A method and system for testing the load-bearing capacity of asphalt pavement

Abstract

The present invention provides a method and system for testing the load-bearing capacity of an asphalt pavement, wherein the method comprises: obtaining first asphalt concrete pavement information; constructing and training a first asphalt concrete pavement analysis model; inputting the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, wherein the first output result comprises a first adjustment parameter; obtaining first environmental information; obtaining first load flow information; constructing and training a first pavement load-bearing performance analysis model; inputting the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement load-bearing performance analysis model to obtain a second output result; performing an asphalt pavement load-bearing capacity test and evaluation according to the second output result to obtain a first load-bearing capacity evaluation result.

Description

Technical Field

[0001] The invention relates to the technical field of road engineering, and in particular to a method and a system for testing the load-bearing capacity of an asphalt pavement. Background Art

[0002] Asphalt concrete is made by mixing asphalt concrete and sand and gravel minerals in a certain proportion. Asphalt concrete paved roads have both strength and toughness. During the use of asphalt concrete, if the temperature is too high or the traffic volume is heavy, it will cause cracks or rutting and other damage to the road, seriously affecting car driving.

[0003] Currently, it is not possible to detect and evaluate the load-bearing capacity of asphalt concrete roads and whether they will be damaged. We can only conduct traffic flow warning adjustments based on the designed load-bearing capacity of the road and carry out maintenance when the road is damaged.

[0004] In the process of implementing the technical solution of the invention in this application, it is found that the above technology has at least the following technical problems:

[0005] There are technical problems in the prior art that the load-bearing capacity of asphalt concrete pavement cannot be accurately detected and evaluated, and the possibility of pavement damage on the asphalt pavement cannot be evaluated. Summary of the invention

[0006] The present application provides a method and system for testing the load-bearing capacity of asphalt pavement, which is used to solve the technical problems existing in the prior art that the load-bearing capacity of asphalt concrete pavement cannot be accurately detected and evaluated, and the possibility of pavement damage on asphalt pavement cannot be evaluated.

[0007] In view of the above problems, the present application provides a method and system for testing the load-bearing capacity of an asphalt pavement.

[0008] The first aspect of the present application provides a method for testing the load-bearing capacity of an asphalt pavement, the method comprising: obtaining first asphalt concrete pavement information; constructing and training a first asphalt concrete pavement analysis model; inputting the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, the first output result including a first adjustment parameter; obtaining first environmental information; obtaining first load flow information; constructing and training a first pavement load-bearing performance analysis model; inputting the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement load-bearing performance analysis model to obtain a second output result; performing an asphalt pavement load-bearing capacity test and evaluation based on the second output result to obtain a first load-bearing capacity evaluation result.

[0009] According to a second aspect of the present application, a system for testing the load-bearing capacity of an asphalt pavement is provided, the system comprising: a first acquisition unit, the first acquisition unit being used to obtain first asphalt concrete pavement information; a first construction unit, the first construction unit being used to construct and train a first asphalt concrete pavement analysis model; a first processing unit, the first processing unit being used to input the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, the first output result comprising a first adjustment parameter; a second acquisition unit, the second acquisition unit being used to obtain first environmental information; a third acquisition unit, the first three acquisition units being used to obtain first load flow information; a second construction unit, the second construction unit being used to construct and train a first pavement load-bearing performance analysis model; a second processing unit, the second processing unit being used to input the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement load-bearing performance analysis model to obtain a second output result; and a third processing unit, the third processing unit being used to perform an asphalt pavement load-bearing capacity test and evaluation based on the second output result to obtain a first load-bearing capacity evaluation result.

[0010] The third aspect of the present application provides a system for testing the load-bearing capacity of asphalt pavement, comprising: a processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the system executes the steps of the method described in the first aspect.

[0011] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] The technical solution provided in the present application collects pavement information related to the laying of asphalt concrete pavement and the use of the pavement, constructs and trains a first asphalt concrete pavement analysis model, inputs the pavement information into the first asphalt concrete pavement analysis model, obtains adjustment parameters based on the pavement use, and then obtains environmental information on the use of the asphalt pavement and load flow information of automobile transportation, constructs and trains a pavement load-bearing performance analysis model, inputs the above-mentioned pavement information, adjustment parameters, environmental information and load flow information into the pavement load-bearing performance analysis model, and conducts an evaluation and analysis of the load-bearing performance of the asphalt pavement and whether road damage is likely to occur. The present application obtains asphalt concrete pavement information and inputs it into the first asphalt concrete pavement analysis model, obtains adjustment parameters for adjusting the evaluation standards according to the usage of the pavement, thereby improving the accuracy of asphalt pavement performance evaluation, and evaluates the pavement bearing capacity based on environmental information, load flow information, pavement information and adjustment parameters. The evaluation is performed through multi-dimensional information, which can improve the accuracy and generalization of pavement performance evaluation. The first pavement bearing performance analysis model in the present application is trained using a method for improving the accuracy of supervised learning, which can effectively improve the accuracy of model evaluation predictions. The present application constructs an intelligent asphalt pavement performance detection and evaluation method, which can intelligently and accurately perform asphalt pavement performance detection and evaluation, predict possible pavement damage problems, and improve the inspection and maintenance efficiency of asphalt pavements, thereby achieving the technical effect of improving the accuracy and efficiency of detecting and evaluating the bearing capacity of asphalt pavements and possible pavement damage problems.

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of a method for testing the load-bearing capacity of an asphalt pavement provided in this application;

[0016] Figure 2 A schematic diagram of a process for obtaining first load flow information in a method for testing the load-bearing capacity of an asphalt pavement provided in the present application;

[0017] Figure 3 A schematic diagram of a process for obtaining a first pavement load-bearing performance analysis model in a method for testing the load-bearing performance of an asphalt pavement provided in the present application;

[0018] Figure 4 A schematic diagram of the system structure for testing the load-bearing capacity of asphalt pavement is provided for this application;

[0019] Figure 5This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0020] Explanation of the figure marks: first obtaining unit 11, first building unit 12, first processing unit 13, second obtaining unit 14, third obtaining unit 15, second building unit 16, second processing unit 17, third processing unit 18, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. DETAILED DESCRIPTION

[0021] The present application provides a method and system for testing the load-bearing capacity of asphalt pavement, which is used to solve the technical problems existing in the prior art that the load-bearing capacity of asphalt concrete pavement cannot be accurately detected and evaluated, and the possibility of pavement damage on asphalt pavement cannot be evaluated.

