Highway subgrade slope collapse monitoring reliability analysis method

Abstract

The invention discloses a highway subgrade slope collapse monitoring reliability analysis method, which belongs to the technical field of subgrade slopes and comprises the following steps: S1, collecting and processing historical data of a highway subgrade slope; s2, determining an optimal highway subgrade slope collapse prediction model; s3, analyzing and predicting the real-time data of the highway subgrade slope, judging the stability condition of the highway subgrade slope, and determining a highway subgrade slope collapse prediction result; and S4, analyzing the reliability of the highway subgrade slope collapse prediction result, and determining a highway subgrade slope collapse monitoring reliability analysis result. The problem that the slope collapse monitoring effect is poor due to the fact that reliability analysis cannot be carried out on highway subgrade slope collapse monitoring in the prior art is solved. According to the method, reliability analysis can be carried out on highway subgrade slope collapse monitoring, whether highway subgrade slope collapse monitoring is accurate or not can be determined, effective prediction can be provided for highway subgrade slope collapse monitoring, and the highway subgrade slope collapse monitoring effect can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of roadbed slopes, in particular to a reliability analysis method for monitoring collapse of roadbed slopes of highways. Background Art

[0002] With the rapid development of expressways in my country, the problem of highway embankment slope collapse has received more and more attention. Among them, the embankment slope refers to the inclined surface connecting the ground on both sides of the embankment cross section, which is divided into embankment slope and cutting slope. It is an important factor affecting the stability of the roadbed. In actual projects, due to the influence of geological environment, hydrological conditions, construction quality and other factors, highway embankment slope collapse accidents occur from time to time, posing a serious threat to road traffic safety.

[0003] Existing technical means have failed to conduct reliability analysis on highway roadbed slope collapse monitoring in a timely manner, failed to promptly determine whether highway roadbed slope collapse monitoring is accurate, and failed to promptly provide effective predictions for highway roadbed slope collapse monitoring, resulting in frequent highway roadbed slope collapse or safety hazards. Summary of the invention

[0004] The purpose of the present invention is to provide a reliability analysis method for highway roadbed slope collapse monitoring, which can perform reliability analysis on highway roadbed slope collapse monitoring, clarify whether the highway roadbed slope collapse monitoring is accurate, provide effective prediction for highway roadbed slope collapse monitoring, improve the effect of highway roadbed slope collapse monitoring, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The reliability analysis method for monitoring the collapse of highway embankment slope includes the following steps: S1: Collecting historical data of highway roadbed slopes based on big data, processing the historical data of highway roadbed slopes based on big data, and determining characteristic data of highway roadbed slopes based on big data; S2: According to the reliability analysis requirements of highway embankment slope collapse monitoring, a highway embankment slope collapse prediction model based on machine learning is constructed, and the highway embankment slope collapse prediction model is tested and optimized to determine the optimal highway embankment slope collapse prediction model; S3: Collect the real-time data of highway embankment slopes, analyze and predict the real-time data of highway embankment slopes based on the optimal highway embankment slope collapse prediction model, judge the stability of highway embankment slopes, and determine the prediction results of highway embankment slope collapse; S4: Analyze the reliability of the highway embankment slope collapse prediction results, determine the matching degree between the highway embankment slope collapse prediction results and the actual results of the highway embankment slope collapse, and determine the reliability analysis results of the highway embankment slope collapse monitoring.

[0006] Preferably, in S1, historical data of highway embankment slopes based on big data is collected, and the following operations are performed: Collect historical design parameters of highway embankment slopes to obtain highway embankment slope design data; Collect historical geological conditions of highway embankment slopes and obtain geological data of highway embankment slopes; Collect historical hydrological conditions of highway embankment slopes to obtain hydrological data of highway embankment slopes; Collect historical construction records of highway embankment slopes to obtain highway embankment slope construction data; Among them, based on the highway roadbed slope design data, highway roadbed slope geological data, highway roadbed slope hydrological data and highway roadbed slope construction data, the highway roadbed slope historical data based on big data is determined.

