road and the tunnel road surface. Some systems use information such as temperature sensors to identify road conditions, but these methods are often subject to many limitations in practical applications. Especially in severe weather conditions, the lens of the monitoring device is easily affected by natural factors such as rain and ice, resulting in blurred images, which greatly reduces the accuracy of identifying the slippery conditions of the tunnel road surface. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for monitoring the slippery condition of a tunnel pavement, which are used to solve the following technical problems: under severe weather conditions, the lens of the monitoring device is easily affected by natural factors such as rain and ice, resulting in blurred images, thereby reducing the accuracy of identifying the slippery condition of the tunnel pavement.

[0005] The present application embodiment adopts the following technical solutions:

[0006] The embodiment of the present application provides a method for monitoring the slippery state of a tunnel pavement. The method comprises: emitting sound wave beams of different angles to the tunnel pavement through an ultrasonic transmitter, and dividing the tunnel pavement into multiple sub-areas based on the position of the sound beams on the tunnel pavement; receiving the sound beams corresponding to the multiple sub-areas in the tunnel through an ultrasonic receiver, and converting the sound beams received by the ultrasonic receiver into sound wave images to obtain sound wave images corresponding to the multiple sub-areas; splicing the sound wave images corresponding to the multiple sub-areas in the same time period to obtain the tunnel pavement sound wave image; arranging the multiple sound wave images corresponding to the multiple sub-areas in the continuous time period in time order to obtain the road surface slippery state change diagram corresponding to each sub-area; predicting the duration of the slippery state of the road surface in the tunnel based on the changes in the sound waves of the images corresponding to the tunnel pavement sound wave image and the road surface slippery state change diagram; determining the environmental coefficient based on the environmental information corresponding to the tunnel, and adjusting the initial prediction result based on the environmental coefficient to determine the final prediction result of the duration of the road surface slippery state.

[0007] The embodiment of the present application transmits sound wave beams of different angles to the tunnel pavement through an ultrasonic transmitter, which can fully cover multiple sub-areas of the tunnel pavement to ensure monitoring without blind spots. By dividing the tunnel pavement into multiple sub-areas, it is convenient for refined management and monitoring, and the sub-area division allows the slippery state of each area to be evaluated separately, which helps to timely discover and deal with potential safety hazards. By splicing the sound wave images of multiple sub-areas, a complete tunnel pavement sound wave image is obtained. Based on the sound wave images in a continuous time period, a road slippery state change diagram of each sub-area is drawn, which can not only comprehensively evaluate the slippery state of the tunnel, but also improve the accuracy of predicting the duration of the slippery state of the road surface in the tunnel, thereby reducing the probability of traffic accidents caused by slippery road surface in the tunnel.

[0008] In one implementation of the present application, based on the position of the sound beam on the tunnel pavement, the tunnel pavement is divided into multiple sub-areas, specifically including: adjusting the ultrasonic transmitter so that the ultrasonic transmitter emits multiple sound beams at different angles; based on the position of the ultrasonic transmitter and the multiple emission angles, determining the reference positions where the multiple sound beams fall on the tunnel pavement respectively; constructing multiple reference sub-areas with each reference position as the midpoint to perform an initial division of the tunnel pavement; determining an area where multiple reference sub-areas intersect, and an area to be divided in the tunnel pavement that has not been divided into any reference sub-area; and performing a secondary division of the area where there is an intersection and the area to be divided so that each sub-area covers the tunnel pavement.

[0009] In one implementation of the present application, the area where the intersection exists and the area to be divided are divided twice so that each sub-area covers the tunnel pavement, specifically including: determining multiple reference sub-areas corresponding to the current intersection, and determining the proportions of the current intersection in the multiple reference sub-areas respectively, so as to divide the current intersection into the reference sub-area with the largest proportion; when the proportions are consistent, randomly assigning the current intersection to any reference sub-area; determining the area of ​​the area to be divided, and when the area is greater than a preset area threshold, adjusting the ultrasonic transmitter based on the position of the area to be divided to emit a sound wave beam to the area to be divided; when the area is not greater than the preset area threshold, determining the reference sub-areas adjacent to the area to be divided, and equally dividing the area to be divided into the adjacent reference sub-areas.

[0010] In one implementation of the present application, an ultrasonic receiver is used to receive sound wave beams corresponding to multiple sub-areas in a tunnel, and the sound beams received by the ultrasonic receiver are converted into sound wave images to obtain sound wave images corresponding to the multiple sub-areas, specifically including: dividing the ultrasonic receiver into multiple sub-blocks; wherein each sub-block corresponds to receiving the sound beam of any sub-area in the tunnel; performing wavelet transform on the received sound wave signal to obtain multiple wavelet coefficients, and performing threshold processing on the wavelet coefficients of a preset high-frequency part; performing inverse wavelet transform on the wavelet coefficients subjected to threshold processing to reconstruct the denoised signal, and based on the reconstructed signal, obtaining the sound wave images corresponding to the multiple sub-areas.

