Method and system for monitoring full life cycle of road based on road data

Abstract

The invention relates to the technical field of road monitoring, in particular to a method and system for monitoring the full life cycle of a road based on road data. The problems of poor stability and response speed and high maintenance cost of the system are solved; the method comprises the steps of obtaining maintenance dynamic data of a target highway section to obtain a data set, performing segmentation processing according to a time sequence, and establishing a full life cycle monitoring model to obtain a monitoring result of the target highway section; and setting a normal threshold value of the full life cycle of the road, determining an abnormal state when the monitoring result of the target road section is higher than the normal threshold value, and giving an alarm for reminding when an abnormality is detected. The system comprises a data acquisition module, a full-life-cycle monitoring model module and a full-life-cycle early warning module. According to the invention, the efficiency and accuracy of data analysis are improved; the dynamic monitoring of the road section state is realized through the model; the response speed and the emergency processing capability of road maintenance management are improved, and the sense of trust and satisfaction of the public to road maintenance management are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of highway monitoring, and particularly relates to a method and system for monitoring the entire life cycle of a highway based on highway data. Background Art

[0002] The entire life cycle of a highway refers to the entire process from planning, design, construction, operation, maintenance to final abandonment, covering all stages during the entire service life of the highway, including the construction period, the operation period, and the final disposal period; in the management of the entire life cycle, cost control and management in each stage aim to achieve the lowest total construction cost and the best comprehensive benefits; the monitoring of the entire life cycle of a highway aims to ensure the safety, durability, and efficiency of highway facilities, and provide data support for future planning and construction.

[0003] Prior Art One, a Chinese patent, application number: 202410563859.7 discloses a detection method for the entire life cycle of a highway bridge project, which relates to the technical field of highway bridge project detection. It constructs a sharing and matching platform for the detection method of the entire life cycle of a highway bridge project and constructs a knowledge graph topology map; uploads the data of the target highway bridge project to the highway bridge detection knowledge base to generate the detection method for the entire life cycle of the target highway bridge project; divides the highway bridge project into several process sub-stages according to the detection method for the entire life cycle of the target highway bridge project, and conducts prospective and retrospective detections on each sub-stage of the target highway bridge project to generate prospective monitoring data and retrospective monitoring data; updates the scene sequence correlation degree between the basic structure features and disease features of the highway bridge in the knowledge graph topology map according to the prospective monitoring data and retrospective monitoring data of each process sub-stage of the target highway bridge project. Although it ensures the real-time performance and accuracy of the highway bridge detection knowledge base, the complexity of data collection and processing may lead to unstable system operation or processing delay, affecting the stability and response speed of the system.

[0004] Prior Art Two, a Chinese patent with application number 202410479486.5, discloses a detection method and system for the whole life cycle of a highway based on highway data, which is used to solve the problems that the existing monitoring methods for the whole life cycle of a highway cannot comprehensively monitor the highway status, cannot timely detect abnormal highway status, and cannot give an alarm in time. It includes: a highway data monitoring module, an impact data detection module, a data analysis module, an analysis data discrimination module, and an abnormal alarm module. Although this detection system can timely discover and warn potential safety problems of the highway by collecting highway data in real time, improve driving safety, improve the efficiency and scientificity of highway management, reduce maintenance costs, and provide an important basis for the long-term planning and management of the highway, with the advantages of comprehensiveness, real-time, and intelligence, and provides strong technical support for highway management and maintenance; however, the construction and maintenance of the system require high capital and technical support, increasing the maintenance cost.

[0005] Prior Art Three, a Chinese patent with application number 202311210755.X, discloses a prediction method for the future carbon effect amount in the whole life cycle. Based on literature research, data research, scientific analysis, etc., it studies and supplements the carbon source and sink factor database of highway engineering and its surrounding built environment, establishes a carbon source calculation model for the whole life cycle of highway engineering, constructs a dynamic measurement method for the carbon source and sink of the highway surrounding environment, uses a grey weighted Markov model to predict the carbon effect trend of highway engineering, enriches the traffic carbon emission method system, provides technical support for the construction of long-life low-carbon highways, and objectively evaluates the carbon emission level and emission reduction potential of the highway industry. Although it is of great significance for improving the domestic highway carbon source factor database and the surrounding environment carbon source and sink factor database, standardizing the carbon emission calculation standard of highway engineering, enriching the traffic carbon emission method system, evaluating the emission reduction potential of highway engineering and formulating measures; however, the applicability of the grey weighted Markov model may be restricted by the characteristics of specific regions or specific highway projects.

[0006] Currently, Prior Art One, Prior Art Two, and Prior Art Three have problems of poor system stability and response speed and high maintenance cost. Therefore, the present invention provides a monitoring method and system for the whole life cycle of a highway based on highway data. Summary of the Invention

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] On the one hand, the present invention provides a monitoring method for the whole life cycle of a highway based on highway data, including:

[0009] Obtain the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, meteorological information, etc., and preprocess the obtained maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set;

[0010] The preprocessed highway section information is segmented according to the time series, and a full-life cycle monitoring model is established based on the historical maintenance dynamic data of the target highway section. The set of maintenance dynamic data after segmentation is input into the full-life cycle monitoring model to obtain the monitoring results of the target highway section;

[0011] Set the normal threshold for the full life cycle of the highway. When the monitoring result of the target highway section is higher than the normal threshold, it is set to the abnormal state; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is issued to remind relevant personnel to handle it in time.

[0012] In an optional implementation manner, the process of obtaining the set of maintenance dynamic data specifically includes the following steps:

[0013] Set traffic flow sensing devices such as cameras on the target highway section to monitor traffic flow data, clean and preprocess the collected data, and integrate and statistically analyze the preprocessed traffic flow data;

[0014] Obtain the historical traffic accident information of the target highway section and divide the historical traffic accident information according to the damage degree of the detected highway section; obtain the historical meteorological information of the area where the detected highway section is located, including the temperature, humidity, temperature difference and rainfall of the target highway section, etc.;

[0015] Analyze and calculate the temperature, humidity, temperature difference and rainfall of the obtained target highway section, obtain the data on the damage of the temperature difference to the target section and the erosion degree of rain and snow to the target section, and input it into the full-life cycle monitoring model.