[0007] Preferably, in S1, the historical data of highway embankment slope based on big data is processed, and the following operations are performed: Obtain historical data of highway embankment slopes based on big data; Clean the historical data of highway embankment slope based on big data, including: Conduct consistency check on historical data of highway embankment slopes based on big data; According to the data consistency requirements, check whether the historical data of highway embankment slopes based on big data contain inconsistent data that is useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the inconsistent data in the historical data of highway embankment slopes based on big data; Check invalid and missing values ​​of historical data of highway embankment slopes based on big data; According to the requirements of data validity and integrity, check whether the historical data of highway embankment slopes based on big data contain invalid values ​​and missing values ​​that are useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the invalid values ​​and missing values ​​in the historical data of highway embankment slopes based on big data; Determine the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring.

[0008] Preferably, in S1, the historical data of highway embankment slope based on big data is processed, and the following operations are further performed: Obtain historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring; The historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring are converted to unify the formats of the historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring; Eliminate the dimensional differences between the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring, and determine standardized historical data of highway embankment slopes; Extract features from standardized historical data of highway embankment slopes; Extract features that can reflect the reliability analysis of highway embankment slope collapse monitoring from standardized highway embankment slope historical data; Determine highway embankment slope characteristic data based on big data.

[0009] Preferably, in S2, a highway embankment slope collapse prediction model based on machine learning is constructed, and the following operations are performed: Obtain highway embankment slope characteristic data based on big data; Divide the highway embankment slope characteristic data based on big data and determine the training set and test set; According to the reliability analysis requirements of highway embankment slope collapse monitoring, a machine learning model framework suitable for highway embankment slope collapse prediction is selected; Based on the training set, the selected machine learning model framework suitable for highway roadbed slope collapse prediction is trained to determine the highway roadbed slope collapse prediction model based on machine learning.

[0010] Preferably, in S2, the highway embankment slope collapse prediction model is tested and optimized, and the following operations are performed: Obtain a highway embankment slope collapse prediction model based on machine learning; Based on the test set, the performance of the highway roadbed slope collapse prediction model based on machine learning is tested to determine whether the highway roadbed slope collapse prediction model based on machine learning can achieve the expected effect; Determine the performance test results of the highway embankment slope collapse prediction model; Based on the performance test results of the highway embankment slope collapse prediction model, the parameters of the highway embankment slope collapse prediction model based on machine learning are adjusted and the structure is optimized, and the highway embankment slope collapse prediction model based on machine learning is repeatedly optimized; Determine the optimal highway embankment slope collapse prediction model.

[0011] Preferably, in S3, the real-time data of the highway embankment slope is analyzed and predicted based on the optimal highway embankment slope collapse prediction model, and the following operations are performed: Obtain the optimal highway embankment slope collapse prediction model and deploy it in actual highway embankment slope collapse monitoring work; According to the reliability analysis requirements of highway embankment slope collapse monitoring, real-time data of highway embankment slopes are collected and input into the optimal highway embankment slope collapse prediction model; Based on the optimal highway embankment slope collapse prediction model, the real-time data of highway embankment slope is analyzed and predicted to determine the stability of highway embankment slope and determine the prediction result of highway embankment slope collapse; Among them, the prediction results of highway roadbed slope collapse include: the stability of the highway roadbed slope is good, that is, the highway roadbed slope has no risk of collapse or the stability of the highway roadbed slope is poor, that is, the highway roadbed slope has a risk of collapse.

[0012] Preferably, in S4, the reliability of the prediction result of the highway embankment slope collapse is analyzed, and the following operations are performed: Obtain the prediction results of highway embankment slope collapse and analyze the reliability of the prediction results of highway embankment slope collapse; Compare and analyze the predicted results of highway embankment slope collapse with the actual results of highway embankment slope collapse, judge the matching degree between the predicted results of highway embankment slope collapse and the actual results of highway embankment slope collapse, and determine the reliability analysis results of highway embankment slope collapse monitoring; Among them, the prediction result of highway embankment slope collapse matches the actual result of highway embankment slope collapse, and the reliability analysis result of highway embankment slope collapse monitoring is that the reliability of highway embankment slope collapse monitoring is high; Among them, if the prediction result of highway roadbed slope collapse does not match the actual result of highway roadbed slope collapse, the reliability analysis result of highway roadbed slope collapse monitoring is that the reliability of highway roadbed slope collapse monitoring is low.