[0011] In one implementation of the present application, the acoustic wave images corresponding to multiple sub-areas in the same time period are spliced ​​to obtain an acoustic wave image of a tunnel pavement, specifically including: obtaining area labels corresponding to multiple sub-areas of the tunnel pavement; wherein the area labels are marked in the order of the tunnel pavement; based on the area labels, the image labels of the acoustic wave images corresponding to the multiple sub-areas in the same time period are determined, and the multiple acoustic wave images are spliced ​​according to the image labels; the original acoustic wave image of each sub-area is compared with the acoustic wave image of the tunnel pavement obtained after splicing to determine the similarity between each sub-area in the acoustic wave image of the tunnel pavement and the original acoustic wave image; when the similarity meets the preset similarity threshold, it is determined that the acoustic wave image of the tunnel pavement meets the requirements.

[0012] In one implementation of the present application, multiple sound wave images in continuous time periods corresponding to multiple sub-areas are arranged in chronological order to obtain a road surface slippery state change diagram corresponding to each sub-area, specifically including: determining the timestamp corresponding to each sound wave image, and sorting the multiple sound wave images corresponding to the same sub-area based on the timestamp to obtain a sound wave change image set; determining the time difference between two adjacent timestamps, and determining the time when the ultrasonic transmitter transmits the sound wave beam, and determining the change of the sound wave propagation time based on the time difference and the time when the ultrasonic transmitter transmits the sound beam; and determining the frequency distribution change in the sound wave images corresponding to adjacent timestamps; wherein the frequency distribution change includes at least the high-frequency component distribution and the low-frequency component distribution; based on the sound wave propagation time change and the frequency distribution change, determining the road surface slippery state corresponding to the sound wave change image set; mapping the road surface slippery state with the sound wave change image set to obtain a road surface slippery state change diagram corresponding to each sub-area.

[0013] In one implementation of the present application, an initial prediction is made on the duration of a slippery state of a road surface in a tunnel based on changes in image sound waves corresponding to a tunnel road surface sound wave image and a road surface slippery state change diagram, specifically comprising: inputting the tunnel road surface sound wave image into a preset first tunnel road surface slippery detection model to determine a slippery area through the preset first tunnel road surface detection model; determining a road surface slippery state change diagram corresponding to the slippery area, comparing the road surface slippery state change diagram corresponding to the slippery area with a historical slippery state change diagram in a preset database, to determine a reference historical slippery state change diagram having a similarity greater than a preset similarity in the database; sorting the reference historical slippery state change diagram based on the similarity, and assigning weights to the reference historical slippery state change diagram based on the sorting order; determining a historical duration corresponding to the reference historical slippery state change diagram, and predicting the duration of a slippery state of the road surface in the tunnel based on the assigned weight and the historical duration.

[0014] In one implementation of the present application, an environmental coefficient is determined based on environmental information corresponding to the tunnel, and the initial prediction result is adjusted based on the environmental coefficient to determine the final prediction result of the duration of the slippery road state, which specifically includes: obtaining the environmental information corresponding to the tunnel; wherein the environmental information includes at least temperature information, wind speed information, humidity information, precipitation information and the number of vehicles; determining the influence coefficient corresponding to each environmental information based on the environmental information, and when the influence coefficient is greater than a preset coefficient threshold, comparing the environmental information with a preset environmental combination feature library to determine the environmental combination coefficient value; wherein the preset environmental combination feature library includes a plurality of different environmental combination information, and also includes environmental combination coefficient values ​​corresponding to different environmental combination information; adding and subtracting the influence coefficient corresponding to each environmental information and the environmental combination coefficient value to determine the final environmental coefficient; multiplying the final environmental coefficient by the predicted duration of the slippery road state in the tunnel to obtain the final duration of the slippery state.

[0015] The embodiment of the present application provides a tunnel pavement slippery state monitoring device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: transmit sound wave beams of different angles to the tunnel pavement through an ultrasonic transmitter, and divide the tunnel pavement into multiple sub-areas based on the positions of the sound beams on the tunnel pavement; receive the sound beams corresponding to the multiple sub-areas in the tunnel through an ultrasonic receiver, and convert the sound beams received by the ultrasonic receiver into sound wave images to obtain sound wave images corresponding to the multiple sub-areas; splice the sound wave images corresponding to the multiple sub-areas in the same time period to obtain the tunnel pavement sound wave image; arrange the multiple sound wave images corresponding to the multiple sub-areas in the continuous time period in time order to obtain the road pavement slippery state change diagram corresponding to each sub-area; predict the duration of the road pavement slippery state in the tunnel based on the image sound wave changes corresponding to the tunnel pavement sound wave image and the road pavement slippery state change diagram; determine the environmental coefficient based on the environmental information corresponding to the tunnel, and adjust the initial prediction result based on the environmental coefficient to determine the final prediction result of the road pavement slippery state duration.

