A method and device for road damage and availability assessment based on vector data support

Abstract

This invention discloses a method and apparatus for road damage and availability assessment based on vector data. The method includes: acquiring road vector data, including road surface vector data and damage patch vector data; processing the road vector data to obtain road damage assessment results and road availability assessment results. Utilizing a damage assessment model based on geometric-grayscale information changes and a minimum traffic distance search algorithm model based on custom step-size expansion, the method processes road network vector data, road damage patch vector data identified from optical remote sensing images before and after damage, and remote sensing image vector data to achieve road damage and availability assessment, providing reliable information support for use decisions regarding damaged roads.

Description

Technical Field

[0001] This invention relates to the field of traffic safety detection technology, and in particular to a method and apparatus for assessing road damage and availability based on vector data. Background Technology

[0002] As a crucial component of national economic and social development, road traffic damage during wartime can severely impact a nation's economic and social stability. Damaged roads not only restrict the lives of ordinary citizens but also significantly affect the transportation of vital strategic materials and the passage of military vehicles. Current international research on road damage assessment primarily focuses on two aspects: model-based analytical methods and the application of remote sensing technology in war damage assessment. Model-based research mainly focuses on constructing different types of spatial analysis models to simulate the impact of damage on road capacity and traffic flow. These studies often predetermine information about damaged roads and do not specifically study how to identify road damage. Research on the application of remote sensing methods focuses on using different types of remote sensing data to identify road damage conditions, such as the location of road damage, providing technical support for real-time damage monitoring.

[0003] In China, relevant research mainly focuses on damage repair technologies, transportation network restoration strategies, and the impact of damage on regional economies. These studies help simulate highway war damage repair methods, optimize repair routes and resource allocation, and provide a basis for emergency response after road damage.

[0004] Space-based remote sensing satellites possess irreplaceable advantages in road damage and availability assessment due to their ability to monitor large areas and repeatably. Existing road damage assessment methods largely rely on optical images for road and damage identification, followed by damage assessment on raster images. However, road and damage edge identification based on optical remote sensing images inherently contains errors, and computations based on raster images experience significant speed and efficiency degradation when the image resolution is high and the coverage area is large, hindering the engineering application of these methods.

[0005] This invention fully utilizes existing precise global road network vector surface layer information, combined with road damage patch vectors identified from two phases of optical remote sensing images and the image information itself. Through vector and raster image spatial calculations, it obtains basic attribute information of road damage, such as damage area and grayscale texture attributes of the damaged area, and constructs a damage assessment model based on geometric-grayscale information changes to calculate specific numerical values ​​of road damage. Furthermore, using road vectors and damage patch vectors, it constructs a minimum passable distance search algorithm based on a custom step-size expansion to identify the narrowest point of passability and its width after damage. Through quantitative assessment and usability analysis, it provides reliable information support for usage decisions regarding damaged roads. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and apparatus for assessing road damage and availability based on vector data. By using a damage assessment model based on geometric-grayscale information changes and a minimum traffic distance search algorithm model based on custom step size expansion, the method and apparatus can assess road damage and availability, and provide reliable information support for the use decision of damaged roads.

[0007] To address the aforementioned technical problems, a first aspect of this invention discloses a method for assessing road damage and availability based on vector data, the method comprising: S1, Obtain road vector data; The road vector data includes road surface vector data and damage patch vector data; S2, The road vector data is processed to obtain road damage assessment results and road availability assessment results.

[0008] As an optional implementation, in a first aspect of the present invention, acquiring road vector data includes: S11, acquire remote sensing image data of the target area; S12, The remote sensing image data is processed to obtain target road data and road surface vector data; S13, Based on the target road data, the optical remote sensing image data is processed to obtain optical remote sensing image data before damage and optical remote sensing image data after damage. S14, the light-sensing remote sensing image data before damage and the light-sensing remote sensing image data after damage are extracted and processed to obtain damage patch vector data.

[0009] As an optional implementation, in the first aspect of the present invention, the processing of the road vector data to obtain road damage assessment results and road availability assessment results includes: S21, Process the road vector data to obtain road damage assessment results; S22, The road vector data is processed to obtain the road availability assessment result.