[0016] In an optional implementation manner, the process of analyzing and calculating the data specifically includes the following steps:

[0017] Obtain the air temperature, solar intensity, thermophysical parameters of the road surface material, rainfall and snowfall data of the target highway section;

[0018] Calculate the temperature difference between the surface and the bottom of the road slab, use the temperature adjustment coefficient to characterize the influence of air temperature conditions at different times on the fatigue cracking of the road surface structure layer and the vertical compression deformation of the subgrade top surface, and input the analysis result into the full-life cycle monitoring model;

[0019] According to the preprocessed rainfall and snowfall data, calculate the rainfall erosion factor, and calculate the influence of rainfall intensity greater than the erosion rate; use the sectional method to calculate the snow surface erosion and deposition flux, and input it into the full-life cycle monitoring model.

[0020] In an optional implementation manner, the process of calculating the temperature difference between the road slabs specifically includes the following steps:

[0021] Obtain the air temperature, solar intensity, and thermophysical parameters of the road surface material of the target road section, including solar radiation absorptivity, thermal conductivity, specific heat, etc., and preprocess the obtained meteorological conditions and thermophysical parameters of the material;

[0022] Calculate the temperatures at different depths and different times of the road surface according to the preprocessed meteorological conditions and thermophysical parameters of the material, and calculate the temperature difference between the road surface and the bottom, that is, the slab temperature difference;

[0023] Use the temperature adjustment coefficient to characterize the influence of air temperature conditions at different times on the fatigue cracking of the road surface structure layer and the vertical compression deformation of the subgrade top surface, and input the analysis results into the full-life cycle monitoring model.

[0024] In an optional implementation manner, the process of calculating rain and snow erosion specifically includes the following steps:

[0025] Obtain the rainfall and snowfall data in the area where the target highway section is located, including information such as rainfall, intensity, duration, and snowfall, and preprocess the collected rainfall and snowfall data;

[0026] Calculate the rainfall erosion factor according to the preprocessed rainfall and snowfall data; calculate the snow surface erosion and deposition flux;

[0027] Calculate the road erosion intensity by using the cross-section method.

[0028] In an optional implementation manner, the process of establishing the full-life cycle monitoring model specifically includes the following steps:

[0029] Segment the preprocessed highway section information according to the time series. Each segmented data set should contain relevant maintenance dynamic data such as temperature difference, rainfall erosion factor, snow surface erosion, deposition flux, and road erosion intensity within that time period;

[0030] Associate and fuse the static data and dynamic data in the data set to establish a full-life cycle monitoring model. The full-life cycle monitoring model generates monitoring results of the target highway section by analyzing the data in the data set, including road surface performance prediction and life cycle monitoring, etc.;

[0031] Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set. Use the full-life cycle training set to evaluate the full-life cycle monitoring model, optimize the model parameters, input the full-life cycle validation set, and compare the obtained results with the actual data to verify the effectiveness of the model.

[0032] In an optional implementation manner, the process of associating and fusing the static data and dynamic data in the data set specifically includes the following steps:

[0033] Divide the temperature difference, rainfall erosion factor, snow surface erosion, sediment flux, and erosion intensity on the road in the data set into static data and dynamic data;

[0034] Perform standardization processing on the static data and dynamic data in the data set, eliminate the deviation caused by different dimensions, and convert the data into a unified format;

[0035] Perform feature analysis on the converted static data and dynamic data, identify the correlation of the data, and combine them after association into a unified fusion data set.

[0036] In an alternative implementation, the process of evaluating the full-life cycle monitoring model specifically includes the following steps:

[0037] Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set, use the full-life cycle training set to train the full-life cycle monitoring model, and continuously improve the parameters of the full-life cycle monitoring model;

[0038] Input the full-life cycle validation set, further adjust the parameters of the full-life cycle monitoring model to obtain the best performance; and evaluate the final performance of the full-life cycle monitoring model;

[0039] Calculate the performance indicators of the full-life cycle monitoring model according to the selected evaluation criteria such as accuracy, recall rate, and F1 score, and adjust the parameters of the full-life cycle monitoring model according to the evaluation results.

[0040] In an alternative implementation, the process of setting the normal threshold for the full life cycle of the highway specifically includes the following steps:

[0041] Determine the risk score according to the fixed value method for the results output by the full-life cycle monitoring model of the highway and perform grade division, setting grades 1-9;

[0042] Obtain the technical grade and indicators of the target highway section, conduct a comprehensive analysis of their importance, and divide the target highway section into a first-level interval, a second-level interval, and a third-level interval from high to low according to the technical condition indicators;

[0043] Set different thresholds for the highways in the three index intervals. Set the threshold for the first-level interval as the 2nd-level risk, and set it as an abnormal state when the detected result is higher than the 2nd-level risk; set the threshold for the second-level interval as the 5th-level risk, and set it as an abnormal state when the detected result is higher than the 5th-level risk; set the threshold for the third-level interval as the 8th-level risk, and set it as an abnormal state when the detected result is higher than the 8th-level risk.

[0044] On the other hand, the present invention provides a monitoring system for the full life cycle of a highway based on highway data, including:

[0045] A data acquisition module, which is used to acquire the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, meteorological information, etc., and preprocess the acquired maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set;

[0046] A full-life cycle monitoring model module, which is used to segment the preprocessed highway section information according to the time series, establish a full-life cycle monitoring model based on the historical maintenance dynamic data of the target highway section, and input the segmented maintenance dynamic data set into the full-life cycle monitoring model to obtain the monitoring result of the target highway section;

[0047] A full-life cycle warning module, which is used to set the normal threshold of the full life cycle of the highway. When the monitoring result of the target highway section is higher than the normal threshold, it is set to an abnormal state; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is issued to remind relevant personnel to handle it in time.