[0013] Preferably, the predicted results of highway embankment slope collapse are compared and analyzed with the actual results of highway embankment slope collapse, including: Obtaining result analysis data for the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse respectively, to obtain first analysis data information and second analysis data information; A first matching value is obtained by performing matching degree analysis and calculation based on the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse; Performing disassembly and data standardization processing on the first analysis data information and the second analysis data information respectively to obtain a plurality of first analysis sub-data and a plurality of second analysis sub-data; Corresponding the first analysis sub-data and the second analysis sub-data, and performing matching analysis calculation according to the corresponding result to obtain a second matching value; A comprehensive analysis is performed on the first matching value and the second matching value to obtain the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse: ; In the above formula, is the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse, is the first weight, As parameters, The prediction results of highway embankment slope collapse are data, The actual result of highway embankment slope collapse is data, is the number of the first analysis sub-data, For the The first analysis of the sub-data location data, For the The second analysis sub-data Location data; Then, the reliability analysis results of highway roadbed slope collapse monitoring are determined according to the matching degree between the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse and the result determination standard.

[0014] Preferably, feature extraction is performed on standardized highway embankment slope historical data, including: The standardized highway embankment slope historical data are analyzed for changes according to the data types, and the standardized highway embankment slope historical data are judged in time sequence whether they have changed, and the analysis and judgment results are obtained; According to the analysis and judgment results, the standardized historical data of the roadbed slope are divided to obtain a first division result and a second division result, wherein the first division result is the standardized historical data of the roadbed slope that has changed, and the second division result is the standardized historical data of the roadbed slope that has not changed; In the first division result, the change characteristics of the first division result sub-data are analyzed, and the corresponding standardized highway roadbed slope historical data are extracted according to the change characteristics to obtain the first division result sub-data characteristics; In the second division result, statistical analysis is performed on the second division result sub-data to obtain the statistical analysis result, and the second division result sub-data features are determined according to the statistical analysis result.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects historical data of highway embankment slopes, processes the historical data of highway embankment slopes, determines characteristic data of highway embankment slopes, constructs a highway embankment slope collapse prediction model based on machine learning according to the reliability analysis requirements of highway embankment slope collapse monitoring, tests and optimizes the highway embankment slope collapse prediction model, determines the optimal highway embankment slope collapse prediction model, collects real-time data of highway embankment slopes, analyzes and predicts the real-time data of highway embankment slopes based on the optimal highway embankment slope collapse prediction model, and judges The stability of highway embankment slopes, determine the prediction results of highway embankment slope collapse, analyze the reliability of highway embankment slope collapse prediction results, judge the matching degree between highway embankment slope collapse prediction results and actual results of highway embankment slope collapse, determine the reliability analysis results of highway embankment slope collapse monitoring, conduct reliability analysis on highway embankment slope collapse monitoring, clarify whether highway embankment slope collapse monitoring is accurate, provide effective prediction for highway embankment slope collapse monitoring, and improve the effect of highway embankment slope collapse monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the reliability analysis method for monitoring the collapse of highway embankment slopes of the present invention; Figure 2 Flow chart of the algorithm for analyzing the reliability of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In order to solve the existing problems of being unable to conduct reliability analysis on highway embankment slope collapse monitoring, being unable to determine whether highway embankment slope collapse monitoring is accurate, and being unable to provide effective predictions for highway embankment slope collapse monitoring, which leads to poor results of highway embankment slope collapse monitoring, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: The reliability analysis method for monitoring the collapse of highway embankment slope includes the following steps: S1: Collecting historical data of highway roadbed slopes based on big data, processing the historical data of highway roadbed slopes based on big data, and determining characteristic data of highway roadbed slopes based on big data; In this embodiment, the historical data of highway embankment slope based on big data is collected, including: Collect historical design parameters of highway embankment slopes to obtain highway embankment slope design data; Specifically, the historical design parameters of highway embankment slopes include slope gradient, slope shape, and slope protection measures; Collect historical geological conditions of highway embankment slopes and obtain geological data of highway embankment slopes; Specifically, the historical geological conditions of the highway embankment slope include soil gravity, internal friction angle and cohesion; among them, soil gravity refers to the weight of soil per unit volume, including the weight of soil particles and the weight of water in the pores. In the stability analysis of the embankment slope, soil gravity directly affects the bearing capacity and stability of the slope; the internal friction angle is the friction resistance angle between the particles inside the soil, reflecting the internal friction characteristics of the soil. The larger the internal friction angle, the higher the shear strength of the soil and the better the stability of the slope; cohesion is the bonding force between soil particles, reflecting the cohesive characteristics of the soil. The larger the cohesion, the better the overall stability of the soil. Collect historical hydrological conditions of highway embankment slopes to obtain hydrological data of highway embankment slopes; Specifically, the historical hydrological conditions of the highway embankment slope include hydraulic conductivity, permeability, water level, water pressure, water volume, aquifer thickness, drainage water level influence radius, suddenness coefficient and hydraulic slope; Among them, the hydraulic conductivity coefficient represents the infiltration flow rate per unit area, which is used to evaluate the water conductivity of the highway embankment slope; the permeability coefficient represents the infiltration flow rate per unit area, which reflects the permeability performance of the highway embankment slope; the water level represents the elevation of the maximum static water level that the water body can reach under water equilibrium conditions; the water pressure represents the pressure of the water body, which affects the stability of the highway embankment slope; the water volume represents the total volume of water filling or the volume of water discharged per unit time, which is used to evaluate the change of water volume; the aquifer thickness represents the vertical distance from the top plate to the bottom plate of the aquifer, which affects the water storage capacity of the highway embankment slope; the drainage water level influence radius represents the radius of the water level reduction funnel centered on the drainage pressure reduction wellbore, which reflects the range of the drainage effect; the protrusion coefficient represents the static water pressure per unit area thickness of the aquiclude, which is used to evaluate the stability of the aquiclude; the hydraulic gradient represents the head difference per unit infiltration length in the direction of water flow, which reflects the power of water flow; Collect historical construction records of highway embankment slopes to obtain highway embankment slope construction data; Specifically, the historical construction records of highway embankment slopes include construction progress records, construction material use and inspection records, equipment operation and maintenance records, quality inspection and control records during the construction process, safety measures implementation records, and project changes and acceptance records; Among them, based on the highway roadbed slope design data, highway roadbed slope geological data, highway roadbed slope hydrological data and highway roadbed slope construction data, the highway roadbed slope historical data based on big data is determined.