[0022] Application Overview

[0023] Asphalt concrete is made by mixing asphalt coagulation and sand and gravel in a certain proportion. Asphalt can be divided into petroleum asphalt, coal tar asphalt, etc. Asphalt concrete pavement has both strength and toughness. It can not only stably support the driving of cars, but also reduce the bumps during the driving of cars. In addition, asphalt can be recycled and used, which is more environmentally friendly. During the use of asphalt concrete, if the temperature is too high or the traffic volume is large, it will cause cracks or rutting on the road. Among them, cracks include: transverse cracks, longitudinal cracks, reflective cracks, etc. Road damage seriously affects the safety of car driving. At present, it is impossible to detect and evaluate the load-bearing performance of asphalt concrete roads and whether they will be damaged. It can only be based on the load-bearing performance of the road design to warn and adjust the traffic flow to avoid damage to the road caused by excessive traffic or overloading of cars, and to repair the road when it is damaged. There are technical problems in the prior art that it is impossible to accurately detect and evaluate the load-bearing capacity of asphalt concrete pavements, and it is impossible to evaluate the possibility of pavement damage on asphalt pavements.

[0024] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0025] The technical solution provided in the present application collects pavement information related to the laying of asphalt concrete pavement and the use of the pavement, constructs and trains a first asphalt concrete pavement analysis model, inputs the pavement information into the first asphalt concrete pavement analysis model, obtains adjustment parameters based on the pavement use, and then obtains environmental information on the use of the asphalt pavement and load flow information of automobile transportation, constructs and trains a pavement load-bearing performance analysis model, inputs the above-mentioned pavement information, adjustment parameters, environmental information and load flow information into the pavement load-bearing performance analysis model, and conducts an evaluation and analysis of the load-bearing performance of the asphalt pavement and whether road damage is likely to occur.

[0026] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0027] Embodiment 1

[0028] like Figure 1 As shown, the present application provides a method for testing the load-bearing capacity of an asphalt pavement, the method comprising:

[0029] S100: obtaining first asphalt concrete pavement information;

[0030] Specifically, the first asphalt concrete pavement is a pavement of any specification paved with any asphalt concrete in the prior art, including a highway pavement or a city road pavement, etc. The first asphalt concrete pavement information includes information of multiple dimensions of the first asphalt concrete pavement, which can reflect the current use status of the first asphalt concrete. For example, the first asphalt concrete pavement information can reflect that the pavement is currently in a period of just being put into use, a period of running-in, or a period of poor quality requiring maintenance.

[0031] Preferably, the first asphalt concrete pavement information includes the paving base material information, paving time limit information and maintenance information during the paving process of the first asphalt concrete pavement. The base material information includes multiple information during the asphalt concrete mixed paving process, exemplarily including asphalt information, gravel information, asphalt and gravel ratio information, base material heating temperature, base material softening point and density information, etc., but not limited thereto. The first asphalt concrete pavement information can be obtained based on the engineering design information during the pavement paving.

[0032] The paving time limit information includes the paving time of the first asphalt concrete pavement, that is, the time from the completion of the paving of the first asphalt concrete pavement and its commissioning to the present. Generally, the service life of an asphalt concrete pavement is 15 to 20 years. The paving time limit information can reflect the usage of the asphalt pavement. If the paving time limit information is large, the pavement condition is correspondingly poor.

[0033] Maintenance information includes the type, number and time of maintenance of the first asphalt concrete pavement from the time it was paved and put into use. During the maintenance of the asphalt concrete pavement, the damaged part is often cleared and a new pavement is laid. The new pavement is different from the old pavement in structure and is more likely to be damaged by reflective cracks under stress during use. Therefore, maintenance information can reflect the use status of the pavement, so maintenance information is used as pavement information. Maintenance information and paving time limit information can be obtained through the logs recorded by maintenance personnel during the use of the pavement.

[0034] Preferably, step S100 in the method provided by the present application includes:

[0035] S110: Obtaining first asphalt concrete base material information, obtaining first road surface paving time limit information, and obtaining first road surface maintenance information;

[0036] S120: constructing a pavement information three-dimensional coordinate system according to the first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information;

[0037] S130: Obtaining a first adjustment parameter vector based on the road surface information three-dimensional coordinate system;

[0038] S140: Obtain a second adjustment parameter based on the first adjustment parameter vector;

[0039] S150: Use the second adjustment parameter to adjust the first asphalt concrete base material information, and use the adjusted first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information as the first asphalt concrete pavement information.

[0040] Specifically, after obtaining the above-mentioned first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information, since the first asphalt concrete base material information, such as the base material structure, composition and performance, has changed compared with the beginning of paving during the use and maintenance of the pavement, the first asphalt concrete base material information needs to be adjusted.

[0041] Specifically, the base material information set, paving time limit information set and maintenance information set of multiple asphalt concrete pavements are obtained based on big data collection, and the base material information set, paving time limit information set and maintenance information set are fitted respectively, and the fitted base material information set, paving time limit information set and maintenance information set are used as three coordinate axes to construct a three-dimensional coordinate system of pavement information. Exemplarily, the three-dimensional coordinate system is constructed with the base material information set as the x-coordinate axis, the paving time limit information set as the y-coordinate axis, and the maintenance information set as the z-coordinate axis.

[0042] Based on the three-dimensional coordinate system, the first asphalt concrete base material information, the first pavement paving time limit information, and the first pavement maintenance information are input into the three-dimensional coordinate system to obtain the point of the current first asphalt concrete pavement usage status in the three-dimensional coordinate system. Based on this point and the origin of the three-dimensional coordinate system, a corresponding vector is obtained, which is the first adjustment parameter vector, and its corresponding second adjustment parameter is obtained based on the first adjustment parameter vector. Among them, if the laying period is longer and the number of maintenance times is more, the modulus of the first adjustment parameter vector is larger, and the corresponding second adjustment parameter is larger.

[0043] If the paving time limit corresponding to the first pavement paving time limit information is smaller, or the number of maintenance times corresponding to the first pavement maintenance information is smaller, the second adjustment parameter is smaller, and the first asphalt concrete base material information is adjusted using the second adjustment parameter, and the base material condition corresponding to the adjusted first asphalt concrete base material information is closer to the base material condition at the time of paving, that is, the current pavement base material information is basically the same as the base material information at the time of paving. The specific adjustment can be made according to the corresponding maintenance type and base material information and other data.