[0048] The present invention improves the efficiency and accuracy of data analysis; segmenting the preprocessed data according to the time series helps to analyze the changes of the highway section in different time periods; realizing the dynamic monitoring of the highway section state through the model improves the timeliness and pertinence of maintenance management; provides a scientific basis for highway maintenance decision-making, reduces maintenance costs, and improves maintenance efficiency; discovers and handles the abnormal conditions of the highway section in time, prevents the problem from deteriorating further, and ensures the safety and unobstructedness of the highway; improves the response speed and emergency handling ability of highway maintenance management, and enhances the public's trust and satisfaction in highway maintenance management. Description of the Drawings

[0049] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0050] Figure 1 It is a flowchart of the monitoring method for the full life cycle of a highway based on highway data provided in Embodiment 1 of the present invention;

[0051] Figure 2 It is a flowchart of obtaining the maintenance dynamic data set provided in Embodiment 2 of the present invention;

[0052] Figure 3 It is a flowchart of analyzing and calculating data provided in Embodiment 3 of the present invention;

[0053] Figure 4 It is a flowchart of calculating the temperature difference of the plate provided in Embodiment 4 of the present invention;

[0054] Figure 5 It is the flowchart for calculating rain and snow erosion provided in Embodiment 5 of the present invention;

[0055] Figure 6 It is the flowchart for establishing a full - life - cycle monitoring model provided in Embodiment 6 of the present invention;

[0056] Figure 7 It is the flowchart for associating and fusing static data and dynamic data in a data set provided in Embodiment 7 of the present invention;

[0057] Figure 8 It is the flowchart for evaluating a full - life - cycle monitoring model provided in Embodiment 8 of the present invention;

[0058] Figure 9 It is the flowchart for setting normal thresholds for the full life cycle of a highway provided in Embodiment 9 of the present invention;

[0059] Figure 10 It is the functional module diagram of the monitoring system for the full life cycle of a highway based on highway data provided in Embodiment 10 of the present invention;

[0060] Figure 11 It is the block diagram of an electronic device provided in Embodiment 11 of the present invention;

[0061] Figure 12 It is the block diagram of a computer - readable storage medium provided in Embodiment 12 of the present invention. Detailed implementation manners

[0062] Next, the technical solutions in the embodiments of the present invention will be described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0063] Hereinafter, terms such as "first" and "second" are only for convenience of description and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality" is two or more.

[0064] In the present invention, unless otherwise clearly defined and limited, the term "connection" shall be understood in a broad sense. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or integral; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. In addition, unless otherwise clearly defined and limited, the term "coupling" shall be understood in a broad sense. For example, "coupling" can be a direct electrical connection. For example, there is physical contact and electrical conduction between two components, and it can also be understood that different components in a circuit structure are electrically connected through an entity line such as a copper foil or a wire of a printed circuit board (PCB) that can transmit electrical signals to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in an air-spaced / non-contact manner. For example, two components are electrically connected by means of capacitive coupling to transmit electrical signals.

[0065] In the embodiments of the present invention, orientation terms such as "upper", "lower", "left", and "right" may include but are not limited to being defined relative to the schematic placement of components in the drawings. It should be understood that these directional terms can be relative concepts, and they are used for relative description and clarification, and they can change correspondingly according to the change in the placement orientation of the components in the drawings.

[0066] The embodiments of the present invention can be used for highway maintenance management. By obtaining and analyzing highway facility conditions, traffic flow, traffic accidents, and meteorological information in real time, the maintenance department can timely understand the highway conditions, predict possible problems, and perform maintenance in advance; through the monitoring model established based on historical data, the lifespan and potential failures of highway facilities can be predicted, so as to perform preventive maintenance and reduce sudden failures and maintenance costs. For traffic safety management, by monitoring traffic accident information and traffic flow, the system can identify high-risk sections, issue early warnings, and reduce the occurrence of traffic accidents; when abnormal conditions are detected, the system can immediately issue an alarm and display the abnormal section to help emergency personnel quickly locate and handle problems. For traffic flow management, by analyzing traffic flow information, the traffic management department can optimize traffic signal control, reduce congestion, and improve road traffic efficiency; according to the real-time traffic flow and road conditions, the system can dynamically adjust the speed limit to ensure traffic safety and efficiency. For meteorological disaster response, combined with meteorological information, the system can issue early warnings for severe weather such as heavy rain and heavy snow to help the traffic management department and drivers take response measures. For disaster recovery: after a disaster occurs, the system can help quickly assess the conditions of the affected sections and guide the recovery work.

[0067] Embodiment 1:

[0068] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a method for monitoring the entire life cycle of a highway based on highway data, including the following steps:

[0069] Step S100: Obtain the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, meteorological information, etc., and preprocess the obtained maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set;

[0070] Step S200: Segment the preprocessed highway section information according to the time series, establish an entire life cycle monitoring model based on the historical maintenance dynamic data of the target highway section, and input the segmented maintenance dynamic data set into the entire life cycle monitoring model to obtain the monitoring result of the target highway section;

[0071] Step S300: Set the normal threshold for the entire life cycle of the highway. When the monitoring result of the target highway section is higher than the normal threshold, it is set as an abnormal state; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is issued to remind relevant personnel to handle it in a timely manner.

[0072] In the above embodiment, step S100 obtains comprehensive maintenance dynamic data of the highway section from multiple sources, cleans and organizes the collected data, removes duplicate, incorrect or invalid data, and ensures the accuracy of the analysis; provides a reliable and complete data basis for model establishment and monitoring; improves the efficiency and accuracy of data analysis; segments the preprocessed data according to the time series, which helps to analyze the changes of the highway section in different time periods; step S200 establishes an entire life cycle monitoring model based on historical data, which can simulate and predict the state changes of the highway section; inputs real-time data into the model to monitor the state changes of the highway section, providing the possibility to detect potential problems in a timely manner; realizes the dynamic monitoring of the state of the highway section through the model, improves the timeliness and pertinence of maintenance management; provides a scientific basis for highway maintenance decision-making, reduces maintenance costs, and improves maintenance efficiency; step S300 sets a reasonable normal threshold according to the characteristics and actual needs of the entire life cycle model of the highway to judge whether the highway section is in an abnormal state; when an abnormality is detected in the highway section, relevant personnel are reminded to handle it in a timely manner through the visualization device and the abnormal alarm sound; timely discover and handle the abnormal situation of the highway section, prevent the problem from deteriorating further, and ensure the safety and smoothness of the highway; improve the response speed and emergency handling ability of highway maintenance management, and enhance the public's trust and satisfaction in highway maintenance management.