[0016] A non-volatile computer storage medium provided by an embodiment of the present application stores computer executable instructions, wherein the computer executable instructions are configured to: emit sound wave beams of different angles to a tunnel pavement through an ultrasonic transmitter, and divide the tunnel pavement into a plurality of sub-areas based on the positions of the sound beams on the tunnel pavement; receive the sound beams corresponding to the plurality of sub-areas in the tunnel through an ultrasonic receiver, and convert the sound beams received by the ultrasonic receiver into sound wave images to obtain sound wave images corresponding to the plurality of sub-areas; splice the sound wave images corresponding to the plurality of sub-areas in the same time period to obtain a sound wave image of the tunnel pavement; arrange the plurality of sound wave images corresponding to the plurality of sub-areas in a continuous time period in time sequence to obtain a road surface slippery state change diagram corresponding to each sub-area; predict the duration of the road surface slippery state in the tunnel based on the changes in the sound waves of the images corresponding to the tunnel pavement sound wave image and the road surface slippery state change diagram; determine an environmental coefficient based on the environmental information corresponding to the tunnel, and adjust the initial prediction result based on the environmental coefficient to determine the final prediction result of the duration of the road surface slippery state.

[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: the embodiments of the present application can fully cover multiple sub-areas of the tunnel pavement by emitting sound wave beams at different angles to the tunnel pavement through an ultrasonic transmitter, thereby ensuring monitoring without blind spots. By dividing the tunnel pavement into multiple sub-areas, it is convenient for refined management and monitoring, and the sub-area division allows the slippery state of each area to be evaluated separately, which helps to timely discover and deal with potential safety hazards. By splicing the sound wave images of multiple sub-areas, a complete sound wave image of the tunnel pavement is obtained. Based on the sound wave images in a continuous time period, a change diagram of the slippery state of the pavement in each sub-area is drawn, which can not only comprehensively evaluate the slippery state of the tunnel, but also improve the accuracy of predicting the duration of the slippery state of the pavement in the tunnel, thereby reducing the probability of traffic accidents caused by slippery pavement in the tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0019] Figure 1 A flow chart of a method for monitoring the slippery state of a tunnel pavement provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a tunnel pavement slippery condition monitoring device provided in an embodiment of the present application.

[0021] Reference numerals:

[0022] 200: Tunnel road surface slippery state monitoring device, 201: processor, 202: memory. DETAILED DESCRIPTION

[0023] Embodiments of the present application provide a method, device and medium for monitoring the slippery state of a tunnel pavement.

[0024] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0025] The technical solution proposed in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A flow chart of a method for monitoring the slippery state of a tunnel pavement provided in an embodiment of the present application is as follows: Figure 1 As shown, the tunnel pavement slippery state monitoring method comprises the following steps:

[0027] Step 101: Use an ultrasonic transmitter to emit sound beams at different angles to a tunnel pavement, and divide the tunnel pavement into a plurality of sub-areas based on the positions of the sound beams on the tunnel pavement.

[0028] In one embodiment of the present application, the ultrasonic transmitter selected in the embodiment of the present application has the ability to generate high-frequency ultrasonic waves (usually above 20 kHz) and can emit sound beams at different angles.

[0029] In one embodiment of the present application, an ultrasonic transmitter is adjusted so that the ultrasonic transmitter emits a plurality of sound beams at different angles. Based on the position of the ultrasonic transmitter and the plurality of emission angles, reference positions where the plurality of sound beams fall on the tunnel pavement are determined. With each reference position as the midpoint, a plurality of reference sub-regions are constructed to perform a primary division of the tunnel pavement. An area where the plurality of reference sub-regions intersect, and an area to be divided in the tunnel pavement that has not been divided into any reference sub-region are determined. The area where the intersection exists and the area to be divided are divided twice so that each sub-region covers the tunnel pavement.

[0030] Specifically, in order to transmit the sound beam to different positions of the tunnel pavement, the emission angle of the ultrasonic transmitter can be adjusted by changing the arrangement of the internal components of the transmitter or using an electronic control system. Based on the position of the ultrasonic transmitter and multiple emission angles, the theoretical landing point of each sound beam on the tunnel pavement, that is, the reference position, is calculated. With each reference position as the midpoint, multiple reference sub-areas are constructed. Among them, the reference sub-area can be rectangular, circular or other shapes. For example, assume that the ultrasonic transmitter is located above the tunnel entrance and can emit sound beams at three different angles, namely 30 degrees, 60 degrees and 90 degrees. Based on these three angles and the position of the transmitter, the reference positions A, B, and C of the three sound beams on the tunnel pavement can be calculated. Then, with A, B, and C as the midpoints, three reference sub-areas are constructed, such as rectangular areas R1, R2, and R3.

[0031] Furthermore, the reference sub-regions after the initial division are analyzed to determine the areas with intersections and the areas to be divided that are not divided into any reference sub-regions. The areas with intersections and the areas to be divided are divided again to ensure that each sub-region can completely cover the tunnel road surface.