[0010] As an optional implementation, in the first aspect of the present invention, processing the road vector data to obtain a road damage assessment result includes: S211, Using a geometric information calculation model, the road vector data is processed to obtain road geometric damage values; The expression for the geometric information calculation model is: in, This represents the total area of ​​all damaged patches on the road after the damage occurred. This indicates the total area of ​​the road before the damage; This represents the geometric damage value of the road; S212, Using a texture information calculation model, the road vector data is processed to obtain road texture damage values; S213, Perform road damage calculation processing on the road geometric damage value and the road texture damage value to obtain the road damage assessment result; The expression for calculating road damage is as follows: in, The numerical value representing road damage is the road damage assessment result. Indicates the weight of road geometric damage; The weights representing road texture damage; This represents the geometric damage value of the road; This represents the road texture damage value.

[0011] As an optional implementation, in the first aspect of the present invention, the step of using a texture information calculation model to calculate and process the road vector data to obtain a road texture damage value includes: S2121, Using a grayscale calculation model, the road vector data is processed to obtain the average grayscale value of the road image; S2122, Perform standard deviation calculation on the mean grayscale value of the road image to obtain the standard deviation value of the grayscale value of the road image; S2123, Perform statistical processing on the road vector data to obtain image content measurement values; S2124, calculate and process the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain the road texture damage value.

[0012] As an optional implementation, in the first aspect of the present invention, the grayscale calculation model expression is: in, This represents the average grayscale value of the road image. This indicates the number of rows in the road vector data; This indicates the number of columns in the road vector data; The first of the road vector data Line 1 The grayscale value of a column of pixels; This represents the row index of the road vector data; The column index representing the road vector data; The expression for calculating the standard deviation is: in, This represents the standard deviation of the grayscale value of the road image; The expression for the statistical processing is: in, The grayscale entropy value of the road image represents the image content metric. Indicates the first The probability of gray levels appearing; Indicates the index of grayscale level.

[0013] As an optional implementation, in a first aspect of the present invention, the step of calculating the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain a road texture damage value includes: S21241, Perform a first damage value calculation on the mean grayscale value of the road image to obtain the mean damage value of the image; The expression for calculating the first damage value is: in, This represents the mean damage value of the image; This represents the average grayscale value of the road image in the damaged area of ​​the remote sensing image before the damage occurred. The grayscale value of the road image in the damaged area of ​​the remote sensing image after damage is represented. S21242, Perform a second damage value calculation on the grayscale standard deviation of the road image to obtain the road image standard deviation damage value; S21243, Perform a third damage value calculation on the image content metric to obtain the road image entropy damage value; S21244, The mean damage value of the image, the standard deviation damage value of the road image, and the entropy damage value of the road image are comprehensively processed to obtain the road texture damage value.

[0014] A second aspect of this invention discloses a road damage and availability assessment device based on vector data, the device comprising: a data acquisition module and an assessment processing module; The data acquisition module is used to acquire road vector data; The road vector data includes road surface vector data and damage patch vector data; The evaluation processing module is used to process the road vector data to obtain road damage assessment results and road availability assessment results.

[0015] A third aspect of this invention discloses a road damage and availability assessment device based on vector data, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the road damage and availability assessment method based on vector data disclosed in the first aspect of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked, execute some or all of the steps in the road damage and availability assessment method based on vector data disclosed in the first aspect of the present invention.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, a damage assessment model based on geometric-grayscale information changes and a minimum traffic distance search algorithm model based on custom step-size expansion are used to process road network vector surface layer information, road damage patch vector data identified by optical remote sensing images before and after damage, and remote sensing image vector data to achieve the assessment of road damage and availability, providing reliable information support for the use decision of damaged roads. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a road damage and availability assessment method system provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of a real-world road damage case, illustrating a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention. Figure 4This is a simulation diagram of road damage and start-end information of a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention. Figure 5 This is a schematic diagram of the remaining road vector data obtained by erasing damage patches in a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating road traffic judgment for a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention. Figure 7 This is the location of the narrowest passable section of a road, calculated using a road damage and availability assessment method based on vector data disclosed in an embodiment of the present invention. Figure 8 This is a schematic diagram of a road damage and availability assessment device based on vector data disclosed in an embodiment of the present invention; Figure 9 This is a schematic diagram of another road damage and availability assessment device based on vector data disclosed in an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0024] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0025] It should be noted that the artificial intelligence-related technologies that may be involved in this application will be briefly described. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is the study of the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making.