[0073] Embodiment 2:

[0074] As Figure 2As shown in the figure, on the basis of Embodiment 1, the process of obtaining the maintenance dynamic data set in step S100 provided by the embodiment of the present invention specifically includes the following steps:

[0075] Step S101: Set traffic flow sensing devices such as cameras on the target highway section to monitor traffic flow data, clean and preprocess the collected data, and integrate and statistically analyze the preprocessed traffic flow data;

[0076] Step S102: Obtain the historical traffic accident information of the target highway section, and divide the historical traffic accident information according to the damage degree of the detected highway section; obtain the historical meteorological information of the area where the detected highway section is located, including the temperature, humidity, temperature difference and rainfall of the target highway section, etc.;

[0077] Step S103: Analyze and calculate the temperature, humidity, temperature difference and rainfall of the obtained target highway section, obtain the data of the damage of the temperature difference to the target section and the erosion degree of rain and snow to the target section, and input it into the whole life cycle monitoring model.

[0078] In the above embodiment, step S101 obtains traffic flow data in real time, providing instant feedback on the highway traffic condition; data cleaning and preprocessing ensure the accuracy and reliability of the data; data integration and statistical analysis provide a basis for traffic flow analysis and prediction; step S102 obtaining historical traffic accident information helps to identify the high-incidence sections and time periods of traffic accidents; historical meteorological information provides data support for analyzing the impact of the natural environment on highway sections; step S103 quantifies the impact of natural environmental factors on highway sections through analysis and calculation, providing a scientific basis for the whole life cycle prediction of highways; inputting the analysis results into the whole life cycle monitoring model can more accurately predict the state changes of highway sections; scientifically quantifying the impact of natural environmental factors on highways provides a basis for highway maintenance; through the whole life cycle detection model, real-time monitoring and early warning of highway conditions are realized, improving the safety and service life of highways.

[0079] Embodiment 3:

[0080] As Figure 3 shown, on the basis of Embodiment 2, the process of analyzing and calculating the data in step S103 provided by the embodiment of the present invention specifically includes the following steps:

[0081] Step S1031: Obtain the air temperature, solar intensity, thermophysical parameters of the road surface material, rainfall and snowfall data of the target highway section;

[0082] Step S1032: Calculate the temperature difference between the surface and the bottom of the road surface, use the temperature adjustment coefficient to characterize the influence of air temperature conditions at different times on the fatigue cracking of the road surface structure layer and the vertical compression deformation of the subgrade top surface, and input the analysis results into the whole life cycle monitoring model;

[0083] Step S1033: Calculate the rainfall erosion factor based on the pre-processed rainfall and snowfall data, and calculate the impact of rainfall intensity greater than the erosion rate; use the sectional method to calculate the snow surface erosion and deposition fluxes and input them into the full life cycle monitoring model.

[0084] In the above embodiments, in step S1031, the key meteorological conditions of the target highway section, the physical properties of the road surface materials, as well as the rainfall and snowfall data are obtained, and these data are pre-processed to ensure the accuracy and consistency of the data; the data quality is ensured, and the accuracy and reliability of the analysis are improved; calculate the temperature difference between the surface and the bottom of the road surface, and introduce a temperature adjustment coefficient to quantify the impact of air temperature conditions at different times on the fatigue cracking of the road surface structure layer and the vertical compression of the top surface of the roadbed; in step S1032, by quantifying the impact of temperature differences and air temperature conditions, the performance changes and potential problems of the road surface structure can be predicted more accurately; it helps to optimize the maintenance plan and design strategy of the highway, reduce the maintenance cost and increase the service life of the highway; in step S1033, calculate the rainfall erosion factor according to the rainfall and snowfall data, and evaluate the erosion impact of rainfall on the road surface structure; calculate the impact of rainfall intensity greater than the erosion rate to quantify the erosion degree of rainfall on the road surface structure; use the sectional method to calculate the snow surface erosion and deposition fluxes and evaluate the impact of snow on the road surface; by quantifying the erosion and deposition impacts of rainfall and snowfall on the road surface, the durability and maintenance requirements of the road surface can be evaluated more accurately; it helps to formulate a more effective maintenance strategy, reduce the road surface damage caused by erosion and deposition, and extend the service life of the highway.

[0085] Embodiment 4:

[0086] As Figure 4 shown, on the basis of Embodiment 3, the process of calculating the temperature difference of the plate in step S1032 provided by the embodiment of the present invention specifically includes the following steps:

[0087] Step S10321: Obtain the air temperature, solar intensity of the target section, and the thermophysical parameters of the road surface materials, including solar radiation absorptivity, thermal conductivity, specific heat, etc., and pre-process the obtained meteorological conditions and the thermophysical parameters of the materials.

[0088] Step S10322: Calculate the temperatures at different depths and different times of the road surface according to the pre-processed meteorological conditions and the thermophysical parameters of the materials, and calculate the temperature difference between the surface and the bottom of the road surface, that is, the temperature difference of the plate.

[0089] Step S10323: Use the temperature adjustment coefficient to characterize the impact of air temperature conditions at different times on the fatigue cracking of the road surface structure layer and the vertical compression of the top surface of the roadbed, and input the analysis results into the full life cycle monitoring model.

[0090] In the above embodiments, in step S10321, the air temperature, solar intensity and other conditions of the target road section, as well as the thermophysical coefficients of the connected materials, are obtained, and these data are preprocessed to ensure the accuracy, integrity and consistency of the data; meteorological conditions and material parameters are important factors affecting the pavement temperature distribution, and data preprocessing can eliminate noise and outliers in the data and improve the accuracy and reliability of the analysis; in step S10322, according to the preprocessed meteorological conditions and material parameters, the temperature distributions at different depths and different times of the pavement are calculated; calculating the temperature difference between the surface and the bottom of the pavement is of great significance for evaluating the thermal stress, fatigue cracking risk of the pavement structure and the vertical compression of the subgrade top surface, etc.; it provides key inputs for the analysis of the temperature coefficient and the full-life cycle monitoring model; in step S10323, a temperature adjustment coefficient is used to characterize the influence of air temperature conditions at different times on the fatigue cracking of the pavement structural layer and the vertical compression of the subgrade top surface; the analysis results are input into the full-life cycle monitoring model to predict and evaluate the long-term performance of the pavement; the temperature adjustment coefficient can obtain the influence of air temperature changes on the pavement structural performance, thereby improving the accuracy of prediction and evaluation; the full-life cycle monitoring model can comprehensively consider various factors, dynamically monitor and evaluate the long-term performance of the pavement, and provide a scientific basis for the maintenance and management of the pavement.