[0032] In one embodiment of the present application, multiple reference sub-areas corresponding to the current intersection are determined, and the proportions of the current intersection in the multiple reference sub-areas are determined to divide the current intersection into the reference sub-areas with the largest proportion. When the proportions are consistent, the current intersection is randomly assigned to any reference sub-area. The area of ​​the area to be divided is determined, and when the area is greater than a preset area threshold, the ultrasonic transmitter is adjusted based on the position of the area to be divided to emit a sound beam to the area to be divided. When the area is not greater than the preset area threshold, the reference sub-areas adjacent to the area to be divided are determined, and the area to be divided is evenly divided into adjacent reference sub-areas.

[0033] Specifically, all reference sub-regions are traversed to determine whether the boundaries between different reference sub-regions overlap, thereby determining the intersection region. For each intersection region, the area covered by each overlapping reference sub-region is calculated, and the ratio of the intersection area covered by each reference sub-region to the total area of ​​the intersection region is calculated to obtain the proportion. The intersection region is divided into the reference sub-region with the largest proportion. If the proportions of multiple reference sub-regions are the same, one of the reference sub-regions is randomly selected to receive the intersection region.

[0034] Furthermore, the area of ​​the area to be divided is calculated. If the area of ​​the area to be divided is greater than the preset area threshold, it means that the area is large and requires additional sound beam coverage. The ultrasonic transmitter is adjusted based on its position, a new sound beam is emitted to cover the area, and a new reference sub-area is constructed. If the area is not greater than the preset area threshold, it can be considered to be merged into the adjacent reference sub-area. Specifically, the adjacent reference sub-areas can be determined and evenly divided into these adjacent sub-areas.

[0035] Step 102: Receive the sound wave beams corresponding to the multiple sub-areas in the tunnel through an ultrasonic receiver, and convert the sound wave beams received by the ultrasonic receiver into sound wave images to obtain sound wave images corresponding to the multiple sub-areas.

[0036] In one embodiment of the present application, the ultrasonic receiver is divided into a plurality of sub-blocks; wherein each sub-block corresponds to a sound wave beam of any sub-area in the receiving tunnel. The received sound wave signal is subjected to wavelet transformation to obtain a plurality of wavelet coefficients, and the wavelet coefficients of the preset high-frequency part are subjected to threshold processing. The wavelet coefficients subjected to threshold processing are subjected to inverse wavelet transformation to reconstruct the denoised signal, and based on the reconstructed signal, the sound wave images corresponding to the plurality of sub-areas are obtained.

[0037] Specifically, according to the structure and layout of the tunnel, the ultrasonic receiver is divided into multiple sub-blocks, and each sub-block is responsible for receiving the sound wave beam in a specific area of ​​the tunnel. The division of sub-blocks should ensure that all areas in the tunnel can be fully covered. The sound wave signal received by each sub-block is subjected to wavelet transform, which can decompose the signal into wavelet coefficients of different frequency components, which is convenient for subsequent denoising. According to the noise level of the signal and the denoising requirements, a suitable threshold is set, and the wavelet coefficients of the high-frequency part obtained after the wavelet transform are subjected to threshold processing. If the absolute value of the wavelet coefficient is greater than the threshold, it is retained or shrunk. If it is less than or equal to the threshold, it is set to zero.

[0038] Furthermore, the wavelet coefficients that have been processed by thresholding are subjected to inverse wavelet transform, and the inverse transform recombines the processed wavelet coefficients into an acoustic wave signal to achieve denoising and signal reconstruction. According to the inverse wavelet transform results of each sub-block, the denoised acoustic wave signal is reconstructed to ensure that the reconstructed signal can accurately reflect the acoustic wave characteristics of each area in the tunnel. The reconstructed acoustic wave signal is converted into a corresponding acoustic wave image, and the generated acoustic wave image is matched with each sub-area in the tunnel. The acoustic wave image can be used to intuitively understand the acoustic wave distribution in different areas of the tunnel.

[0039] Step 103: splice the acoustic wave images corresponding to the multiple sub-areas in the same time period to obtain the acoustic wave image of the tunnel road surface.

[0040] In one embodiment of the present application, the region labels corresponding to the multiple sub-regions of the tunnel pavement are obtained; wherein the region labels are marked in the order of the tunnel pavement. Based on the region labels, the image labels of the acoustic wave images corresponding to the multiple sub-regions in the same time period are determined, and the multiple acoustic wave images are spliced ​​according to the image labels. The original acoustic wave image of each sub-region is compared with the acoustic wave image of the tunnel pavement obtained after splicing to determine the similarity between each sub-region in the acoustic wave image of the tunnel pavement and the original acoustic wave image. When the similarity meets the preset similarity threshold, it is determined that the acoustic wave image of the tunnel pavement meets the requirements.

[0041] Specifically, first, the tunnel pavement needs to be divided into multiple sub-areas. Each sub-area should have a unique area label, which is usually marked in a certain order of the tunnel pavement (such as from left to right, from top to bottom). In each sub-area, an ultrasonic receiver is used to capture acoustic signals in the same period of time and convert these signals into acoustic images. The acoustic image of each sub-area should have an image label corresponding to the area label so that the acoustic image of each sub-area can be accurately matched and identified later.