[0044] In this way, the present application uses the adjusted first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information as the first asphalt concrete pavement information, wherein the base material information can reflect the current base material condition of the pavement after adjustment, and combined with the paving time limit information and the maintenance information, it can reflect the current condition information of the pavement, thereby establishing a corresponding data foundation for the detection and evaluation of the pavement's load-bearing performance, and can perform targeted performance evaluation based on the pavement usage conditions, thereby achieving the technical effect of improving the accuracy of pavement performance evaluation.

[0045] S200: constructing and training a first asphalt concrete pavement analysis model;

[0046] The first asphalt concrete pavement analysis model is used to output the corresponding first adjustment parameter according to the above-mentioned first asphalt concrete pavement information, and the first adjustment parameter is used to adjust the evaluation result when the pavement performance evaluation is performed according to the first asphalt concrete pavement information. For example, the longer the paving period in the first asphalt concrete pavement information is or the worse the base material condition is, the larger the first adjustment parameter is, and the greater the influence of the first adjustment parameter on the pavement performance evaluation is. For example, when evaluating whether the first adjustment parameter is likely to be damaged, the evaluation result is more likely to be inclined to the possibility of pavement damage.

[0047] Step S200 in the method provided in this application includes:

[0048] S210: Obtain historical asphalt concrete pavement information set;

[0049] S220: obtaining multiple sets of training data and multiple sets of verification data, wherein each set of data in the multiple sets of training data and the multiple sets of verification data includes: asphalt concrete pavement information and identification information for identifying the first adjustment parameter;

[0050] S230: constructing a neural network model, and using the multiple sets of training data to train the neural network model until the output result reaches convergence or a preset accuracy rate;

[0051] S240: Using the multiple groups of verification data to verify the accuracy of the neural network model, and after the preset accuracy is met, obtaining the first asphalt concrete pavement analysis model.

[0052] Specifically, based on the aforementioned step of obtaining the first asphalt concrete pavement information, historical asphalt concrete pavement information of multiple asphalt concrete pavements is obtained based on big data collection as a historical asphalt concrete pavement information set. The historical asphalt concrete pavement information set includes a historical asphalt concrete base material information set adjusted according to the second adjustment parameter set, a historical pavement paving time limit information set, and a historical pavement maintenance information set. Among them, the adjustment parameters in the second adjustment parameter set can be obtained based on step S100.

[0053] Based on the above historical asphalt concrete pavement information set, multiple sets of training data and multiple sets of verification data are obtained, and the multiple sets of training data and the multiple sets of verification data both include: asphalt concrete pavement information and identification information for identifying the first adjustment parameter. Exemplarily, the historical asphalt concrete pavement information set is divided according to a ratio of 7:3 to obtain multiple sets of training data and multiple sets of verification data.

[0054] Construct a neural network model, which is a network model built on the structure of the human brain and the logic of processing information. It can perform complex logical operations and nonlinear operations based on input information, and can simulate the human brain to make judgments and predictions based on information. The neural network model in this application includes an input layer, an implicit processing layer, and an output layer, and includes multiple neurons inside. Correspondingly, the neurons are divided into input units, processing units, and output units. Each input unit can be used to input asphalt concrete pavement information. The processing unit is used to perform logical analysis and judgment based on the input asphalt concrete pavement information, and it cannot be observed from the outside. The output unit is used to output the final first adjustment parameter. The connection between neurons is a weight value, and the weight value identifies the importance of different information processing judgments between neurons.

[0055] The present application trains the above-mentioned neural network model through multiple sets of training data, forms the structure and weights in the model during the training process, and completes the training when the output result of the model reaches convergence or reaches a preset accuracy. Then, the accuracy of the model is verified using the above-mentioned multiple sets of verification data to avoid underfitting or overfitting of the model. If the accuracy of the output result of the model meets the requirements, the above-mentioned first asphalt concrete pavement analysis model is obtained.

[0056] The present application collects historical pavement information, obtains training data and verification data, and trains a neural network model. It can output an accurate first adjustment parameter based on the pavement information, and then perform a final pavement performance test and evaluation based on the first adjustment parameter. It can accurately make relevant adjustments to the evaluation results based on the pavement information, thereby improving the accuracy of data processing and the accuracy of pavement performance detection and evaluation.

[0057] S300: Inputting the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, wherein the first output result includes a first adjustment parameter;

[0058] Specifically, based on the trained first asphalt concrete pavement analysis model, the first asphalt concrete pavement information of the current first asphalt concrete pavement is input into the model to obtain an output result, which includes the first adjustment parameter.

[0059] S400: Obtaining first environment information;

[0060] The first environmental information is used to reflect the external environment during the use of the asphalt concrete road surface, which is the external environmental information during the time period of the current first asphalt concrete road pavement performance evaluation. The first environmental information includes data such as the external environmental temperature and environmental humidity during the use of the first asphalt concrete road surface.

[0061] Among them, the ambient temperature has a great influence on the performance of asphalt concrete pavement. When the temperature is high, the strength of the asphalt concrete pavement decreases. Under large loads and tensile stresses, it is easy to be damaged by dents, ruts, cracks, etc. Therefore, it is necessary to detect and evaluate the performance of the concrete pavement based on the ambient information. In general, it can be considered that the worse the first ambient information is relative to the pavement, for example, the higher the temperature, the worse the performance of the asphalt concrete pavement, and the more likely it is to be damaged.

[0062] S500: Obtaining first load flow information;

[0063] Specifically, the first load flow information is the load flow information brought by the vehicle flow during the use of the first asphalt concrete pavement. The greater the vehicle flow and the more heavy vehicles there are, the greater the load flow information. If the load flow exceeds the load-bearing performance of the pavement for a period of time, the pavement may be damaged by cracks or other damages under the action of tensile stress. Therefore, it is necessary to detect and evaluate the pavement performance based on the first load flow information.

[0064] like Figure 2 As shown, step S500 in the method provided by this application includes:

[0065] S510: Collecting a plurality of first load and flow data sets on the first asphalt concrete pavement according to a first time span;

[0066] S520: Obtaining a data set with the largest load flow among the plurality of first load flow data sets as a second load flow data set;

[0067] S530: Collecting a plurality of third load flow data sets within the second load flow data set according to a second time span;

[0068] S540: performing least squares fitting on the plurality of third load flow data sets according to the time sequence of the second time span to obtain a first load flow fitting function;

[0069] S550: Obtaining the first load flow information based on the first load flow fitting function.

