A cloud-edge collaborative asphalt pavement intelligent construction method

Abstract

The present invention provides a cloud-edge collaborative asphalt pavement intelligent construction method, which constructs a road twin model corresponding to a target road based on configuration information input by a management end, wherein the road twin model includes a pavement twin layer and a multi-layer rolling twin layer located above the pavement twin layer; receives construction information of each asphalt construction equipment, parses the construction information to obtain equipment type and construction path, obtains twin update data according to the equipment type and construction path, updates the road twin model based on the twin update data, and obtains a progress model; when the twin update data meets the verification conditions, obtains a road verification image, calls the verification strategy and road image data to verify the twin update data, and obtains verification data; updates the progress model according to the verification data to obtain a verification model.

Description

Technical Field

[0001] The present invention relates to cloud-edge collaborative technology, and in particular to a cloud-edge collaborative asphalt pavement intelligent construction method. Background Art

[0002] When constructing an asphalt road, the asphalt layer needs to be laid on the roadbed and then compacted using a paver.

[0003] At present, in the process of asphalt road paving, due to the short engineering cycle and tight construction time, in order to be able to understand the construction progress of the asphalt road in real time, the project management side usually dispatches staff to the construction site to follow up the construction progress. However, due to the large human influence factors, the construction progress will not be updated in time, and because there is a lot of data that needs to be updated, the updated data will be inaccurate.

[0004] Therefore, how to automatically update the construction progress of asphalt pavement in real time and improve accuracy so that the management end can remotely monitor the construction status of asphalt pavement has become an urgent problem that needs to be solved today. Summary of the invention

[0005] An embodiment of the present invention provides a cloud-edge collaborative intelligent asphalt pavement construction method, which can automatically update the construction progress of the asphalt pavement in real time and improve the accuracy, so that the management end can remotely monitor the construction status of the asphalt pavement.

[0006] A first aspect of an embodiment of the present invention provides a cloud-edge collaborative asphalt pavement intelligent construction method, including:

[0007] Building a road twin model corresponding to the target road based on the configuration information input by the management end, wherein the road twin model includes a pavement twin layer and a multi-layer rolling twin layer located above the pavement twin layer;

[0008] receiving construction information of each asphalt construction equipment, parsing the construction information to obtain equipment type and construction path, obtaining twin update data according to the equipment type and construction path, and updating the road twin model based on the twin update data to obtain a progress model;

[0009] When the twin update data meets the verification condition, a road verification image is acquired, and a verification strategy and the road image data are retrieved to verify the twin update data to obtain verification data;

[0010] The progress model is updated according to the verification data to obtain a verification model.

[0011] Optionally, in a possible implementation manner of the first aspect, obtaining twin update data according to the equipment type and the construction path, and updating the road twin model based on the twin update data to obtain a progress model includes:

[0012] Determine a construction width and construction attributes according to the equipment type of the asphalt construction equipment, wherein the construction attributes include paving attributes and rolling attributes, determine a virtual renewal width based on the construction width, and determine a virtual renewal attribute based on the construction attributes;

[0013] Acquire GIS information of each of the asphalt construction equipment in real time, generate a construction path according to the GIS information, and determine a virtual update path based on the construction path;

[0014] Obtaining a virtual update area based on the virtual update width and the virtual update path, and determining the corresponding pavement twin layer or rolling twin layer as an update layer according to the virtual update attribute;

[0015] The update layer is updated based on the virtual update area to obtain a progress model.

[0016] Optionally, in a possible implementation of the first aspect, obtaining a virtual update area based on the virtual update width and the virtual update path, and determining the corresponding pavement twin layer or rolling twin layer as the update layer according to the virtual update attribute includes:

[0017] If the virtual update attribute is a paving attribute, determining the pavement twin layer as the update layer;

[0018] If the virtual update attribute is a rolling attribute, the rolling times of the actual rolling area corresponding to the virtual update area are obtained, and the number of rolling layers is determined according to the rolling times;

[0019] Determine the rolled twin layer corresponding to the number of rolled layers as the update layer.