[0042] Furthermore, after obtaining the acoustic wave images of all sub-areas, it is necessary to splice these images according to their image labels (i.e., area labels) and their relative positions on the tunnel pavement. The purpose of splicing is to obtain a complete acoustic wave image of the tunnel pavement, which can show the acoustic wave distribution of the entire tunnel pavement. After obtaining the spliced ​​tunnel pavement acoustic wave image, the original acoustic wave image of each sub-area is compared with the sub-area at the corresponding position in the spliced ​​image. In this way, it is evaluated whether the acoustic wave information of each sub-area in the spliced ​​image is consistent with the original image, and then the quality and accuracy of the spliced ​​image are judged.

[0043] Furthermore, in the comparison process, the similarity between the original acoustic wave image of each sub-region and the corresponding sub-region in the spliced ​​image is calculated. The similarity can be calculated using similarity measurement methods in image processing, such as mean square error, peak signal-to-noise ratio, etc. Then, the calculated similarity is compared with a preset similarity threshold. If the similarity of all sub-regions reaches or exceeds the preset threshold, it is considered that the spliced ​​tunnel pavement acoustic wave image meets the requirements.

[0044] Step 104 , arranging the multiple sound wave images in the continuous time periods corresponding to the multiple sub-regions respectively in time sequence, and obtaining the road slippery state change diagram corresponding to each sub-region respectively.

[0045] In one embodiment of the present application, the timestamp corresponding to each acoustic wave image is determined, and multiple acoustic wave images corresponding to the same sub-area are sorted based on the timestamp to obtain an acoustic wave change image set. The time difference between two adjacent timestamps is determined, and the time when the ultrasonic transmitter transmits the acoustic wave beam is determined. Based on the time difference and the time when the ultrasonic transmitter transmits the acoustic beam, the change in the acoustic wave propagation time is determined. And, the frequency distribution change in the acoustic wave images corresponding to adjacent timestamps is determined; wherein the frequency distribution change includes at least the high-frequency component distribution and the low-frequency component distribution. Based on the acoustic wave propagation time change and the frequency distribution change, the slippery road state corresponding to the acoustic wave change image set is determined. The slippery road state is mapped with the acoustic wave change image set to obtain the slippery road state change corresponding to each sub-area.

[0046] Specifically, each acoustic wave image will have a corresponding time point when it is collected, that is, a timestamp. For the same sub-area, multiple acoustic wave images at different time points will be collected. These images need to be sorted in the order of the timestamps to form a set of acoustic wave change images in order to observe the changes of the acoustic waves in the sub-area over time. The time difference between two adjacent timestamps is calculated. The different slippery conditions of the road surface correspond to different acoustic wave propagation velocities. Therefore, the change in the slippery state of the road surface can be determined by determining the changes in adjacent timestamps. For example, if the time difference between adjacent timestamps gradually increases, it means that the ultrasonic propagation speed is decreasing, that is, the slippery state of the road surface is gradually becoming more serious. On the contrary, if the time difference gradually decreases, it means that the ultrasonic propagation speed is increasing, that is, the slippery state of the road surface is gradually decreasing.

[0047] Furthermore, the sound wave image contains information about the frequency distribution, that is, the intensity distribution of different frequency components in the sound wave. If the sound wave propagation time increases, the high-frequency component decreases and the low-frequency component increases, this may indicate that the road surface has become slippery. This is because a slippery road surface will slow down the propagation speed of the sound wave, and the reflection and scattering of the sound wave on the slippery surface will attenuate the high-frequency component and enhance the low-frequency component. The slippery state of each sub-area is mapped with its corresponding sound wave change image set to form a complete road slippery state change map.

[0048] Step 105 : Based on the image sound wave changes corresponding to the tunnel road surface sound wave image and the road surface slippery state change diagram, an initial prediction is made on the duration of the road surface slippery state in the tunnel.

[0049] In one embodiment of the present application, the tunnel pavement acoustic wave image is input into a preset first tunnel pavement slippery detection model to determine a slippery area through the preset first tunnel pavement detection model. A pavement slippery state change graph corresponding to the slippery area is determined, and the pavement slippery state change graph corresponding to the slippery area is compared with a historical slippery state change graph in a preset database to determine a reference historical slippery state change graph having a similarity greater than a preset similarity in the database. The reference historical slippery state change graph is sorted based on the similarity, and weights are assigned to the reference historical slippery state change graph based on the sorting order. The historical duration corresponding to the reference historical slippery state change graph is determined, and an initial prediction is made of the duration of the slippery state of the pavement in the tunnel based on the assigned weight and the historical duration.

[0050] Specifically, the embodiment of the present application is provided with a preset first tunnel pavement slippery detection model, and the training process of the model is to take the preset tunnel pavement acoustic wave image sample set as input, and the image sample set corresponding to the preset tunnel pavement acoustic wave image sample set with the slippery area marked as output, and train the preset neural network model to obtain the preset first tunnel pavement slippery detection model. The tunnel pavement acoustic wave image currently to be detected is input into the preset first tunnel pavement slippery detection model, so that the slippery area therein is marked by the model.

