Construction road crack prediction method based on time sequence monitoring data

Abstract

The application relates to the technical field of road monitoring, and relates to a construction road crack prediction method and system based on time sequence monitoring data, which comprises the following steps: obtaining a historical monitoring time based on a road monitoring device, obtaining a plurality of unit time periods based on the historical monitoring time, obtaining a plurality of historical images based on the unit time periods, obtaining denoising images based on the historical images, obtaining unit impact data based on the plurality of denoising images, summarizing the unit impact data to obtain a unit impact data set, obtaining a denoising image sequence, obtaining an initial crack area sequence based on the denoising image sequence, screening the initial crack area sequence to obtain a to-be-interpolated sequence, performing interpolation calculation on the to-be-interpolated sequence to obtain a crack area evolution sequence, constructing a crack evolution model according to the crack area evolution sequence and the unit impact data set, and obtaining a predicted crack value based on the crack evolution model. The application can realize road crack prediction by using historical data.

Description

Technical Field

[0001] The present invention relates to the field of road monitoring technology, and in particular to a construction road crack prediction method, system, electronic device and computer-readable storage medium based on time series monitoring data. Background Art

[0002] With the development of the times, all parties and regions have deeply implemented the rural revitalization policy. Correspondingly, expanding the scope of urbanization and building roads to every home have become the people's day and night expectations. Therefore, road construction has become a key area of ​​development.

[0003] Before road construction begins, it's often necessary to assess and predict the road's performance. This ensures that the completed road meets national and operational standards, preventing cracks from appearing quickly after construction, or even rapidly developing once cracks appear, disrupting traffic. Conventional technology typically relies on the experience of road construction teams and existing construction standards, and crack control measures are typically implemented after cracks have appeared.

[0004] Although the above method can complete road construction and predict and maintain cracks that appear, for areas where road construction has never been carried out, such as rural roads and urban fringe roads, it is relatively subjective based on experience. There is a lack of a method that can predict future road conditions and the evolution of possible cracks based on the actual situation of the road to be constructed, thereby realizing the prediction of road cracks using historical data. Summary of the Invention

[0005] The present invention provides a construction road crack prediction method based on time series monitoring data and a computer-readable storage medium, the main purpose of which is to predict road cracks using historical data.

[0006] To achieve the above objectives, the present invention provides a method for predicting cracks in construction roads based on time series monitoring data, comprising:

[0007] receiving a road monitoring instruction, activating a road monitoring device according to the road monitoring instruction, obtaining historical monitoring time based on the activated road monitoring device, and dividing the historical monitoring time into a plurality of unit time periods;

[0008] Extracting unit time periods sequentially from the plurality of unit time periods, and performing the following operations on the extracted unit time periods: acquiring a plurality of historical images based on the extracted unit time periods, sequentially extracting historical images from the plurality of historical images, and performing the following operations on the extracted historical images:

[0009] Performing a sharpening operation on the extracted historical image to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the extracted unit impact data for a unit time period based on the set of vehicle images to be identified;

[0010] Summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised images to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, and filter the initial crack area sequence to obtain a sequence to be interpolated;

[0011] The interpolation method is used to interpolate the sequence to be interpolated to obtain the crack area evolution sequence;

[0012] A crack evolution model is constructed based on the crack area evolution sequence and the unit impact data set. The time period to be predicted and the impact to be predicted are obtained. The time period to be predicted and the impact to be predicted are input into the crack evolution model to obtain the predicted crack value. Based on the predicted crack value, the prediction of construction road cracks in the unit time period monitoring data is completed.

[0013] Optionally, performing a clearing operation on the extracted historical image to obtain a clear image includes:

[0014] Multiple Gaussian difference curves are generated using the pre-built Gaussian difference operator and the extracted historical images, where the Gaussian difference operator is:

[0015]

[0016] Among them, z represents the horizontal coordinate of the historical image in the image coordinate system, and w represents the vertical coordinate of the historical image in the image coordinate system. and Both represent the variance of the Gaussian filter, σ1 and σ2 both represent the standard deviation of the Gaussian filter, and σ1≠σ2, DOG represents the Gaussian difference operator;

[0017] A zero-crossing point of each Gaussian difference curve in a plurality of Gaussian difference curves is obtained to obtain a plurality of zero-crossing points, a plurality of contour coordinates are generated based on the plurality of zero-crossing points, the plurality of contour coordinates are spliced ​​using a pre-built edge connection algorithm to obtain a historical image contour, an image transition area is obtained based on the historical image contour, and a clear image is obtained based on the image transition area, wherein the contour coordinates correspond one-to-one to the Gaussian difference curves.

[0018] Optionally, performing a noise reduction operation on the clear image to obtain a noise-reduced image includes:

[0019] Construct a mean filter unit and a median filter unit;

[0020] Obtain a standard image, extract the spatial distribution characteristics of pixel grayscale values ​​from the standard image, and import the spatial distribution characteristics of pixel grayscale values ​​into a mean filter unit to obtain fixed pattern noise, wherein the filter kernel size of the mean filter unit is 9×9;

[0021] A noise reduction operation is performed on the clear image using fixed pattern noise to obtain a noise-reduced image. The formula for a noise reduction operation is as follows:

[0022] k(x,y)=j(x,y)-δ×l(x,y)+β×h

[0023] Wherein, k(x, y) represents the pixel grayscale value corresponding to the pixel coordinate (x, y) in the primary denoised image, x represents the abscissa of the pixel coordinate (x, y), y represents the ordinate of the pixel coordinate (x, y), j(x, y) represents the pixel grayscale value corresponding to the pixel coordinate (x, y) in the clear image, l(x, y) represents the pixel grayscale value of the pixel coordinate (x, y) in the fixed pattern noise, δ and β represent the first correction coefficient and the second correction coefficient, respectively, and δ = 0.7, β = 1.2, and h represents the average grayscale value of all pixels in the clear image;

[0024] The median filter unit is used to perform a secondary denoising operation on the primary denoised image to obtain a denoised image, wherein the size of the filter kernel of the median filter unit is 3×3.

[0025] Optionally, extracting a set of vehicle images to be identified based on the denoised image, and calculating the extracted unit impact data per unit time period based on the set of vehicle images to be identified, includes:

[0026] Using a target detection algorithm to identify the denoised image, a set of vehicle images to be identified is obtained, wherein the set of vehicle images to be identified includes a plurality of vehicle images to be identified;

[0027] The following operations are performed on the vehicle images to be identified in the vehicle image set:

[0028] Identify the vehicle model of the vehicle image to be identified, and determine whether the license plate number can be extracted from the vehicle image to be identified. If the license plate number can be extracted from the vehicle image to be identified, identify the extracted license plate number to obtain the target license plate number; otherwise, confirm the license plate number of the vehicle image to be identified as 1 to obtain the target license plate number;

[0029] Construct vehicle data based on vehicle model and target license plate number, aggregate vehicle data, and obtain the extracted vehicle data set for the unit time period;

[0030] Calculate unit impact data from the vehicle dataset.

[0031] Optionally, calculating unit impact data based on the vehicle data set includes:

[0032] extracting vehicle data from the vehicle data set in sequence, and confirming a target license plate number of the extracted vehicle data;

[0033] if the target license plate number is 1, confirming the extracted vehicle data as identification data, and eliminating the vehicle data from the vehicle data set;

[0034] if the target license plate number is not 1, confirming the extracted vehicle data as identification data, extracting a same license plate set from the vehicle data set, eliminating the same license plate set from the vehicle data set, obtaining a reserved identification data set, confirming the reserved identification data set as a vehicle data set, and returning to the step of extracting vehicle data from the vehicle data set in sequence until the vehicle data set is an empty set;

[0035] summarizing the identification data to obtain an extracted unit period identification data set, and inputting the identification data set into a pre-constructed prediction model to obtain an extracted unit period unit impact data.

[0036] Optionally, the initial crack area sequence is obtained by using a pre-constructed edge recognition algorithm to recognize the denoised image sequence, and the method comprises the following steps of:

[0037] screening the denoised image sequence to obtain an initial crack image sequence, and performing the following operations on each initial crack image in the initial crack image sequence:

[0038] obtaining a pixel gradient sequence based on the initial crack image, calculating a first pixel probability, a second pixel probability and a third pixel probability based on the pixel gradient sequence, calculating a first pixel gray mean value, a second pixel gray mean value and a third pixel gray mean value based on the first pixel probability, the second pixel probability and the third pixel probability respectively, and calculating a maximum total inter-class variance by using the first pixel probability, the second pixel probability, the third pixel probability, the first pixel gray mean value, the second pixel gray mean value and the third pixel gray mean value.

[0039] calculating a low threshold value and a high threshold value based on the maximum total inter-class variance, and dividing the initial crack image by using the low threshold value and the high threshold value to obtain a strong edge point set and a non-edge point set;

[0040] constructing a crack image according to the strong edge point set and the non-edge point set, and calculating an initial crack area according to the crack image;

[0041] summarizing the initial crack areas to obtain the initial crack area sequence.