[0019] In this embodiment, the historical data of highway embankment slope based on big data is processed, including: Obtain historical data of highway embankment slopes based on big data; Clean the historical data of highway embankment slope based on big data, including: Conduct consistency check on historical data of highway embankment slopes based on big data; According to the data consistency requirements, check whether the historical data of highway embankment slopes based on big data contain inconsistent data that is useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the inconsistent data in the historical data of highway embankment slopes based on big data; Check invalid and missing values ​​of historical data of highway embankment slopes based on big data; According to the requirements of data validity and integrity, check whether the historical data of highway embankment slopes based on big data contain invalid values ​​and missing values ​​that are useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the invalid values ​​and missing values ​​in the historical data of highway embankment slopes based on big data; Determine the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring; It should be noted that data cleaning refers to the process of processing and sorting the historical data of highway embankment slopes to improve the quality and availability of the historical data of highway embankment slopes; Among them, the consistency check is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each parameter, and find out the data that exceeds the normal range, is logically unreasonable or contradictory; for example, the variable measured by the 1-7 scale has a value of 0, and the weight has a negative number, which should be regarded as exceeding the normal value range; computer software such as SPSS, SAS and Excel can automatically identify each variable value that exceeds the range according to the defined value range; logically inconsistent answers may appear in many forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or the respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale; when inconsistencies are found, the questionnaire number, record number, variable name, error category, etc. should be listed to facilitate further verification and correction; Therefore, by cleaning the historical data of highway roadbed slopes based on big data, inconsistent data, invalid values ​​and missing values ​​that are useless for the reliability analysis of highway roadbed slope collapse monitoring in the historical data of highway roadbed slopes based on big data can be removed, and the historical data of highway roadbed slopes that are useful for the reliability analysis of highway roadbed slope collapse monitoring can be determined, which can improve the processing accuracy and efficiency of subsequent highway roadbed slope historical data and improve the quality of highway roadbed slope historical data.

[0020] The historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring are converted to unify the formats of the historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring; Eliminate the dimensional differences between the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring, and determine standardized historical data of highway embankment slopes; Extract features from standardized historical data of highway embankment slopes; Extract features that can reflect the reliability analysis of highway embankment slope collapse monitoring from standardized highway embankment slope historical data; Determine highway embankment slope characteristic data based on big data.