[0020] Optionally, in a possible implementation of the first aspect, when the twin update data meets the verification condition, obtaining a road verification image, retrieving a verification strategy and the road image data to verify the twin update data, and obtaining the verification data, includes:

[0021] Obtaining the path length of the construction path, and when the path length is greater than or equal to a preset path length, determining that the twin update data meets a verification condition;

[0022] Determine the virtual update area corresponding to the construction path in the progress model, obtain the road verification image collected by the drone based on the construction path, call the verification strategy and verify the virtual update area in the twin update data according to the road verification image to obtain verification data.

[0023] Optionally, in a possible implementation manner of the first aspect, determining a virtual update area corresponding to the construction path in the progress model, acquiring a road verification image collected by a drone based on the construction path, retrieving a verification strategy, and verifying the virtual update area in the twin update data according to the road verification image to obtain verification data, including:

[0024] Receiving configuration information input by the management end, parsing the configuration information to obtain a preset collection distance, and determining a plurality of collection location points on the construction path based on the preset collection distance and a construction starting point of the construction path;

[0025] Acquire the road verification images collected by the drone at each of the collection locations, determine the verification area in each of the road verification images, intercept the verification area to obtain a verification image, and splice the verification images according to the construction path to obtain a target verification image;

[0026] The virtual update area in the twin update data is verified according to the target verification image to obtain verification data.

[0027] Optionally, in a possible implementation of the first aspect, obtaining road verification images collected by the drone at each of the collection locations, determining a verification area in each of the road verification images, intercepting the verification area to obtain a verification image, and splicing the verification images according to the construction path to obtain a target verification image, includes:

[0028] Acquire a reference center line of each road verification image in a shooting direction, determine an image verification width according to the construction width, obtain a verification area based on the reference center line and the image verification width, and intercept the verification area in each road verification image to obtain a verification image;

[0029] Generate an image positioning path corresponding to the construction path, wherein the image positioning path includes positioning position points corresponding to each of the acquisition position points;

[0030] The image center point of each verification image is obtained, the image center point of the corresponding verification image is located based on the positioning position point, and the corresponding verification image is located on the positioning path for image stitching to obtain a target verification image.

[0031] Optionally, in a possible implementation manner of the first aspect, verifying the virtual update area in the twin update data according to the target verification image to obtain the verification data includes:

[0032] Counting the pixel values ​​of all pixels in the target verification image to obtain a total pixel value, and averaging the total pixel values ​​to obtain an average pixel value;

[0033] Determine a preset pixel value corresponding to the virtual update area according to the construction attribute, and obtain a pixel value difference according to the average pixel value and the preset pixel value;

[0034] If the pixel value difference is less than the preset pixel value difference, the virtual update area is used as a normal construction area;

[0035] If the pixel value difference is greater than or equal to a preset pixel value difference, the virtual update area is regarded as an abnormal construction area;

[0036] Verification data is generated according to the normal construction area or the abnormal construction area.

[0037] Optionally, in a possible implementation manner of the first aspect, updating the progress model according to the verification data to obtain the verification model includes:

[0038] The pixel values ​​of the abnormal area are retrieved, and the pixel values ​​of the abnormal construction area in the verification data are updated according to the pixel values ​​of the abnormal area to obtain a verification model.

[0039] Optionally, in a possible implementation of the first aspect, the method further includes:

[0040] Acquire virtual update areas whose positional relationship is an adjacent relationship as a group of gap verification groups, and determine the gap distance between the virtual update areas in each of the gap verification groups;

[0041] If the gap distance is not 0, a gap abnormality region is generated according to the virtual update region in the corresponding gap verification group, and the verification model is updated based on the gap abnormality region to obtain an updated verification model.

[0042] Optionally, in a possible implementation manner of the first aspect, if the gap distance is not 0, generating a gap abnormality region according to a virtual update region in the corresponding gap verification group, and updating the verification model based on the gap abnormality region to obtain an updated verification model, including:

[0043] If the gap distance is not 0, obtaining two adjacent edges corresponding to two virtual update areas in the gap verification group as gap edges;

[0044] Connecting the starting points of the two gap edges to obtain a first connected edge, and connecting the end points of the two gap edges to obtain a second connected edge;

[0045] A gap abnormality region is generated according to the gap edge, the first connecting edge and the second connecting edge, and the pixel value of the gap abnormality region is updated according to the pixel value of the abnormal region to obtain an updated verification model.