[0091] Embodiment 5:

[0092] As Figure 5 shown, on the basis of Embodiment 3, the process of calculating rain and snow erosion in step S1033 provided by the embodiment of the present invention specifically includes the following steps:

[0093] Step S10331: Obtain the rainfall and snowfall data of the area where the target highway section is located, including information such as rainfall amount, intensity, duration, and snowfall amount, and preprocess the collected rainfall and snowfall data;

[0094] Step S10332: Calculate the rainfall erosion factor according to the preprocessed rainfall and snowfall data; calculate the snow surface erosion and deposition flux;

[0095] Step S10333: Calculate the road erosion intensity by using the cross-section method.

[0096] Among them, the calculation formula of the rainfall erosion factor:

[0097]

[0098] In the formula, represents the rainfall erosion factor; I represents the rainfall intensity, with the unit of millimeters per hour (mm / h); represents the annual average rainfall, with the unit of millimeters (mm); Denotes the rainfall duration, in hours (h); Denotes the annual average rainfall duration, in hours (h); Denotes the vegetation coverage rate; - Denotes the reference vegetation coverage rate; , , , Denotes the empirical coefficient, which depends on local soil type, vegetation cover, terrain, and other factors.

[0099] Calculation formula for snow surface erosion and deposition flux:

[0100]

[0101] In the formula, Denotes the snow surface erosion and deposition flux; Denotes the snowfall amount, in millimeters (mm); Denotes the reference snowfall amount, in millimeters (mm); Denotes the wind speed, in meters per second (m / s); - Denotes the reference wind speed, in meters per second (m / s); Denotes the terrain height, in meters (m); Denotes the reference terrain height, in meters (m); Denotes the slope angle, in degrees (°); Denotes the reference slope angle, in degrees (°); , , , , Denotes the empirical coefficient, which depends on local climate conditions, terrain, vegetation cover, and other factors.

[0102] Calculation formula for road erosion intensity:

[0103]

[0104] In the formula, Denotes the road erosion intensity; Denotes the erosion amount at the i-th section, in tons per square meter (t / m²); Denotes the length of the i-th section, in meters (m); Denotes the soil permeability coefficient at the i-th section; Denotes the reference soil permeability coefficient; Denotes the vehicle flow at the i-th section, in vehicles per hour (veh / h); Denotes the reference vehicle flow, in vehicles per hour (veh / h); It represents the vegetation coverage rate at the i-th cross-section; It represents the reference vegetation coverage rate; It represents the total area of the road, with the unit of square meters (m²); N represents the total number of cross-sections; , , It represents the empirical coefficient, which depends on factors such as local soil type, vegetation coverage, traffic flow, etc. It can more accurately reflect the actual situation.

[0105] In the above embodiment, in step S10331, rainfall and snowfall data in the area where the target highway section is located are obtained, including key information such as rainfall amount, intensity, duration, and snowfall amount; the collected data is preprocessed to ensure the accuracy and availability of the data; it provides basic data support for calculating the rainfall erosion factor and snow surface erosion and deposition fluxes; in step S10332, according to the preprocessed rainfall and snowfall data, the rainfall erosion factor, snow surface erosion flux and deposition flux, and the change amount of snow height per unit time are calculated; the rainfall erosion factor can reflect the erosion degree of rainfall on the highway pavement and provide a basis for evaluating the highway erosion risk; the calculation of snow surface erosion and deposition fluxes helps to understand the impact of snow on the highway pavement, including the erosion and accumulation of snow; the change amount of snow height per unit time can reflect the dynamic process of snow surface change and provide an important reference for highway maintenance and management; in step S10333, using the cross-section method, combined with the weight, height and density of the road erosion material, the road erosion intensity is calculated; the road erosion area is calculated by multiplying the road length and width; the calculation of road erosion intensity can quantify the erosion degree of the highway pavement and provide a scientific basis for highway maintenance and repair.

[0106] Embodiment 6:

[0107] As Figure 6 shown, on the basis of Embodiment 1, the process of establishing the full-life cycle monitoring model in step S200 provided by the embodiment of the present invention specifically includes the following steps:

[0108] Step S201: Segment the preprocessed highway section information according to the time series. Each segmented data set should contain relevant maintenance dynamic data such as temperature difference, rainfall erosion factor, snow surface erosion, deposition flux, and erosion intensity on the road during this time period;

[0109] Step S202: Correlate and fuse the static data and dynamic data in the data set to establish a full-life cycle monitoring model. The full-life cycle monitoring model generates monitoring results of the target highway section by analyzing the data in the data set, including pavement performance prediction and life cycle monitoring, etc.;

[0110] Step S203: Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set. Use the full-life cycle training set to evaluate the full-life cycle monitoring model, optimize the model parameters, and input the full-life cycle validation set. Compare the obtained results with the actual data to verify the effectiveness of the model.

[0111] In the above embodiment, in step S201, the preprocessed highway section information is segmented according to the time series, ensuring that each segmented data set contains key maintenance dynamic data such as temperature difference, rainfall erosion factor, snow surface erosion, sediment flux, and erosion intensity of the road; it helps to organize a large amount of data into a form that is easy to manage and analyze, while retaining the key information in the time series; by segmenting the data, the maintenance requirements and changes of the highway in different time periods can be captured more accurately; in step S202, the static data and dynamic data in the data set are associated and fused to establish a monitoring model that can comprehensively reflect the state of the highway throughout its life cycle; it can generate monitoring results including pavement performance prediction and life cycle monitoring by analyzing the data in the data set; by establishing a full-life cycle monitoring model, real-time monitoring and prediction of the highway state can be achieved, providing a scientific basis for highway maintenance and management, extending the service life of the highway, and reducing maintenance costs; in step S203, the historical data of the target highway section is divided into a full-life cycle training set and a full-life cycle validation set for model training and validation; by using the full-life cycle training set to evaluate the model, optimizing the model parameters, and inputting the full-life cycle validation set for verification, the effectiveness and accuracy of the model are ensured, providing more accurate prediction and decision-making support for highway maintenance and management.

[0112] Embodiment 7:

[0113] As Figure 7 shown, on the basis of Embodiment 6, the process of associating and fusing the static data and dynamic data in the data set in step S202 provided by the embodiment of the present invention specifically includes the following steps:

[0114] Step S2021: Divide the temperature difference, rainfall erosion factor, snow surface erosion, sediment flux, and erosion intensity of the road in the data set into static data and dynamic data;

[0115] Step S2022: Perform standardization processing on the static data and dynamic data in the data set to eliminate the deviation caused by different dimensions and convert the data into a unified format;

[0116] Step S2023: Perform feature analysis on the converted static data and dynamic data, identify the correlation of the data, and associate and merge them into a unified fusion data set.