[0021] It should be noted that by converting and extracting the features of historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring, the characteristic data of highway embankment slopes based on big data can be determined, which is convenient for the subsequent construction of a highway embankment slope collapse prediction model based on machine learning, and provides data support for the construction of a highway embankment slope collapse prediction model based on machine learning.

[0022] S2: According to the reliability analysis requirements of highway embankment slope collapse monitoring, a highway embankment slope collapse prediction model based on machine learning is constructed, and the highway embankment slope collapse prediction model is tested and optimized to determine the optimal highway embankment slope collapse prediction model; In this embodiment, a highway embankment slope collapse prediction model based on machine learning is constructed, including: Obtain highway embankment slope characteristic data based on big data; Divide the highway embankment slope characteristic data based on big data and determine the training set and test set; According to the reliability analysis requirements of highway embankment slope collapse monitoring, a machine learning model framework suitable for highway embankment slope collapse prediction is selected; Based on the training set, the selected machine learning model framework suitable for highway roadbed slope collapse prediction is trained to determine the highway roadbed slope collapse prediction model based on machine learning.

[0023] In this embodiment, the highway embankment slope collapse prediction model is tested and optimized, including: Obtain a highway embankment slope collapse prediction model based on machine learning; Based on the test set, the performance of the highway roadbed slope collapse prediction model based on machine learning is tested to determine whether the highway roadbed slope collapse prediction model based on machine learning can achieve the expected effect; Determine the performance test results of the highway embankment slope collapse prediction model; Based on the performance test results of the highway embankment slope collapse prediction model, the parameters of the highway embankment slope collapse prediction model based on machine learning are adjusted and the structure is optimized, and the highway embankment slope collapse prediction model based on machine learning is repeatedly optimized; Determine the optimal highway embankment slope collapse prediction model.

[0024] S3: Collect the real-time data of highway embankment slopes, analyze and predict the real-time data of highway embankment slopes based on the optimal highway embankment slope collapse prediction model, judge the stability of highway embankment slopes, and determine the prediction results of highway embankment slope collapse; In this embodiment, the real-time data of highway embankment slopes are analyzed and predicted based on the optimal highway embankment slope collapse prediction model, including: Obtain the optimal highway embankment slope collapse prediction model and deploy it in actual highway embankment slope collapse monitoring work; According to the reliability analysis requirements of highway embankment slope collapse monitoring, real-time data of highway embankment slopes are collected and input into the optimal highway embankment slope collapse prediction model; Based on the optimal highway embankment slope collapse prediction model, the real-time data of highway embankment slope is analyzed and predicted to determine the stability of highway embankment slope and determine the prediction result of highway embankment slope collapse; Among them, the prediction results of highway roadbed slope collapse include: the stability of the highway roadbed slope is good, that is, the highway roadbed slope has no risk of collapse or the stability of the highway roadbed slope is poor, that is, the highway roadbed slope has a risk of collapse.

[0025] S4: Analyze the reliability of the highway embankment slope collapse prediction results, determine the matching degree between the highway embankment slope collapse prediction results and the actual results of the highway embankment slope collapse, and determine the reliability analysis results of the highway embankment slope collapse monitoring.

[0026] In this embodiment, the reliability of the prediction result of highway embankment slope collapse is analyzed, including: Obtain the prediction results of highway embankment slope collapse and analyze the reliability of the prediction results of highway embankment slope collapse; Compare and analyze the predicted results of highway embankment slope collapse with the actual results of highway embankment slope collapse, judge the matching degree between the predicted results of highway embankment slope collapse and the actual results of highway embankment slope collapse, and determine the reliability analysis results of highway embankment slope collapse monitoring; Among them, the prediction result of highway embankment slope collapse matches the actual result of highway embankment slope collapse, and the reliability analysis result of highway embankment slope collapse monitoring is that the reliability of highway embankment slope collapse monitoring is high; Among them, if the prediction result of highway roadbed slope collapse does not match the actual result of highway roadbed slope collapse, the reliability analysis result of highway roadbed slope collapse monitoring is that the reliability of highway roadbed slope collapse monitoring is low.

