Highway toughness evaluation method and system based on multi-disaster mountainous area, electronic equipment and storage medium

Abstract

The invention discloses a multi-disaster mountainous area road toughness evaluation method and system, electronic equipment and a storage medium, and relates to the technical field of road evaluation methods. According to the multi-disaster mountainous area road toughness evaluation method, index data is established for an evaluation object through an entropy weight method; the method comprises the following steps: collecting index data, carrying out standardization processing on the data, building a road model by adopting a numerical terrain model method and a triangulation network interpolation and fitting technology based on the obtained data, carrying out entropy calculation on the index data based on the standardized data, and carrying out weight calculation on the index data based on the calculated index data. And based on the weight and entropy calculation result, applying the normalized weight to the evaluation result of each index, obtaining comprehensive evaluation processing, outputting an evaluation report, visualizing the report, determining the weakness and vulnerability of the highway system through toughness evaluation, and providing the evaluation report for a decision maker, thereby improving and enhancing the anti-disaster ability of the highway.

Description

Technical Field

[0001] The present invention relates to the field of BIM technology, and more particularly to a method, system, electronic device and storage medium for evaluating the resilience of roads in disaster-prone mountainous areas. Background Art

[0002] Highways in disaster-prone mountainous areas are usually exposed to damage from natural disasters, such as earthquakes, landslides, mud-rock flows, etc. The damage to highway infrastructure caused by the above disasters may lead to traffic interruption, casualties and economic losses. Highways in disaster-prone mountainous areas have the following characteristics: First, complex geological environment. Disaster-prone mountain roads are usually located in areas with complex geological conditions and steep terrain, with high risks of natural disasters such as earthquakes, landslides, and mud-rock flows. Second, long-term exposure to harsh environments: Highways in disaster-prone mountainous areas are often exposed to adverse climatic conditions, such as high temperatures, low temperatures, high humidity, and strong winds, which can easily lead to material aging and structural damage; Third, the impact of traffic disruption is serious. The damage to roads will lead to traffic disruption, which in turn will affect emergency rescue, material transportation and economic activities.

[0003] The above characteristics make it difficult to conduct data and comprehensive performance evaluation of disaster-prone mountain roads through conventional means. Their disaster resistance capabilities are unknown. When encountering disasters, traffic interruptions are likely to occur, affecting material transportation or emergency rescue.

[0004] Therefore, it is urgent to propose a resilience assessment method for highways in disaster-prone mountainous areas. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the prior art and provide a method, system, electronic device and storage medium for evaluating the resilience of highways in disaster-prone mountainous areas. The method aims to use the entropy weight method to evaluate the resilience and recovery capacity of the highway system when facing natural disasters, determine the weaknesses and vulnerabilities of the highway system through resilience assessment, and provide assessment reports for decision makers, thereby improving and strengthening the disaster resistance of highways.

[0006] To this end, this application provides a road resilience assessment method based on disaster-prone mountainous areas, including Based on the entropy weight method, establish indicator data for the evaluation object, collect and process data based on the indicator data, and standardize the collected data; Based on the obtained data, the numerical terrain model method is adopted, and the highway model is collaboratively established using triangulated network interpolation and fitting techniques; Based on the processed data, the entropy value of the indicator data is calculated, and for each different indicator, its information content or uncertainty is calculated and measured; Based on the indicator data after entropy calculation, the indicator data is weighted and normalized to ensure that the sum of the weights is 1; Based on the weight and entropy calculation results, the normalized weights are applied to the evaluation results of each indicator to obtain a comprehensive evaluation process; Based on the comprehensive evaluation processing results, an evaluation report is output and a visual mapping is provided for the evaluation report.

[0007] In some specific implementations, index data is established for the evaluation object, and data collection and processing are performed based on the index data, and the collected data is standardized, specifically including: Assume that the specific value of the collected indicator data is X; By finding the indicator data and , for each indicator data , subtract it , get the corresponding difference, and then divide the difference by minus The result is the standardized data, that is, , its specific value range is between; If you need to substitute a numerical value for indicator calculation; Suppose there is a set of indicator data that needs to be standardized and calculated; By finding and , for each indicator data , subtract it ; Divide the difference by minus ; After reducing the fraction, the specific standardized value is obtained: The processed data can eliminate the dimensional differences between different indicators, making them more accurate when compared and analyzed on the same scale.

