Highway information evaluation method based on data management

Abstract

The invention discloses a highway information evaluation method based on data management, and relates to the technical field of highway evaluation, and the method comprises the evaluation steps: S1, collecting highway traffic flow data, pavement state data and local meteorological data, and carrying out the preprocessing of the collected data; s2, extracting a correlation calculation characteristic value from the preprocessed data, synchronizing the processed data according to a time dimension, and enabling the collected data to correspond to each other; and S3, calculating a pavement damage rate, a traffic jam rate and a meteorological influence rate based on the data. According to the invention, the evaluation score is obtained by comprehensively considering each factor, a scientific basis is provided for the management decision, the grading and maintenance decision can determine the condition grade, make the maintenance strategy according to the grade, realize reasonable resource allocation, improve the overall operation and maintenance management level of the expressway, and avoid the problem of dependence on single data evaluation. The processing flow is effectively improved, and accurate evaluation and analysis are carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of highway evaluation, and in particular to a highway information evaluation method based on data management. Background Art

[0002] In the modern transportation system, highways play an extremely important role in transportation. The quality of their operation is directly related to transportation efficiency, traffic safety, and socioeconomic development. However, traditional highway condition assessment methods have many limitations. In the past, highway evaluations often relied on a single or a few types of data. For example, they only focused on traffic flow data to determine whether the road was congested, ignoring the combined impact of road surface conditions and meteorological factors on highway operation. This one-sided assessment method is difficult to fully and accurately reflect the true condition of the highway. When judging road conditions based solely on traffic flow, potential damage to the road surface caused by long-term use may not be detected in time. These damages can easily cause traffic accidents and affect normal road traffic under severe weather conditions. For the limited data collected, there is a lack of systematic and effective preprocessing processes, resulting in uneven data quality, making it difficult to conduct accurate analysis. There is often a lack of comprehensive and accurate data support, which greatly reduces the pertinence and effectiveness of decision-making. To this end, we propose a highway information evaluation method based on data management. Summary of the Invention

[0003] In order to solve the above technical problems, a highway information evaluation method based on data management is provided. This technical solution solves the above-mentioned problems of relying solely on single data evaluation, lacking a systematic and effective processing flow, and being difficult to accurately evaluate and analyze.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a highway information evaluation method based on data management, wherein the evaluation steps are as follows:

[0005] S1. Collect highway traffic flow data, road surface condition data, and local meteorological data, and pre-process the collected data;

[0006] S2. Extract relevant calculation feature values from the preprocessed data, synchronize the processed data according to the time dimension, and make the collected data correspond to each other;

[0007] S3. Calculate the road surface damage rate, traffic congestion rate, and weather impact rate based on the data, calculate the weight vector of each indicator, and calculate the evaluation score of the current expressway based on the weighted average method;

[0008] S4. Based on big data, the evaluation scores are divided into different levels. Based on the calculated evaluation scores, the corresponding values are substituted into different levels to evaluate the current highway status information and make corresponding maintenance decisions for different levels.

[0009] Preferably, traffic flow data is collected based on cameras, and traffic flow data within a unit time period is determined based on image analysis. Road condition data is collected in real time based on a laser detection vehicle. The vehicle is equipped with a laser sensor and a navigation system. The laser sensor emits a laser beam, and the three-dimensional topography data of the road surface is accurately obtained by measuring the laser reflection time, and the flatness index of the road surface is calculated; local meteorological data is obtained based on meteorological data from a weather station.

[0010] Preferably, the laser sensor emits a laser beam and measures the reflection time during the vehicle's travel, using the formula:

[0011] d=c×t / 2

[0012] Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor.

[0013] Based on the installation height of the laser sensor and the measured distance value, the elevation value of each measuring point on the road surface is calculated to obtain the road surface longitudinal section elevation data sequence z i , i=1,2,…,n, where n is the number of measurement points;

[0014] Calculate the elevation difference between adjacent measuring points. The calculation formula is: Δz i =z i+1 -z i ;

[0015] Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle when the simulated vehicle is driving. i ; where k is the spatial frequency;

[0016] Calculate the approximate value of IRI using the following formula:

[0017]

[0018] Where L is the length of the measured road section and IRI is the International Roughness Index.