[0117] Among them, the formula for standardization:

[0118] Suppose there is a data set , and standardize it into a normal distribution ; Calculate the mean of the data It is expressed as:

[0119]

[0120] Calculate the standard deviation of the data It is expressed as:

[0121]

[0122] Calculate the cumulative distribution function (CDF) of the data. It is expressed as:

[0123] For each data point , calculate its corresponding cumulative distribution function value It is expressed as:

[0124]

[0125] Standardize each data point into a new value , so that the standardized data follows the standard normal distribution N(0, 1). It is expressed as:

[0126]

[0127] Among them, is the inverse cumulative distribution function CDF of the standard normal distribution; it not only considers the mean and standard deviation of the data, but also considers the probability distribution characteristics of the data, so it is more complex and comprehensive; it can perform more accurate and complex standardization processing on the static data and dynamic data in the data set.

[0128] In the above embodiments, in step S2021, the data is classified, distinguishing static data and dynamic data, which is helpful for subsequent data processing and analysis; by classifying the data, the nature of the data can be understood more clearly, providing guidance for data processing and analysis; classification also helps to optimize the data processing process, improving the efficiency and accuracy of data processing; the standardization process in step S2022 is a common step in data preprocessing, aiming to eliminate the deviation caused by different dimensions, enabling comparison and analysis between different data; by converting the data into a unified format, the consistency and accuracy of subsequent processing can be ensured; the unified format helps to simplify the data processing process, reducing the possibility of errors and repetitive work; step S2023: by correlating static data and dynamic data, potential relationships between them can be mined; the merged fusion dataset contains more comprehensive information, helping to improve the accuracy and generalization ability of the model; feature analysis and data fusion help to discover hidden rules and patterns in the data, and the fusion dataset contains richer information, which can support the construction of more complex and accurate models; by mining and utilizing the correlation between data, the prediction ability of the model and the level of decision support can be improved.

[0129] Embodiment 8:

[0130] As Figure 8 shown, on the basis of Embodiment 6, the process of evaluating the full-life cycle monitoring model in step S203 provided by the embodiment of the present invention specifically includes the following steps:

[0131] Step S2031: Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set, use the full-life cycle training set to train the full-life cycle monitoring model, and continuously improve the parameters of the full-life cycle monitoring model;

[0132] Step S2032: Input the full-life cycle validation set, further adjust the parameters of the full-life cycle monitoring model to obtain the best performance; and evaluate the final performance of the full-life cycle monitoring model;

[0133] Step S2033: Calculate the performance indicators of the full-life cycle monitoring model according to the selected evaluation criteria such as accuracy, recall rate, and F1 score, and adjust the parameters of the full-life cycle monitoring model according to the evaluation results.

[0134] In the above embodiments, step S2031 scientifically and reasonably divides the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set, ensuring the representativeness and integrity of the data; uses the full-life cycle training set to train the full-life cycle monitoring model, enabling the model to learn the laws and characteristics of the full life cycle of the highway section; improves the fitting ability and prediction accuracy of the model by continuously improving the parameters of the full-life cycle monitoring model; provides a reliable data basis for the establishment of the full-life cycle monitoring model; step S2032 inputs the full-life cycle validation set to verify the full-life cycle monitoring model, which can objectively evaluate the performance of the model; further adjusts the parameters of the full-life cycle monitoring model through the feedback of the validation set to obtain the best performance; comprehensively evaluates the final performance of the full-life cycle monitoring model, ensuring the accuracy and reliability of the model; optimizes the performance of the model and improves the generalization ability of the model through the verification and parameter adjustment of the validation set; the comprehensive performance evaluation provides strong support for the practical application of the model; step S2033 calculates the performance indicators of the full-life cycle monitoring model according to the selected evaluation criteria, which can quantitatively evaluate the performance of the model; fine-tunes the parameters of the full-life cycle monitoring model according to the evaluation results, which can further improve the performance of the model; through quantitative evaluation, the performance of the model can be intuitively understood, providing a clear direction for the optimization of the model; the fine-tuning of the parameters can further improve the accuracy and reliability of the model, providing better support for practical applications.

[0135] Embodiment 9:

[0136] As Figure 9 shown, on the basis of Embodiment 1, the process of setting the normal threshold of the full life cycle of the highway in step S300 provided by the embodiment of the present invention specifically includes the following steps:

[0137] Step S301: Determine the risk score according to the fixed value method for the results output by the full-life cycle highway monitoring model and conduct grade division, setting grades 1-9;

[0138] Step S302: Obtain the technical grade and indicators of the target highway section, comprehensively analyze their importance, and divide the target highway section into a first-level interval, a second-level interval, and a third-level interval from high to low according to the technical condition indicators;

[0139] Step S303: Set different thresholds for the highways in the three index intervals. The first-level interval is set with a threshold of level 2 risk, and when the detected result is higher than level 2 risk, it is set to an abnormal state; the second-level interval is set with a threshold of level 5 risk, and when the detected result is higher than level 5 risk, it is set to an abnormal state; the third-level interval is set with a threshold of level 8 risk, and when the detected result is higher than level 8 risk, it is set to an abnormal state.

[0140] In the above embodiments, in step S301, the output result is converted into a risk score through a fixed numerical method and classified into levels; this realizes the conversion of the abstract output result into a specific and quantifiable risk level, which is convenient for understanding and application; it provides a clear basis for decision-making, enabling management personnel to quickly judge the risk situation of the highway; through the level classification, it is easier to identify which highway sections or parts have higher risks, so as to prioritize the treatment; in step S302, by comprehensively considering the technical level and indicators of the highway, as well as its importance, a scientific classification of highway sections is realized; the divided first-level, second-level, and third-level intervals reflect the technical conditions and risk levels of different sections; it enables management personnel to take corresponding management measures and maintenance strategies according to the characteristics of different intervals; it helps to optimize the allocation of resources, use limited resources in the places where they are most needed, and improve the maintenance efficiency; in step S303, reasonable risk thresholds are set for highways in different intervals as the basis for judging whether they are in an abnormal state; this realizes the dynamic monitoring and early warning of highway risks; when it is detected that the risk level of the highway exceeds the set threshold, an early warning can be issued in a timely manner to remind management personnel to take countermeasures; it helps to prevent accidents and ensure the safe operation of the highway; by setting different thresholds, it is possible to more accurately identify highway sections with different risk levels, improving the pertinence and effectiveness of management.

