Road construction safety monitoring method and system

Abstract

The present invention relates to the technical field of safety engineering management, and specifically discloses a road construction safety monitoring method and system. The method collects road surface humidity and vibration data by evenly distributing humidity sensors and acceleration sensors on the road surface during a monitoring period. The method calculates the fluctuation amplitude of humidity and vibration and the global impact index through filtering processing and time series analysis. The monitoring area is divided into several sections, and the abnormal fluctuation of humidity and vibration in each section is comprehensively analyzed to evaluate the safety of each section. Based on the evaluation results, the sections are divided into high-risk and low-risk categories, and different management measures are taken for each section. For low-risk sections, the monitoring frequency is increased, sensors are regularly inspected and calibrated, and long-term maintenance plans are intelligently planned. For high-risk sections, real-time early warning, emergency response, dynamic adjustment of traffic signals, issuance of traffic prompts and other measures are implemented to ensure the safety and service life of the road.

Description

Technical Field

[0001] The present invention relates to the technical field of safety engineering management, and in particular to a road construction safety monitoring method and system. Background Art

[0002] With the acceleration of urbanization, road traffic safety issues are becoming increasingly prominent, becoming a key factor affecting sustainable urban development. Traditional road safety management methods rely primarily on manual inspections and regular maintenance, which are not only inefficient but also difficult to promptly detect and address sudden safety hazards. This is particularly true in high-risk road sections, such as those prone to waterlogging, icing, or severe road damage. Traditional methods often fail to provide real-time and effective warnings and responses, leading to frequent traffic accidents and posing significant safety risks to the public.

[0003] However, most current intelligent transportation systems still have problems such as incomplete data collection, single analysis methods, and imperfect response mechanisms. They lack a method that deploys humidity sensors and accelerometers on the road to collect real-time humidity and vibration data from the road surface, and combines time series analysis, filtering processing, and Fourier transform to accurately assess road safety conditions. Based on this real-time data, the intelligent transportation system can implement real-time warnings for high-risk sections, dynamically adjust traffic signals, and issue traffic prompts, thereby effectively preventing traffic accidents and improving road safety and service life. Summary of the Invention

[0004] The purpose of the present invention is to provide a road construction safety monitoring method and system to solve the above-mentioned problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A road construction safety monitoring method comprises the following steps:

[0007] S1: During a monitoring period, the road surface moisture data is collected and a corresponding time series is established. Based on the fluctuation amplitude of the road surface moisture in the time series, the impact of abnormal fluctuations in road surface moisture on road safety engineering during the monitoring period is determined;

[0008] S2: During the monitoring period, road surface vibration amplitude data is collected and a corresponding time series is established. By analyzing the fluctuation of the road surface vibration amplitude within the time series, the impact of abnormal fluctuations in the road surface vibration amplitude on the road safety project is determined;

[0009] S3: Divide the monitoring area into several monitoring sections, conduct a comprehensive analysis of the abnormal fluctuations in road surface moisture and road vibration amplitude in each monitoring section, and evaluate the safety of each monitoring section;

[0010] S4: Based on the assessment results, each road section is marked and divided into high-risk sections and low-risk sections;

[0011] S5: For low-risk sections, increase the monitoring frequency; for high-risk sections, provide real-time warnings, emergency responses, dynamically adjust traffic signals, and issue traffic reminders and maintenance plans.

[0012] As a further solution of the present invention, the road surface moisture data within the monitoring period is collected and a corresponding time series is established, and the global impact index is calculated according to the road surface moisture fluctuation amplitude within the time series, specifically including:

[0013] Generate humidity time series for each monitoring point;

[0014] Decompose the time series into trend component, seasonal component and random component, and calculate the filtered humidity data;

[0015] The expression of the filtered humidity data is:

[0016] ;

[0017] Where, Indicates the number of sensors, =1,2,......,m, Indicates the A humidity sensor at time The filtered humidity data, represents the trend component of the humidity time series, represents the seasonal component of the humidity time series, represents the random component of the humidity time series;

[0018] Calculate the fluctuation range, the calculation expression is:

[0019] ;

[0020] Where, represents the fluctuation amplitude of the random component of humidity, represents the random component of the maximum humidity time series, represents the random component of the minimum humidity time series;

[0021] The fluctuation amplitude obtained by each sensor is combined to calculate the global impact index;

[0022] The calculation expression of the global impact index is:

[0023] ;

[0024] Where, Indicates the maximum number of humidity sensors, represents the global impact index, is the preset weight factor.