[0051] Furthermore, based on the determined slippery area, the corresponding road slippery state change map is determined, and the slippery state change map is compared with the historical slippery state change map pre-stored in the database, and the similarity value is output through a preset similarity algorithm, such as a structural similarity index algorithm. After comparing all historical maps, reference historical slippery state change maps with a similarity greater than a preset threshold are screened out according to the similarity. Assuming that the preset similarity threshold is 0.8 (indicating that the two maps have at least 80% similarity), all historical maps with a similarity greater than 0.8 are found and used as reference maps.

[0052] Furthermore, the filtered reference history graphs are sorted, usually from high to low according to the similarity. Then, different weights are assigned to these graphs according to the sorting order, and the weight of the graph with higher similarity is greater. According to the historical duration of the reference history graph and the assigned weight, the duration of the current slippery state is predicted. Assume that the duration of the slippery state corresponding to the three reference history graphs is 3 hours, 2.5 hours and 2 hours respectively. According to the assigned weights (0.4, 0.3 and 0.3), the weighted average duration is calculated, such as (3×0.4 + 2.5×0.3 + 2×0.3) = 2.75 hours, which is used as the initial predicted duration of the current slippery state.

[0053] Step 106: Determine an environmental coefficient based on the environmental information corresponding to the tunnel, and adjust the initial prediction result based on the environmental coefficient to determine a final prediction result of the duration of the slippery road state.

[0054] In one embodiment of the present application, environmental information corresponding to the tunnel is obtained; wherein the environmental information includes at least temperature information, wind speed information, humidity information, precipitation information and the number of vehicles. Based on the environmental information, the influence coefficient corresponding to each environmental information is determined. When the influence coefficient is greater than the preset coefficient threshold, the environmental information is compared with the preset environmental combination feature library to determine the environmental combination coefficient value; wherein the preset environmental combination feature library includes a plurality of different environmental combination information, and also includes environmental combination coefficient values ​​corresponding to different environmental combination information. The influence coefficient corresponding to each environmental information and the environmental combination coefficient value are added and subtracted to determine the final environmental coefficient. The final environmental coefficient is multiplied by the predicted duration of the slippery state of the road surface in the tunnel to obtain the final duration of the slippery state.

[0055] Specifically, when evaluating the duration of the slippery state of the road surface in the tunnel, in addition to image analysis, environmental information outside the tunnel needs to be considered. This environmental information includes temperature, wind speed, humidity, precipitation, and the number of vehicles. For example, through the weather station and sensors installed outside the tunnel, the temperature, wind speed, humidity and precipitation information of the tunnel can be obtained in real time. At the same time, the traffic flow monitoring equipment in the tunnel can count the number of vehicles. Each type of environmental information has a different degree of influence on the slippery state of the road surface, so it is necessary to set an influence coefficient for each environmental information based on historical data. For example, assuming that humidity has the greatest impact on the slippery state of the road surface, an influence coefficient of 0.4 is assigned to it; followed by precipitation, with an influence coefficient of 0.3; temperature, wind speed and the number of vehicles have a smaller impact, and influence coefficients of 0.1, 0.1 and 0.1 are assigned respectively. Furthermore, some combinations of environmental information may have a special impact on the slippery state of the road surface. Therefore, when the influence coefficient of a certain environmental information exceeds a certain threshold, it needs to be combined with other environmental information and compared with the preset environmental combination feature library to determine the environmental combination coefficient value. For example, the combination of humidity and precipitation has a particularly significant impact on road slipperiness, and their influence coefficients exceed a preset threshold (such as 0.2). At this time, the combination of humidity and precipitation is compared with the preset environmental combination feature library to find the most similar combination and obtain its corresponding environmental combination coefficient value, such as 0.6.

[0056] Furthermore, the influence coefficients of all environmental information and the determined environmental combination coefficient values ​​are added and subtracted to obtain a final environmental coefficient. For example, the influence coefficient of humidity is 0.4, the influence coefficient of precipitation is 0.3, and the influence coefficients of temperature, wind speed and number of vehicles are 0.1 each, to obtain a basic influence coefficient of 0.9. Adding the environmental combination coefficient value of humidity and precipitation of 0.6, the final environmental coefficient is 1.5. The predicted duration of the slippery state is multiplied by the determined environmental coefficient to obtain the final duration of the slippery state. For example, the predicted duration of the slippery state is 2.75 hours, and the determined environmental coefficient is 1.5. Multiplying the two together, the final duration of the slippery state is 4.125 hours. This means that under the current environmental conditions, the slippery state of the tunnel pavement may last for about 4.125 hours.