[0046] The beneficial effects of the present invention are as follows:

[0047] 1. The present invention can automatically update the construction progress of the asphalt pavement in real time and improve the accuracy, so that the management end can remotely monitor the construction status of the asphalt pavement. The present invention will update the constructed road twin model based on the construction information of multiple asphalt construction equipment received by the server to obtain a progress model, and update the construction progress of the asphalt pavement in real time through the progress model, so that the management end can understand the construction progress of the asphalt pavement based on the progress model. In order to improve the accuracy of the updated data, the present invention will also verify the updated data based on the road verification images collected by the drone, and update the progress model again based on the verification results to obtain a verification model. The verification model can improve the accuracy of the construction progress data and provide the management end with more accurate viewing data.

[0048] 2. When the present invention updates the constructed road twin model based on the construction information, it will obtain the equipment type and construction path based on the construction information, determine different construction attributes and construction widths through the equipment type, and then update the corresponding pavement twin layer and rolling twin layer in the road twin model based on the construction attributes, construction width and construction path, so that the construction data can be updated in different ways through the different attributes of the equipment, so that the management end can intuitively understand the construction data of each construction step through the progress model. When the present invention updates the rolling twin layer, it will also determine the corresponding number of layers of rolling twin layers based on the number of rolling times and update them accordingly, so that the management end can view the corresponding number of rolling times through the rolling twin layer.

[0049] 3. When verifying the update data, the present invention will first obtain multiple road verification images collected by the drone based on the construction path, and then cut out the verification area corresponding to the virtual update area in each road verification image to obtain multiple verification images, and then splice the verification images according to the construction path to obtain a complete target verification image, and judge whether the corresponding virtual update area is abnormal by the difference between the pixel value of the target verification image and the standard pixel value, so as to improve the accuracy of data verification, and the construction data can also be verified by the verification data, so as to improve the accuracy of the construction data. In addition, during verification, the present invention will also highlight the area where the gap is abnormal, so that the management end can take targeted treatment measures for the area where the construction is abnormal and the area where the gap is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present invention;

[0051] Figure 2 It is a structural schematic diagram of a cloud-edge collaborative asphalt pavement intelligent construction system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 , is a schematic diagram of an application scenario provided by an embodiment of the present invention. In order to update the construction progress of the asphalt pavement in real time, the present invention will update the constructed road twin model according to the construction information of multiple asphalt construction equipment received by the server to obtain a progress model, and then display the progress model to the management end, so that the management end can understand the construction progress of the asphalt pavement according to the progress model. And in order to improve the accuracy of the updated data, the present invention will also verify the updated data based on the road verification images collected by the drone, and update the progress model again according to the verification results to obtain a verification model. The verification model can improve the accuracy of the construction progress data and provide the management end with more accurate viewing data.

[0054] The execution subject of this application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDA) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S4, as follows:

[0055] S1, constructing a road twin model corresponding to the target road based on the configuration information input by the management end, wherein the road twin model includes a pavement twin layer and a multi-layer rolling twin layer located above the pavement twin layer.

[0056] In actual applications, when the target road is under construction, asphalt is usually laid on the target road with a paver, and then the laid asphalt is rolled multiple times by a roller to complete the construction of the asphalt pavement. In order to monitor and update the construction progress of both the laying and rolling steps, this solution will build a corresponding pavement twin layer to monitor and update the data during laying, and build a corresponding rolling twin layer to monitor and update the data during rolling when building the road twin model.

[0057] The number of layers of the rolling twin layer can be set by the staff according to the actual number of times of rolling. For example, if the paved asphalt needs to be rolled 5 times, the rolling twin layer can be set to 5 layers.

[0058] S2, receiving construction information of each asphalt construction equipment, parsing the construction information to obtain equipment type and construction path, obtaining twin update data according to the equipment type and construction path, updating the road twin model based on the twin update data to obtain a progress model.