[0042] Optionally, a calculation formula of the maximum total inter-class variance is as follows:

[0043]

[0044] Where T represents the maximum total inter-class variance, ω j represents the j-th pixel probability, a j Indicates the grayscale mean of the j-th class pixel corresponding to the j-th pixel probability, represents the mean grayscale value of all pixels in the initial crack image, and j represents the j-th type of pixel.

[0045] Optionally, dividing the initial crack image by using a low threshold and a high threshold to obtain a strong edge point set and a non-edge point set includes:

[0046] Based on the low threshold and high threshold, the non-edge pixel interval, weak edge pixel interval and strong edge pixel interval are constructed, and the crack pixels are extracted from the initial crack image in sequence. The following operations are performed on the extracted crack pixels:

[0047] Obtaining a crack grayscale value based on the extracted crack pixels, and comparing the crack grayscale value with the size of the non-edge pixel interval, the weak edge pixel interval, and the strong edge pixel interval;

[0048] If the crack grayscale value belongs to the non-edge pixel interval, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point;

[0049] If the crack grayscale value belongs to the weak edge pixel interval, a judgment neighborhood is obtained based on the extracted crack pixels, and the maximum neighborhood pixel grayscale value is extracted from the judgment neighborhood. If the maximum neighborhood pixel grayscale value is greater than the high threshold, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point. If the maximum neighborhood pixel grayscale value is not greater than the high threshold, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point.

[0050] If the crack grayscale value belongs to the strong edge pixel interval, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point;

[0051] Summarize the strong edge points to get the strong edge point set, and summarize the non-edge points to get the non-edge point set.

[0052] Optionally, the interpolation calculation of the sequence to be interpolated by using an interpolation method to obtain a fracture area evolution sequence includes:

[0053] Constructing an interpolation model, wherein the interpolation model includes: a generation unit and a discrimination unit, wherein the discrimination unit includes a main discrimination unit and an auxiliary discrimination unit;

[0054] Based on the interpolation model, a generation unit is started, and according to the initial fracture area sequence and the sequence to be interpolated and the missing data set, the missing data set, the initial fracture area sequence and the sequence to be interpolated are input into the started generation unit to obtain a plurality of initial interpolation areas, and the plurality of initial interpolation areas are inserted into the sequence to be interpolated to obtain a filled sequence, wherein the missing data set includes a plurality of missing data, and the initial interpolation areas correspond to the missing data in a one-to-one manner;

[0055] Detect the data distribution of the sequence to be interpolated to obtain the original distribution, detect the data distribution of the filled sequence to obtain the filled distribution, and import the original distribution and the filled distribution into the discriminant unit;

[0056] If the original distribution is consistent with the filled distribution, the filled sequence is confirmed to be the fracture area evolution sequence based on the auxiliary discrimination unit;

[0057] If the original distribution is inconsistent with the filled distribution, a missing value set is identified from the multiple initial interpolation areas based on the main discrimination unit, the missing value set is confirmed as a missing data set, and the process returns to the step of inputting the missing data set, the initial crack area sequence, and the sequence to be interpolated into the generation unit until the filled sequence is confirmed as a crack area evolution sequence based on the auxiliary discrimination unit.

[0058] To achieve the above objectives, the present invention further provides a construction road crack prediction system based on time series monitoring data, comprising:

[0059] a historical image acquisition module, configured to receive a road monitoring instruction, activate a road monitoring device according to the road monitoring instruction, acquire a historical monitoring time based on the activated road monitoring device, divide the historical monitoring time into a plurality of unit time periods, sequentially extract unit time periods from the plurality of unit time periods, and perform the following operations on the extracted unit time periods: acquire a plurality of historical images based on the extracted unit time periods;

[0060] an impact data acquisition module, configured to sequentially extract historical images from a plurality of historical images and perform the following operations on the extracted historical images: performing a sharpening operation on the extracted historical images to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the extracted unit impact data for a unit time period based on the set of vehicle images to be identified;

[0061] The crack area evolution sequence module is used to summarize the unit impact data to obtain the unit impact data set corresponding to the historical monitoring time, summarize the noise reduction image to obtain the noise reduction image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the noise reduction image sequence to obtain an initial crack area sequence, filter the initial crack area sequence to obtain a sequence to be interpolated, and use an interpolation method to perform interpolation calculation on the sequence to be interpolated to obtain a crack area evolution sequence;

[0062] The crack prediction module is used to build a crack evolution model based on the crack area evolution sequence and the unit impact data set, obtain the time period to be predicted and the impact to be predicted, input the time period to be predicted and the impact to be predicted into the crack evolution model, obtain the predicted crack value, and complete the prediction of construction road cracks based on the unit time period monitoring data according to the predicted crack value.

[0063] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0064] a memory storing at least one instruction;

[0065] The processor executes the instructions stored in the memory to implement the above-mentioned construction road crack prediction method based on time series monitoring data.

[0066] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned construction road crack prediction method based on time series monitoring data.

[0067] The present application is to solve the problems described in the background art, and the present application is based on the road monitoring device after starting to obtain historical monitoring time, divide the historical monitoring time, obtain a plurality of unit time periods, and fully utilize historical data to construct a crack evolution model. The present application performs a sharpening operation on the extracted historical image to obtain a clear image, performs a noise reduction operation on the clear image to obtain a noise reduction image, extracts a to-be-identified vehicle image set based on the noise reduction image, calculates the unit impact data of the extracted unit time period based on the to-be-identified vehicle image set, and provides a basis for identifying the initial crack area through noise reduction and sharpening of the historical image. At the same time, the clear image corresponds to the to-be-identified vehicle image set, and a plurality of clear images correspond to a plurality of to-be-identified vehicle sets, so that the number of vehicles passing through the unit time period can be identified, and the size of the vehicle can also be identified, thereby calculating the unit impact data to quantify the impact on the road. The present application uses a pre-constructed edge recognition algorithm to identify the noise reduction image sequence to obtain an initial crack area sequence, filters the initial crack area sequence to obtain a to-be-interpolated sequence, and uses an interpolation method to perform interpolation calculation on the to-be-interpolated sequence to obtain a crack area evolution sequence. The abnormal initial crack area caused by weather, light, machine noise and the like is eliminated from the initial crack area sequence, and the interpolation method is used to perfect the to-be-interpolated sequence to construct a crack area evolution sequence that can accurately represent the evolution of the road crack in the historical monitoring time. The present application constructs a crack evolution model according to the crack area evolution sequence and the unit impact data set, obtains a to-be-predicted time period and a to-be-predicted impact, inputs the to-be-predicted time period and the to-be-predicted impact into the crack evolution model, obtains a predicted crack value, and completes the prediction of the construction road crack of the unit time period monitoring data according to the predicted crack value. The evolution of the crack in the historical monitoring time is described according to the time sequence, the development of the road crack area, and the unit impact data set. Therefore, the present application can predict the road crack by using historical data. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The flowchart of the construction road crack prediction monitoring method based on time sequence monitoring data provided by an embodiment of the present application is shown in the figure.

[0069] Figure 2 The functional module diagram of the construction road crack prediction monitoring device based on time sequence monitoring data provided by an embodiment of the present application is shown in the figure.

[0070] Figure 3 The structural diagram of the electronic device for implementing the construction road crack prediction model method based on time sequence monitoring data provided by an embodiment of the present application is shown in the figure.

[0071] The implementation of the present application, the functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0073] The embodiments of the present application provide a method for predicting cracks on construction roads based on time-series monitoring data. The execution entity of the method for predicting cracks on construction roads based on time-series monitoring data includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for predicting cracks on construction roads based on time-series monitoring data can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0074] Reference Figure 1 FIG. 1 is a flow chart of a method for predicting cracks on a construction road based on time series monitoring data according to an embodiment of the present invention. In this embodiment, the method for predicting cracks on a construction road based on time series monitoring data includes:

[0075] S1. Receive a road monitoring instruction, start a road monitoring device according to the road monitoring instruction, obtain historical monitoring time based on the started road monitoring device, divide the historical monitoring time into multiple unit time periods.

[0076] It is understood that the road monitoring instruction is an instruction to start the road monitoring device, which is initiated by the monitoring personnel. The road monitoring device is a device for monitoring the road.

[0077] For example,

[0078] Construction is now required on a road that has already been used by vehicles. Xiao Zhang, as a technician, wants to understand the usage status of the road over a period of time in order to determine the possible cracks and crack evolution after the renovation. Therefore, he issues a road monitoring instruction to extract the usage status of the road over a period of time from the road monitoring device.

[0079] It is understandable that the historical monitoring time is a period of time for monitoring the road in the past, and the unit period is a time that is shorter than the historical monitoring time.