[0008] In some specific implementations, a numerical terrain model method is used, and a triangulated network interpolation and fitting technology is used to collaboratively establish a highway model, specifically: The model is established based on the collected and processed data, using the triangular interpolation method; According to the location of discrete data points, the data points are connected into a triangular mesh using a triangulation algorithm, and then the given interpolation points are , you need to calculate its centroid coordinates in each triangle; The barycentric coordinates are composed of three weights Express, satisfy ; For triangle : In the formula, is the interpolation point and the area of ​​the triangle, , , The triangle area; Based on the barycentric coordinates and the values ​​of discrete data points, interpolation calculations can be performed. Assume that there are three data point values , , Corresponding to the vertices of the triangle , , , then the interpolation point The estimated value at It can be calculated by the following formula: In the formula, , , is the triangle vertex , , The corresponding data point value; Based on the specific results obtained by interpolation, the terrain data is approximated by fitting curves to describe the shape of the terrain. After interpolation and fitting processing, a highway model is generated, and a visualization mapping is provided for the generated highway model based on the visualization module.

[0009] In some specific implementations, entropy calculation is performed on the indicator data, and for each different indicator, the information content or uncertainty is calculated and measured, specifically: Shannon entropy is used to measure the degree of data dispersion. The larger the value, the more dispersed the data is, and vice versa, the more concentrated the data is. In the formula, Indicators The information entropy of Indicates the number of different values ​​of the indicator. Indicator In the value The probability of being at Assume that pavement shear strength is selected as the indicator data, and the following data are collected: First, calculate the frequency of each value: Substitute into the Shannon entropy formula for calculation: According to the calculated information entropy value, subsequent weight calculation can be performed according to the obtained information entropy value.

[0010] In some specific implementations, based on the index data after the entropy value is calculated, the index data is weighted and the weights are normalized to ensure that the sum of the weights is 1, specifically: Based on the entropy weight method, the information entropy value is used to calculate the weight of each indicator. The weight is calculated based on the information entropy of the indicator and is determined using the normalized form of the information entropy value. In the formula, Indicator The weight of Indicator The information entropy of Indicates the total number of indicators; Assume that there are three indicators: pavement shear strength, pavement layer thickness and pavement crack resistance, and their information entropy values ​​have been calculated: , , ; Substitute the above information entropy value into the weight calculation formula: The weight value of each indicator is calculated by the above formula.

[0011] In some specific implementations, based on the weight and entropy value calculation results, the normalized weights are applied to the evaluation results of each indicator to obtain a comprehensive evaluation process, which specifically includes: When the standardized indicator data is multiplied by the corresponding weight and summed to obtain the final comprehensive evaluation result, the weighted summation calculation formula is used; In the formula, Indicates the comprehensive evaluation results. Represents the standardized indicator data, represents the corresponding weight, and n represents the total number of indicators; Assume that there are three indicators, namely, pavement shear strength, pavement layer thickness and pavement crack resistance, and the indicators have been standardized and calculated to obtain the corresponding weight values; , , , , , Multiply the standardized indicator data by the corresponding weight and sum them up: The comprehensive evaluation result R is obtained by calculation, which represents the evaluation value of pavement toughness; If other index data need to be calculated, such as the road surface water erosion resistance performance, the road surface water erosion resistance performance can be set as P, and it can also be calculated by the formula: Where P represents the road surface's water erosion resistance. Represents the standardized indicator data, Represents the corresponding weight, and n represents the total number of indicators.

[0012] This application also provides a road resilience assessment system based on disaster-prone mountainous areas, including The data collection module establishes index data based on the evaluation object and collects elevation data based on the index data; The processing module processes the data based on the collected indicator data, and performs weight reduction and cleaning on the data; The cloud server, based on the processed data, is used to provide entropy value calculation for the indicator data in real time; The 3D modeling module generates a 3D highway model based on the triangular interpolation method; A visualization module, used to provide visualization mapping processing; The evaluation module evaluates the comprehensive performance of the highway based on the calculation results of the entropy weight method and outputs an evaluation report.