[0059] Among them, asphalt construction equipment can be intelligent pavers and intelligent rollers. In practical applications, intelligent paving technology can be used to construct the target road during asphalt road construction, thereby realizing the automated construction of intelligent pavers and intelligent rollers, improving construction accuracy and quality, and enabling workers to remotely view the construction progress based on the management end, thereby reducing manual labor. The above-mentioned intelligent pavers and intelligent rollers are existing technologies, which can use GPS positioning technology for positioning and construction planning. The positioning accuracy error of the intelligent pavers and intelligent rollers in the existing technology is within millimeters.

[0060] It is understandable that since there are many types of asphalt construction equipment, the construction paths of asphalt construction equipment of different types may be different during construction. Therefore, after obtaining the construction information, this solution will obtain the equipment type and construction path based on the construction information, and obtain the twin update data corresponding to the corresponding asphalt construction equipment based on the equipment type and construction path, and then update the road twin model through the twin update data to obtain the progress model.

[0061] Based on the above embodiment, the specific implementation of step S2 may be:

[0062] S21, determining a construction width and construction attributes according to the equipment type of the asphalt construction equipment, wherein the construction attributes include paving attributes and rolling attributes, determining a virtual renewal width based on the construction width, and determining a virtual renewal attribute based on the construction attributes.

[0063] In actual applications, the construction widths of asphalt construction equipment with different construction attributes may also be different. For example, the construction width of asphalt construction equipment with paving attributes may be wider than the construction width of asphalt construction equipment with rolling attributes. After obtaining the construction width and construction attributes, the construction width can be virtually converted to obtain a virtual update width, and the virtual update attributes can be obtained according to the construction attributes.

[0064] S22, acquiring GIS information of each of the asphalt construction equipment in real time, generating a construction path according to the GIS information, and determining a virtual update path based on the construction path.

[0065] When determining the virtual update path based on the construction path, the virtual update path can be determined through the GIS information corresponding to the construction path.

[0066] S23, obtaining a virtual update area based on the virtual update width and the virtual update path, and determining the corresponding pavement twin layer or rolling twin layer as the update layer according to the virtual update attribute.

[0067] When determining the virtual update area, multiple location points passed by the asphalt construction equipment can be obtained through the virtual update path, and the virtual construction width of the asphalt construction equipment at each location point can be obtained through the virtual update width, so that a virtual update area can be generated through the virtual update width and the virtual update path, wherein the virtual update width can be pre-set by the staff according to the equipment.

[0068] Specifically, step S23 can be implemented through steps S231 to S233, which are specifically as follows:

[0069] S231: If the virtual update attribute is a paving attribute, determine the pavement twin layer as the update layer.

[0070] It can be understood that since the virtual update attributes of asphalt construction equipment are different, the type corresponding to the update layer also corresponds to the virtual update attribute. If the virtual update attribute is the paving attribute, it means that the corresponding asphalt construction equipment may be a paver, and the corresponding data is the paving data. Therefore, the pavement twin layer can be determined as the update layer.

[0071] S232: If the virtual update attribute is a rolling attribute, the rolling times of the actual rolling area corresponding to the virtual update area are obtained, and the number of rolling layers is determined according to the rolling times.

[0072] If the virtual update attribute is a rolling attribute, it corresponds to rolling data, so the rolling twin layer can be determined as the update layer. When rolling the asphalt pavement, in order to make the pavement compact, multiple rollings may be performed. Therefore, when determining the rolling twin layer, this solution will also determine the corresponding number of rolling twin layers as the update layer according to the number of rollings, so that the number of rollings can be updated accordingly through the update data.

[0073] It is worth mentioning that, when obtaining the rolling times of each asphalt construction equipment in this scheme, the rolling path of each asphalt construction equipment during repeated rolling is the same, that is, the rolling area of ​​each asphalt construction equipment during each rolling is the same.

[0074] S233, determining the rolled twin layer corresponding to the number of rolled layers as the update layer.

[0075] For example, if the number of rolled layers is the second layer, the rolled twin layer of the second layer can be determined as the updated layer.

[0076] S24, updating the update layer based on the virtual update area to obtain a progress model.

[0077] Through the above method, the road twin model can be updated according to the construction information of each asphalt construction equipment to obtain a progress model, so that the management end can grasp the progress of the construction according to the progress model.