[0080] It should be noted that the roads to be constructed in the embodiments of the present invention already have a certain amount of traffic. Therefore, acquiring and analyzing historical images over a period of time allows us to understand the past usage of the roads to be constructed, including the traffic volume and cracks on the original roads. This allows us to similarly predict when cracks are likely to appear after the construction of the roads to be constructed, as well as their subsequent evolution. The crack evolution model for the construction roads constructed in the embodiments of the present invention is constructed before direct construction on the current road begins.

[0081] S2. Extract unit time periods in sequence from the multiple unit time periods, and perform the following operations on the extracted unit time periods: obtain multiple historical images based on the extracted unit time periods, extract historical images in sequence from the multiple historical images, and perform the following operations on the extracted historical images: perform a sharpening operation on the extracted historical images to obtain a clear image, and perform a noise reduction operation on the clear image to obtain a noise-reduced image.

[0082] It is understood that historical images are images of the current road captured by the road monitoring device during a unit time period. In embodiments of the present invention, the image acquisition frequency of the road monitoring device is preset. Therefore, during the extracted unit time period, the road monitoring device captures multiple images of the current road based on the image acquisition frequency. These multiple images of the current road constitute multiple historical images. For example, if the historical monitoring period is from the first to the sixth month of year a, a total of six months, and the historical monitoring period is divided into days, then the unit time period is one day, and the image acquisition frequency is 10 seconds, then 144 historical images will be captured within one day.

[0083] Furthermore, performing a clearing operation on the extracted historical image to obtain a clear image includes:

[0084] Multiple Gaussian difference curves are generated using the pre-built Gaussian difference operator and the extracted historical images, where the Gaussian difference operator is:

[0085]

[0086] Among them, z represents the horizontal coordinate of the historical image in the image coordinate system, and w represents the vertical coordinate of the historical image in the image coordinate system. and Both represent the variance of the Gaussian filter, σ1 and σ2 both represent the standard deviation of the Gaussian filter, and σ1≠σ2, DOG represents the Gaussian difference operator;

[0087] A zero-crossing point of each Gaussian difference curve in a plurality of Gaussian difference curves is obtained to obtain a plurality of zero-crossing points, a plurality of contour coordinates are generated based on the plurality of zero-crossing points, the plurality of contour coordinates are spliced ​​using a pre-built edge connection algorithm to obtain a historical image contour, an image transition area is obtained based on the historical image contour, and a clear image is obtained based on the image transition area, wherein the contour coordinates correspond one-to-one to the Gaussian difference curves.

[0088] It is understood that the sharpening operation utilizes a Gaussian difference operator to obtain a sharp image. A Gaussian difference curve is obtained by convolving the Gaussian difference operator with the historical image. The Gaussian difference curve is a curve that runs from peak to trough, and the zero-crossing points in the Gaussian difference curve correspond to the contour coordinates in the generated historical image. This technology is prior art and will not be further described here. The construction of the image coordinate system is also prior art and will not be further described here.

[0089] It should be noted that the pre-built edge connection algorithm is used to splice the multiple contour coordinates: a smooth curve is used to splice the multiple contour coordinates in sequence according to a preset counterclockwise direction. The image formed by the spliced ​​closed curve is the historical image contour, and the image transition area is the area jointly formed by the inner and outer areas of the closed curve in the historical image contour.

[0090] For example, taking the plane rectangular coordinate system as an example, the multiple contour coordinates are: (0,0), (1,1) and 0,1). Taking (0,0) as the starting point, (0,0), (1,1), (0,1) and (0,0) are connected in sequence to form a closed curve. The image formed by the closed curve is the historical image contour.

[0091] Specifically, the reason why a clear image can be obtained in the image transition area is that in the historical image, the position of the image contour has not changed, but the spatial distribution of the pixel grayscale values ​​in the adjacent area of ​​the image contour has changed. Therefore, a clear image can be obtained based on the image transition area.

[0092] For example, the pixel coordinates of the pixel in the historical image contour are (0,0), and its grayscale value is 54. Assume that in a clear image, the grayscale value of the pixel coordinate (0,1) should be 0. In the historical image, due to atmospheric disturbances, inaccurate focus, or vehicle shaking, the historical image is blurred, so the grayscale value of the pixel coordinate (0,1) is affected by the pixel coordinate (0,0), so that the grayscale value of the pixel coordinate (0,1) is also 54, and the pixel coordinate (0,1) is classified as the image transition area.

[0093] Furthermore, performing a noise reduction operation on the clear image to obtain a noise-reduced image includes:

[0094] Construct a mean filter unit and a median filter unit;

[0095] The standard image is acquired, the pixel gray value space distribution feature is extracted from the standard image, the pixel gray value space distribution feature is introduced into a mean filter unit, and a fixed pattern noise is obtained, wherein a size of a filter kernel of the mean filter unit is 9*9.

[0096] A clear image is obtained by performing a first denoising operation on the clear image by using the fixed pattern noise, wherein a formula of the first denoising operation is as follows:

[0097] k(x, y) = j(x, y) - δ * l(x, y) + β * h

[0098] wherein k(x, y) represents a pixel gray value corresponding to a pixel coordinate (x, y) in the first denoising image, x represents an abscissa of the pixel coordinate (x, y), y represents an ordinate of the pixel coordinate (x, y), j(x, y) represents a pixel gray value corresponding to the pixel coordinate (x, y) in the clear image, l(x, y) represents a pixel gray value of the pixel coordinate (x, y) in the fixed pattern noise, δ and β represent a first correction coefficient and a second correction coefficient respectively, and δ = 0.7 and β = 1.2, and h represents an average gray value of all pixels in the clear image.

[0099] A denoising image is obtained by performing a second denoising operation on the first denoising image by using the median filter unit, wherein a size of a filter kernel of the median filter unit is 3*3.

[0100] It should be noted that the mean filter unit is a unit for performing a mean filtering operation by using an average pixel value of a filter kernel, the median filter unit is a unit for performing a median filtering operation by using a median value in the filter kernel, and the mean filtering and the median filtering are prior art and will not be described herein.

[0101] Further, the standard image is an image of a complete road without foreign matters and cracks. The pixel gray value space distribution feature is a feature map for describing a space distribution of gray values in the standard image. The pixel gray value space distribution feature is constructed as follows: a three-dimensional space distribution is constructed by taking an image column of the standard image as a Y axis, taking an image row of the standard image as an X axis, and taking a gray value of a pixel in the standard image as a Z axis, wherein the image column is a column formed by a column of pixels in the standard image, the image row is a row formed by a row of pixels in the standard image, and a corresponding manner of the standard image on the Y axis and the X axis in the three-dimensional space distribution is the same as a corresponding manner of the standard image on the Y axis and the X axis in an image coordinate system.

[0102] Optionally, in the embodiment, the image without foreign matters and cracks is captured on a road to be predicted by using a camera device, and the standard image can be obtained after mean filtering and median filtering are performed on the image.

[0103] Further, the fixed pattern noise is a kind of noise commonly seen in image sensors, mainly manifested as deviation in the same mode of fixed position, fixed brightness, fixed color and the like in the image, which does not change with the change of the collected image, and is a special term in the professional field, which will not be repeated here. After introducing the pixel gray value space distribution feature into the mean filter unit, the standard image is filtered by using the mean filter unit, and the fixed pattern noise can be obtained through the filtered standard image. The first denoising image is an image obtained by performing a denoising operation on a clear image. The first denoising operation is a denoising operation for removing the fixed pattern noise same as the standard image in the clear image. The pixel gray value is the gray value of the pixel, the pixel coordinate is the coordinate of the pixel in the image coordinate system, the first correction coefficient is a coefficient for ensuring that the gray value after the first denoising operation is within the range of [0, 255], and the second correction coefficient is a coefficient for adjusting the overall gray value of the denoising image. The second correction coefficient solves the problem of insufficient overall brightness of the denoising image after denoising. The first correction coefficient and the second correction coefficient are limited in the embodiments of the present application, and the first correction coefficient and the second correction coefficient can be adjusted according to the actual situation under different road materials.

[0104] It should be explained that the average gray value is the average of the gray values of all pixels in the clear image. The second denoising operation is an operation of denoising the first denoising image by using the median filter unit.

[0105] S3, extracting a set of to-be-identified vehicle images based on the denoising image, and calculating unit impact data of the extracted unit time period based on the set of to-be-identified vehicle images.

[0106] Further, the set of to-be-identified vehicle images is extracted based on the denoising image, and the unit impact data of the extracted unit time period is calculated based on the set of to-be-identified vehicle images, which comprises:

[0107] The target detection algorithm is used to identify the denoising image to obtain a set of to-be-identified vehicle images, wherein the set of to-be-identified vehicle images comprises a plurality of to-be-identified vehicle images;

[0108] The to-be-identified vehicle images in the set of to-be-identified vehicle images are subjected to the following operations:

[0109] The vehicle model of the to-be-identified vehicle image is identified, and it is judged whether the license plate number can be extracted in the to-be-identified vehicle image. If the license plate number can be extracted in the to-be-identified vehicle image, the extracted license plate number is identified to obtain a target license plate number, otherwise, the license plate number of the to-be-identified vehicle image is confirmed as 1 to obtain the target license plate number;

[0110] The vehicle data is constructed according to the vehicle model and the target license plate number, and the vehicle data is summarized to obtain a set of vehicle data of the extracted unit time period;

[0111] The unit impact data is calculated according to the set of vehicle data.