[0070] Specifically, the first time span and the second time span are a time period during the use of the first asphalt concrete, the first time span is greater than the second time span, and the first time span includes multiple second time spans. For example, the first time span may be one year, and the second time span may be one month. Alternatively, the first time span may be one month, and the second time span may be one day.

[0071] In order to collect representative and accurate first load flow information, firstly, a plurality of first load flow data sets are collected on the first asphalt concrete pavement according to the first time span. The first load flow data set includes vehicle flow information and load information corresponding to various types of vehicles on the first asphalt concrete pavement within the first time span, which can be obtained through monitoring at toll stations at the entrance and exit of the expressway, as well as through monitoring image information of the highway.

[0072] Based on the above-mentioned multiple first load flow data sets, a first load flow data set with the largest load flow data is selected as the second load flow data set. The load flow data in the second load flow data set is continuously collected according to the second time span to obtain multiple third load flow data sets.

[0073] Based on the multiple third load flow data sets, the multiple third load flow data sets are sorted in time sequence within the second time span, and then least squares fitting is performed to fit the multiple third load flow data sets into a function curve in time sequence to obtain a first load flow fitting function. If the first load flow fitting function is overfitted, the function can be adjusted by regularization.

[0074] Based on the above-mentioned first load flow fitting function, all the stagnation points therein are calculated and obtained. The load flow at the stagnation point is relatively stable. The point with the largest load flow among all the stagnation points is further obtained as the above-mentioned first load flow information, which can represent the larger and more stable load flow information within the second time span.

[0075] For example, in the method provided by the present application, the first time span is set to one month, the second time span is set to one day, and the load flow data sets within multiple days are collected according to the first time span, that is, multiple first load flow data sets. Then, the largest first load flow data set is selected as the second load flow data set, and then the load flow data sets within 24 hours in one day are obtained according to the second time span, that is, 24 third load flow data sets, and then fitting is performed to obtain a function, and finally a load flow with relatively large and relatively stable load flow information within one day is obtained, such as 9:00 AM or 18:00 PM in one day, thereby obtaining the first load flow information.

[0076] Since it is necessary to consider whether the pavement may be damaged under a large load during the performance detection and evaluation of asphalt concrete pavement, the present application selects a first load flow data set with the largest load flow, and then obtains and selects the inflection point with the largest load flow in the first load flow fitting function as the first load flow information, which can improve the representativeness and accuracy of the pavement performance evaluation and avoid the problem that the performance evaluation results cannot meet the requirements of larger load flow operations when using smaller load flow information for pavement performance evaluation.

[0077] S600: construct and train the first pavement load-bearing performance analysis model;

[0078] like Figure 3 As shown, step S600 in the method provided by this application includes:

[0079] S610: Collect and obtain a historical environment information set and a historical load flow information set;

[0080] S620: obtaining multiple sets of training sample data, each set of training sample data including the historical asphalt concrete pavement information, the historical environmental information, and the historical load and flow information;

[0081] S630: constructing a first sub-pavement load-bearing performance analysis model;

[0082] S640: inputting the plurality of sets of training sample data into the first sub-pavement load-bearing performance analysis model to obtain a first analysis result;

[0083] S650: Iterating according to the first analysis result and the multiple sets of training sample data to construct a second sub-pavement load-bearing performance analysis model to obtain a second analysis result;

[0084] S660: Calculate a first total accuracy rate of the first analysis result and the second analysis result, and determine whether the first total accuracy rate meets a first total accuracy rate threshold;

[0085] S670: If the first total accuracy rate meets the first total accuracy rate threshold, obtaining the first road surface load-bearing performance analysis model;

[0086] S680: If the first total accuracy rate does not meet the first total accuracy rate threshold, continue to iterate until the total accuracy rates of all sub-pavement load-bearing performance analysis models meet the first total accuracy rate threshold, thereby obtaining the first pavement load-bearing performance analysis model.

[0087] Specifically, based on the aforementioned steps S400 and S500, historical environmental information sets and historical load and flow information sets of multiple types and multiple asphalt concrete roads are collected and obtained. And based on the aforementioned step S100, historical asphalt concrete pavement information sets of multiple types and multiple asphalt concrete roads are obtained. At the same time, when collecting the historical environmental information sets, historical load and flow information sets, and historical asphalt concrete pavement information sets, the pavement condition information corresponding to each historical environmental information, historical load and flow information, and historical asphalt concrete pavement information is obtained according to the maintenance records of the asphalt concrete pavement, and the pavement condition information set is obtained. The pavement condition information includes whether the corresponding pavement is damaged, and the type of damage when it occurs.

[0088] Then, the above historical environment information set, historical load and flow information set and historical asphalt concrete pavement information set are used as training data to construct and train the first pavement load-bearing performance analysis model. Each set of training data includes: corresponding historical asphalt concrete pavement information, historical environment information and historical load and flow information.

[0089] Specifically, first, a first sub-road bearing performance analysis model is constructed. The first sub-road bearing performance analysis model is a classifier, which performs classification regression prediction according to the three-dimensional data of environmental information, road surface information, and load flow information. The prediction result is whether the corresponding road surface will be damaged, and the type of damage that may occur. Then, the prediction result is judged according to the historical road surface maintenance data. For example, the first sub-road bearing performance analysis model is based on historical environmental information, historical road surface information, and historical load flow information, and sets a certain classification threshold for classification. The road surface corresponding to the above data is predicted to be not damaged, and then the road surface is judged according to the historical maintenance data. If no damage occurs, the prediction result of the first sub-road bearing performance analysis model is wrong. If damage occurs, the prediction result is correct.

[0090] The thresholds within the first sub-pavement load-bearing performance analysis model can be set according to needs, including environmental information thresholds, load and flow information thresholds, and pavement information thresholds. During the prediction process, the historical environmental information, historical pavement information, and historical load and flow information input into the first sub-pavement load-bearing performance analysis model are compared and classified with the corresponding thresholds, and the prediction is completed based on the decision tree principle.

[0091] In addition, the first sub-pavement load-bearing performance analysis model can also be set to predict whether the pavement will have load cracks, non-load cracks, or no damage based on historical environmental information, historical pavement information, and historical load flow information. For example, if the load information is too large and the environmental information is normal, it may be predicted that the pavement will have load cracks, and then the correctness of the prediction result can be determined based on the historical maintenance records of the pavement.