[0026] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0027] Computer vision (CV) is a science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes in recognizing and measuring targets, and then performs image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0028] Monomodal information refers to data of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information refers to data that includes at least two types of monomodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved in these tasks.

[0029] Large models refer to artificial neural network models with a very large number of parameters. In the field of artificial intelligence, large models typically refer to models with hundreds of millions to trillions of parameters. These models usually need to be trained on large-scale datasets and require a significant amount of computing resources for optimization and tuning. Large models are commonly used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is a type of AI that can create new content and ideas, including dialogues, stories, images, videos, and music. In this embodiment, the large model can be a language model of the scale of ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot-AI model, ChatGLM model, Qianyitongwen model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunque model, vivoLM model, and Wenxin Yiyan, etc., and this embodiment does not limit the scope of the large model.

[0030] This application provides a method, system, device, computer equipment, and computer-readable storage medium for road damage and availability assessment based on vector data, which will be described in detail below.

[0031] Please see Figure 1 , Figure 1This is a schematic diagram of a road safety monitoring system provided in an embodiment of this application. The system may include a computer device 100, which integrates a road damage and availability assessment device based on vector data. Figure 1 Computer equipment in the country.

[0032] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0033] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0034] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the system may also include one or more other services, which are not limited here.

[0035] In addition, such as Figure 1 As shown, the road safety monitoring system may also include a memory 200 for storing processing result data and remote sensing image sample data, such as evaluation result data.

[0036] It should be noted that, Figure 1 The schematic diagram of the road safety monitoring system shown is merely an example. The road safety monitoring system and scenario described in this application are intended to more clearly illustrate the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of road safety monitoring systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0037] This invention discloses a method and apparatus for assessing road damage and availability based on vector data. Utilizing a damage assessment model based on geometric-grayscale information changes and a minimum traffic distance search algorithm model based on custom step-size expansion, it processes road network vector surface layer information, road damage patch vector data identified from optical remote sensing images before and after damage, and remote sensing image vector data to assess road damage and availability, providing reliable information support for decisions regarding the use of damaged roads. Detailed descriptions follow.

[0038] Example 1 Please see Figure 2 , Figure 2 This is a flowchart illustrating a road damage and availability assessment method based on vector data, as disclosed in an embodiment of the present invention. Figure 2 The described road damage and availability assessment method based on vector data is applied to road safety monitoring systems, such as local servers or cloud servers used in road management systems; however, this invention does not limit its application. Figure 1 As shown, this road damage and availability assessment method based on vector data may include the following operations: S1, Obtain road vector data; It should be noted that the road vector data includes road surface vector data and damage patch vector data; S2, The road vector data is processed to obtain road damage assessment results and road availability assessment results.

[0039] As can be seen, by implementing the road damage and availability assessment method based on vector data as described in the embodiments of the present invention, the road vector data is processed to obtain road damage assessment results and road availability assessment results, thereby realizing the assessment of road damage and availability and providing reliable information support for the use decision of damaged roads.