[0019] Preferably, the relevant calculated characteristic values in step S2 include: traffic flow data of the congested period of the highway within a unit time period, traffic capacity data of the highway within the period, total road surface damaged area, total road surface area and number of meteorological factors; the steps of synchronization according to the time dimension are: in each data collection process, a timestamp is attached to the collected data. For traffic flow data, it changes frequently, and the timestamp accuracy is accurate to seconds and milliseconds; road surface status data changes relatively slowly, and the timestamp accuracy is accurate to minutes; meteorological data is based on the monitoring frequency and is accurate to seconds and minutes, that is, traffic flow data is recorded as [timestamp 1, vehicle flow value 1], road surface flatness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data is processed accordingly.

[0020] Preferably, the road surface damage rate in step S3 is used to reflect the proportion of the damaged area on the highway to the entire road surface area, and the calculation formula is:

[0021] A=(b / e)*100%

[0022] Where A is the calculated pavement damage rate, b is the total pavement damage area, and e is the total area of the pavement;

[0023] Traffic congestion rate is used to reflect the congestion situation of expressways. The calculation formula is:

[0024] F'=(G / H)*100%

[0025] Where F' is the calculated traffic congestion rate, G is the traffic flow of the current highway when the data was collected, and H is the traffic capacity of the highway at the time of design.

[0026] Preferably, the meteorological impact rate calculation formula in step S3 is:

[0027]

[0028] Where J is the calculated meteorological influence rate, n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed and visibility; w i is the weight of the i-th meteorological factor, and the weight is determined by the expert scoring method. i (x i ) is the influence function of the i-th meteorological factor, and the actual value of the meteorological factor x i Converted into the degree of impact on traffic, for the visibility factor, its impact function is a piecewise function, expressed as:

[0029]

[0030] V1 and V2 are pre-set visibility thresholds. When visibility is greater than or equal to V1, it is judged that there is no impact on traffic; when visibility is less than or equal to V2, it is considered that the impact on traffic has reached the maximum.

[0031] Preferably, the steps for calculating the weight vector of each indicator are as follows: standardizing the calculated values, constructing a judgment matrix using the hierarchical analysis method, comparing the relative importance of the two indicators, constructing a 3×3 judgment matrix M, determining that the road surface damage rate is slightly more important than the traffic congestion rate, and the corresponding element M in the judgment matrix is 12 The value is 3, M 21 Then The main diagonal element M of the judgment matrix ii =1, after the judgment matrix is constructed, a consistency test is performed and the consistency index CI is used to calculate:

[0032]

[0033] where λ max is the maximum eigenvalue of the judgment matrix, na is the matrix order, na = 3, by looking up the random consistency index RI, for na = 3, there is a corresponding standard value, calculate the consistency ratio CR, the calculation formula is:

[0034]

[0035] When CR<0.1, the judgment matrix is considered to have acceptable consistency. Otherwise, the judgment matrix is readjusted. After passing the consistency test, the eigenvector method is used to calculate the weight vector. The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector obtained is the weight vector W of each indicator.

[0036] Preferably, the evaluation score of the current expressway is comprehensively calculated based on the weighted average method, and the evaluation score calculation formula is:

[0037] P=W1*A+W2*F'+W3*J

[0038] Where P is the evaluation score of the current highway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values of the corresponding indicators respectively.

[0039] Preferably, in step S4, the evaluation classification level of the highway is obtained through big data, and the threshold intervals of different levels are divided. The levels are set as excellent, good, medium, pass and fail categories, and different levels have clear meanings and application values.