[0092] The above-mentioned multiple groups of training sample data are input into the first sub-pavement bearing performance analysis model to obtain multiple first analysis results. Among the multiple first analysis results, predictions are made based on the asphalt concrete pavement information, historical environmental information, and historical load and flow information in the multiple groups of training sample data to obtain the above-mentioned first analysis results.

[0093] Then, based on the above-mentioned multiple first analysis results and multiple sets of training sample data input into the first sub-road bearing performance analysis model, iteration is performed to construct a second sub-road bearing performance analysis model to obtain multiple second analysis results. Specifically, step S650 in the method provided by the present application includes:

[0094] S651: Obtain a first sub-model accuracy and a first set of erroneous training sample data according to the first analysis result, where the first set of erroneous training sample data is included in the multiple sets of training sample data;

[0095] S652: Obtain a first weight value and a second weight value according to the accuracy of the first sub-model;

[0096] S653: performing weight allocation on the multiple groups of training sample data and the first set of erroneous training sample data according to the first weight value to obtain a first weight allocation result;

[0097] S654: Construct the load-bearing performance analysis model of the second sub-pavement;

[0098] S655: Input the first weight distribution result and the multiple groups of training sample data into the second sub-pavement load-bearing performance analysis model to obtain the second analysis result.

[0099] Specifically, after obtaining multiple first analysis results, based on the above-mentioned road condition information set, it is determined whether the prediction results in the multiple first analysis results are correct, and the first sub-model accuracy is obtained. The first sub-model accuracy is the ratio of the number of correct first analysis results to the number of all first analysis results. Then, the training sample data corresponding to the first analysis results with incorrect predictions in the multiple first analysis results are extracted to obtain a first error training sample data set. Each first error training sample data includes the above-mentioned historical asphalt concrete pavement information, historical environmental information, and historical load and flow information.

[0100] Then, according to the accuracy of the above-mentioned first sub-model, the first weight value and the second weight value are obtained. Among them, the first weight value is the weight value of the erroneous first analysis results in multiple first analysis results. In the embodiment of the present application, the first pavement bearing performance analysis model finally obtained is formed by merging multiple sub-pavement bearing performance analysis models, and the second weight value is the weight value of the analysis result of the first sub-pavement bearing performance analysis model in the final analysis result of the merged first pavement bearing performance analysis model.

[0101] For example, if there are M groups of training sample data, M is a positive integer, and there are M first analysis results, then the weight of each group of original training sample data is , there are N wrong first analysis results in the first analysis results, N is a positive integer, then the error rate of the first sub-pavement load-bearing performance analysis model is , then the second weight value is:

[0102]

[0103] Then, the first weight value is:

[0104]

[0105] in, is the second weight value, is the first weight value, is the natural logarithm. According to the first weight value, the weight of the correct first analysis result among the multiple first analysis results can be calculated, which is equal to .

[0106] Based on the above first weight value and the weight value of the correct first analysis result, weights are allocated to multiple sets of training sample data and the first set of erroneous training sample data so that the sum of the weight values ​​of the multiple sets of training sample data is 1, and a first weight allocation result is obtained.

[0107] The weight of the correct first analysis result corresponding to the training data sample is:

[0108]

[0109] The weight of the training data sample corresponding to the wrong first analysis result is:

[0110]

[0111] Since the original weight values ​​of the M group training data are , the weights are redistributed according to the multiple first analysis results of the first sub-pavement bearing performance analysis model, wherein the weight value of the training sample data corresponding to the erroneous first analysis result is relatively large. When constructing the second sub-pavement bearing performance analysis model and performing analysis and prediction, the training sample data corresponding to the erroneous first analysis result can be analyzed emphatically, thereby obtaining a better classification threshold and ultimately obtaining a more accurate prediction result.

[0112] Similar to the principle of the first sub-pavement load-bearing performance analysis model, a second sub-pavement load-bearing performance analysis model is constructed, wherein the classification threshold in the second sub-pavement load-bearing performance analysis model is different from the threshold in the first sub-pavement load-bearing performance analysis model.

[0113] The above-mentioned first weight distribution result and the corresponding multiple groups of training sample data are input into the second sub-road bearing performance analysis model, and then multiple second analysis results are obtained. The multiple second analysis results also include multiple error results, but the number of error results in the multiple first analysis results may be different, and the corresponding training sample data are not the same. Then, based on the multiple second analysis results and the multiple erroneous second analysis results, the second sub-model accuracy of the multiple second analysis results is obtained, as in the above steps, and then the third weight value and the fourth weight value of the second sub-road bearing performance analysis model are obtained. The fourth weight value is the weight of the second analysis result of the second sub-road bearing performance analysis model in the final analysis result of the first road bearing performance analysis model.

[0114] Furthermore, step S660 in the method provided by the present application includes:

[0115] S661: Obtain the second analysis result of the second sub-pavement load-bearing performance analysis model;

[0116] S662: Obtain a second sub-model accuracy rate according to the second analysis result and the first weight allocation result;

[0117] S663: Obtain a third weight value and a fourth weight value according to the accuracy of the second sub-model;

[0118] S664: performing weighted calculation on the accuracy of the first sub-model according to the second weight value, and performing weighted calculation on the accuracy of the second sub-model using the fourth weight value;

[0119] S665: Add the weighted first sub-model accuracy and the second sub-model accuracy to obtain the first total accuracy.

[0120] Specifically, as described above, multiple second analysis results of the second sub-road bearing performance analysis model are obtained, and the second sub-model accuracy is obtained according to the multiple second analysis results and the first weight distribution result. For example, if there are P wrong second analysis results among M second analysis results, P is a positive integer, then the second sub-model accuracy is However, due to the first weight distribution result, the weight of each group of training sample data changes, and the accuracy of the second sub-model also changes. It needs to be calculated according to the first weight distribution result. The accuracy of the second sub-model is as follows:

[0121]

[0122] According to the accuracy of the second sub-model, a third weight value and a fourth weight value are obtained. Exemplarily, the fourth weight value is:

[0123]

[0124] According to the accuracy of the second sub-model and the first weight distribution result, the third weight value is obtained:

[0125]

[0126] In this way, the third weight value and the fourth weight value of the second sub-pavement load-bearing performance analysis model are calculated and obtained.

[0127] The first sub-model accuracy is weighted based on the second weight value, the second sub-model accuracy is weighted based on the fourth weight value, and then summed to obtain the first total accuracy, as follows:

[0128]

[0129] If the first total accuracy does not meet the preset accuracy threshold, continue to iterate, build a third sub-pavement load-bearing performance analysis model, weight multiple groups of training sample data according to the third weight value and the fourth weight value, obtain the second weight allocation result, and then input the second weight allocation result and the multiple groups of training sample data into the third sub-pavement load-bearing performance analysis model until the total accuracy of all sub-pavement load-bearing performance analysis models meets the first total accuracy threshold.