[0013] In some specific implementations, a three-dimensional highway model is generated based on a triangular interpolation method, specifically: Based on the elevation data collected in the data collection module, the UV mapping and triangular interpolation methods are used to coordinate the model generation process. First, the elevation value of the highway is mapped to the UV coordinate, and the elevation value is assigned to each vertex on the highway. For each vertex, according to its elevation value, it is converted into a UV coordinate; Based on the assigned vertex UV coordinates and elevation values, the UV coordinates of the points inside the triangle are calculated by interpolation, and the UV coordinates of the points inside the triangle are calculated using the triangular interpolation method; Modeling is performed based on the calculated UV coordinates. The highway area is divided into a series of triangles according to the geometric shape of the highway model. For each triangle, texture mapping is performed using the calculated vertex UV coordinates and elevation values, as well as the internal point UV coordinates obtained by interpolation. The texture image can be applied to each triangle to match the terrain elevation data.

[0014] The present application further provides an electronic device, including: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described.

[0015] The present application further provides a computer-readable medium having a computer program stored thereon, which implements any of the methods described above when the program is executed by a processor.

[0016] The present application provides a method for assessing the resilience of highways in disaster-prone mountainous areas. The entropy weight method is used to establish index data for the assessment object. The data is collected and standardized based on the index data. A numerical terrain model method is used based on the obtained data, and a highway model is jointly established using triangulated network interpolation and fitting technology. The entropy value of the index data is calculated based on the standardized data, and the weight is calculated based on the calculated index data. The weight is normalized to ensure that the sum of the weights is 1. Based on the weight and entropy calculation results, the normalized weight is applied to the assessment results of each indicator to obtain a comprehensive assessment process, output an assessment report, and visualize the report. Through resilience assessment, the weaknesses and vulnerabilities of the highway system are determined, and an assessment report is provided to decision makers, thereby improving and strengthening the disaster resistance of highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a method for assessing the resilience of highways in disaster-prone mountainous areas provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The present application will describe the embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein, but rather these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0021] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0022] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0023] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0024] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0025] Please refer to Figure 1 , which shows the process of an embodiment of the method for evaluating the resilience of highways in disaster-prone mountainous areas according to the present disclosure.

[0026] like Figure 1 As shown, the survey method based on BIM fusion includes the following steps: Step 100: Based on the entropy weight method, establish indicator data for the evaluation object, collect and process data based on the indicator data, and standardize the collected data.

[0027] Here, it is assumed that the specific value of the collected indicator data is X; By finding the indicator data and , for each indicator data , subtract it , get the corresponding difference, and then divide the difference by minus The result is the standardized data, that is, , its specific value range is between; If you need to substitute a numerical value for indicator calculation; Suppose there is a set of indicator data that needs to be standardized and calculated; By finding and , for each indicator data , subtract it ; Divide the difference by minus ; After reducing the fraction, the specific standardized value is obtained: The processed data can eliminate the dimensional differences between different indicators, making them more accurate when compared and analyzed on the same scale.

[0028] Step 200: Based on the obtained data, a numerical terrain model method is adopted, and a triangulation interpolation and fitting technology is used to collaboratively establish a highway model.

[0029] Here, the model is established based on the data obtained after collection and processing, and the triangular interpolation method is used; According to the location of discrete data points, the data points are connected into a triangular mesh using a triangulation algorithm, and then the given interpolation points are , you need to calculate its centroid coordinates in each triangle; Among them, the triangulation algorithm in the above content adopts Triangulation; Specifically: take the point set to be segmented as input and create a super triangle that contains all the points. The vertices of the super triangle must be guaranteed to contain all the points and exceed the boundary of the point set; Iterative subdivision: For each point, iterative subdivision is performed according to the following steps; first, find the triangle containing the current point: starting from the super triangle, check each triangle and find the triangle containing the current point; second, delete the triangles related to the current point: starting from the found triangle containing the current point, delete these triangles; then, build new triangles: for each edge of the deleted triangle, if the edge has no shared adjacent triangles, build a new triangle connecting the current point with the two vertices of the edge.

[0030] The triangles formed during the subdivision process may intersect with the boundaries of the super triangle. Therefore, these triangles that intersect with the boundaries of the super triangle need to be deleted. After the subdivision is completed, the output result is a set of non-overlapping triangles that satisfy condition.

[0031] Here, the barycentric coordinates are composed of three weights Express, satisfy ; For triangle : In the formula, is the interpolation point and the area of ​​the triangle, , , The triangle area; Based on the barycentric coordinates and the values ​​of discrete data points, interpolation calculations can be performed. Assume that there are three data point values , , Corresponding to the vertices of the triangle , , , then the interpolation point The estimated value at It can be calculated by the following formula: In the formula, , , is the triangle vertex , , The corresponding data point value; Based on the specific results obtained by interpolation, the terrain data is approximated by fitting curves to describe the shape of the terrain. After interpolation and fitting processing, a highway model is generated, and a visualization mapping is provided for the generated highway model based on the visualization module.