[0057] Figure 2 This is a schematic diagram of a tunnel pavement slippery state monitoring device provided in an embodiment of the present application. Figure 2 As shown, a tunnel pavement slippery state monitoring device 200 includes: at least one processor 201; and a memory 202 in communication with the at least one processor 201; wherein the memory 202 stores instructions that can be executed by the at least one processor 201, and the instructions are executed by the at least one processor 201, so that the at least one processor 201 can: transmit sound beams of different angles to the tunnel pavement through an ultrasonic transmitter, and divide the tunnel pavement into multiple sub-areas based on the positions of the sound beams on the tunnel pavement; receive the sound beams corresponding to the multiple sub-areas in the tunnel through an ultrasonic receiver, and convert the sound beams received by the ultrasonic receiver into sound wave images, To obtain the acoustic wave images corresponding to the multiple sub-areas respectively; to splice the acoustic wave images corresponding to the multiple sub-areas in the same time period to obtain the tunnel pavement acoustic wave image; to arrange the multiple acoustic wave images corresponding to the multiple sub-areas in the continuous time period in chronological order to obtain the road surface slippery state change diagram corresponding to each sub-area; to predict the duration of the road surface slippery state in the tunnel based on the image acoustic wave changes corresponding to the tunnel pavement acoustic wave image and the road surface slippery state change diagram; to determine the environmental coefficient based on the environmental information corresponding to the tunnel, and to adjust the initial prediction result based on the environmental coefficient to determine the final prediction result of the road surface slippery state duration.

[0058] The embodiment of the present application also provides a non-volatile computer storage medium, storing computer executable instructions, wherein the computer executable instructions are configured to: transmit sound wave beams of different angles to a tunnel pavement through an ultrasonic transmitter, and divide the tunnel pavement into a plurality of sub-areas based on the positions of the sound beams on the tunnel pavement; receive the sound beams corresponding to the plurality of sub-areas in the tunnel through an ultrasonic receiver, and convert the sound beams received by the ultrasonic receiver into sound wave images to obtain sound wave images corresponding to the plurality of sub-areas; splice the sound wave images corresponding to the plurality of sub-areas in the same time period to obtain a sound wave image of the tunnel pavement; arrange the plurality of sound wave images corresponding to the plurality of sub-areas in a continuous time period in time sequence to obtain a road surface slippery state change diagram corresponding to each sub-area; predict the duration of the road surface slippery state in the tunnel based on the changes in the sound waves of the images corresponding to the tunnel pavement sound wave image and the road surface slippery state change diagram; determine an environmental coefficient based on the environmental information corresponding to the tunnel, and adjust the initial prediction result based on the environmental coefficient to determine the final prediction result of the duration of the road surface slippery state.

[0059] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0060] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various changes and variations. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for monitoring the slippery state of a tunnel pavement, characterized in that: The method comprises: Transmitting sound beams at different angles to a tunnel pavement through an ultrasonic transmitter, and dividing the tunnel pavement into a plurality of sub-areas based on the positions of the sound beams on the tunnel pavement; Receiving the sound wave beams respectively corresponding to the plurality of sub-areas in the tunnel through an ultrasonic receiver, and performing sound wave image conversion on the sound wave beams received by the ultrasonic receiver to obtain sound wave images respectively corresponding to the plurality of sub-areas; splicing the acoustic wave images corresponding to the plurality of sub-areas in the same period of time to obtain an acoustic wave image of the tunnel pavement; Arranging the multiple sound wave images in the continuous time periods respectively corresponding to the multiple sub-areas in time sequence to obtain a road slippery state change diagram respectively corresponding to each sub-area; Based on the image sound wave changes corresponding to the tunnel road surface sound wave image and the road surface slippery state change diagram, an initial prediction is made on the duration of the road surface slippery state in the tunnel; Determining an environmental coefficient based on environmental information corresponding to the tunnel, and adjusting the initial prediction result based on the environmental coefficient to determine a final prediction result of the duration of the slippery road state; The step of determining an environmental coefficient based on environmental information corresponding to the tunnel and adjusting the initial prediction result based on the environmental coefficient to determine a final prediction result of the duration of the slippery road surface specifically includes: Acquire environmental information corresponding to the tunnel; wherein the environmental information includes at least temperature information, wind speed information, humidity information, precipitation information and the number of vehicles; Determine the influence coefficients corresponding to the respective environmental information based on the environmental information, and when the influence coefficients are greater than a preset coefficient threshold, compare the environmental information with a preset environmental combination feature library to determine the environmental combination coefficient value; wherein the preset environmental combination feature library includes a plurality of different environmental combination information, and also includes environmental combination coefficient values ​​corresponding to the different environmental combination information; Add and subtract the influence coefficients corresponding to the environmental information and the environmental combination coefficient values ​​to determine the final environmental coefficient; The final environmental coefficient is multiplied by the predicted duration of the slippery state of the road surface in the tunnel to obtain the final duration of the slippery state.

2. A tunnel pavement slippery state monitoring method according to claim 1, characterized in that: The step of dividing the tunnel pavement into a plurality of sub-areas based on the position of the acoustic beam on the tunnel pavement specifically includes: Adjusting the ultrasonic transmitter so that the ultrasonic transmitter emits a plurality of sound beams at different angles; Based on the position of the ultrasonic transmitter and the multiple emission angles, determining the reference positions where the multiple sound beams fall on the tunnel pavement respectively; Taking each of the reference positions as a midpoint, constructing a plurality of reference sub-areas to perform a preliminary division of the tunnel pavement; Determine an area where a plurality of the reference sub-areas intersect, and an area to be divided in the tunnel pavement that is not divided into any of the reference sub-areas; The intersecting area and the area to be divided are divided twice so that each sub-area covers the tunnel road surface.