[0078] S3, when the twin update data meets the verification conditions, obtain a road verification image, call the verification strategy and the road image data to verify the twin update data, and obtain verification data.

[0079] It can be understood that in order to improve the accuracy of the twin update data, this solution will verify the twin update data based on the road verification image after obtaining it, and obtain the verified verification data.

[0080] Based on the above embodiment, the specific implementation of step S3 may be:

[0081] S31, obtaining the path length of the construction path, and when the path length is greater than or equal to the preset path length, determining that the twin update data meets the verification condition.

[0082] It is understandable that if the construction is verified after it is completed, some problems that arise during the construction process may not be dealt with in a timely manner. Therefore, this solution will verify the corresponding construction area when the path length is greater than or equal to the preset path length (for example, 10 meters, 20 meters), so that problems can be dealt with in a timely manner when they arise in the corresponding construction area.

[0083] S32, determine the virtual update area corresponding to the construction path in the progress model, obtain the road verification image collected by the drone based on the construction path, call the verification strategy and verify the virtual update area in the twin update data according to the road verification image to obtain verification data.

[0084] From the above embodiments, it can be known that each construction path has its corresponding virtual update area in the progress model. Therefore, during verification, the corresponding virtual update area can be verified based on the road verification image collected by the drone based on the construction path.

[0085] In some embodiments, step S32 may be implemented through steps S321 to S323, as follows:

[0086] S321, receiving configuration information input by the management end, parsing the configuration information to obtain a preset collection distance, and determining a plurality of collection location points on the construction path based on the preset collection distance and a construction starting point of the construction path.

[0087] In practical applications, the preset collection distance can correspond to the collection range of the drone during collection, so that the drone can obtain a relatively complete road image when collecting images based on the collection location points.

[0088] S322, obtaining the road verification images collected by the drone at each of the collection locations, determining the verification area in each of the road verification images, intercepting the verification area to obtain a verification image, and splicing the verification images according to the construction path to obtain a target verification image.

[0089] It is understandable that the road verification image may contain multiple areas, such as areas in the road that need to be verified and areas that do not need to be verified, as well as areas outside the road. However, this scheme only needs to perform corresponding verification in the verification area in the road during verification. Therefore, after obtaining the road verification image, the verification image corresponding to the verification area in the road verification image can be intercepted, and corresponding verification can be performed based on the verification image.

[0090] It can also be understood that since there may be multiple road verification images, after obtaining the verification image, the multiple verification images can be spliced ​​according to the construction path to obtain the target verification image, thereby obtaining a complete road image corresponding to the construction path.

[0091] Specifically, step S322 includes step S3221 to step S3223, which are as follows:

[0092] S3221, obtaining a reference center line of each of the road verification images in the shooting direction, determining an image verification width according to the construction width, obtaining a verification area based on the reference center line and the image verification width, and intercepting the verification area in each of the road verification images to obtain a verification image.

[0093] It can be understood that when the drone in this solution collects road verification images, it collects them on the construction path, that is, it is located at the center of the corresponding construction area to collect images, and the verification area corresponds to the construction area. Therefore, when obtaining the verification area, the reference center line in the shooting direction can be obtained first, and then the construction width can be converted into the image verification width corresponding to the verification area. For example, a 1-meter construction width corresponds to a 1-cm image verification width. The image verification width can also be preset, and then half of the image verification width is extended to both sides from the reference center line to obtain the verification area, and the verification area is intercepted to obtain the verification image.

[0094] S3222: Generate an image positioning path corresponding to the construction path, wherein the image positioning path includes positioning position points corresponding to each of the acquisition position points.

[0095] In practical applications, the image positioning path can be generated through the GIS information of the construction path, and the corresponding positioning position points on the image positioning path can be determined according to the GIS information of each collection position point.

[0096] S3223, obtaining the image center point of each of the verification images, locating the image center point of the corresponding verification image based on the positioning position point, locating the corresponding verification image on the positioning path, and performing image stitching to obtain a target verification image.

[0097] When positioning the verification image, the positioning point corresponding to the position information of the verification image can be obtained according to the position information corresponding to the verification image, and then the verification image is positioned at the corresponding positioning point, so as to realize the splicing of multiple verification images through the positioning path.