[0130] Iterations are performed based on the above steps to finally obtain a first pavement bearing performance analysis model formed by merging multiple sub-pavement bearing performance analysis models. For each sub-pavement bearing performance analysis model, analysis and judgment are performed based on multiple sets of input training sample data to determine whether the pavement corresponding to the historical asphalt concrete pavement information, historical environmental information, and historical load and flow information in each set of training sample data is likely to be damaged, and the type of damage that may occur.

[0131] Moreover, except for the first sub-pavement bearing performance analysis model, other sub-pavement bearing performance analysis models will obtain weights according to the accuracy corresponding to the analysis results of the previous sub-pavement bearing performance analysis model, and then perform more accurate threshold division on the training sample data corresponding to the wrong analysis results of the previous sub-pavement bearing performance analysis model, and then obtain more accurate analysis results. And, the analysis results of the first pavement bearing performance analysis model finally obtained are obtained by weighted calculation of the analysis results of each sub-pavement bearing performance analysis model, and the weight value also comes from the analysis accuracy of the sub-pavement bearing performance analysis model. The higher the accuracy of a sub-pavement bearing performance analysis model, the more weight value it occupies, so that the first pavement bearing performance analysis model finally obtained can accurately analyze according to the input first asphalt concrete pavement information, first environmental information, and first load flow information, obtain the most accurate analysis results, and analyze the bearing performance of the current first asphalt concrete pavement to determine whether it may be damaged.

[0132] The embodiment of the present application constructs and trains a first pavement bearing performance analysis model through the above-mentioned method, performs iterative training based on multiple sub-pavement bearing performance analysis models, and finally obtains the first pavement bearing performance analysis model. When performing pavement performance analysis, the analysis results of multiple sub-pavement bearing performance analysis models are weighted and added based on the weight of each sub-pavement bearing performance analysis model, so as to obtain the most accurate final evaluation and analysis result, construct a model and method for intelligently analyzing the performance of asphalt concrete pavement, and achieve the technical effect of accurately and intelligently performing pavement performance evaluation and analysis.

[0133] S700: Inputting the first asphalt concrete pavement information, the first environmental information, the first load flow information, and the first adjustment parameter into the first pavement bearing performance analysis model to obtain a second output result;

[0134] S800: Performing a load-bearing capacity test and evaluation of the asphalt pavement according to the second output result to obtain a first load-bearing capacity evaluation result.

[0135] Specifically, after training to obtain the above-mentioned first pavement load-bearing performance analysis model, the first asphalt concrete pavement information, the first environmental information, and the first load flow information are input into the first pavement load-bearing performance analysis model, and the analysis results of multiple sub-pavement load-bearing performance analysis models can be preliminarily obtained. Then, according to the weight of each sub-pavement load-bearing performance analysis model in the first pavement load-bearing performance analysis model, weighted calculation is performed to obtain preliminary analysis results.

[0136] Then, based on the preliminary analysis results, the first pavement bearing performance analysis model also includes a part for adjusting the calculation. The preliminary analysis results are input into this part. The preliminary analysis results can be adjusted according to the above-mentioned first adjustment parameters to achieve adjustments based on the usage of the asphalt concrete pavement. For example, if the pavement has been in use for a long time or has been repaired many times, the preliminary analysis results will be adjusted and modified to obtain a final second output result. Then, the second output result is used as the final evaluation result of the asphalt concrete pavement's bearing capacity and other performance.

[0137] In summary, the method provided in the present application constructs a three-dimensional coordinate system according to asphalt concrete base material information, paving time information and maintenance information, adjusts the base material information according to vector parameters, obtains asphalt concrete pavement information, and inputs it into the first asphalt concrete pavement analysis model, obtains adjustment parameters for adjusting the evaluation standard according to the use of the pavement, and can improve the accuracy of asphalt pavement performance evaluation, evaluates the pavement bearing capacity based on environmental information, representative load flow information, pavement information and adjustment parameters, and evaluates through multi-dimensional information, which can improve the accuracy and generalization of pavement performance evaluation. The first pavement bearing performance analysis model in the present application is trained using a method to improve the accuracy of supervised learning, which can effectively improve the accuracy of model evaluation prediction. The present application constructs an intelligent asphalt pavement performance detection and evaluation method, which can intelligently and accurately perform asphalt pavement performance detection and evaluation, predict possible pavement damage problems, and improve the inspection and maintenance efficiency of asphalt pavements, achieving the technical effect of improving the accuracy and efficiency of detecting and evaluating the bearing capacity of asphalt pavements and possible pavement damage problems.

[0138] Embodiment 2

[0139] Based on the same inventive concept as the method for testing the load-bearing property of asphalt pavement in the aforementioned embodiment, Figure 4 As shown, the present application provides a system for testing the load-bearing capacity of an asphalt pavement, wherein the system comprises:

[0140] A first obtaining unit 11, wherein the first obtaining unit 11 is used to obtain first asphalt concrete pavement information;

[0141] A first construction unit 12, the first construction unit 12 is used to construct and train a first asphalt concrete pavement analysis model;

[0142] A first processing unit 13, the first processing unit 13 is used to input the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, wherein the first output result includes a first adjustment parameter;

[0143] A second obtaining unit 14, wherein the second obtaining unit 14 is used to obtain first environment information;

[0144] A third obtaining unit 15, wherein the first third obtaining unit 15 is used to obtain first load flow information;

[0145] A second construction unit 16, the second construction unit 16 is used to construct and train a first pavement load-bearing performance analysis model;

[0146] A second processing unit 17, the second processing unit 17 is used to input the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement bearing performance analysis model to obtain a second output result;

[0147] The third processing unit 18 is used to perform a load-bearing capacity test and evaluation of the asphalt pavement according to the second output result to obtain a first load-bearing capacity evaluation result.

[0148] Furthermore, the system also includes:

[0149] A fourth obtaining unit, the fourth obtaining unit is used to obtain first asphalt concrete base material information, obtain first pavement paving time limit information, and obtain first pavement maintenance information;

[0150] A third construction unit, the third construction unit is used to construct a pavement information three-dimensional coordinate system according to the first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information;

[0151] a fifth obtaining unit, the fifth obtaining unit being configured to obtain a first adjustment parameter vector based on the road surface information three-dimensional coordinate system;

[0152] a sixth obtaining unit, configured to obtain a second adjustment parameter based on the first adjustment parameter vector;

[0153] The fourth processing unit is used to adjust the first asphalt concrete base material information by using the second adjustment parameter, and use the adjusted first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information as the first asphalt concrete pavement information.