[0032] Step 300: Based on the processed data, entropy values ​​are calculated for the indicator data, and for each different indicator, the amount of information or uncertainty is calculated and measured.

[0033] Here, Shannon entropy is used to measure the degree of discreteness of data. The larger the value, the more dispersed the data is, and vice versa, the more concentrated the data is. In the formula, Indicator The information entropy of Indicates the number of different values ​​of the indicator. Indicator In the value The probability of being at Assume that pavement shear strength is selected as the indicator data, and the following data are collected: First, calculate the frequency of each value: Substitute into the Shannon entropy formula for calculation: According to the calculated information entropy value, subsequent weight calculation can be performed according to the obtained information entropy value.

[0034] Step 400: Based on the indicator data after entropy value calculation, weight calculation is performed on the indicator data, and the weights are normalized to ensure that the sum of the weights is 1.

[0035] Here, based on the entropy weight method, the information entropy value is used to calculate the weight of each indicator. The weight is calculated based on the information entropy of the indicator and is determined using the normalized form of the information entropy value. In the formula, Indicator The weight of Indicator The information entropy of Indicates the total number of indicators; Assume that there are three indicators: pavement shear strength, pavement layer thickness and pavement crack resistance, and their information entropy values ​​have been calculated: , , ; Substitute the above information entropy value into the weight calculation formula: The weight value of each indicator is calculated by the above formula.

[0036] Step 500: Based on the weight and entropy value calculation results, the normalized weight is applied to the evaluation results of each indicator to obtain a comprehensive evaluation process.

[0037] Here, specifically include: When the standardized indicator data is multiplied by the corresponding weight and summed to obtain the final comprehensive evaluation result, the weighted summation calculation formula is used; In the formula, Indicates the comprehensive evaluation results. Represents the standardized indicator data, represents the corresponding weight, and n represents the total number of indicators; Assume that there are three indicators, namely, pavement shear strength, pavement layer thickness and pavement crack resistance, and the indicators have been standardized and calculated to obtain the corresponding weight values; , , , , , Multiply the standardized indicator data by the corresponding weight and sum them up: The comprehensive evaluation result R is obtained by calculation, which represents the evaluation value of pavement toughness; If other index data need to be calculated, such as the road surface water erosion resistance performance, the road surface water erosion resistance performance can be set as P, and it can also be calculated by the formula: Where P represents the road surface's water erosion resistance. Represents the standardized indicator data, Represents the corresponding weight, and n represents the total number of indicators.

[0038] Step 600: Based on the weight and entropy value calculation results, the normalized weight is applied to the evaluation results of each indicator to obtain a comprehensive evaluation process.

[0039] The present application provides a method for assessing the resilience of highways in disaster-prone mountainous areas. The entropy weight method is used to establish index data for the assessment object. The data is collected and standardized based on the index data. A numerical terrain model method is used based on the obtained data, and a highway model is jointly established using triangulated network interpolation and fitting technology. The entropy value of the index data is calculated based on the standardized data, and the weight is calculated based on the calculated index data. The weight is normalized to ensure that the sum of the weights is 1. Based on the weight and entropy calculation results, the normalized weight is applied to the assessment results of each indicator to obtain a comprehensive assessment process, output an assessment report, and visualize the report. Through resilience assessment, the weaknesses and vulnerabilities of the highway system are determined, and an assessment report is provided to decision makers, thereby improving and strengthening the disaster resistance of highways.

[0040] Further, as an implementation of the above method, the present disclosure provides a system for evaluating the resilience of highways in disaster-prone mountainous areas. Figure 1 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0041] The present application also provides a highway resilience assessment system based on disaster-prone mountainous areas, including a data collection module, which establishes index data based on the assessment object and collects elevation data based on the index data; a processing module, which processes data based on the collected index data and reduces and cleans the data; a cloud server, based on the processed data, is used to provide entropy value calculation for the index data in real time; a three-dimensional modeling module, which generates a three-dimensional model of the highway based on the triangular interpolation method; a visualization module, which is used to provide visualization mapping processing; an evaluation module, which evaluates the comprehensive performance of the highway based on the calculation results of the entropy weight method, and outputs an evaluation report.