[0112] It is understood that the target detection algorithm is a prior art technology capable of identifying vehicles in a denoised image. Optionally, the YOLOX algorithm is used as the target detection algorithm. The vehicle image to be identified is an image of a vehicle identified using the target detection algorithm. The target license plate number is the license plate number identified from the vehicle image to be identified. Vehicle models include: large passenger vehicles, medium-sized passenger vehicles, small passenger vehicles, micro-passenger vehicles, and non-motor vehicles. The vehicle model classification standard is the Chinese Automobile Classification Standard, which is a prior art standard and will not be further described here.

[0113] It should be noted that vehicle data is data used to describe the vehicles in the images to be identified. It should be noted that during vehicle identification using the target detection algorithm, multiple de-noised images are acquired per unit time period. Each de-noised image corresponds to a set of vehicle images to be identified. Since the extracted unit time period includes multiple de-noised images, the vehicle data set corresponding to that unit time period includes all vehicle data from the multiple sets of vehicle images to be identified within that unit time period. Unit impact data is the impact of vehicles on the road during that unit time period, calculated based on the traffic volume and vehicle model within that unit time period.

[0114] It is understood that the current road defined in the embodiments of the present invention does not include traffic lights. Therefore, when using the target detection algorithm to detect the set of vehicles to be identified in the denoised image, it is relatively rare for a vehicle's license plate number to be obscured by other vehicles and thus unrecognizable. In addition, the following situations may also result in the inability to extract the license plate number: the vehicle being at the edge of the image, the vehicle not having a license plate installed, or the vehicle's license plate being obscured by dirt, etc., which are not listed here. If vehicles with unrecognizable license plates are ignored, the actual impact and traffic volume on the road will be far greater than the predicted unit impact data. If these vehicles with unrecognizable license plates are included in the calculation of the unit impact data, the actual impact and traffic volume on the road will be less than the predicted unit impact data. Therefore, based on the current unit impact data, road repairs and road planning can be used to increase the road's ability to withstand future impacts and traffic volumes. When the actual impact and traffic volume are applied to the repaired road, cracks will appear later, thereby increasing the road's lifespan.

[0115] It should be noted that the to-be-identified vehicle image set is a collection of multiple to-be-identified vehicle images. When there is no vehicle traveling on the current road, the to-be-identified vehicle image set is an empty set.

[0116] Furthermore, the calculating of unit impact data based on the vehicle data set includes:

[0117] Extracting vehicle data from the vehicle data set in sequence and confirming the target license plate number of the extracted vehicle data;

[0118] If the target license plate number is 1, the extracted vehicle data is confirmed as identification data and the vehicle data is removed from the vehicle data set;

[0119] If the target license plate number is not 1, the extracted vehicle data is confirmed as identification data, and a same license plate set is extracted from the vehicle data set, the same license plate set is removed from the vehicle data set to obtain a retained identification data set, the retained identification data set is confirmed to be the vehicle data set, and the process returns to the step of sequentially extracting vehicle data from the vehicle data set until the vehicle data set is an empty set;

[0120] The identification data are aggregated to obtain an identification data set of the extracted unit time period, and the identification data set is input into a pre-built prediction model to obtain unit impact data of the extracted unit time period.

[0121] Furthermore, the identification data includes the vehicle model and license plate number. In the identification data set, no two vehicles with the same target license plate number exist, except for the vehicle with the target license plate number 1. The purpose is to use the identification data with the target license plate number 1 to improve the safety factor of the calculated road impact tolerance, while using the identification data with target license plate numbers other than 1 to improve the accuracy of the unit impact data.

[0122] For example, the vehicle dataset is: Large Passenger Vehicle-1, Medium Passenger Vehicle-1, Small Passenger Vehicle-XXX01, Small Passenger Vehicle-XXX01, Small Passenger Vehicle-XXX02, Non-Motor Vehicle-1, Non-Motor Vehicle-XXX09. Vehicle data is extracted sequentially from the vehicle dataset. When the vehicle data is Large Passenger Vehicle-1, and the target license plate number is confirmed to be 1, Large Passenger Vehicle-1 is confirmed as identification data and removed from the vehicle dataset. The vehicle dataset is now: Medium Passenger Vehicle-1, Small Passenger Vehicle-XXX01, Small Passenger Vehicle-XXX01, Small Passenger Vehicle-XXX02, Non-Motor Vehicle-1, Non-Motor Vehicle-XXX09. When the vehicle data is for medium-sized passenger car 1, the target license plate number is confirmed to be 1. Medium-sized passenger car 1 is then identified as identification data and removed from the vehicle dataset. The vehicle dataset is now: small passenger car XXX01, small passenger car XXX01, small passenger car XXX01, small passenger car XXX02, non-motor vehicle 1, and non-motor vehicle XXX09. When the vehicle data is for small passenger car XXX01, the target license plate number is not 1. Small passenger car XXX01 is identified as identification data, and the same license plate set is extracted from the vehicle dataset. The same license plate set is the collection of all vehicle data for small passenger car XXX01 in the vehicle dataset. In this example, the vehicle data includes three vehicles with the same license plate number of small passenger car XXX01, so the same license plate set is: small passenger car XXX01, small passenger car XXX01, and small passenger car XXX01. Remove the set of identical license plates from the vehicle dataset to obtain the retained identification dataset. The retained identification dataset is: Small Passenger Car - XXX02, Non-Motor Vehicle - 1, Non-Motor Vehicle - XXX09. This continues in this manner until the vehicle dataset is empty. In this example, the final identification dataset is: Large Passenger Car - 1, Medium Passenger Car - 1, Small Passenger Car - XXX01, Small Passenger Car - XXX02, Non-Motor Vehicle - 1, Non-Motor Vehicle - XXX09.

[0123] It can be understood that the prediction model is a model that predicts the impact based on the identification data set. For example, in the extracted unit time period, the identification data set is: large passenger car-1, medium-sized passenger car-1, small passenger car-XXX01, small passenger car-XXX02, non-motor vehicle-1, non-motor vehicle-XXX09. There is one large passenger car, one medium-sized passenger car, two small passenger cars, and two non-motor vehicles. Since cars of different masses have different impacts on the road, different vehicle models need to be calculated separately. Based on one large passenger car, one medium-sized passenger car, two small passenger cars, and two non-motor vehicles, the impact that the road withstands in the unit time period can be calculated.

[0124] S4. Summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised images to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, filter the initial crack area sequence, and obtain a sequence to be interpolated.

[0125] It should be noted that the denoised image sequence is a chronological sequence of multiple denoised images. The purpose of screening the initial crack area sequence is to remove abnormal initial crack areas from the initial crack area sequence. Calculating crack areas using image recognition methods is affected by factors such as weather, lighting, and machine noise. Therefore, not every initial crack image accurately represents the actual area of ​​the road crack at the time, necessitating screening of the initial crack area sequence. After removing outliers from the initial crack area sequence, the multiple initial crack areas that remain constitute the sequence to be interpolated.

[0126] Furthermore, the method of using a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence includes:

[0127] The denoised image sequence is filtered to obtain an initial crack image sequence, and the following operations are performed on the initial crack images in the initial crack image sequence:

[0128] A pixel gradient sequence is obtained based on the initial crack image, a first pixel probability, a second pixel probability, and a third pixel probability are calculated based on the pixel gradient sequence, a first class pixel grayscale mean, a second class pixel grayscale mean, and a third class pixel grayscale mean are calculated based on the first pixel probability, the second pixel probability, and the third pixel probability, respectively, and a maximum total inter-class variance is calculated using the first pixel probability, the second pixel probability, the third pixel probability, the first class pixel grayscale mean, the second class pixel grayscale mean, and the third class pixel grayscale mean;

[0129] Calculating a low threshold and a high threshold based on the maximum total inter-class variance, and using the low threshold and the high threshold to divide the initial crack image to obtain a strong edge point set and a non-edge point set;

[0130] Construct a crack image based on a strong edge point set and a non-edge point set, and calculate the initial crack area based on the crack image;

[0131] The initial crack areas are summarized to obtain the initial crack area sequence.

[0132] Furthermore, the calculation formula for the maximum total inter-class variance is:

[0133]

[0134] Where T represents the maximum total inter-class variance, ω jrepresents the j-th pixel probability, a j Indicates the grayscale mean of the j-th class pixel corresponding to the j-th pixel probability, Represents the mean grayscale value of all pixels in the denoised image, and j represents the j-th type of pixel.