[0040] Preferably, in step S4, the calculated evaluation score is substituted into the corresponding range interval for the divided threshold range interval to obtain the current state information of the highway, and maintenance measures are taken based on different levels.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention ensures data availability by comprehensively collecting traffic flow, road conditions, and meteorological data and preprocessing them, comprehensively grasping key operation information of highways, and simultaneously mining key information. It synchronizes and corresponds to different data by time to improve analysis accuracy, calculates indicators and evaluation scores to achieve quantitative evaluation, and comprehensively considers various factors to derive evaluation scores, providing a scientific basis for management decisions. The classification and maintenance decision-making can clarify the condition level, formulate maintenance strategies according to the level, realize reasonable allocation of resources, and improve the overall operation and maintenance management level of highways. It avoids the problem of relying on single data evaluation, effectively improves the processing flow, and conducts accurate evaluation and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the highway information evaluation steps of the present invention;

[0044] Figure 2 This is the framework diagram of the highway information evaluation of the present invention. DETAILED DESCRIPTION

[0045] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0046] Reference Figure 1 As shown in FIG, a highway information evaluation method based on data management, the evaluation steps are as follows:

[0047] S1. Collect highway traffic flow data, road surface condition data, and local meteorological data, and pre-process the collected data;

[0048] S2. Extract relevant calculation feature values from the preprocessed data, synchronize the processed data according to the time dimension, and make the collected data correspond to each other;

[0049] S3. Calculate the road surface damage rate, traffic congestion rate, and weather impact rate based on the data, calculate the weight vector of each indicator, and calculate the evaluation score of the current expressway based on the weighted average method;

[0050] S4. Based on big data, the evaluation scores are divided into different levels. Based on the calculated evaluation scores, the corresponding values are substituted into different levels to evaluate the current highway status information and make corresponding maintenance decisions for different levels.

[0051] This application can comprehensively cover the key factors affecting highway operation by simultaneously collecting traffic flow, road surface conditions, and local meteorological data. Traffic flow reflects the real-time usage load of the road; road surface conditions are related to driving safety and comfort; and meteorological data has a dynamic impact on traffic and road conditions. In rainstorms, combining meteorological data with traffic flow can determine the degree of obstruction to traffic caused by flooded sections. The preprocessing step removes noise, outliers, and missing values in the collected data, improving data accuracy and usability. It corrects erroneous counts in traffic flow data to ensure that subsequent analysis is based on reliable data, laying a solid foundation for accurately evaluating highway conditions.

[0052] Extracting relevant calculated characteristic values, such as extracting average vehicle speed and flow rate change rate from traffic flow data, can deeply reveal the laws and trends behind the data. These characteristics provide a quantitative basis for evaluating highways and help to accurately judge the traffic operation status. For example, an excessively large flow rate change rate may indicate an impending traffic congestion. Synchronizing different types of data by time dimension and making them correspond to each other establishes an intrinsic connection between the data. This allows for a comprehensive analysis of the synergistic effects of traffic, road surface, and meteorological factors on the same time scale. At a certain moment, the road surface becomes slippery due to rainfall. Combined with the heavy traffic flow and reduced speed at this time, the safety of the highway during that period can be more accurately assessed. Calculating the road surface damage rate, traffic congestion rate, and meteorological impact rate converts complex highway conditions into specific quantitative indicators. These indicators intuitively reflect the severity of various problems. For example, a high road surface damage rate indicates that the road surface needs timely maintenance, while a high traffic congestion rate requires optimized traffic management measures.

[0053] Make corresponding maintenance decisions based on the evaluation score level to improve management efficiency and rational resource utilization. For sections with poor evaluation levels, large-scale road surface repairs, optimized traffic organization, or strengthened meteorological monitoring and early warning can be arranged in a timely manner; for sections with better evaluation levels, regular inspections and small-scale maintenance can be carried out to achieve precise management and ensure safe and efficient operation of expressways.

[0054] Traffic flow data is collected based on cameras, and traffic flow data within a unit time period is determined based on image analysis. Road condition data is collected in real time based on a laser detection vehicle. The vehicle is equipped with a laser sensor and a navigation system. The laser sensor emits a laser beam and measures the laser reflection time to accurately obtain the three-dimensional topography data of the road surface and calculate the road surface flatness index. Local meteorological data is obtained based on meteorological data from the weather station.