[0042] Among the above contents, it should be noted that the three-dimensional model of the highway is generated based on the triangular interpolation method, specifically: Based on the elevation data collected in the data collection module, the UV mapping and triangular interpolation methods are used to coordinate the model generation process. First, the elevation value of the highway is mapped to the UV coordinate, and the elevation value is assigned to each vertex on the highway. For each vertex, according to its elevation value, it is converted into a UV coordinate; Based on the assigned vertex UV coordinates and elevation values, the UV coordinates of the points inside the triangle are calculated by interpolation, and the UV coordinates of the points inside the triangle are calculated using the triangular interpolation method; Modeling is performed based on the calculated UV coordinates. The highway area is divided into a series of triangles according to the geometric shape of the highway model. For each triangle, texture mapping is performed using the calculated vertex UV coordinates and elevation values, as well as the internal point UV coordinates obtained by interpolation. The texture image can be applied to each triangle to match the terrain elevation data.

[0043] The present application further provides an electronic device, which may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0044] An electronic device may include a processing system (e.g., a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory or a program loaded from a storage system into a random access memory. Various programs and data required for the operation of the electronic device are also stored in the ROM. The processing system, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0045] Typically, the following systems can be connected to the I / O interface: input systems including, for example, touch screens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output systems including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage systems including, for example, magnetic tapes, hard disks, etc.; and communication systems. The communication system can allow an electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0046] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication system, or installed from a storage system, or installed from a ROM. When the computer program is executed by a processing system, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0047] It should be noted that the storage medium of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any storage medium other than computer readable storage media that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, system or device. The program code contained on the storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0048] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0049] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0050] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

[0051] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0052] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for evaluating the resilience of highways in disaster-prone mountainous areas, characterized by: Based on the entropy weight method, establish indicator data for the evaluation object, collect and process data based on the indicator data, and standardize the collected data; Based on the obtained data, the numerical terrain model method is adopted, and the highway model is collaboratively established using triangulated network interpolation and fitting techniques; Based on the processed data, the entropy value of the indicator data is calculated, and for each different indicator, its information content or uncertainty is calculated and measured; Based on the indicator data after entropy calculation, the indicator data is weighted and the weights are normalized to ensure that the sum of the weights is 1; Based on the weight and entropy calculation results, the normalized weights are applied to the evaluation results of each indicator to obtain a comprehensive evaluation process; Based on the comprehensive evaluation processing results, an evaluation report is output and a visual mapping is provided for the evaluation report.

2. The method for evaluating the resilience of highways in disaster-prone mountainous areas according to claim 1 is characterized in that: Based on the entropy weight method, index data is established for the evaluation object, and data collection and processing are carried out based on the index data, and the collected data is standardized, including: Assume that the specific value of the collected indicator data is X; By finding the indicator data and , for each indicator data , subtract it , get the corresponding difference, and then divide the difference by minus The result is the standardized data, that is , its specific value range is between; If you need to substitute a numerical value for indicator calculation; Suppose there is a set of indicator data that needs to be standardized and calculated; By finding and , for each indicator data , subtract it ; Divide the difference by minus ; After reducing the fraction, the specific standardized value is obtained: The processed data can eliminate the dimensional differences between different indicators, making them more accurate when compared and analyzed on the same scale.

3. The method for evaluating the resilience of highways in disaster-prone mountainous areas according to claim 2 is characterized in that: Based on the obtained data, the numerical terrain model method is adopted, and the triangulation interpolation and fitting technology are used to collaboratively establish the highway model, specifically: The model is established based on the collected and processed data, using the triangular interpolation method; According to the location of discrete data points, the data points are connected into a triangular mesh using a triangulation algorithm, and then the interpolation points are given. , you need to calculate its centroid coordinates in each triangle; The barycentric coordinates are composed of three weights express, satisfy ; For triangle : In the formula, is the interpolation point and the area of ​​the triangle, , , The triangle area; Based on the barycentric coordinates and the values ​​of discrete data points, interpolation calculations can be performed. Assume that there are three data point values , , Corresponding to the vertices of the triangle , , , then the interpolation point The estimated value at It can be calculated by the following formula: In the formula, , , is the triangle vertex , , The corresponding data point value; Based on the specific results obtained by interpolation, the terrain data is approximated by fitting curves to describe the shape of the terrain. After interpolation and fitting processing, a highway model is generated, and a visualization mapping is provided for the generated highway model based on the visualization module.