[0135] It should be noted that in the denoised image sequence, there must be images in which vehicles block road cracks. Therefore, the denoised image sequence is screened to obtain an initial crack image sequence, so that the initial crack images in the initial crack image sequence are all images in which road cracks are exposed.

[0136] Furthermore, the pixel gradient sequence is a sequence of pixel grayscale values ​​in the initial crack image. This technology is prior art and will not be described in detail here. The first pixel probability, the second pixel probability, and the third pixel probability are the probabilities of the pixels in the pixel gradient sequence being in the first, second, and third categories, respectively. In this embodiment, j represents the jth category pixel. When j = 1, it represents the first category pixel, corresponding to the first pixel probability and the grayscale mean of the first category pixel. When j = 2, it represents the second category pixel, corresponding to the second pixel probability and the grayscale mean of the second category pixel. When j = 3, it represents the third category pixel, corresponding to the third pixel probability and the grayscale mean of the third category pixel.

[0137] It should be noted that before calculating the first pixel probability, the second pixel probability and the third pixel probability, an initial high threshold and an initial low threshold are preset, wherein the initial high threshold is greater than the initial low threshold, and the initial high threshold and the initial low threshold are both grayscale values ​​in the pixel gradient sequence. The pixel gradient sequence is divided into three categories according to the initial high threshold and the initial low threshold. Pixels corresponding to grayscale values ​​lower than the initial low threshold are first-category pixels, pixels corresponding to grayscale values ​​between the initial low threshold and the initial high threshold are second-category pixels, and pixels corresponding to grayscale values ​​higher than the initial high threshold are third-category pixels. At this time, the grayscale mean of the first-category pixels, the grayscale mean of the second-category pixels and the grayscale mean of the third-category pixels are the average grayscale value of all first-category pixels, the average grayscale value of all second-category pixels and the average grayscale value of all third-category pixels, respectively.

[0138] Furthermore, different initial high thresholds and initial low thresholds calculate different total inter-class variances. The total inter-class variance is a value used to determine the degree of dispersion between the first-class pixels, the second-class pixels, and the third-class pixels. When the maximum total inter-class variance is obtained, the difference in grayscale values ​​between the first-class pixels, the second-class pixels, and the third-class pixels is the largest. Therefore, the initial crack image is processed using the initial high threshold and the initial low threshold corresponding to the maximum total inter-class variance, so that the edge information of the crack can be extracted from the initial crack image. In an embodiment of the present invention, the initial high threshold and the initial low threshold corresponding to the maximum total inter-class variance are identified as the high threshold and the low threshold, and the high threshold and the low threshold are used to divide the initial crack image.

[0139] Specifically, the crack image is a contour image of a road crack after edge detection, and the initial crack area is the actual crack area calculated from the crack image. This is achieved using existing techniques. For example, a smooth contour image of the road crack is drawn based on the set of strong edges in the crack image. The actual crack area is calculated based on the ratio between the crack image and the actual road crack. The initial crack area sequence is a chronologically ordered sequence of the initial crack areas.

[0140] Furthermore, the method of dividing the initial crack image by using a low threshold and a high threshold to obtain a strong edge point set and a non-edge point set includes:

[0141] Based on the low threshold and high threshold, the non-edge pixel interval, weak edge pixel interval and strong edge pixel interval are constructed, and the crack pixels are extracted from the initial crack image in sequence. The following operations are performed on the extracted crack pixels:

[0142] Obtaining a crack grayscale value based on the extracted crack pixels, and comparing the crack grayscale value with the size of the non-edge pixel interval, the weak edge pixel interval, and the strong edge pixel interval;

[0143] If the crack grayscale value belongs to the non-edge pixel interval, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point;

[0144] If the crack grayscale value belongs to the weak edge pixel interval, a judgment neighborhood is obtained based on the extracted crack pixels, and the maximum neighborhood pixel grayscale value is extracted from the judgment neighborhood. If the maximum neighborhood pixel grayscale value is greater than the high threshold, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point. If the maximum neighborhood pixel grayscale value is not greater than the high threshold, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point.

[0145] If the crack grayscale value belongs to the strong edge pixel interval, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point;

[0146] Summarize the strong edge points to get the strong edge point set, and summarize the non-edge points to get the non-edge point set.

[0147] Furthermore, the non-edge pixel interval is an interval composed of grayscale values ​​greater than or equal to zero 0 and less than a low threshold, the weak edge pixel interval is an interval composed of grayscale values ​​greater than or equal to the low threshold and less than a high threshold, the strong edge pixel interval is an interval composed of grayscale values ​​greater than or equal to the high threshold and less than 255, the crack pixel is a pixel in the initial crack image, and the crack grayscale value is the grayscale value of the crack pixel.

[0148] It can be understood that the judgment neighborhood is a set of pixels in the neighborhood around the extracted pixel, and the maximum neighborhood pixel grayscale value is the maximum grayscale value in the judgment neighborhood. For example, the judgment neighborhood takes a window size of 3×3. For ease of representation, the 3×3 window size is set to a 3×3 matrix, and the elements in the 3×3 matrix represent grayscale values. The grayscale value corresponding to the extracted pixel is located in the second row and second column. When the maximum grayscale value in the 3×3 matrix is ​​greater than the high threshold, the grayscale value of the extracted pixel is output as 255, otherwise, the grayscale value of the extracted pixel is output as 0.

[0149] Furthermore, the strong edge points are pixel points of the edge contour of the crack, and the non-edge points are pixel points of the edge contour of the non-crack.

[0150] S5. Use the interpolation method to perform interpolation calculation on the interpolation sequence to obtain the crack area evolution sequence.

[0151] Furthermore, the interpolation method is used to perform interpolation calculation on the sequence to be interpolated to obtain the fracture area evolution sequence, including:

[0152] Constructing an interpolation model, wherein the interpolation model includes: a generation unit and a discrimination unit, wherein the discrimination unit includes a main discrimination unit and an auxiliary discrimination unit;

[0153] Based on the interpolation model, a generation unit is started, and according to the initial fracture area sequence and the sequence to be interpolated and the missing data set, the missing data set, the initial fracture area sequence and the sequence to be interpolated are input into the started generation unit to obtain a plurality of initial interpolation areas, and the plurality of initial interpolation areas are inserted into the sequence to be interpolated to obtain a filled sequence, wherein the missing data set includes a plurality of missing data, and the initial interpolation areas correspond to the missing data in a one-to-one manner;

[0154] Detect the data distribution of the sequence to be interpolated to obtain the original distribution, detect the data distribution of the filled sequence to obtain the filled distribution, and import the original distribution and the filled distribution into the discriminant unit;

[0155] If the original distribution is consistent with the filled distribution, the filled sequence is confirmed to be the fracture area evolution sequence based on the auxiliary discrimination unit;

[0156] If the original distribution is inconsistent with the filled distribution, a missing value set is identified from the multiple initial interpolation areas based on the main discrimination unit, the missing value set is confirmed as a missing data set, and the process returns to the step of inputting the missing data set, the initial crack area sequence, and the sequence to be interpolated into the generation unit until the filled sequence is confirmed as a crack area evolution sequence based on the auxiliary discrimination unit.

[0157] Furthermore, the interpolation model is a model used to interpolate a sequence to be interpolated. The generation unit is used to generate interpolation values, the determination unit is used to determine whether the generated interpolation values ​​are valid interpolation values, the main determination unit is used to identify initial interpolation areas that cannot be effectively used from multiple initial interpolation areas, and the auxiliary determination unit is used to determine the data distribution.

[0158] It should be noted that the missing data set is the set of data removed from the initial crack area sequence, the initial interpolation area is the area value generated by the generation unit, and the filled sequence is the sequence after all the initial interpolation areas are inserted into the positions corresponding to the missing data set.

[0159] For example, the initial fracture area sequence is: 10, 20, 16, 16, 16, 17, 28, 18. This initial fracture area sequence is filtered to obtain the sequence to be interpolated, and the sequence to be interpolated is: 10, [*], 16, 16, 16, 17, [*], 18, where [*] and [*] are the two data to be interpolated. Since the sequence to be interpolated includes time information, [*] and [*] respectively constitute the missing data set with the corresponding time information. Assuming that the multiple initial interpolation areas generated by the generation unit are 12 and 17, the multiple initial interpolation areas are interpolated into the sequence to be interpolated, and the interpolated sequence obtained is: 10, 12, 16, 16, 16, 17, 17, 18.

[0160] Furthermore, the original distribution is the data distribution of the sequence to be interpolated, and the filling distribution is the data distribution of the filled sequence.