[0055] With the help of cameras and image analysis methods, this application can comprehensively and accurately monitor traffic flow in various road sections, provide real-time feedback on dynamic changes in traffic flow, and distinguish vehicle types to provide a basis for traffic management, such as adjusting traffic lights and planning truck traffic during peak hours; laser inspection vehicles use laser sensors and navigation systems to quickly and accurately collect three-dimensional road surface data and calculate flatness indicators. The data is comprehensive and intuitive, helping road maintenance personnel to understand road conditions and reasonably arrange maintenance resources; weather stations collect meteorological data in real time to help management departments understand meteorological conditions in advance and take countermeasures to ensure traffic safety, such as severe weather warnings and traffic control. At the same time, it also provides support for road maintenance and reasonably arranges maintenance work.

[0056] The laser sensor emits a laser beam while the vehicle is moving and measures the reflection time, using the formula:

[0057] d=c×t / 2

[0058] Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor.

[0059] Based on the installation height of the laser sensor and the measured distance value, the elevation value of each measuring point on the road surface is calculated to obtain the road surface longitudinal section elevation data sequence z i , i=1,2,…,n, where n is the number of measurement points;

[0060] Calculate the elevation difference between adjacent measuring points. The calculation formula is: Δz i =z i+1 -z i ;

[0061] Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle when the simulated vehicle is driving. i ; where k is the spatial frequency;

[0062] Calculate the approximate value of IRI using the following formula:

[0063]

[0064] Where L is the length of the measured road section and IRI is the International Roughness Index.

[0065] This application uses a laser sensor to emit a laser beam and measure the reflection time, and uses a formula to convert the reflection time into a distance value. This can accurately determine the distance between the road surface and the sensor. On this basis, the elevation value of each measuring point on the road surface is calculated in combination with the installation height of the laser sensor, forming a road surface longitudinal section elevation data sequence. This provides accurate data support for a comprehensive and detailed understanding of the undulating conditions of the road surface and helps to accurately locate uneven areas and potential diseased areas on the road surface. The elevation difference between adjacent measuring points is calculated and filtered based on the transfer function of the quarter-car model to obtain a vertical displacement difference sequence between the rear and front axles when simulating vehicle driving. This process simulates the actual situation of the vehicle when driving on the road and can more realistically reflect the impact of road surface smoothness on vehicle driving. Finally, by calculating the approximate value of the IRI, a scientific and objective indicator is provided for the quantitative evaluation of road surface smoothness, enabling accurate comparison of the smoothness of different road sections and facilitating the management department to formulate targeted maintenance and repair plans.

[0066] The relevant calculated characteristic values in step S2 include: traffic flow data of the congested period of the highway within a unit time period, the traffic capacity data of the highway within the period, the total area of the road surface, the total area of the road surface and the number of meteorological factors; the steps for synchronization according to the time dimension are: in each data collection process, a timestamp is attached to the collected data. For traffic flow data, it changes frequently, and the timestamp accuracy is accurate to seconds and milliseconds; road surface status data changes relatively slowly, and the timestamp accuracy is accurate to minutes; meteorological data is based on the monitoring frequency and is accurate to seconds and minutes, that is, traffic flow data is recorded as [timestamp 1, vehicle flow value 1], road surface flatness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data is processed accordingly.

[0067] This application can accurately analyze traffic congestion by obtaining traffic flow and capacity data during congested periods, helping traffic management departments to formulate traffic diversion plans; calculate the total area and total area of road damage to evaluate road conditions and assist maintenance departments in carrying out targeted work; clarify the number of meteorological factors to facilitate comprehensive consideration of the impact of weather on highways and provide a basis for traffic and maintenance decisions in severe weather; attach different precision timestamps to the collected data according to the data change characteristics, with traffic flow accurate to seconds or milliseconds, road conditions accurate to minutes, and meteorological data accurate to seconds or minutes depending on the monitoring frequency to ensure data timeliness, and use timestamps as a link to realize the correlation of multi-source data such as traffic, road surface, and meteorology, explore potential connections between factors, and provide support for the formulation of comprehensive management strategies.