[0141] Embodiment 10:

[0142] As Figure 10 shown, on the basis of Embodiments 1 - 9, the monitoring system for the whole life cycle of a highway based on highway data provided by the embodiments of the present invention includes:

[0143] A data acquisition module 1, which is used to acquire the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, meteorological information, etc., and preprocess the acquired maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set;

[0144] A whole life cycle monitoring model module 2, which is used to perform segmented processing on the preprocessed highway section information according to the time series, establish a whole life cycle monitoring model based on the historical maintenance dynamic data of the target highway section, and input the segmented maintenance dynamic data set into the whole life cycle monitoring model to obtain the monitoring result of the target highway section;

[0145] A whole life cycle early warning module 3, which is used to set the normal threshold for the whole life cycle of the highway, and set it to an abnormal state when the monitoring result of the target highway section is higher than the normal threshold; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is issued to remind relevant personnel to process it in a timely manner.

[0146] In the above embodiments, the data acquisition module 1 can comprehensively collect various maintenance dynamic data of the target highway section. By preprocessing the acquired data, a structured set of maintenance dynamic data is obtained. Comprehensive data collection and processing provide a more accurate and reliable basis for highway maintenance decision-making, helping to formulate a more scientific and reasonable maintenance plan. By timely acquiring and analyzing data, potential problems can be discovered and solved in a timely manner, avoiding higher maintenance costs caused by the deterioration of problems. The full life cycle monitoring model 2 can better capture trends and changes in the data by segmenting the highway section information according to the time series, improving the prediction accuracy of the model. Establishing a monitoring model based on historical maintenance dynamic data can comprehensively reflect the maintenance situation of the highway section during the full life cycle, providing strong support for predicting future maintenance needs. Through the monitoring model, potential maintenance problems of the highway section can be discovered in a timely manner, providing the possibility of taking measures in advance and reducing the occurrence of sudden failures. According to the monitoring results, maintenance resources can be more reasonably allocated to ensure the timely maintenance of key sections and facilities and improve the maintenance efficiency. The full life cycle warning module 3 automatically sets the abnormal state when the detection result is higher than the threshold by setting a normal threshold, realizing automatic monitoring and warning. The abnormal section is displayed on the visualization device and an abnormal alarm sound is emitted, intuitively and quickly reminding relevant personnel to handle it. Through the visualization and alarm functions, the attention of relevant personnel can be quickly attracted, improving the emergency response speed and reducing the losses caused by delays. Through real-time monitoring and warning, potential safety hazards can be discovered and handled in a timely manner, improving the safety management level of the highway section and ensuring driving safety.

[0147] Figure 11 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention is shown.

[0148] The electronic device may include a central processing unit / microprocessor / master control chip, etc. 4; a storage medium 5, coupled to the central processing unit / microprocessor / master control chip, etc. 4, and storing computer-executable instructions therein for performing the steps of the various methods of the embodiments of the present invention when executed by the processor.

[0149] The central processing unit / microprocessor / master control chip, etc. 4 may include, but are not limited to, for example, one or more processors or microprocessors, etc.

[0150] The storage medium 5 may include, but are not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disks, floppy disks, solid state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0151] In addition, the electronic device may further include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus, etc. 7, a display 8, and input / output devices 9 (such as a keyboard, a mouse, a speaker, etc.).

[0152] The central processing unit / microprocessor / master control chip, etc. 4 can communicate with external devices (8, 9, etc.) via the I / O bus 7 through a wired or wireless network (not shown).

[0153] The storage medium 5 can also store at least one computer-executable instruction for performing the various functions and / or method steps in the embodiments described in the present technology when run by the central processing unit / microprocessor / master control chip, etc. 4.

[0154] In one embodiment, the at least one computer-executable instruction can also be compiled into or form a software product, and when one or more computer-executable instructions are run by a processor, the various functions and / or method steps in the embodiments described in the present technology are performed.

[0155] Figure 12 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0156] As Figure 12 shown, instructions are stored on the non-transitory computer-readable storage medium 11, and the instructions are, for example, computer-readable instructions 10. When the computer-readable instructions 10 are run by a processor, the various methods described above can be executed. The non-transitory computer-readable storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. Non-transitory non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. For example, the non-transitory computer-readable storage medium 11 can be connected to a computing device such as a computer. Then, when the computing device runs the computer-readable instructions 10 stored on the non-transitory computer-readable storage medium 11, the various methods described above can be performed.

[0157] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0158] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, the functional units in various embodiments of the present invention may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods in various embodiments of the present invention by a computer device (which may be a personal computer, a server, or a network device, etc.). The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks, or optical discs that can store program codes.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A monitoring method for the whole life cycle of a highway based on highway data, characterized in that, The steps include: Obtain the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, and meteorological information, and preprocess the obtained maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set; Perform segmented processing on the preprocessed highway section information according to the time series, establish a full-life cycle monitoring model based on the historical maintenance dynamic data of the target highway section, and input the segmented maintenance dynamic data set into the full-life cycle monitoring model to obtain the monitoring results of the target highway section; Set the normal threshold for the full life cycle of the highway. When the monitoring result of the target highway section is higher than the normal threshold, it is set to the abnormal state; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is emitted to remind relevant personnel to handle it in time.

2. The monitoring method for the whole life cycle of a highway based on highway data according to claim 1, wherein, The process of obtaining the maintenance dynamic data set includes the following steps: Set up camera traffic flow sensing devices on the target highway section to monitor traffic flow data, clean and preprocess the collected data, and integrate and statistically analyze the preprocessed traffic flow data; Obtain the historical traffic accident information of the target highway section and divide the historical traffic accident information according to the damage degree of the detected highway section; obtain the historical meteorological information of the area where the detected highway section is located, including the temperature, humidity, temperature difference, and rainfall of the target highway section; Analyze and calculate the temperature, humidity, temperature difference, and rainfall of the obtained target highway section to obtain the data on the damage of the temperature difference to the target section and the erosion degree of rain and snow to the target section, and input it into the full-life cycle monitoring model.