[0161] For example, in an embodiment of the present invention, if a road crack remains unfilled, the crack will continue to grow larger. Therefore, if the original distribution is a straight line fitted using the least squares method, then when determining the filled distribution, the least squares method is also used to fit the filled sequence data into a straight line. When comparing the filled distribution with the original distribution, the slope and intercept corresponding to the original distribution are extracted. The slope corresponding to the original distribution is subtracted from the slope corresponding to the filled distribution and the slope and intercept are taken as the absolute value to obtain the slope absolute difference. The intercept corresponding to the original distribution is subtracted from the intercept corresponding to the filled distribution and the intercept and intercept are taken as the absolute value to obtain the intercept absolute difference. If the slope absolute difference is less than a preset difference threshold and the intercept absolute difference is less than a preset intercept threshold, then the original distribution is considered to be consistent with the filled distribution. The difference threshold is the maximum absolute difference in slope for which the original distribution and the filled distribution are considered to be consistent, and the intercept threshold is the maximum absolute difference in intercept for which the original distribution and the filled distribution are considered to be consistent.

[0162] It should be noted that the crack area evolution sequence is a sequence that can describe the evolution of road cracks after interpolation. The missing value set is a collection of data whose initial interpolation areas disrupt the distribution of the sequence to be interpolated. For example, the sequence to be interpolated is: 10, [*], 16, 16, 16, 17, [*], 18, where [*] and [*] are two data points that need to be interpolated. Assuming that the multiple initial interpolation areas generated by the generation unit are 22 and 17, and it is determined that 22 disrupts the data distribution of the sequence to be interpolated, the main discriminant unit can be used to confirm the data corresponding to 22 as an element in the missing value set, and the missing value set can be confirmed as a missing data set.

[0163] S6. Construct a crack evolution model based on the crack area evolution sequence and the unit impact data set, obtain the time period to be predicted and the impact to be predicted, input the time period to be predicted and the impact to be predicted into the crack evolution model, obtain the predicted crack value, and complete the prediction of the construction road cracks in the unit time period monitoring data based on the predicted crack value.

[0164] It should be noted that the crack area evolution sequence is a sequence of the areas of road cracks sorted in chronological order and is constructed by multiple crack area evolution sequences. For example, at time a, the corresponding initial crack image sequence is: 0, 0, 1, 2; at time (a+1), the corresponding initial crack image sequence is: 2, 2, 2.2, 3. If there is no missing data at time a and time (a+1), the crack area evolution sequence is: 0, 0, 1, 2, 2, 2, 2.2, 3.

[0165] Furthermore, a unit impact dataset is a collection of multiple unit impact data. In this embodiment, each unit time period corresponds to a unit impact data set, and each unit time period also corresponds to an initial crack image sequence. The initial crack area corresponds one-to-one with the initial crack image. For example, if the unit impact data set corresponding to a unit time period is at, where t is tons, and the initial crack image sequence is 0, 0, 1, 2, then the initial crack area sequence corresponds to the unit impact data set at. Therefore, the evolution of cracks during the historical monitoring period can be described based on the chronological sequence, the development of road crack areas, and the unit impact dataset. Therefore, the crack evolution model is a model used to describe the evolution of cracks during the historical monitoring period.

[0166] Furthermore, the time period to be predicted is the time period set by a person for predicting the expected road crack situation, the impact to be predicted is the data on the impact on the road set by a person, and the predicted crack value is the area of ​​the road crack predicted after the time period to be predicted and the impact to be predicted are input into the crack evolution model.

[0167] The present application is to solve the problems described in the background art, and the present application is based on the road monitoring device after starting to obtain historical monitoring time, divide the historical monitoring time, obtain a plurality of unit time periods, and fully utilize historical data to construct a crack evolution model. The present application performs a sharpening operation on the extracted historical image to obtain a clear image, performs a noise reduction operation on the clear image to obtain a noise reduction image, extracts a to-be-identified vehicle image set based on the noise reduction image, calculates the unit impact data of the extracted unit time period based on the to-be-identified vehicle image set, and provides a basis for identifying the initial crack area through noise reduction and sharpening of the historical image. At the same time, the clear image corresponds to the to-be-identified vehicle image set, and a plurality of clear images correspond to a plurality of to-be-identified vehicle sets, so that the number of vehicles passing through the unit time period can be identified, and the size of the vehicle can also be identified, thereby calculating the unit impact data to quantify the impact on the road. The present application uses a pre-constructed edge recognition algorithm to identify the noise reduction image sequence to obtain an initial crack area sequence, filters the initial crack area sequence to obtain a to-be-interpolated sequence, uses an interpolation method to perform interpolation calculation on the to-be-interpolated sequence to obtain a crack area evolution sequence, removes abnormal initial crack areas in the initial crack area sequence caused by weather, light, machine noise and the like, and uses the interpolation method to perfect the to-be-interpolated sequence to construct a crack area evolution sequence that can accurately represent the evolution of road cracks in the historical monitoring time. The present application constructs a crack evolution model according to the crack area evolution sequence and the unit impact data set, obtains a to-be-predicted time period and a to-be-predicted impact, inputs the to-be-predicted time period and the to-be-predicted impact into the crack evolution model to obtain a predicted crack value, and completes the prediction of the construction road crack of the unit time period monitoring data according to the predicted crack value. The evolution of the crack in the historical monitoring time is described according to the time sequence, the development of the road crack area, and the unit impact data set. Therefore, the present application can predict road cracks using historical data.

[0168] As Figure 2 shown, it is a functional module diagram of a construction road crack prediction system based on time sequence monitoring data provided by an embodiment of the present application.

[0169] The construction road crack prediction system based on time sequence monitoring data 100 described in the present application can be installed in an electronic device. According to the functions implemented, the construction road crack prediction system based on time sequence monitoring data 100 can include a historical image acquisition module 101, an impact data acquisition module 102, a crack area evolution sequence module 103, and a crack prediction module 104. The modules described in the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0170] The historical image acquisition module 101 is configured to receive a road monitoring instruction, activate a road monitoring device according to the road monitoring instruction, acquire a historical monitoring time based on the activated road monitoring device, divide the historical monitoring time into a plurality of unit time periods, sequentially extract a unit time period from the plurality of unit time periods, and perform the following operations on the extracted unit time periods: acquire a plurality of historical images based on the extracted unit time periods;

[0171] The impact data acquisition module 102 is configured to sequentially extract historical images from a plurality of historical images and perform the following operations on the extracted historical images: performing a sharpening operation on the extracted historical images to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the unit impact data for the extracted unit time period based on the set of vehicle images to be identified;

[0172] The crack area evolution sequence module 103 is used to summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the noise reduction image to obtain a noise reduction image sequence corresponding to the historical monitoring time, identify the noise reduction image sequence using a pre-built edge recognition algorithm to obtain an initial crack area sequence, filter the initial crack area sequence to obtain a sequence to be interpolated, and perform interpolation calculation on the sequence to be interpolated using an interpolation method to obtain a crack area evolution sequence;

[0173] The crack prediction module 104 is used to construct a crack evolution model based on the crack area evolution sequence and the unit impact data set, obtain the time period to be predicted and the impact to be predicted, input the time period to be predicted and the impact to be predicted into the crack evolution model, obtain the predicted crack value, and complete the prediction of construction road cracks in the unit time period monitoring data based on the predicted crack value.

[0174] In detail, each module in the construction road crack prediction system 100 based on time series monitoring data in the embodiment of the present invention adopts the same method as above when in use. Figure 1 The same technical means are used as the construction road crack prediction method based on time series monitoring data described in , and can produce the same technical effects, so they will not be repeated here.

[0175] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a construction road crack prediction method based on time series monitoring data provided by an embodiment of the present invention.

[0176] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a construction road crack prediction method program based on time series monitoring data.

[0177] The memory 11 includes at least one type of readable storage medium, including a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of a construction road crack prediction method program based on time series monitoring data, but can also be used to temporarily store data that has been output or is to be output.

[0178] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (e.g., a construction road crack prediction method program based on time-series monitoring data) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0179] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0180] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0181] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0182] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0183] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0184] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0185] The construction road crack prediction method program based on time series monitoring data stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0186] receiving a road monitoring instruction, activating a road monitoring device according to the road monitoring instruction, obtaining a historical monitoring time based on the activated road monitoring device, and dividing the historical monitoring time into a plurality of unit time periods;

[0187] Extracting unit time periods sequentially from the plurality of unit time periods, and performing the following operations on the extracted unit time periods: acquiring a plurality of historical images based on the extracted unit time periods, sequentially extracting historical images from the plurality of historical images, and performing the following operations on the extracted historical images:

[0188] Performing a sharpening operation on the extracted historical image to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the extracted unit impact data for a unit time period based on the set of vehicle images to be identified;

[0189] Summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised images to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, and filter the initial crack area sequence to obtain a sequence to be interpolated;

[0190] The interpolation method is used to interpolate the sequence to be interpolated to obtain the crack area evolution sequence;

[0191] A crack evolution model is constructed based on the crack area evolution sequence and the unit impact data set. The time period to be predicted and the impact to be predicted are obtained. The time period to be predicted and the impact to be predicted are input into the crack evolution model to obtain the predicted crack value. Based on the predicted crack value, the prediction of construction road cracks in the unit time period monitoring data is completed.