[0068] The pavement damage rate in step S3 is used to reflect the proportion of the damaged area on the highway to the entire pavement area. The calculation formula is:

[0069] A=(b / e)*100%

[0070] Where A is the calculated pavement damage rate, b is the total pavement damage area, and e is the total area of the pavement;

[0071] Traffic congestion rate is used to reflect the congestion situation of expressways. The calculation formula is:

[0072] F'=(G / H)*100%

[0073] Where F' is the calculated traffic congestion rate, G is the traffic flow of the current highway when the data was collected, and H is the traffic capacity of the highway at the time of design.

[0074] This application can quantitatively assess road conditions, helping maintenance departments to formulate plans and allocate resources based on damage rates to ensure safe and comfortable driving; it can quantitatively assess road conditions, helping maintenance departments to formulate plans and allocate resources based on damage rates to ensure safe and comfortable driving; it provides a scientific and objective basis for highway management decisions and facilitates the formulation of maintenance and traffic control strategies.

[0075] The calculation formula for the meteorological impact rate in step S3 is:

[0076]

[0077] Where J is the calculated meteorological influence rate, n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed and visibility; w i is the weight of the i-th meteorological factor, and the weight is determined by the expert scoring method. i (x i ) is the influence function of the i-th meteorological factor, which converts the actual value xi of the meteorological factor into the degree of influence on traffic. For the visibility factor, its influence function is a piecewise function, expressed as:

[0078]

[0079] V1 and V2 are pre-set visibility thresholds. When visibility is greater than or equal to V1, it is judged that there is no impact on traffic; when visibility is less than or equal to V2, it is considered that the impact on traffic has reached the maximum.

[0080] The formula of this application covers meteorological factors such as temperature, precipitation, wind speed, and visibility, and comprehensively reflects their comprehensive impact on highway traffic, avoiding the limitations of single-factor evaluation. It uses the expert scoring method to determine the weight of each factor, and reasonably assigns values based on actual conditions and experience, so that the calculation results can more truly reflect the role of meteorological factors on traffic, providing a reliable basis for management. The impact of meteorological factors on traffic is quantified with the help of influence functions, such as the visibility piecewise function, which clearly presents the changes in impact at different levels, and uses specific numerical values to facilitate analysis and comparison by management departments, and to formulate measures. The formula can be adjusted according to the meteorological, traffic, and road conditions in different regions. By changing the weights and function parameters, the calculation accuracy and practicality can be improved, and it can be improved with the development of research. Accurate calculation of meteorological impact rate helps traffic management departments make decisions in severe weather, formulate plans in advance, and reasonably arrange resources. It can also evaluate the effectiveness of management measures and optimize strategies.

[0081] The steps for calculating the weight vector of each indicator are as follows: standardize the calculated values, use the hierarchical analysis method to construct a judgment matrix, compare the relative importance of the two indicators, and construct a 3×3 judgment matrix M. It is determined that the road damage rate is slightly more important than the traffic congestion rate. In the judgment matrix, the corresponding element M 12 The value is 3, M 21 Then The main diagonal element M of the judgment matrix ii =1, after the judgment matrix is constructed, a consistency test is performed and the consistency index CI is used to calculate:

[0082]

[0083] where λ max is the maximum eigenvalue of the judgment matrix, na is the matrix order, na = 3, by looking up the random consistency index RI, for na = 3, there is a corresponding standard value, calculate the consistency ratio CR, the calculation formula is:

[0084]

[0085] When CR<0.1, the judgment matrix is considered to have acceptable consistency. Otherwise, the judgment matrix is readjusted. After passing the consistency test, the eigenvector method is used to calculate the weight vector. The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector obtained is the weight vector W of each indicator.