[0040] In an optional embodiment, step S1 above, obtaining road vector data, includes: S11, acquire remote sensing image data of the target area; It should be noted that the remote sensing image data mentioned is the remote sensing image data of the existing precise road network; S12, The remote sensing image data is processed to obtain target road data and road surface vector data; It should be noted that the processing of the remote sensing image data to obtain target road data and road surface vector data includes road surface vectors extracted from remote sensing images and road surface vector data obtained by subsequent manual edge correction; or road surface vector data obtained by manual drawing based on remote sensing images; this embodiment does not impose any limitations. S13, Based on the target road data, the optical remote sensing image data is processed to obtain optical remote sensing image data before damage and optical remote sensing image data after damage. It should be noted that the optical remote sensing image data mentioned can be space-based satellite optical remote sensing image data, or optical remote sensing image data collected by airborne UAVs or manned aircraft; It should be noted that the process of processing optical remote sensing image data based on the target road data to obtain optical remote sensing image data before damage and optical remote sensing image data after damage includes: Based on the target road data, optical remote sensing image data is matched to obtain the pre-damage optical remote sensing image data and post-damage optical remote sensing image data of the area where the target road is located. S14, the optical remote sensing image data before damage and the optical remote sensing image data after damage are extracted and processed to obtain damage patch vector data; It should be noted that the extraction and processing of the pre-damage and post-damage optical remote sensing image data to obtain damage patch vector data includes manually outlining the pre-damage and post-damage optical remote sensing image data to obtain damage patch vector data; or, extracting the pre-damage and post-damage optical remote sensing image data based on a remote sensing image change detection algorithm to obtain damage patch vector data; this embodiment does not impose any limitations.

[0041] As can be seen, by implementing the road damage and availability assessment method based on vector data as described in the embodiments of the present invention, the road vector data is obtained, providing data support for road damage assessment and road availability assessment, realizing the assessment of road damage and availability, and providing reliable information support for the use decision of damaged roads.

[0042] In another optional embodiment, step S2 above, processing the road vector data to obtain road damage assessment results and road availability assessment results, includes: S21, Process the road vector data to obtain road damage assessment results; S22, The road vector data is processed to obtain the road availability assessment result.

[0043] As can be seen, by implementing the road damage and availability assessment method based on vector data as described in the embodiments of the present invention, the road vector data is processed to obtain road damage assessment results and road availability assessment results, providing reliable information support for the use decision of damaged roads.

[0044] In another optional embodiment, step S21 above, processing the road vector data to obtain road damage assessment results, includes: S211, Using a geometric information calculation model, the road vector data is processed to obtain road geometric damage values; The expression for the geometric information calculation model is: in, This represents the total area of ​​all damaged patches on the road after the damage occurred. This indicates the total area of ​​the road before the damage; This represents the geometric damage value of the road; S212, Using a texture information calculation model, the road vector data is processed to obtain road texture damage values; S213, Perform road damage calculation processing on the road geometric damage value and the road texture damage value to obtain the road damage assessment result; The expression for calculating road damage is as follows: in, This represents the numerical value of road damage, i.e., the road damage assessment result; Indicates the weight of road geometric damage; Indicates the weight of road texture damage; This represents the geometric damage value of the road; This represents the road texture damage value; It should be noted that the sum of the road geometric damage weight and the road texture damage weight is 1; Based on the characteristics of remote sensing images, values ​​are generally assigned according to experience; the default values ​​are set as follows: the weight of road geometric damage is set to 0.5, and the weight of road texture damage is set to 0.5. It should be noted that in this embodiment, the road geometric damage weight is set to 0.7, and the road texture damage weight is set to 0.3.

[0045] As can be seen, the road damage and availability assessment method based on vector data described in the embodiments of the present invention processes the road vector data to obtain road damage assessment results, providing reliable information support for the use decision of damaged roads.

[0046] In another optional embodiment, in step S212 above, the step of using a texture information calculation model to calculate and process the road vector data to obtain road texture damage values ​​includes: S2121, Using a grayscale calculation model, the road vector data is processed to obtain the average grayscale value of the road image; S2122, Perform standard deviation calculation on the mean grayscale value of the road image to obtain the standard deviation value of the grayscale value of the road image; S2123, Perform statistical processing on the road vector data to obtain image content measurement values; S2124, calculate and process the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain the road texture damage value.

[0047] As can be seen, the road damage and availability assessment method based on vector data described in the embodiments of the present invention uses a texture information calculation model to calculate and process the road vector data to obtain road texture damage values, providing data support for subsequent road damage assessment and road availability assessment.