[0192] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0193] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0194] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0195] receiving a road monitoring instruction, activating a road monitoring device according to the road monitoring instruction, obtaining a historical monitoring time based on the activated road monitoring device, and dividing the historical monitoring time into a plurality of unit time periods;

[0196] Extracting unit time periods sequentially from the plurality of unit time periods, and performing the following operations on the extracted unit time periods: acquiring a plurality of historical images based on the extracted unit time periods, sequentially extracting historical images from the plurality of historical images, and performing the following operations on the extracted historical images:

[0197] Performing a sharpening operation on the extracted historical image to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the extracted unit impact data for a unit time period based on the set of vehicle images to be identified;

[0198] Summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised images to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, and filter the initial crack area sequence to obtain a sequence to be interpolated;

[0199] The interpolation method is used to interpolate the sequence to be interpolated to obtain the crack area evolution sequence;

[0200] A crack evolution model is constructed based on the crack area evolution sequence and the unit impact data set. The time period to be predicted and the impact to be predicted are obtained. The time period to be predicted and the impact to be predicted are input into the crack evolution model to obtain the predicted crack value. Based on the predicted crack value, the prediction of construction road cracks in the unit time period monitoring data is completed.

[0201] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0202] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0203] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0204] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0205] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A construction road crack prediction method based on time series monitoring data, characterized in that: The method comprises: receiving a road monitoring instruction, activating a road monitoring device according to the road monitoring instruction, obtaining historical monitoring time based on the activated road monitoring device, and dividing the historical monitoring time into a plurality of unit time periods; Extracting unit time periods sequentially from the plurality of unit time periods, and performing the following operations on the extracted unit time periods: acquiring a plurality of historical images based on the extracted unit time periods, sequentially extracting historical images from the plurality of historical images, and performing the following operations on the extracted historical images: Performing a sharpening operation on the extracted historical image to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the extracted unit impact data of a unit time period based on the set of vehicle images to be identified, wherein the extracting the set of vehicle images to be identified based on the noise-reduced image and calculating the extracted unit impact data of a unit time period based on the set of vehicle images to be identified include: Using a target detection algorithm to identify the denoised image, a set of vehicle images to be identified is obtained, wherein the set of vehicle images to be identified includes a plurality of vehicle images to be identified; The following operations are performed on the vehicle images to be identified in the vehicle image set: Identify the vehicle model of the vehicle image to be identified, and determine whether the license plate number can be extracted from the vehicle image to be identified. If the license plate number can be extracted from the vehicle image to be identified, identify the extracted license plate number to obtain the target license plate number; otherwise, confirm the license plate number of the vehicle image to be identified as 1 to obtain the target license plate number; Construct vehicle data based on vehicle model and target license plate number, aggregate vehicle data, and obtain the extracted vehicle data set for the unit time period; Calculate unit impact data based on the vehicle dataset; The step of calculating the unit impact data based on the vehicle data set includes: Extracting vehicle data from the vehicle data set in sequence and confirming the target license plate number of the extracted vehicle data; If the target license plate number is 1, the extracted vehicle data is confirmed as identification data and the vehicle data is removed from the vehicle data set; If the target license plate number is not 1, the extracted vehicle data is confirmed as identification data, and a same license plate set is extracted from the vehicle data set, the same license plate set is removed from the vehicle data set to obtain a retained identification data set, the retained identification data set is confirmed to be the vehicle data set, and the process returns to the step of sequentially extracting vehicle data from the vehicle data set until the vehicle data set is an empty set; Aggregating the identification data to obtain the identification data set of the extracted unit time period, inputting the identification data set into a pre-built prediction model to obtain the unit impact data of the extracted unit time period; Summarize the unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised images to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, filter the initial crack area sequence to obtain a sequence to be interpolated, wherein the purpose of filtering the initial crack area sequence is to eliminate abnormal initial crack areas in the initial crack area sequence. After eliminating the abnormal values ​​in the initial crack area sequence, the multiple initial crack areas retained are the sequence to be interpolated; The interpolation method is used to interpolate the sequence to be interpolated to obtain the crack area evolution sequence; The interpolation calculation of the sequence to be interpolated by using the interpolation method to obtain the fracture area evolution sequence includes: Constructing an interpolation model, wherein the interpolation model includes: a generation unit and a discrimination unit, wherein the discrimination unit includes a main discrimination unit and an auxiliary discrimination unit; Based on the interpolation model, a generation unit is started, and according to the initial fracture area sequence and the sequence to be interpolated and the missing data set, the missing data set, the initial fracture area sequence and the sequence to be interpolated are input into the started generation unit to obtain a plurality of initial interpolation areas, and the plurality of initial interpolation areas are inserted into the sequence to be interpolated to obtain a filled sequence, wherein the missing data set includes a plurality of missing data, and the initial interpolation areas correspond to the missing data in a one-to-one manner; Detect the data distribution of the sequence to be interpolated to obtain the original distribution, detect the data distribution of the filled sequence to obtain the filled distribution, and import the original distribution and the filled distribution into the discriminant unit; If the original distribution is consistent with the filled distribution, the filled sequence is confirmed to be the fracture area evolution sequence based on the auxiliary discrimination unit; If the original distribution is inconsistent with the filled distribution, a missing value set is identified from the multiple initial interpolation areas based on the main discriminant unit, the missing value set is confirmed as a missing data set, and the process returns to the step of inputting the missing data set, the initial fracture area sequence, and the sequence to be interpolated into the generation unit until the filled sequence is confirmed to be a fracture area evolution sequence based on the auxiliary discriminant unit; A crack evolution model is constructed based on the crack area evolution sequence and the unit impact data set. The time period to be predicted and the impact to be predicted are obtained. The time period to be predicted and the impact to be predicted are input into the crack evolution model to obtain the predicted crack value. Based on the predicted crack value, the prediction of construction road cracks in the unit time period monitoring data is completed.

2. The construction road crack prediction method based on time series monitoring data according to claim 1, characterized in that: The performing a clearing operation on the extracted historical image to obtain a clear image includes: Multiple Gaussian difference curves are generated using the pre-built Gaussian difference operator and the extracted historical images, where the Gaussian difference operator is: Among them, z represents the horizontal coordinate of the historical image in the image coordinate system, and w represents the vertical coordinate of the historical image in the image coordinate system. and Both represent the variance of the Gaussian filter, σ1 and σ2 both represent the standard deviation of the Gaussian filter, and σ1≠σ2, DOG represents the Gaussian difference operator; A zero-crossing point of each Gaussian difference curve in a plurality of Gaussian difference curves is obtained to obtain a plurality of zero-crossing points, a plurality of contour coordinates are generated based on the plurality of zero-crossing points, the plurality of contour coordinates are spliced ​​using a pre-built edge connection algorithm to obtain a historical image contour, an image transition area is obtained based on the historical image contour, and a clear image is obtained based on the image transition area, wherein the contour coordinates correspond one-to-one to the Gaussian difference curves.

3. The construction road crack prediction method based on time series monitoring data according to claim 2, characterized in that: The performing of a noise reduction operation on the clear image to obtain a noise-reduced image includes: Construct a mean filter unit and a median filter unit; Obtain a standard image, extract the spatial distribution characteristics of pixel grayscale values ​​from the standard image, and import the spatial distribution characteristics of pixel grayscale values ​​into a mean filter unit to obtain fixed pattern noise, wherein the filter kernel size of the mean filter unit is 9×9; A noise reduction operation is performed on the clear image using fixed pattern noise to obtain a noise-reduced image. The formula for a noise reduction operation is as follows: k(x,y)=j(x,y)-δ×l(x,y)+β×h Wherein, k(x, y) represents the pixel grayscale value corresponding to the pixel coordinate (x, y) in the primary denoised image, x represents the abscissa of the pixel coordinate (x, y), y represents the ordinate of the pixel coordinate (x, y), j(x, y) represents the pixel grayscale value corresponding to the pixel coordinate (x, y) in the clear image, l(x, y) represents the pixel grayscale value of the pixel coordinate (x, y) in the fixed pattern noise, δ and β represent the first correction coefficient and the second correction coefficient, respectively, and δ = 0.7, β = 1.2, and h represents the average grayscale value of all pixels in the clear image; The median filter unit is used to perform a secondary denoising operation on the primary denoised image to obtain a denoised image, wherein the size of the filter kernel of the median filter unit is 3×3.