[0154] Furthermore, the system also includes:

[0155] A seventh obtaining unit, the seventh obtaining unit is used to obtain a historical asphalt concrete pavement information set;

[0156] An eighth obtaining unit, the eighth obtaining unit is used to obtain multiple sets of training data and multiple sets of verification data, wherein each set of data in the multiple sets of training data and the multiple sets of verification data includes: asphalt concrete pavement information and identification information for identifying the first adjustment parameter;

[0157] A fourth construction unit, the fourth construction unit is used to construct a neural network model, and train the neural network model using the multiple sets of training data until the output result reaches convergence or a preset accuracy rate;

[0158] The fifth processing unit is used to verify the accuracy of the neural network model using the multiple groups of verification data, and obtain the first asphalt concrete pavement analysis model after meeting the preset accuracy.

[0159] Furthermore, the system also includes:

[0160] a ninth obtaining unit, the ninth obtaining unit being used to collect a plurality of first load and flow data sets on the first asphalt concrete pavement according to a first time span;

[0161] a tenth obtaining unit, the tenth obtaining unit being used to obtain one of the plurality of first load flow data sets with the largest load flow as a second load flow data set;

[0162] an eleventh obtaining unit, the eleventh obtaining unit being configured to collect a plurality of third load flow data sets within the second load flow data set according to a second time span;

[0163] a sixth processing unit, the sixth processing unit being configured to perform least squares fitting on the plurality of third load flow data sets according to the time sequence of the second time span to obtain a first load flow fitting function;

[0164] A twelfth obtaining unit is used to obtain the first load flow information based on the first load flow fitting function.

[0165] Furthermore, the system also includes:

[0166] A thirteenth obtaining unit, the thirteenth obtaining unit is used to collect and obtain a historical environment information set and a historical load and flow information set;

[0167] A fourteenth obtaining unit, the fourteenth obtaining unit is used to obtain multiple groups of training sample data, each group of training sample data includes the historical asphalt concrete pavement information, the historical environmental information, and the historical load and flow information;

[0168] A fifth construction unit, the fifth construction unit is used to construct a load-bearing performance analysis model of the first sub-pavement;

[0169] A seventh processing unit, the seventh processing unit is used to input the first sub-pavement load-bearing performance analysis model using the multiple sets of training sample data to obtain a first analysis result;

[0170] an eighth processing unit, the eighth processing unit being configured to iterate according to the first analysis result and the plurality of sets of training sample data, to construct a second sub-pavement load-bearing performance analysis model, and to obtain a second analysis result;

[0171] a first judgment unit, the first judgment unit being configured to calculate a first total accuracy rate of the first analysis result and the second analysis result, and to judge whether the first total accuracy rate meets a first total accuracy rate threshold;

[0172] a fifteenth obtaining unit, configured to obtain the first pavement load-bearing performance analysis model if the first total accuracy rate satisfies the first total accuracy rate threshold;

[0173] The ninth processing unit is used to continue iterating if the first total accuracy does not meet the first total accuracy threshold, until the total accuracy of all sub-pavement bearing performance analysis models meets the first total accuracy threshold, so as to obtain the first pavement bearing performance analysis model.

[0174] Furthermore, the system also includes:

[0175] A sixteenth obtaining unit, the sixteenth obtaining unit being used to obtain a first sub-model accuracy and a first erroneous training sample data set according to the first analysis result, wherein the first erroneous training sample data set is included in the multiple sets of training sample data;

[0176] a tenth processing unit, configured to obtain a first weight value and a second weight value according to the accuracy of the first sub-model;

[0177] an eleventh processing unit, configured to perform weight allocation on the plurality of groups of training sample data and the first set of erroneous training sample data according to the first weight value to obtain a first weight allocation result;

[0178] A sixth construction unit, the sixth construction unit being used to construct a load-bearing performance analysis model of the second sub-pavement;

[0179] A twelfth processing unit is used to input the first weight distribution result and the multiple groups of training sample data into the second sub-pavement load-bearing performance analysis model to obtain the second analysis result.

[0180] Furthermore, the system also includes:

[0181] a seventeenth obtaining unit, the seventeenth obtaining unit being used to obtain the second analysis result of the second sub-pavement load-bearing performance analysis model;

[0182] an eighteenth obtaining unit, the eighteenth obtaining unit being configured to obtain a second sub-model accuracy rate according to the second analysis result and the first weight allocation result;

[0183] a nineteenth obtaining unit, the nineteenth obtaining unit being configured to obtain a third weight value and a fourth weight value according to the accuracy rate of the second sub-model;

[0184] a thirteenth processing unit, the thirteenth processing unit being configured to perform weighted calculation on the accuracy of the first sub-model according to the second weight value, and to perform weighted calculation on the accuracy of the second sub-model using the fourth weight value;

[0185] A fourteenth processing unit is used to add the weighted first sub-model accuracy and the second sub-model accuracy to obtain the first total accuracy.

[0186] Embodiment 3

[0187] Based on the same inventive concept as the method for testing the load-bearing capacity of an asphalt pavement in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in embodiment one is implemented.

[0188] Exemplary Electronic Devices

[0189] Reference below Figure 5 To describe the electronic device of this application,

[0190] Based on the same inventive concept as a method for testing the load-bearing capacity of an asphalt pavement in the aforementioned embodiment, the present application also provides a system for testing the load-bearing capacity of an asphalt pavement, comprising: a processor, the processor is coupled to a memory, the memory is used to store a program, and when the program is executed by the processor, the system executes the steps of the method described in embodiment one.

[0191] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0192] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0193] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access networks, etc.

[0194] The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus architecture 304. The memory may also be integrated with the processor.

[0195] The memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301, thereby realizing a method for testing the load-bearing capacity of an asphalt pavement provided in the above embodiment of the present application.