3. The monitoring method for the entire life cycle of a highway based on highway data according to claim 2, characterized in that, The process of analyzing and calculating the data includes the following steps: Obtain the air temperature, solar intensity, thermophysical parameters of the pavement material, rainfall, and snowfall data of the target highway section; Calculate the temperature difference between the surface and the bottom of the pavement slab, use the temperature adjustment coefficient to characterize the influence of air temperature conditions at different times on the fatigue cracking of the pavement structure layer and the vertical compression deformation of the subgrade top surface, and input the analysis result into the full-life cycle monitoring model; According to the preprocessed rainfall and snowfall data, calculate the rainfall erosion factor, and calculate the influence of rainfall intensity greater than the erosion rate; use the section method to calculate the snow surface erosion and deposition flux, and input it into the full-life cycle monitoring model.

4. The monitoring method for the whole life cycle of a highway based on highway data according to claim 3, characterized in that, The process of calculating the slab temperature difference includes the following steps: Obtain the air temperature, solar intensity, and thermophysical parameters of the pavement material of the target section, including solar radiation absorption rate, thermal conductivity, and specific heat, and preprocess the obtained meteorological conditions and thermophysical parameters of the material; According to the preprocessed meteorological conditions and thermophysical parameters of the material, calculate the temperature at different depths and different times of the pavement, and calculate the temperature difference between the surface and the bottom of the pavement, that is, the slab temperature difference; Use the temperature adjustment coefficient to characterize the influence of air temperature conditions at different times on the fatigue cracking of the pavement structure layer and the vertical compression deformation of the subgrade top surface, and input the analysis result into the full-life cycle monitoring model.

5. The monitoring method for the whole life cycle of a highway based on highway data according to claim 3, characterized in that, The process of calculating rain and snow erosion includes the following steps: Obtain rainfall and snowfall data in the area where the target highway section is located, including rainfall amount, intensity, duration, and snowfall amount information, and preprocess the collected rainfall and snowfall data; Calculate the rainfall erosion factor based on the preprocessed rainfall and snowfall data; calculate the snow surface erosion and deposition fluxes; Calculate the road erosion intensity using the cross-section method.

6. The monitoring method for the whole life cycle of a highway based on highway data according to claim 1, characterized in that, The process of establishing a full-life cycle monitoring model includes the following steps: Segment the preprocessed highway section information by time series. Each segmented data set should contain temperature difference, rainfall erosion factor, snow surface erosion, deposition flux, and relevant maintenance dynamic data on the road erosion intensity within that time period; Associate and fuse the static data and dynamic data in the data set to establish a full-life cycle monitoring model. The full-life cycle monitoring model generates monitoring results for the target highway section by analyzing the data in the data set, including pavement performance prediction and life cycle monitoring; Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set. Use the full-life cycle training set to evaluate the full-life cycle monitoring model, optimize the model parameters, input the full-life cycle validation set, and compare the obtained results with the actual data to verify the effectiveness of the model.

7. The monitoring method for the whole life cycle of a highway based on highway data according to claim 6, characterized in that, The process of associating and fusing the static data and dynamic data in the data set includes the following steps: Divide the temperature difference, rainfall erosion factor, snow surface erosion, deposition flux, and road erosion intensity in the data set into static data and dynamic data; Perform standardization processing on the static data and dynamic data in the data set to eliminate the deviation caused by different dimensions and convert the data into a unified format; Perform feature analysis on the converted static data and dynamic data, identify the correlation of the data, and associate and merge them into a unified fusion data set.

8. The monitoring method for the whole life cycle of a highway based on highway data as claimed in claim 6, wherein, The process of evaluating the full-life cycle monitoring model includes the following steps: Divide the historical data of the target highway section into a full-life cycle training set and a full-life cycle validation set. Use the full-life cycle training set to train the full-life cycle monitoring model and continuously improve the full-life cycle monitoring model parameters; Input the full-life cycle validation set, further adjust the full-life cycle monitoring model parameters to obtain the best performance; and evaluate the final performance of the full-life cycle monitoring model; Calculate the performance indicators of the full-life cycle monitoring model according to the selected evaluation criteria of accuracy, recall rate, and F1 score, and adjust the full-life cycle monitoring model parameters according to the evaluation results.

9. The monitoring method for the whole life cycle of a highway based on highway data according to claim 1, characterized in that, The process of setting the normal threshold for the full life cycle of the highway includes the following steps: Determine the risk score and classify the levels according to the fixed value method for the results output by the full-life cycle monitoring model of the highway, and set levels 1-9; Obtain the technical grade and indicators of the target highway section, conduct a comprehensive analysis of their importance, and divide the target highway section into a first-level interval, a second-level interval, and a third-level interval from high to low according to the technical condition indicators; Different thresholds are set for roads in three index intervals. The threshold for the first-level interval is set as the 2nd-level risk. When the detected result is higher than the 2nd-level risk, it is set as the abnormal state; the threshold for the second-level interval is set as the 5th-level risk. When the detected result is higher than the 5th-level risk, it is set as the abnormal state; the threshold for the third-level interval is set as the 8th-level risk. When the detected result is higher than the 8th-level risk, it is set as the abnormal state.

10. A monitoring system for the whole life cycle of a highway based on highway data according to any one of claims 1 to 9, characterized in that, Including: A data acquisition module, which is used to acquire the maintenance dynamic data of the target highway section, including highway facility conditions, traffic flow information, traffic accident information, and meteorological information, and preprocess the acquired maintenance dynamic data of the target highway section to obtain a maintenance dynamic data set; A full-life cycle monitoring model module, which is used to perform segmented processing on the preprocessed highway section information according to the time series, establish a full-life cycle monitoring model based on the historical maintenance dynamic data of the target highway section, and input the segmented maintenance dynamic data set into the full-life cycle monitoring model to obtain the monitoring result of the target highway section; A full-life cycle early warning module, which is used to set the normal threshold for the full life cycle of the highway. When the monitoring result of the target highway section is higher than the normal threshold, it is set as the abnormal state; if an abnormality is detected in the highway section, the abnormal section is displayed on the visualization device and an abnormal alarm sound is emitted to remind relevant personnel to handle it in time.

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