[0025] As a further solution of the present invention, the determination of the degree of influence of abnormal fluctuations in road surface moisture on road safety engineering during the monitoring period specifically includes:

[0026] It is determined whether the global impact index is greater than or equal to a preset global impact index threshold. If so, the humidity data of the corresponding road section has a high impact on road safety. If not, the humidity data of the corresponding road section has a low impact on road safety.

[0027] As a further solution of the present invention, the road surface vibration amplitude data is collected and a corresponding time series is established, and the road surface vibration amplitude fluctuation within the time series is analyzed to calculate the comprehensive vibration fluctuation index, which specifically includes:

[0028] Collect vibration amplitude data in time series;

[0029] The collected vibration amplitude data is transmitted to the central data processing platform via the Internet of Things;

[0030] Use a bandpass filter to remove ambient noise and high-frequency interference signals;

[0031] The vibration amplitude data of each acquisition point is formed into a time series;

[0032] Eliminate the DC component in the time series to obtain a zero-mean time series;

[0033] The calculation expression of the zero-mean time series is:

[0034] ;

[0035] Where, Indicates the number of acceleration sensors, is a positive integer greater than 0, Indicates the time point of collection, is a positive integer greater than 0, Indicates the The accelerometer The zero mean value of the vibration amplitude data collected at each time point, Indicates the The accelerometer The vibration amplitude data collected at each time point, Indicates the The average value of the vibration amplitude data collected by the acceleration sensors;

[0036] Perform discrete Fourier transform on the zero-mean sequence to obtain frequency domain components, and calculate the Fourier coefficients corresponding to each frequency domain component;

[0037] The calculation expression of the Fourier coefficient is:

[0038] ;

[0039] Where, Indicates the maximum number of acquisition time points, represents the number of frequency components, represents the imaginary unit, Indicates the The accelerometer The Fourier coefficients of the frequency components, Indicates the frequency components, Represents the logarithm of the base of a natural number;

[0040] Calculate the vibration amplitude data in frequency components The power spectral density of

[0041] The calculation expression of the power spectrum density is:

[0042] ;

[0043] Where, Indicates the The accelerometer The power spectral density of the frequency components;

[0044] Calculate the cumulative value of the power spectrum within the frequency range to obtain the cumulative power spectrum;

[0045] The calculation expression of the cumulative power spectrum is:

[0046] ;

[0047] Where, Indicates the The accelerometer The cumulative power spectrum of the frequency components;

[0048] Calculate the vibration fluctuation index based on the cumulative power spectrum;

[0049] The calculation expression of the vibration fluctuation index is:

[0050] ;

[0051] Where, Indicates the The vibration fluctuation index of the vibration amplitude data collected by the acceleration sensor, Indicates the An accelerometer The maximum power spectral density among the power spectral densities of frequency components;

[0052] The vibration fluctuation indexes of all acceleration sensors on the surface of the same monitored road section are obtained, and the average of all vibration fluctuation indices is calculated to obtain the comprehensive vibration fluctuation index.

[0053] As a further solution of the present invention, the determination of the impact of abnormal fluctuations in road vibration amplitude on road safety engineering specifically includes:

[0054] Determine whether the comprehensive vibration fluctuation index is greater than or equal to a preset threshold. If so, the abnormal fluctuation of the road surface vibration amplitude has a high impact on the road safety project. If not, the abnormal fluctuation of the road surface vibration amplitude has a low impact on the road safety project.

[0055] As a further solution of the present invention, the monitoring area is divided into several monitoring sections, and the abnormal fluctuations of the road surface moisture and the abnormal fluctuations of the road surface vibration amplitude in each monitoring section are comprehensively analyzed to calculate the safety factor, which specifically includes:

[0056] Divide the monitoring area into several monitoring sections;

[0057] The global impact index and comprehensive vibration fluctuation index of each monitored section are obtained, the global impact index and comprehensive vibration fluctuation index of each monitored section are normalized, and the safety factor of each section is calculated.