4. The construction road crack prediction method based on time series monitoring data according to claim 1, characterized in that: The method of using a pre-built edge recognition algorithm to identify the noise reduction image sequence to obtain an initial crack area sequence includes: The denoised image sequence is filtered to obtain an initial crack image sequence, and the following operations are performed on the initial crack images in the initial crack image sequence: A pixel gradient sequence is obtained based on the initial crack image, and a first pixel probability, a second pixel probability and a third pixel probability are calculated based on the pixel gradient sequence. The grayscale mean of the first class of pixels, the grayscale mean of the second class of pixels and the grayscale mean of the third class of pixels are calculated according to the first pixel probability, the second pixel probability and the third pixel probability respectively. The maximum total inter-class variance is calculated using the first pixel probability, the second pixel probability, the third pixel probability, the grayscale mean of the first class of pixels, the grayscale mean of the second class of pixels and the grayscale mean of the third class of pixels, wherein the first pixel probability, the second pixel probability and the third pixel probability are the probabilities that the pixels in the pixel gradient sequence are in the first class of pixels, the second class of pixels and the third class of pixels respectively. Before calculating the probability and the third pixel probability, an initial high threshold and an initial low threshold are preset, wherein the initial high threshold is greater than the initial low threshold, and both the initial high threshold and the initial low threshold are grayscale values ​​in the pixel gradient sequence. The pixel gradient sequence is divided into three categories according to the initial high threshold and the initial low threshold, pixels corresponding to grayscale values ​​lower than the initial low threshold are pixels of the first category, pixels corresponding to grayscale values ​​between the initial low threshold and the initial high threshold are pixels of the second category, and pixels corresponding to grayscale values ​​higher than the initial high threshold are pixels of the third category. At this time, the grayscale mean of the pixels of the first category, the grayscale mean of the pixels of the second category, and the grayscale mean of the pixels of the third category are the average grayscale value of all pixels of the first category, the average grayscale value of all pixels of the second category, and the average grayscale value of all pixels of the third category, respectively. Calculating a low threshold and a high threshold based on the maximum total inter-class variance, and using the low threshold and the high threshold to divide the initial crack image to obtain a strong edge point set and a non-edge point set; Construct a crack image based on a strong edge point set and a non-edge point set, and calculate the initial crack area based on the crack image; The initial crack areas are summarized to obtain the initial crack area sequence.

5. The construction road crack prediction method based on time series monitoring data according to claim 4, characterized in that: The calculation formula of the maximum total inter-class variance is: Where T represents the maximum total inter-class variance, ω j represents the j-th pixel probability, a j Indicates the grayscale mean of the j-th class pixel corresponding to the j-th pixel probability, represents the mean grayscale value of all pixels in the initial crack image, and j represents the j-th type of pixel.

6. The construction road crack prediction method based on time series monitoring data according to claim 5, characterized in that: The method of dividing the initial crack image by using a low threshold and a high threshold to obtain a strong edge point set and a non-edge point set includes: Based on the low threshold and high threshold, the non-edge pixel interval, weak edge pixel interval and strong edge pixel interval are constructed, and the crack pixels are extracted from the initial crack image in sequence. The following operations are performed on the extracted crack pixels: Obtaining a crack grayscale value based on the extracted crack pixels, and comparing the crack grayscale value with the size of the non-edge pixel interval, the weak edge pixel interval, and the strong edge pixel interval; If the crack grayscale value belongs to the non-edge pixel interval, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point; If the crack grayscale value belongs to the weak edge pixel interval, a judgment neighborhood is obtained based on the extracted crack pixels, and the maximum neighborhood pixel grayscale value is extracted from the judgment neighborhood. If the maximum neighborhood pixel grayscale value is greater than the high threshold, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point. If the maximum neighborhood pixel grayscale value is not greater than the high threshold, the grayscale value of the extracted crack pixel is output as 0 to obtain a non-edge point. If the crack grayscale value belongs to the strong edge pixel interval, the grayscale value of the extracted crack pixel is output as 255 to obtain a strong edge point; Summarize the strong edge points to get the strong edge point set, and summarize the non-edge points to get the non-edge point set.

7. A construction road crack prediction system based on time series monitoring data, applied to the construction road crack prediction method based on time series monitoring data as claimed in claim 1, characterized in that: The system comprises: a historical image acquisition module, configured to receive a road monitoring instruction, activate a road monitoring device according to the road monitoring instruction, acquire a historical monitoring time based on the activated road monitoring device, divide the historical monitoring time into a plurality of unit time periods, sequentially extract unit time periods from the plurality of unit time periods, and perform the following operations on the extracted unit time periods: acquire a plurality of historical images based on the extracted unit time periods; The impact data acquisition module is configured to sequentially extract historical images from a plurality of historical images and perform the following operations on the extracted historical images: performing a sharpening operation on the extracted historical images to obtain a sharp image, performing a noise reduction operation on the sharp image to obtain a noise-reduced image, extracting a set of vehicle images to be identified based on the noise-reduced image, and calculating the unit impact data of the extracted unit time period based on the set of vehicle images to be identified, wherein the steps of extracting the set of vehicle images to be identified based on the noise-reduced image and calculating the unit impact data of the extracted unit time period based on the set of vehicle images to be identified include: Using a target detection algorithm to identify the denoised image, a set of vehicle images to be identified is obtained, wherein the set of vehicle images to be identified includes a plurality of vehicle images to be identified; The following operations are performed on the vehicle images to be identified in the vehicle image set: Identify the vehicle model of the vehicle image to be identified, and determine whether the license plate number can be extracted from the vehicle image to be identified. If the license plate number can be extracted from the vehicle image to be identified, identify the extracted license plate number to obtain the target license plate number; otherwise, confirm the license plate number of the vehicle image to be identified as 1 to obtain the target license plate number; Construct vehicle data based on vehicle model and target license plate number, aggregate vehicle data, and obtain the extracted vehicle data set for the unit time period; Calculate unit impact data based on the vehicle dataset; The step of calculating the unit impact data based on the vehicle data set includes: Extracting vehicle data from the vehicle data set in sequence and confirming the target license plate number of the extracted vehicle data; If the target license plate number is 1, the extracted vehicle data is confirmed as identification data and the vehicle data is removed from the vehicle data set; If the target license plate number is not 1, the extracted vehicle data is confirmed as identification data, and a same license plate set is extracted from the vehicle data set, the same license plate set is removed from the vehicle data set to obtain a retained identification data set, the retained identification data set is confirmed to be the vehicle data set, and the process returns to the step of sequentially extracting vehicle data from the vehicle data set until the vehicle data set is an empty set; Aggregating the identification data to obtain the identification data set of the extracted unit time period, inputting the identification data set into a pre-built prediction model to obtain the unit impact data of the extracted unit time period; The crack area evolution sequence module is used to summarize unit impact data to obtain a unit impact data set corresponding to the historical monitoring time, summarize the denoised image to obtain a denoised image sequence corresponding to the historical monitoring time, use a pre-built edge recognition algorithm to identify the denoised image sequence to obtain an initial crack area sequence, filter the initial crack area sequence to obtain a sequence to be interpolated, and use an interpolation method to perform interpolation calculation on the sequence to be interpolated to obtain a crack area evolution sequence, wherein the purpose of filtering the initial crack area sequence is to eliminate abnormal initial crack areas in the initial crack area sequence. After eliminating the outliers in the initial crack area sequence, the multiple initial crack areas retained are the sequence to be interpolated, wherein the interpolation method is used to perform interpolation calculation on the sequence to be interpolated to obtain the crack area evolution sequence, including: Constructing an interpolation model, wherein the interpolation model includes: a generation unit and a discrimination unit, wherein the discrimination unit includes a main discrimination unit and an auxiliary discrimination unit; Based on the interpolation model, a generation unit is started, and according to the initial fracture area sequence and the sequence to be interpolated and the missing data set, the missing data set, the initial fracture area sequence and the sequence to be interpolated are input into the started generation unit to obtain a plurality of initial interpolation areas, and the plurality of initial interpolation areas are inserted into the sequence to be interpolated to obtain a filled sequence, wherein the missing data set includes a plurality of missing data, and the initial interpolation areas correspond to the missing data in a one-to-one manner; Detect the data distribution of the sequence to be interpolated to obtain the original distribution, detect the data distribution of the filled sequence to obtain the filled distribution, and import the original distribution and the filled distribution into the discriminant unit; If the original distribution is consistent with the filled distribution, the filled sequence is confirmed to be the fracture area evolution sequence based on the auxiliary discrimination unit; If the original distribution is inconsistent with the filled distribution, a missing value set is identified from the multiple initial interpolation areas based on the main discriminant unit, the missing value set is confirmed as a missing data set, and the process returns to the step of inputting the missing data set, the initial fracture area sequence, and the sequence to be interpolated into the generation unit until the filled sequence is confirmed to be a fracture area evolution sequence based on the auxiliary discriminant unit; The crack prediction module is used to build a crack evolution model based on the crack area evolution sequence and the unit impact data set, obtain the time period to be predicted and the impact to be predicted, input the time period to be predicted and the impact to be predicted into the crack evolution model, obtain the predicted crack value, and complete the prediction of construction road cracks based on the unit time period monitoring data according to the predicted crack value.

Images