3. A tunnel pavement slippery state monitoring method according to claim 2, characterized in that: The second division of the area with the intersection and the area to be divided so that each sub-area covers the tunnel road surface specifically includes: Determine a plurality of reference sub-regions corresponding to the current intersection, and determine the proportions of the current intersection in the plurality of reference sub-regions, respectively, so as to divide the current intersection into the reference sub-region with the largest proportion; In the case where the proportions are consistent, the current intersection is randomly assigned to any of the reference sub-regions; Determine the area of ​​the region to be divided, and if the area is greater than a preset area threshold, adjust the ultrasonic transmitter based on the position of the region to be divided to transmit a sound wave beam to the region to be divided; When the area is not greater than the preset area threshold, a reference sub-region adjacent to the region to be divided is determined, and the region to be divided is evenly divided into the adjacent reference sub-regions.

4. A tunnel pavement slippery state monitoring method according to claim 1, characterized in that: The step of receiving the sound wave beams respectively corresponding to the plurality of sub-areas in the tunnel by an ultrasonic receiver and converting the sound wave beams received by the ultrasonic receiver into sound wave images to obtain sound wave images respectively corresponding to the plurality of sub-areas specifically includes: Divide the ultrasonic receiver into a plurality of sub-blocks; wherein each sub-block corresponds to receiving the sound wave beam of any sub-area in the tunnel; Performing wavelet transformation on the received sound wave signal to obtain multiple wavelet coefficients, and performing threshold processing on the wavelet coefficients of the preset high-frequency part; The wavelet coefficients subjected to the threshold processing are subjected to inverse wavelet transformation to reconstruct the denoised signal, and based on the reconstructed signal, the acoustic wave images corresponding to the plurality of sub-regions are obtained.

5. A tunnel pavement slippery state monitoring method according to claim 1, characterized in that: The step of splicing the acoustic wave images corresponding to the plurality of sub-areas in the same period of time to obtain the acoustic wave image of the tunnel pavement specifically includes: Obtaining area labels corresponding to a plurality of sub-areas of the tunnel pavement, respectively; wherein the area labels are marked in the order of the tunnel pavement; Based on the region labels, determining image labels of acoustic wave images corresponding to the plurality of sub-regions in the same time period, and splicing the plurality of acoustic wave images according to the image labels; Comparing the original acoustic wave image of each sub-region with the tunnel pavement acoustic wave image obtained after splicing to determine the similarity between each sub-region in the tunnel pavement acoustic wave image and the original acoustic wave image; When the similarity meets the preset similarity threshold, it is determined that the tunnel pavement acoustic wave image meets the requirements.

6. A tunnel pavement slippery state monitoring method according to claim 1, characterized in that: Arranging the multiple acoustic wave images in the continuous time periods corresponding to the multiple sub-regions in time sequence to obtain the road slippery state change diagram corresponding to each sub-region specifically includes: Determine a timestamp corresponding to each acoustic wave image, and sort multiple acoustic wave images corresponding to the same sub-region based on the timestamp to obtain an acoustic wave change image set; Determine the time difference between two adjacent timestamps and the time when the ultrasonic transmitter transmits the sound beam, and determine the change of the sound wave propagation time based on the time difference and the time when the ultrasonic transmitter transmits the sound beam; And, determining the frequency distribution change in the acoustic wave image corresponding to the adjacent time stamps; wherein the frequency distribution change includes at least the high frequency component distribution and the low frequency component distribution; Determining a slippery road state corresponding to the sound wave change image set based on the change in the sound wave propagation time and the change in the frequency distribution; The road surface slippery state is mapped with the sound wave change image set to obtain a road surface slippery state change map corresponding to each of the sub-areas.

7. A tunnel pavement slippery state monitoring method according to claim 1, characterized in that: The initial prediction of the duration of the slippery state of the road surface in the tunnel based on the image sound wave changes corresponding to the tunnel road surface sound wave image and the road surface slippery state change diagram specifically includes: Inputting the tunnel pavement acoustic wave image into a preset first tunnel pavement slippery detection model to determine a slippery area through the preset first tunnel pavement detection model; Determine a road surface slippery state change map corresponding to the slippery area, and compare the road surface slippery state change map corresponding to the slippery area with a historical slippery state change map in a preset database to determine a reference historical slippery state change map having a similarity greater than a preset similarity in the database; sorting the reference historical slippery state change graphs based on the similarity, and assigning weights to the reference historical slippery state change graphs based on the sorting order; The historical duration corresponding to the reference historical slippery state change diagram is determined, and based on the assigned weight and the historical duration, an initial prediction is made on the duration of the slippery state of the road surface in the tunnel.

8. A tunnel pavement slippery state monitoring device, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 7.

9. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions can execute the method according to any one of claims 1 to 7.

Images