[0196] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions

[0197] The instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0198] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for testing the load-bearing capacity of an asphalt pavement, characterized in that: The method comprises: Obtaining first asphalt concrete pavement information; Construct and train the first asphalt concrete pavement analysis model; Inputting the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, wherein the first output result includes a first adjustment parameter; Obtaining first environment information; Obtaining first load flow information; Construct and train the first pavement load-bearing performance analysis model; Inputting the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement bearing performance analysis model to obtain a second output result; Performing a load-bearing capacity test and evaluation of the asphalt pavement according to the second output result to obtain a first load-bearing capacity evaluation result; The obtaining of the first asphalt concrete pavement information includes: Obtaining first asphalt concrete base material information, obtaining first road surface paving time limit information, and obtaining first road surface maintenance information; Constructing a pavement information three-dimensional coordinate system according to the first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information; Based on the road surface information three-dimensional coordinate system, obtaining a first adjustment parameter vector; Based on the first adjustment parameter vector, obtaining a second adjustment parameter; The first asphalt concrete base material information is adjusted using the second adjustment parameter, and the adjusted first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information are used as the first asphalt concrete pavement information.

2. The method according to claim 1, characterized in that: The step of constructing and training a first asphalt concrete pavement analysis model includes: Obtain historical asphalt concrete pavement information collection; Obtaining multiple sets of training data and multiple sets of verification data, wherein each set of data in the multiple sets of training data and the multiple sets of verification data includes: asphalt concrete pavement information and identification information for identifying the first adjustment parameter; Constructing a neural network model, and using the multiple sets of training data to train the neural network model until the output results reach convergence or a preset accuracy rate; The accuracy of the neural network model is verified using the multiple groups of verification data, and the first asphalt concrete pavement analysis model is obtained after the preset accuracy is met.

3. The method according to claim 1, characterized in that The obtaining of the first load flow information includes: Collecting a plurality of first load and flow data sets on the first asphalt concrete pavement according to a first time span; Obtaining one of the plurality of first load flow data sets with the largest load flow as a second load flow data set; collecting a plurality of third load flow data sets within the second load flow data set according to a second time span; performing least squares fitting on the plurality of third load flow data sets according to the time sequence of the second time span to obtain a first load flow fitting function; Based on the first load flow fitting function, the first load flow information is obtained.

4. The method according to claim 2, characterized in that: The step of constructing and training a first road surface load-bearing performance analysis model includes: Collect and obtain historical environmental information sets and historical load and flow information sets; Obtaining multiple sets of training sample data, each set of training sample data includes the historical asphalt concrete pavement information, the historical environmental information, and the historical load and flow information; Construct the first sub-pavement load-bearing performance analysis model; Using the plurality of sets of training sample data to input the first sub-pavement load-bearing performance analysis model to obtain a first analysis result; Iterate according to the first analysis result and the multiple groups of training sample data to construct a second sub-pavement load-bearing performance analysis model to obtain a second analysis result; Calculating a first total accuracy rate of the first analysis result and the second analysis result, and determining whether the first total accuracy rate meets a first total accuracy rate threshold; If the first total accuracy rate meets the first total accuracy rate threshold, obtaining the first pavement load-bearing performance analysis model; If the first total accuracy rate does not meet the first total accuracy rate threshold, the iteration is continued until the total accuracy rates of all sub-pavement load-bearing performance analysis models meet the first total accuracy rate threshold, thereby obtaining the first pavement load-bearing performance analysis model.

5. The method according to claim 4, characterized in that The iterative process to construct a second sub-pavement load-bearing performance analysis model and obtain a second analysis result includes: According to the first analysis result, a first sub-model accuracy rate and a first set of erroneous training sample data are obtained, wherein the first set of erroneous training sample data is included in the multiple sets of training sample data; Obtaining a first weight value and a second weight value according to the accuracy of the first sub-model; Performing weight allocation on the multiple groups of training sample data and the first set of erroneous training sample data according to the first weight value to obtain a first weight allocation result; Construct the load-bearing performance analysis model of the second sub-pavement; The first weight distribution result and the multiple groups of training sample data are input into the second sub-pavement load-bearing performance analysis model to obtain the second analysis result.

6. The method according to claim 5, characterized in that The calculating a first total accuracy rate of the first analysis result and the second analysis result includes: Obtaining the second analysis result of the second sub-pavement load-bearing performance analysis model; Obtaining a second sub-model accuracy rate according to the second analysis result and the first weight allocation result; Obtaining a third weight value and a fourth weight value according to the accuracy of the second sub-model; Performing weighted calculation on the accuracy of the first sub-model according to the second weight value, and performing weighted calculation on the accuracy of the second sub-model using the fourth weight value; The weighted first sub-model accuracy and the second sub-model accuracy are added together to obtain the first total accuracy.

7. A system for testing the load-bearing capacity of an asphalt pavement, characterized in that: The system comprises: A first obtaining unit, the first obtaining unit is used to obtain first asphalt concrete pavement information; A first construction unit, the first construction unit is used to construct and train a first asphalt concrete pavement analysis model; a first processing unit, the first processing unit being used to input the first asphalt concrete pavement information into the first asphalt concrete pavement analysis model to obtain a first output result, wherein the first output result includes a first adjustment parameter; A second obtaining unit, the second obtaining unit is used to obtain first environment information; A third obtaining unit, the third obtaining unit is used to obtain first load flow information; A second construction unit, the second construction unit is used to construct and train a first pavement load-bearing performance analysis model; a second processing unit, the second processing unit being used to input the first asphalt concrete pavement information, the first environmental information, the first load flow information and the first adjustment parameter into the first pavement bearing performance analysis model to obtain a second output result; a third processing unit, the third processing unit being used to perform a load-bearing capacity test and evaluation of an asphalt pavement according to the second output result to obtain a first load-bearing capacity evaluation result; A fourth obtaining unit, the fourth obtaining unit is used to obtain first asphalt concrete base material information, obtain first pavement paving time limit information, and obtain first pavement maintenance information; A third construction unit, the third construction unit is used to construct a pavement information three-dimensional coordinate system according to the first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information; a fifth obtaining unit, the fifth obtaining unit being configured to obtain a first adjustment parameter vector based on the road surface information three-dimensional coordinate system; a sixth obtaining unit, configured to obtain a second adjustment parameter based on the first adjustment parameter vector; The fourth processing unit is used to adjust the first asphalt concrete base material information by using the second adjustment parameter, and use the adjusted first asphalt concrete base material information, the first pavement paving time limit information and the first pavement maintenance information as the first asphalt concrete pavement information.

8. A system for testing the load-bearing capacity of an asphalt pavement, characterized in that: include: A processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the system is enabled to perform the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which implements the steps of the method according to any one of claims 1 to 6 when executed by a processor.

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