[0048] In another optional embodiment, in step S2121 above, the grayscale calculation model expression is: in, This represents the average grayscale value of the road image. This indicates the number of rows in the road vector data; This indicates the number of columns in the road vector data; The first of the road vector data Line 1 The grayscale value of a column of pixels; This represents the row index of the road vector data; The column index representing the road vector data; It should be noted that the mean gray level of the road image reflects the overall level of the gray level distribution in the image. In another optional embodiment, in step S2122 above, the expression for the standard deviation calculation is: in, This represents the standard deviation of the grayscale value of the road image; It should be noted that the standard deviation of grayscale in the road image can be understood as the contrast of the image. The larger the standard deviation of grayscale in the road image, the greater the difference between grayscale values ​​and the higher the contrast. In another optional embodiment, in step S2123 above, the expression for the statistical processing is: in, The grayscale entropy value of the road image represents the image content metric. Indicates the first The probability of gray levels appearing; An index representing the grayscale level; It should be noted that the image grayscale entropy value of the road image is used to represent the measure of the randomness of the image content. The greater the information content, the greater the entropy value. When all pixels have the same gray level (uniform distribution), the entropy value reaches the minimum value.

[0049] As can be seen, the road damage and availability assessment method based on vector data described in the embodiments of the present invention uses a texture information calculation model to calculate and process the road vector data to obtain road texture damage values, providing data support for subsequent road damage assessment and road availability assessment.

[0050] In another optional embodiment, step S2124 above, which involves calculating the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain the road texture damage value, includes: S21241, Perform a first damage value calculation on the mean grayscale value of the road image to obtain the mean damage value of the image; The expression for calculating the first damage value is: in, This represents the mean damage value of the image; This represents the average grayscale value of the road image in the damaged area of ​​the remote sensing image before the damage occurred. The grayscale value of the road image in the damaged area of ​​the remote sensing image after damage is represented. S21242, Perform a second damage value calculation on the grayscale standard deviation of the road image to obtain the road image standard deviation damage value; S21243, Perform a third damage value calculation on the image content metric to obtain the road image entropy damage value; S21244, The mean damage value of the image, the standard deviation damage value of the road image, and the entropy damage value of the road image are comprehensively processed to obtain the road texture damage value.

[0051] As can be seen, the road damage and availability assessment method based on vector data described in the embodiments of the present invention uses a texture information calculation model to calculate and process the road vector data to obtain road texture damage values, providing data support for subsequent road damage assessment and road availability assessment.

[0052] In another optional embodiment, in step S21242 above, the expression for calculating the second damage value is: in, This represents the standard deviation of the road image damage value; This represents the standard deviation of the road image in the damaged area of ​​the remote sensing image before the damage occurred; The standard deviation of the road image in the damaged area of ​​the remote sensing image after damage is represented. In another optional embodiment, in step S21243 above, the expression for calculating the third damage value is: in, This represents the entropy value and damage value of the road image; This represents a metric value indicating the image content of the damaged area in the remotely sensed image before the damage occurred. This represents a metric value indicating the image content of the damaged area in the remotely sensed image after damage. In another optional embodiment, in step S21244 above, the expression for the synthesis process is: in, This represents the road texture damage value; As can be seen, the road damage and availability assessment method based on vector data described in the embodiments of the present invention uses a texture information calculation model to calculate and process the road vector data to obtain road texture damage values, providing data support for subsequent road damage assessment and road availability assessment.