[0098] S323, verifying the virtual update area in the twin update data according to the target verification image to obtain verification data.

[0099] Specifically, step S323 includes steps S3231 to S3235, which are specifically as follows:

[0100] S3231, counting the pixel values ​​of all pixels in the target verification image to obtain a total pixel value, and averaging the total pixel values ​​to obtain an average pixel value.

[0101] It is worth mentioning that during verification, the first two layers of the pavement twin layer and the rolling twin layer can be verified. This is because the pixel values ​​during paving and just after rolling will change greatly. Therefore, the pixel values ​​can be used to verify the first two layers of the pavement twin layer and the rolling twin layer. For the rolling twin layer with more than three layers, the amplitude of its state change will be relatively small, and the verification effect may not be as ideal as the first two layers. Therefore, for the rolling twin layer with more than three layers, it is not necessary to perform verification operations on it, and only perform update operations.

[0102] During verification, the average pixel value in the target verification image may be obtained first, and then subsequent verification processing may be performed using the average pixel value.

[0103] S3232: Determine a preset pixel value corresponding to the virtual update area according to the construction attribute, and obtain a pixel value difference according to the average pixel value and the preset pixel value.

[0104] It is understandable that asphalt construction equipment with different construction attributes performs different construction operations during construction, and the corresponding construction images may also be different. Therefore, when obtaining the preset pixel value, the preset pixel value of the virtual update area can be determined by the construction attribute.

[0105] S3233: If the pixel value difference is less than a preset pixel value difference, the virtual update area is used as a normal construction area.

[0106] If the pixel value difference is less than the preset pixel value difference, it means that the difference between the pixel value of the target verification image and the standard pixel value is relatively small, and the corresponding virtual update area may be normal, so it can be used as a normal construction area.

[0107] S3234: If the pixel value difference is greater than or equal to a preset pixel value difference, the virtual update area is used as an abnormal construction area.

[0108] If the pixel value difference is greater than or equal to the preset pixel value difference, it means that the difference between the pixel value of the target verification image and the standard pixel value is relatively large, and the corresponding virtual update area may be abnormal, so it can be used as an abnormal construction area.

[0109] S3235, generating verification data according to the normal construction area or the abnormal construction area.

[0110] Through the above method, the accuracy of data verification can be improved, and the construction data can also be verified by the verification data, thereby improving the accuracy of the construction data.

[0111] S4, updating the progress model according to the verification data to obtain a verification model.

[0112] In some embodiments, the verification model can be obtained by the following steps:

[0113] S41, retrieve the pixel value of the abnormal area, and update the pixel value of the abnormal construction area in the verification data according to the pixel value of the abnormal area to obtain a verification model.

[0114] In practical applications, the pixel values ​​of abnormal areas can be set in advance by the staff, so that the abnormal construction areas can be highlighted by changing the pixel values, allowing the staff to intuitively view the abnormal construction areas and make corresponding treatments.

[0115] In addition, based on the above embodiments, this solution also includes the following embodiments:

[0116] S42, acquiring virtual update areas whose positional relationship is an adjacent relationship as a group of gap verification groups, and determining the gap distances between the virtual update areas in each of the gap verification groups.

[0117] When gap verification is performed on the virtual update area, gap verification can be performed on the virtual update area corresponding to the compacted twin layer. It is understandable that in actual applications, the tidal flat area of ​​the paver is generally large, and the tidal flat can be completed in one time, while the rolling area of ​​the roller is small, and multiple rollers need to cooperate to complete the rolling of the asphalt pavement, which is prone to gaps.

[0118] Therefore, there may be no gaps in the virtual update areas corresponding to the paver, while gaps may appear between the virtual update areas corresponding to the roller due to insufficient rolling. In this case, the gaps may need to be supplemented with rolling. Therefore, when gap verification is performed on adjacent virtual update areas, gap verification can be performed on the virtual update areas corresponding to the rolled twin layers.

[0119] In practical applications, when determining the gap distance between virtual update areas in the gap verification group, the adjacent edges between the virtual update areas can be obtained, and then the gap distance can be obtained based on the difference between the two adjacent edges. It is worth mentioning that the repeated rolling path of each roller needs to be the same, so that the repeated rolling road surface of each roller can be the same, and the path does not need to be configured multiple times.