[0086] This application eliminates the differences in dimensions and magnitudes of different indicators, enables comparative analysis of various indicators on the same scale, objectively reflects relative importance, avoids weight bias, transforms the problem of multi-indicator weights into pairwise comparisons, uses professional knowledge and experience to determine weights, accurately expresses the relative importance of indicators, sorts out indicator relationships, lays the foundation for weight calculation, ensures the logical consistency of the judgment matrix, discovers inconsistencies by calculating related indicators, and if the conditions are met, the results are reliable, otherwise the matrix needs to be adjusted to improve the accuracy and reliability of the weights. The weight vector is calculated based on the mathematical properties of the judgment matrix, and the result accurately reflects the relative importance of the indicators. It has a strict theoretical basis, is objective and stable, and normalized processing facilitates comparative analysis, providing a scientific and reliable basis for weights.

[0087] The evaluation score of the current expressway is calculated based on the weighted average method. The evaluation score calculation formula is:

[0088] P=W1*A+W2*F'+W3*J

[0089] Where P is the evaluation score of the current highway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values of the corresponding indicators respectively.

[0090] The weighted average method used in this application is highly flexible and the weights can be adjusted according to actual needs and specific circumstances. As the highway develops and changes, the importance of each indicator may change. New traffic management measures may reduce the impact of traffic congestion rates, and new pavement materials may improve the pavement's resistance to damage.

[0091] In step S4, the assessment classification level of the highway is obtained through big data, and the threshold ranges of different levels are divided. The levels are set as excellent, good, medium, passing and failing. Different levels have clear meanings and application values. In step S4, the calculated evaluation score is substituted into the corresponding range interval for the divided threshold range interval to obtain the current status information of the highway, and maintenance measures are taken based on different levels.

[0092] The clear level classification and corresponding status information of this application provide a scientific basis for managers to formulate maintenance measures. Managers can formulate targeted and feasible maintenance plans based on different levels and the actual situation and development plan of the highway.

[0093] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A highway information evaluation method based on data management, characterized in that: The evaluation steps are: S1. Collect highway traffic flow data, road surface condition data, and local meteorological data, and pre-process the collected data; S2. Extract relevant calculation feature values from the preprocessed data, synchronize the processed data according to the time dimension, and make the collected data correspond to each other; S3. Calculate the road surface damage rate, traffic congestion rate, and weather impact rate based on the data, calculate the weight vector of each indicator, and calculate the evaluation score of the current expressway based on the weighted average method; S4. Based on big data, the evaluation scores are divided into different levels. Based on the calculated evaluation scores, the corresponding values are substituted into different levels to evaluate the current highway status information and make corresponding maintenance decisions for different levels.

2. A highway information evaluation method based on data management according to claim 1, characterized in that: Traffic flow data is collected based on cameras, and traffic flow data within a unit time period is determined based on image analysis. Road condition data is collected in real time based on a laser detection vehicle. The vehicle is equipped with a laser sensor and a navigation system. The laser sensor emits a laser beam and measures the laser reflection time to accurately obtain the three-dimensional topography data of the road surface and calculate the road surface flatness index. Local meteorological data is obtained based on meteorological data from the weather station.

3. A highway information evaluation method based on data management according to claim 2, characterized in that: The laser sensor emits a laser beam while the vehicle is moving and measures the reflection time, using the formula: d=c×t / 2 Where d is the distance value, c is the speed of light, and t is the reflection time. The reflection time is converted into the distance value between the road surface and the sensor. Based on the installation height of the laser sensor and the measured distance value, the elevation value of each measuring point on the road surface is calculated to obtain the road surface longitudinal section elevation data sequence z i , i=1,2,…,n, where n is the number of measurement points; Calculate the elevation difference between adjacent measuring points. The calculation formula is: Δz i =z i+1 -z i ; Based on the transfer function H(k) of the quarter-car model, the elevation difference sequence is filtered to obtain the vertical displacement difference sequence Δy between the rear axle and the front axle when the simulated vehicle is driving. i ; where k is the spatial frequency; Calculate the approximate value of IRI using the following formula: Where L is the length of the measured road section and IRI is the International Roughness Index.