[0027] Specifically, the reliability of the prediction results of highway embankment slope collapse is analyzed, and the reliability analysis results of highway embankment slope collapse monitoring are shown in Table 1: Table 1: Reliability analysis results of highway embankment slope collapse monitoring Based on the actual results of highway embankment slope collapse, the reliability of highway embankment slope collapse prediction results is analyzed. Reliability analysis results of highway embankment slope collapse monitoring The prediction results of highway embankment slope collapse match the actual results of highway embankment slope collapse Highway roadbed slope collapse monitoring has high reliability The prediction results of highway embankment slope collapse do not match the actual results of highway embankment slope collapse The reliability of highway embankment slope collapse monitoring is low ; Therefore, based on the actual results of highway roadbed slope collapse, the reliability of the highway roadbed slope collapse prediction results is analyzed, and the reliability analysis results of highway roadbed slope collapse monitoring are determined. The highway roadbed slope collapse monitoring can be analyzed for reliability, and it can be determined whether the highway roadbed slope collapse monitoring is accurate. When the highway roadbed slope collapse monitoring is accurate, it can provide effective prediction for highway roadbed slope collapse monitoring, facilitate timely adoption of protective measures to avoid the occurrence of highway roadbed slope collapse accidents, and improve the highway roadbed slope collapse monitoring effect.

[0028] In this embodiment, the predicted result of highway embankment slope collapse is compared with the actual result of highway embankment slope collapse, including: Obtaining result analysis data for the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse respectively, to obtain first analysis data information and second analysis data information; A first matching value is obtained by performing matching degree analysis and calculation based on the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse; Performing disassembly and data standardization processing on the first analysis data information and the second analysis data information respectively to obtain a plurality of first analysis sub-data and a plurality of second analysis sub-data; Corresponding the first analysis sub-data and the second analysis sub-data, and performing matching analysis calculation according to the corresponding result to obtain a second matching value; A comprehensive analysis is performed on the first matching value and the second matching value to obtain the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse: ; In the above formula, is the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse, is the first weight, As parameters, The prediction results of highway embankment slope collapse are data, The actual result of highway embankment slope collapse is data, is the number of the first analysis sub-data, For the The first analysis of the sub-data location data, For the The second analysis sub-data Location data; Then, the reliability analysis results of highway roadbed slope collapse monitoring are determined according to the matching degree between the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse and the result determination standard.

[0029] The above technical scheme obtains result analysis data for the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse respectively, so that when performing comparative analysis, not only the matching degree analysis is performed based on the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse, but also the matching degree analysis is performed in combination with the analysis data of the predicted results of highway roadbed slope collapse and the analysis data of the actual results of highway roadbed slope collapse, thereby improving the comprehensiveness of the matching degree between the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse, making the matching degree between the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse more accurate, thereby improving the accuracy of the reliability analysis results of highway roadbed slope collapse monitoring.

[0030] In this embodiment, feature extraction is performed on standardized highway embankment slope historical data, including: The standardized highway embankment slope historical data are analyzed for changes according to the data types, and the standardized highway embankment slope historical data are judged in time sequence whether they have changed, and the analysis and judgment results are obtained; According to the analysis and judgment results, the standardized historical data of the roadbed slope are divided to obtain a first division result and a second division result, wherein the first division result is the standardized historical data of the roadbed slope that has changed, and the second division result is the standardized historical data of the roadbed slope that has not changed; In the first division result, the change characteristics of the first division result sub-data are analyzed, and the corresponding standardized highway roadbed slope historical data are extracted according to the change characteristics to obtain the first division result sub-data characteristics; In the second division result, statistical analysis is performed on the second division result sub-data to obtain the statistical analysis result, and the second division result sub-data features are determined according to the statistical analysis result.

[0031] The above technical solution divides the standardized historical data of highway embankment slopes so that different feature extraction methods are used to determine data features according to the characteristics of the standardized historical data of highway embankment slopes during feature extraction. This can not only improve the efficiency of feature extraction, but also ensure the accuracy of data features, thereby improving the guarantee for analysis and prediction, avoiding deviations in the prediction results of highway embankment slope collapse, and ensuring the reliability analysis of highway embankment slope collapse monitoring.