[0053] In another optional embodiment, step S22 above, processing the road vector data to obtain the road availability assessment result, includes: S2201, Set initial step size and the current number of processes; It should be noted that, in this embodiment, the initial step size is set to 0.1 meters; It should be noted that the current processing quantity is set to 1; S2202, determine the vehicle's starting and ending points to obtain route vector data. and road surface vector data; It should be noted that the route vector data intersects with the road vector data; like Figure 3 and Figure 4 As shown, where, Figure 3 The image is a real-world example. The green and yellow lines represent the surface vector information of the two roads, the purple lines represent the start and end point vectors of the two roads, and the red circled areas in the roads represent the damage patch vectors. Figure 4 The simulation diagram shows the route vector with the red line as the start and end point, the area circled in black as the simulated road surface vector, and the black ellipse as the simulated damage patch vector. S2203, Process the initial step size and the route vector data to obtain the total amount of data to be processed. ; It should be noted that the processing expression is: It should be noted that, among them, This represents the route vector data; This represents the initial step size; Indicates rounding up; S2204, Based on the total amount of data to be processed, the route vector data and the road surface vector data are segmented to obtain the route vector dataset to be processed. and road surface vector dataset; S2205, determine whether the current processing quantity is greater than the total amount of data to be processed, and obtain the processing quantity determination result; S2206, when the processing quantity judgment result is yes, the road availability assessment result of the road being passable is obtained; If the result of the processing quantity determination is negative, execute S2207; S2207, Match the current processing quantity and obtain the unprocessed route vector data corresponding to the current processing quantity. And compare road surface vector data; S2208, The vector data of the route to be processed and the vector data of the road surface to be compared are superimposed and erased to obtain the remaining road vector data information after removing the damaged area; It should be noted that, as Figure 5 As shown, the remaining road vector data information is obtained after overlay and erasure processing; S2209, The remaining road vector data information is processed to obtain the road passage judgment result; like Figure 6As shown, the judgment process involves determining whether the starting point and the ending point are on the same surface layer. If the starting point and the ending point are on the same surface layer, the road traffic judgment result is "connected"; if the starting point and the ending point are not on the same surface layer, the road traffic judgment result is "disconnected". S2210, when the road passage determination result is yes, execute S2211; When the road passability determination result is negative, the road is deemed impassable, and the road availability assessment result is obtained. S2211, delete the unprocessed route vector data and the compared road surface vector data corresponding to the current processing quantity from the unprocessed route vector dataset and the road surface vector dataset, increase the current processing quantity by 1, and execute S2205.

[0054] It can be seen that, as Figure 7 As shown, the road damage and availability assessment method based on vector data described in the embodiments of the present invention is implemented. The road vector data is processed to obtain the road availability assessment results, providing reliable information support for the use decision of damaged roads.

[0055] Example 2 Please see Figure 8 , Figure 8 This is a schematic diagram of a road damage and availability assessment device based on vector data, as disclosed in an embodiment of the present invention. Figure 8 The described device can be applied in road management systems, such as local servers or cloud servers used in road safety monitoring systems, and the embodiments of the present invention are not limited thereto. Figure 8 As shown, the device may include: Data acquisition module 101 and evaluation processing module 102; The data acquisition module 101 is used to acquire road vector data; The road vector data includes road surface vector data and damage patch vector data; The evaluation processing module 102 is used to process the road vector data to obtain road damage evaluation results and road availability evaluation results.

[0056] Example 3 Please see Figure 9 , Figure 9 This is a schematic diagram of a road damage and availability assessment device based on vector data, as disclosed in an embodiment of the present invention. Figure 9 The described device can be applied in road management systems, such as local servers or cloud servers used in road safety monitoring systems, and the embodiments of the present invention are not limited thereto. Figure 9As shown, the device may include: Memory 201 storing executable program code; Processor 202 coupled to memory 201; The processor 202 calls the executable program code stored in the memory 201 to execute the steps in the road damage and availability assessment method based on vector data described in Embodiment 1.

[0057] Example 4 This invention discloses a computer-readable storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the steps in the road damage and availability assessment method based on vector data described in Embodiment 1.

[0058] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the road damage and availability assessment method based on vector data described in Embodiment 1.

[0059] The device embodiments described above are merely illustrative. 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 modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0060] It should be noted that all calculation expressions or mathematical functions in the embodiments of the present invention have undergone dimensionless processing of the variables involved before calculation.

[0061] It should be noted that in all the calculation expressions or mathematical functions in the embodiments of the present invention, the values ​​of the input independent variables all meet the reasonable requirements of the input value range of the calculation expression or mathematical function, and can ensure that the calculation expression or mathematical function can be calculated smoothly without violating physical laws or mathematical rules.

[0062] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platform, or of course by hardware. Based on this understanding, the above-mentioned technical solution, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact-disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0063] Finally, it should be noted that the road damage and availability assessment method, system, and apparatus based on vector data disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing road damage and availability based on vector data, characterized in that, The method includes: S1, Obtain road vector data; The road vector data includes road surface vector data and damage patch vector data; S2, The road vector data is processed to obtain road damage assessment results and road availability assessment results.