[0058] As a further solution of the present invention: each road section is marked according to the evaluation results and divided into high-risk sections and low-risk sections, specifically including:

[0059] Determine whether the safety factor of each road section is greater than or equal to the preset safety factor threshold. If so, it is recorded as a low-risk section; if not, it is recorded as a high-risk section.

[0060] As a further solution of the present invention, the monitoring frequency is increased for low-risk sections, and real-time warning, emergency response, dynamic adjustment of traffic signals, issuance of traffic warnings, driver training and long-term maintenance plans are carried out for high-risk sections to ensure the safety and service life of the roads, specifically including:

[0061] For road sections assessed as low-risk, the sampling frequency of humidity and vibration monitoring parameters will be increased to monitor changes in the road section in real time. The accuracy and continuity of data collection will be ensured through regular inspection and calibration of sensors and deployment of backup equipment.

[0062] Intelligently plan long-term maintenance plans for low-risk road sections based on historical data analysis, road conditions, and traffic flow forecasts;

[0063] For high-risk road sections, an early warning mechanism will be automatically triggered, using IoT technology to link with traffic lights, road sign screens, and traffic broadcast systems;

[0064] If an abnormal situation is detected, the system will issue a warning message to the driver;

[0065] Based on real-time monitoring data, the intelligent transportation system can dynamically adjust traffic signal control strategies for high-risk sections of roads, use big data analysis to predict traffic flow changes, conduct traffic flow scheduling, guide vehicles to avoid high-risk areas, and reduce the probability of accidents.

[0066] A road construction safety monitoring system comprising:

[0067] A data acquisition module, which is used to collect road surface moisture data and road surface vibration amplitude data within a monitoring period;

[0068] A road surface moisture assessment module is used to collect road surface moisture data within a monitoring period and establish a corresponding time series. Based on the road surface moisture fluctuation amplitude within the time series, the module determines the impact of abnormal road surface moisture fluctuations on road safety engineering within the monitoring period.

[0069] a pavement vibration amplitude assessment module, which is used to collect pavement vibration amplitude data and establish a corresponding time series. By analyzing the pavement vibration amplitude fluctuations within the time series, the module determines the impact of abnormal pavement vibration amplitude fluctuations on road safety engineering;

[0070] A comprehensive evaluation module, which is used to divide the monitoring area into several monitoring sections, and comprehensively analyze the abnormal fluctuations of road surface moisture and abnormal fluctuations of road surface vibration amplitude in each monitoring section to evaluate the safety of each monitoring section;

[0071] A risk section classification module, which marks each road section according to the assessment results and divides it into high-risk sections and low-risk sections;

[0072] An early warning response module strengthens the monitoring frequency for low-risk sections, and provides real-time early warnings, emergency responses, dynamic adjustment of traffic signals, issuance of traffic reminders and maintenance plans for high-risk sections to ensure the safety and service life of the roads.

[0073] Beneficial effects of the present invention:

[0074] (1) The present invention collects road surface humidity and vibration amplitude data at a high frequency during the monitoring period, establishes a time series, and uses filtering, Fourier transform and other technical means to analyze the fluctuation amplitude of humidity and vibration data and the degree of their impact on road safety. Specifically, by evenly distributing high-frequency sampling humidity sensors and acceleration sensors on the road surface, the humidity changes and vibration conditions of the road surface can be monitored in real time. After filtering, these data are used to generate a time series of humidity and vibration. Then, by decomposing the time series, calculating the fluctuation amplitude and the global impact index, the impact of road surface humidity and vibration on road safety is accurately evaluated. For high-risk sections, the system automatically activates the early warning mechanism, and through the Internet of Things technology, the real-time data is linked with traffic lights, road section indicator screens, and traffic broadcast systems to quickly issue early warning information to drivers, such as automatically adjusting the signal light cycle when the road surface is slippery, reducing traffic flow and avoiding congestion. These measures can improve the driver's alertness, reduce traffic accidents caused by slippery roads or abnormal vibration, thereby improving the overall safety of the road and reducing the traffic accident rate.