[0120] S43, if the gap distance is not 0, generating a gap abnormality area according to the virtual update area in the corresponding gap verification group, and updating the verification model based on the gap abnormality area to obtain an updated verification model.

[0121] If the gap distance is not 0, it means that there are gaps between the virtual update areas in the gap verification group. Therefore, the corresponding gap abnormal area can be obtained. The verification model is updated through the gap abnormal area to obtain the updated verification model, so that the management end can view the area where the gap abnormality occurs and make targeted processing on it.

[0122] In some embodiments, step S43 may be implemented through steps S431 to S433, as follows:

[0123] S431: If the gap distance is not 0, two adjacent edges corresponding to two virtual update areas in the gap verification group are obtained as gap edges.

[0124] S432, connecting the starting points of the two gap edges to obtain a first connecting edge, and connecting the end points of the two gap edges to obtain a second connecting edge.

[0125] In practical applications, the start and end points of the gap edge are the two endpoints of the gap edge.

[0126] S433, generating a gap abnormality region according to the gap edge, the first connecting edge and the second connecting edge, and updating the pixel value of the gap abnormality region according to the pixel value of the abnormal region to obtain an updated verification model.

[0127] Through the above method, the area with abnormal gap can be highlighted, so that the management end can take targeted processing measures for the area with abnormal gap.

[0128] See also Figure 2 , is a structural diagram of a cloud-edge-coordinated asphalt pavement intelligent construction system provided by an embodiment of the present invention, and the cloud-edge-coordinated asphalt pavement intelligent construction system includes:

[0129] A configuration module, used to construct a road twin model corresponding to the target road based on the configuration information input by the management end, wherein the road twin model includes a pavement twin layer and a multi-layer rolling twin layer located above the pavement twin layer;

[0130] A progress module is used to receive construction information of each asphalt construction equipment, parse the construction information to obtain equipment type and construction path, obtain twin update data according to the equipment type and construction path, and update the road twin model based on the twin update data to obtain a progress model;

[0131] A verification module, used to obtain a road verification image when the twin update data meets the verification conditions, and to retrieve the verification strategy and the road image data to verify the twin update data to obtain verification data;

[0132] An updating module is used to update the progress model according to the verification data to obtain a verification model.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cloud-edge-coordinated asphalt pavement intelligent construction method, characterized in that: include: Building a road twin model corresponding to the target road based on the configuration information input by the management end, wherein the road twin model includes a pavement twin layer and a multi-layer rolling twin layer located above the pavement twin layer; receiving construction information of each asphalt construction equipment, parsing the construction information to obtain equipment type and construction path, obtaining twin update data according to the equipment type and construction path, and updating the road twin model based on the twin update data to obtain a progress model; When the twin update data meets the verification condition, a road verification image is acquired, and a verification strategy and the road image data are retrieved to verify the twin update data to obtain verification data; The progress model is updated according to the verification data to obtain a verification model; Obtaining twin update data according to the equipment type and the construction path, and updating the road twin model based on the twin update data to obtain a progress model, including: Determine a construction width and construction attributes according to the equipment type of the asphalt construction equipment, wherein the construction attributes include paving attributes and rolling attributes, determine a virtual renewal width based on the construction width, and determine a virtual renewal attribute based on the construction attributes; Acquire GIS information of each of the asphalt construction equipment in real time, generate a construction path according to the GIS information, and determine a virtual update path based on the construction path; Obtaining a virtual update area based on the virtual update width and the virtual update path, and determining the corresponding pavement twin layer or rolling twin layer as an update layer according to the virtual update attribute; The update layer is updated based on the virtual update area to obtain a progress model.

2. The method according to claim 1, characterized in that The method comprises: obtaining a virtual update area based on the virtual update width and the virtual update path, and determining the corresponding pavement twin layer or rolling twin layer as the update layer according to the virtual update attribute, including: If the virtual update attribute is a paving attribute, determining the pavement twin layer as the update layer; If the virtual update attribute is a rolling attribute, the rolling times of the actual rolling area corresponding to the virtual update area are obtained, and the number of rolling layers is determined according to the rolling times; Determine the rolled twin layer corresponding to the number of rolled layers as the update layer.