4. The method for evaluating the resilience of highways in disaster-prone mountainous areas as claimed in claim 3 is characterized in that: Based on the processed data, the entropy value of the indicator data is calculated. For each different indicator, the amount of information or uncertainty is calculated, specifically: Shannon entropy is used to measure the degree of discreteness of data. The larger the value, the more dispersed the data is, and vice versa, the more concentrated the data is. In the formula, Indicators The information entropy of Indicates the number of different values ​​of the indicator. Indicators In the value The probability of being at Assume that pavement shear strength is selected as the indicator data, and the following data are collected: First, calculate the frequency of each value: Substitute into the Shannon entropy formula for calculation: According to the calculated information entropy value, subsequent weight calculation can be performed according to the obtained information entropy value.

5. The method for evaluating the resilience of highways in disaster-prone mountainous areas according to claim 4 is characterized in that: Based on the indicator data after entropy calculation, the indicator data is weighted and normalized to ensure that the sum of the weights is 1. Specifically: Based on the entropy weight method, the information entropy value is used to calculate the weight of each indicator. The weight is calculated based on the information entropy of the indicator and is determined using the normalized form of the information entropy value. In the formula, Indicators The weight of Indicators The information entropy of Indicates the total number of indicators; Assume that there are three indicators: pavement shear strength, pavement layer thickness and pavement crack resistance, and their information entropy values ​​have been calculated: ， ， ； Substitute the above information entropy value into the weight calculation formula: The weight value of each indicator is calculated by the above formula.

6. The method for evaluating the resilience of highways in disaster-prone mountainous areas according to claim 5 is characterized in that: Based on the weight and entropy calculation results, the normalized weights are applied to the evaluation results of each indicator to obtain a comprehensive evaluation process, including: When multiplying the standardized indicator data by the corresponding weights and summing them to obtain the final comprehensive evaluation results, the weighted summation calculation formula is used; In the formula, Indicates the comprehensive evaluation results. Represents the standardized indicator data, represents the corresponding weight, and n represents the total number of indicators; Assume that there are three indicators, namely, pavement shear strength, pavement layer thickness and pavement crack resistance, and the indicators have been standardized and calculated to obtain the corresponding weight values; ， ， ， ， ， Multiply the standardized indicator data by the corresponding weight and sum them: The comprehensive evaluation result R is obtained by calculation, which represents the evaluation value of pavement toughness; If other index data need to be calculated, such as the road surface water erosion resistance performance, the road surface water erosion resistance performance can be set as P, which can also be calculated by the formula: Where P represents the road surface's water erosion resistance. Represents the standardized indicator data, Represents the corresponding weight, and n represents the total number of indicators.

7. Based on the disaster-prone mountainous highway resilience assessment system, it is characterized by: include The data collection module establishes index data based on the evaluation object and collects elevation data based on the index data; The processing module processes the data based on the collected indicator data, and performs weight reduction and cleaning on the data; The cloud server, based on the processed data, is used to provide entropy value calculation for the indicator data in real time; The 3D modeling module generates a 3D highway model based on the triangular interpolation method; A visualization module, used to provide visualization mapping processing; The evaluation module evaluates the comprehensive performance of the highway based on the calculation results of the entropy weight method and outputs an evaluation report.

8. The system for evaluating the resilience of highways in disaster-prone mountainous areas as claimed in claim 7 is characterized in that: Based on the triangular interpolation method, a three-dimensional highway model is generated, specifically: Based on the elevation data collected in the data collection module, the UV mapping and triangular interpolation methods are used to coordinate the model generation process. First, the elevation value of the highway is mapped to the UV coordinate, and the elevation value is assigned to each vertex on the highway. For each vertex, according to its elevation value, it is converted into a UV coordinate; Based on the assigned vertex UV coordinates and elevation values, the UV coordinates of the points inside the triangle are calculated by interpolation, and the UV coordinates of the points inside the triangle are calculated using the triangular interpolation method; Modeling is performed based on the calculated UV coordinates. The highway area is divided into a series of triangles according to the geometric shape of the highway model. For each triangle, texture mapping is performed using the calculated vertex UV coordinates and elevation values, as well as the internal point UV coordinates obtained by interpolation. The texture image can be applied to each triangle to match the terrain elevation data.

9. An electronic device, characterized in that include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, characterized in that When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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