4. The highway information evaluation method based on data management according to claim 1 is characterized in that: The relevant calculated characteristic values in step S2 include: traffic flow data of the congested period of the highway within a unit time period, the traffic capacity data of the highway within the period, the total area of the road surface, the total area of the road surface and the number of meteorological factors; the steps for synchronization according to the time dimension are: in each data collection process, a timestamp is attached to the collected data. For traffic flow data, it changes frequently, and the timestamp accuracy is accurate to seconds and milliseconds; road surface status data changes relatively slowly, and the timestamp accuracy is accurate to minutes; meteorological data is based on the monitoring frequency and is accurate to seconds and minutes, that is, traffic flow data is recorded as [timestamp 1, vehicle flow value 1], road surface flatness data is recorded as [timestamp 2, IRI value 2], and meteorological data is recorded as [timestamp 3, temperature value 3]; the collected data is processed accordingly.

5. The highway information evaluation method based on data management according to claim 1 is characterized in that: The pavement damage rate in step S3 is used to reflect the proportion of the damaged area on the highway to the entire pavement area. The calculation formula is: A=(b / e)*100% Where A is the calculated pavement damage rate, b is the total pavement damage area, and e is the total area of the pavement; Traffic congestion rate is used to reflect the congestion situation of expressways. The calculation formula is: F'=(G / H)*100% Where F' is the calculated traffic congestion rate, G is the traffic flow of the current highway when the data was collected, and H is the traffic capacity of the highway at the time of design.

6. The highway information evaluation method based on data management according to claim 1 is characterized in that: The calculation formula for the meteorological impact rate in step S3 is: Where J is the calculated meteorological influence rate, n' represents the number of meteorological factors considered, including temperature, precipitation, wind speed and visibility; w i is the weight of the i-th meteorological factor, and the weight is determined by the expert scoring method. i (x i ) is the influence function of the i-th meteorological factor, and the actual value of the meteorological factor x i Converted into the degree of impact on traffic, for the visibility factor, its impact function is a piecewise function, expressed as: V1 and V2 are pre-set visibility thresholds. When visibility is greater than or equal to V1, it is judged that there is no impact on traffic; when visibility is less than or equal to V2, it is considered that the impact on traffic has reached the maximum.

7. The highway information evaluation method based on data management according to claim 1 is characterized in that: The steps for calculating the weight vector of each indicator are as follows: standardize the calculated values, use the hierarchical analysis method to construct a judgment matrix, compare the relative importance of the two indicators, and construct a 3×3 judgment matrix M. It is determined that the road damage rate is slightly more important than the traffic congestion rate. In the judgment matrix, the corresponding element M 12 The value is 3, M 21 Then The main diagonal element M of the judgment matrix ii =1, after the judgment matrix is constructed, a consistency test is performed and the consistency index CI is used to calculate: where λ max is the maximum eigenvalue of the judgment matrix, na is the matrix order, na = 3, by looking up the random consistency index RI, for na = 3, there is a corresponding standard value, calculate the consistency ratio CR, the calculation formula is: When CR<0.1, the judgment matrix is considered to have acceptable consistency. Otherwise, the judgment matrix is readjusted. After passing the consistency test, the eigenvector method is used to calculate the weight vector. The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the eigenvector is normalized. The normalized eigenvector obtained is the weight vector W of each indicator.

8. The highway information evaluation method based on data management according to claim 1 is characterized in that: The evaluation score of the current expressway is calculated based on the weighted average method. The evaluation score calculation formula is: P=W1*A+W2*F'+W3*J Where P is the evaluation score of the current highway obtained by weighted comprehensive calculation, and W1, W2 and W3 are the weight vector values of the corresponding indicators respectively.

9. The highway information evaluation method based on data management according to claim 1, characterized in that: In step S4, the evaluation classification level of the highway is obtained through big data, and the threshold intervals of different levels are divided. The levels are set as excellent, good, medium, pass and fail. Different levels have clear meanings and application values.

10. The highway information evaluation method based on data management according to claim 1, characterized in that: In step S4, the calculated evaluation score is substituted into the corresponding range interval for the divided threshold range interval to obtain the current status information of the highway and make maintenance measures based on different levels.

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