[0032] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reliability analysis method for monitoring highway embankment slope collapse, characterized in that: The steps include: S1: Collecting historical data of highway roadbed slopes based on big data, processing the historical data of highway roadbed slopes based on big data, and determining characteristic data of highway roadbed slopes based on big data; S2: According to the reliability analysis requirements of highway embankment slope collapse monitoring, a highway embankment slope collapse prediction model based on machine learning is constructed, and the highway embankment slope collapse prediction model is tested and optimized to determine the optimal highway embankment slope collapse prediction model; S3: Collect the real-time data of highway embankment slopes, analyze and predict the real-time data of highway embankment slopes based on the optimal highway embankment slope collapse prediction model, judge the stability of highway embankment slopes, and determine the prediction results of highway embankment slope collapse; S4: Analyze the reliability of the highway embankment slope collapse prediction results, determine the matching degree between the highway embankment slope collapse prediction results and the actual results of the highway embankment slope collapse, and determine the reliability analysis results of the highway embankment slope collapse monitoring.

2. The highway embankment slope collapse monitoring reliability analysis method according to claim 1 is characterized in that: In S1, historical data of highway embankment slopes based on big data is collected, and the following operations are performed: Collect historical design parameters of highway embankment slopes to obtain highway embankment slope design data; Collect historical geological conditions of highway embankment slopes and obtain geological data of highway embankment slopes; Collect historical hydrological conditions of highway embankment slopes to obtain hydrological data of highway embankment slopes; Collect historical construction records of highway embankment slopes to obtain highway embankment slope construction data; Among them, based on the highway roadbed slope design data, highway roadbed slope geological data, highway roadbed slope hydrological data and highway roadbed slope construction data, the highway roadbed slope historical data based on big data is determined.

3. The highway embankment slope collapse monitoring reliability analysis method according to claim 2 is characterized in that: In S1, the historical data of highway embankment slope based on big data is processed, and the following operations are performed: Obtain historical data of highway embankment slopes based on big data; Clean the historical data of highway embankment slope based on big data, including: Conduct consistency check on historical data of highway embankment slopes based on big data; According to the data consistency requirements, check whether the historical data of highway embankment slopes based on big data contain inconsistent data that is useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the inconsistent data in the historical data of highway embankment slopes based on big data; Check invalid and missing values ​​of historical data of highway embankment slopes based on big data; According to the requirements of data validity and integrity, check whether the historical data of highway embankment slopes based on big data contain invalid values ​​and missing values ​​that are useless for the reliability analysis of highway embankment slope collapse monitoring, and remove the invalid values ​​and missing values ​​in the historical data of highway embankment slopes based on big data; Determine the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring.

4. The highway embankment slope collapse monitoring reliability analysis method according to claim 3 is characterized in that: In S1, the historical data of highway embankment slope based on big data is processed, and the following operations are performed: Obtain historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring; The historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring are converted to unify the formats of the historical data of highway embankment slopes useful for reliability analysis of highway embankment slope collapse monitoring; Eliminate the dimensional differences between the historical data of highway embankment slopes that are useful for reliability analysis of highway embankment slope collapse monitoring, and determine standardized historical data of highway embankment slopes; Extract features from standardized historical data of highway embankment slopes; Extract features that can reflect the reliability analysis of highway embankment slope collapse monitoring from standardized highway embankment slope historical data; Determine highway embankment slope characteristic data based on big data.

5. The highway embankment slope collapse monitoring reliability analysis method according to claim 4 is characterized in that: In S2, a highway embankment slope collapse prediction model based on machine learning is constructed, and the following operations are performed: Obtain highway embankment slope characteristic data based on big data; Divide the highway embankment slope characteristic data based on big data and determine the training set and test set; According to the reliability analysis requirements of highway embankment slope collapse monitoring, a machine learning model framework suitable for highway embankment slope collapse prediction is selected; Based on the training set, the selected machine learning model framework suitable for highway roadbed slope collapse prediction is trained to determine the highway roadbed slope collapse prediction model based on machine learning.

6. The highway embankment slope collapse monitoring reliability analysis method according to claim 5 is characterized in that: In S2, the highway embankment slope collapse prediction model is tested and optimized, and the following operations are performed: Obtain a highway embankment slope collapse prediction model based on machine learning; Based on the test set, the performance of the highway roadbed slope collapse prediction model based on machine learning is tested to determine whether the highway roadbed slope collapse prediction model based on machine learning can achieve the expected effect; Determine the performance test results of the highway embankment slope collapse prediction model; Based on the performance test results of the highway embankment slope collapse prediction model, the parameters of the highway embankment slope collapse prediction model based on machine learning are adjusted and the structure is optimized, and the highway embankment slope collapse prediction model based on machine learning is repeatedly optimized; Determine the optimal highway embankment slope collapse prediction model.