2. The road damage and availability assessment method based on vector data according to claim 1, characterized in that, The acquisition of road vector data includes: S11, acquire remote sensing image data of the target area; S12, The remote sensing image data is processed to obtain target road data and road surface vector data; S13, Based on the target road data, the optical remote sensing image data is processed to obtain optical remote sensing image data before damage and optical remote sensing image data after damage. S14, the light-sensing remote sensing image data before damage and the light-sensing remote sensing image data after damage are extracted and processed to obtain damage patch vector data.

3. The road damage and availability assessment method based on vector data according to claim 1, characterized in that, The process of processing the road vector data to obtain road damage assessment results and road availability assessment results includes: S21, Process the road vector data to obtain road damage assessment results; S22, The road vector data is processed to obtain the road availability assessment result.

4. The road damage and availability assessment method based on vector data according to claim 3, characterized in that, The process of processing the road vector data to obtain road damage assessment results includes: S211, Using a geometric information calculation model, the road vector data is processed to obtain road geometric damage values; The expression for the geometric information calculation model is: in, This represents the total area of ​​all damaged patches on the road after the damage occurred. This indicates the total area of ​​the road before the damage; This represents the geometric damage value of the road; S212, Using a texture information calculation model, the road vector data is processed to obtain road texture damage values; S213, Perform road damage calculation processing on the road geometric damage value and the road texture damage value to obtain the road damage assessment result; The expression for calculating road damage is as follows: in, The numerical value representing road damage is the road damage assessment result. Indicates the road geometric damage weight; The weights representing road texture damage; This represents the geometric damage value of the road; This represents the road texture damage value.

5. The road damage and availability assessment method based on vector data according to claim 4, characterized in that, The method of using a texture information calculation model to process the road vector data to obtain road texture damage values ​​includes: S2121, Using a grayscale calculation model, the road vector data is processed to obtain the average grayscale value of the road image; S2122, Perform standard deviation calculation on the mean grayscale value of the road image to obtain the standard deviation value of the grayscale value of the road image; S2123, Perform statistical processing on the road vector data to obtain image content measurement values; S2124, calculate and process the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain the road texture damage value.

6. The road damage and availability assessment method based on vector data according to claim 5, characterized in that, The grayscale calculation model expression is as follows: in, This represents the average grayscale value of the road image. This indicates the number of rows in the road vector data; This indicates the number of columns in the road vector data; The first of the road vector data Line number The grayscale value of a column of pixels; This represents the row index of the road vector data; The column index representing the road vector data; The expression for calculating the standard deviation is: in, This represents the standard deviation of the grayscale value of the road image; The expression for the statistical processing is: in, The grayscale entropy value of the road image represents the image content metric. Indicates the first The probability of gray levels appearing; Indicates the index of grayscale level.

7. The road damage and availability assessment method based on vector data according to claim 5, characterized in that, The process of calculating the mean gray level of the road image, the standard deviation of the gray level of the road image, and the image content metric to obtain the road texture damage value includes: S21241, Perform a first damage value calculation on the mean grayscale value of the road image to obtain the mean damage value of the image; The expression for calculating the first damage value is: in, This represents the mean damage value of the image; This represents the average grayscale value of the road image in the damaged area of ​​the remote sensing image before the damage occurred. The grayscale value of the road image in the damaged area of ​​the remote sensing image after damage is represented. S21242, Perform a second damage value calculation on the grayscale standard deviation of the road image to obtain the road image standard deviation damage value; S21243, Perform a third damage value calculation on the image content metric to obtain the road image entropy damage value; S21244, The mean damage value of the image, the standard deviation damage value of the road image, and the entropy damage value of the road image are comprehensively processed to obtain the road texture damage value.

8. A road damage and availability assessment device based on vector data, characterized in that, The device includes: a data acquisition module and an evaluation processing module; The data acquisition module is used to acquire road vector data; The road vector data includes road surface vector data and damage patch vector data; The evaluation processing module is used to process the road vector data to obtain road damage assessment results and road availability assessment results.

9. A road damage and availability assessment device based on vector data, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the road damage and availability assessment method based on vector data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the road damage and availability assessment method based on vector data as described in any one of claims 1-7.

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