3. The method according to claim 1, characterized in that When the twin update data meets the verification condition, a road verification image is obtained, and a verification strategy and the road image data are retrieved to verify the twin update data to obtain verification data, including: Obtaining the path length of the construction path, and when the path length is greater than or equal to a preset path length, determining that the twin update data meets a verification condition; Determine the virtual update area corresponding to the construction path in the progress model, obtain the road verification image collected by the drone based on the construction path, call the verification strategy and verify the virtual update area in the twin update data according to the road verification image to obtain verification data.

4. The method according to claim 3, characterized in that Determine a virtual update area corresponding to the construction path in the progress model, obtain a road verification image collected by a drone based on the construction path, call a verification strategy and verify the virtual update area in the twin update data according to the road verification image to obtain verification data, including: Receiving configuration information input by the management end, parsing the configuration information to obtain a preset collection distance, and determining a plurality of collection location points on the construction path based on the preset collection distance and a construction starting point of the construction path; Acquire the road verification images collected by the drone at each of the collection locations, determine the verification area in each of the road verification images, intercept the verification area to obtain a verification image, and splice the verification images according to the construction path to obtain a target verification image; The virtual update area in the twin update data is verified according to the target verification image to obtain verification data.

5. The method according to claim 4, characterized in that Acquiring the road verification images collected by the drone at each of the collection locations, determining the verification area in each of the road verification images, intercepting the verification area to obtain a verification image, and splicing the verification images according to the construction path to obtain a target verification image, including: Acquire a reference center line of each road verification image in a shooting direction, determine an image verification width according to the construction width, obtain a verification area based on the reference center line and the image verification width, and intercept the verification area in each road verification image to obtain a verification image; Generate an image positioning path corresponding to the construction path, wherein the image positioning path includes positioning position points corresponding to each of the acquisition position points; The image center point of each verification image is obtained, the image center point of the corresponding verification image is located based on the positioning position point, and the corresponding verification image is located on the positioning path for image stitching to obtain a target verification image.

6. The method according to claim 4, characterized in that Verifying the virtual update area in the twin update data according to the target verification image to obtain verification data, including: Counting the pixel values ​​of all pixels in the target verification image to obtain a total pixel value, and averaging the total pixel values ​​to obtain an average pixel value; Determine a preset pixel value corresponding to the virtual update area according to the construction attribute, and obtain a pixel value difference according to the average pixel value and the preset pixel value; If the pixel value difference is less than the preset pixel value difference, the virtual update area is used as a normal construction area; If the pixel value difference is greater than or equal to a preset pixel value difference, the virtual update area is regarded as an abnormal construction area; Verification data is generated according to the normal construction area or the abnormal construction area.

7. The method according to claim 6, characterized in that The progress model is updated according to the verification data to obtain a verification model, including: The pixel values ​​of the abnormal area are retrieved, and the pixel values ​​of the abnormal construction area in the verification data are updated according to the pixel values ​​of the abnormal area to obtain a verification model.

8. The method according to claim 7, characterized in that Also includes: Acquire virtual update areas whose positional relationship is an adjacent relationship as a group of gap verification groups, and determine the gap distance between the virtual update areas in each of the gap verification groups; If the gap distance is not 0, a gap abnormality region is generated according to the virtual update region in the corresponding gap verification group, and the verification model is updated based on the gap abnormality region to obtain an updated verification model.

9. The method according to claim 8, characterized in that If the gap distance is not 0, generating a gap abnormal area according to the virtual update area in the corresponding gap verification group, and updating the verification model based on the gap abnormal area to obtain an updated verification model, including: If the gap distance is not 0, obtaining two adjacent edges corresponding to two virtual update areas in the gap verification group as gap edges; Connecting the starting points of the two gap edges to obtain a first connected edge, and connecting the end points of the two gap edges to obtain a second connected edge; A gap abnormality region is generated according to the gap edge, the first connecting edge and the second connecting edge, and the pixel value of the gap abnormality region is updated according to the pixel value of the abnormal region to obtain an updated verification model.

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