7. The highway embankment slope collapse monitoring reliability analysis method according to claim 6 is characterized in that: In S3, the real-time data of the highway embankment slope is analyzed and predicted based on the optimal highway embankment slope collapse prediction model, and the following operations are performed: Obtain the optimal highway embankment slope collapse prediction model and deploy it in actual highway embankment slope collapse monitoring work; According to the reliability analysis requirements of highway embankment slope collapse monitoring, real-time data of highway embankment slopes are collected and input into the optimal highway embankment slope collapse prediction model; Based on the optimal highway embankment slope collapse prediction model, the real-time data of highway embankment slope is analyzed and predicted to determine the stability of highway embankment slope and determine the prediction result of highway embankment slope collapse; Among them, the prediction results of highway roadbed slope collapse include: the stability of the highway roadbed slope is good, that is, the highway roadbed slope has no risk of collapse or the stability of the highway roadbed slope is poor, that is, the highway roadbed slope has a risk of collapse.

8. The highway embankment slope collapse monitoring reliability analysis method according to claim 7 is characterized in that: In S4, the reliability of the prediction result of highway embankment slope collapse is analyzed, and the following operations are performed: Obtain the prediction results of highway embankment slope collapse and analyze the reliability of the prediction results of highway embankment slope collapse; Compare and analyze the predicted results of highway embankment slope collapse with the actual results of highway embankment slope collapse, judge the matching degree between the predicted results of highway embankment slope collapse and the actual results of highway embankment slope collapse, and determine the reliability analysis results of highway embankment slope collapse monitoring; Among them, the prediction result of highway embankment slope collapse matches the actual result of highway embankment slope collapse, and the reliability analysis result of highway embankment slope collapse monitoring is that the reliability of highway embankment slope collapse monitoring is high; Among them, if the prediction result of highway roadbed slope collapse does not match the actual result of highway roadbed slope collapse, the reliability analysis result of highway roadbed slope collapse monitoring is that the reliability of highway roadbed slope collapse monitoring is low.

9. The highway embankment slope collapse monitoring reliability analysis method according to claim 8 is characterized in that: Compare and analyze the predicted results of highway embankment slope collapse with the actual results of highway embankment slope collapse, including: Obtaining result analysis data for the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse respectively, to obtain first analysis data information and second analysis data information; A first matching value is obtained by performing matching degree analysis and calculation based on the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse; Performing disassembly and data standardization processing on the first analysis data information and the second analysis data information respectively to obtain a plurality of first analysis sub-data and a plurality of second analysis sub-data; Corresponding the first analysis sub-data and the second analysis sub-data, and performing matching analysis calculation according to the corresponding result to obtain a second matching value; A comprehensive analysis is performed on the first matching value and the second matching value to obtain the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse: ； In the above formula, is the matching degree between the predicted result of highway embankment slope collapse and the actual result of highway embankment slope collapse, is the first weight, As parameters, The prediction results of highway embankment slope collapse are data, The actual result of highway embankment slope collapse is data, is the number of the first analysis sub-data, For the The first analysis of the sub-data location data, For the The second analysis sub-data Location data; Then, the reliability analysis results of highway roadbed slope collapse monitoring are determined according to the matching degree between the predicted results of highway roadbed slope collapse and the actual results of highway roadbed slope collapse and the result determination standard.

10. The highway embankment slope collapse monitoring reliability analysis method according to claim 4, characterized in that: Feature extraction of standardized highway embankment slope historical data, including: The standardized highway embankment slope historical data are analyzed for changes according to the data types, and the standardized highway embankment slope historical data are judged in time sequence whether they have changed, and the analysis and judgment results are obtained; According to the analysis and judgment results, the standardized historical data of the roadbed slope are divided to obtain a first division result and a second division result, wherein the first division result is the standardized historical data of the roadbed slope that has changed, and the second division result is the standardized historical data of the roadbed slope that has not changed; In the first division result, the change characteristics of the first division result sub-data are analyzed, and the corresponding standardized highway roadbed slope historical data are extracted according to the change characteristics to obtain the first division result sub-data characteristics; In the second division result, statistical analysis is performed on the second division result sub-data to obtain the statistical analysis result, and the second division result sub-data features are determined according to the statistical analysis result.

Images