[0075] (2) The present invention evaluates the safety of each road section by comprehensively analyzing the abnormal fluctuations in humidity and vibration of each monitored road section, and accordingly divides the road sections into high-risk and low-risk categories; for low-risk sections, the present invention proposes a series of optimization measures, such as increasing the monitoring frequency, regularly checking and calibrating sensors, and deploying spare sensors, to ensure the accuracy and continuity of data collection; at the same time, the system will also intelligently plan the long-term maintenance cycle of low-risk sections based on historical data and traffic flow forecasts, and automatically arrange regular maintenance operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The present invention will be further described below with reference to the accompanying drawings.

[0077] Figure 1 This is a flowchart of the specific steps of a road construction safety monitoring method of the present invention;

[0078] Figure 2 It is a flow chart of a road construction safety monitoring system in the present invention. DETAILED DESCRIPTION

[0079] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0080] See also Figure 1 As shown, the present invention is a road construction safety monitoring method, comprising the following steps:

[0081] S1: During a monitoring period, the road surface moisture data is collected and a corresponding time series is established. Based on the fluctuation amplitude of the road surface moisture in the time series, the impact of abnormal fluctuations in road surface moisture on road safety engineering during the monitoring period is determined;

[0082] S2: During the monitoring period, road surface vibration amplitude data is collected and a corresponding time series is established. By analyzing the fluctuation of the road surface vibration amplitude within the time series, the impact of abnormal fluctuations in the road surface vibration amplitude on the road safety project is determined;

[0083] S3: Divide the monitoring area into several monitoring sections, conduct a comprehensive analysis of the abnormal fluctuations in road surface moisture and road vibration amplitude in each monitoring section, and evaluate the safety of each monitoring section;

[0084] S4: Based on the assessment results, each road section is marked and divided into high-risk sections and low-risk sections;

[0085] S5: For low-risk sections, increase the monitoring frequency; for high-risk sections, conduct real-time warnings, emergency responses, dynamically adjust traffic signals, issue traffic tips, driver training and long-term maintenance plans to ensure the safety and service life of the roads.

[0086] In S1, during a monitoring period, the road surface moisture data within the monitoring period is collected and a corresponding time series is established. Based on the road surface moisture fluctuation amplitude within the time series, the impact of abnormal road surface moisture fluctuations on road safety engineering during the monitoring period is determined, specifically including:

[0087] Evenly distribute humidity sensors on the road surface, choose capacitive or resistive humidity sensors, and collect road surface humidity data;

[0088] It should be noted that each sensor has a high-frequency sampling function, and the sampling interval is set (such as every minute). The humidity data collected by the sensor is uploaded to the cloud data center for subsequent humidity data analysis.

[0089] Filter the collected humidity data to remove noise and use a low-pass filter to eliminate environmental interference;

[0090] Generate humidity time series for each monitoring point;

[0091] Decompose the time series into trend component, seasonal component and random component, and calculate the filtered humidity data;

[0092] The expression of the filtered humidity data is:

[0093] ;

[0094] Where, Indicates the number of sensors, =1,2,......,m, Indicates the A humidity sensor at time The filtered humidity data, represents the trend component of the humidity time series, represents the seasonal component of the humidity time series, represents the random component of the humidity time series;

[0095] Calculate the fluctuation range, the calculation expression is:

[0096] ;

[0097] Where, represents the fluctuation amplitude of the random component of humidity, represents the random component of the maximum humidity time series, represents the random component of the minimum humidity time series;

[0098] The fluctuation amplitude obtained by each sensor is combined to calculate the global impact index;

[0099] The calculation expression of the global impact index is:

[0100] ;

[0101] Where, Indicates the maximum number of humidity sensors, represents the global impact index, is the preset weight factor;

[0102] comparing the global impact index with a preset global impact index threshold;

[0103] If the global impact index is greater than or equal to the preset global impact index threshold, it means that the humidity data of the corresponding road section has a high impact on road safety;

[0104] If the global impact index is less than the preset global impact index threshold, it means that the humidity data of the corresponding road section has a low impact on road safety;

[0105] It should be noted that the global impact index reflects the impact of humidity data on the friction coefficient of the road. The larger the value of the global impact index, the lower the safety of vehicle driving on the corresponding road section.

[0106] In S2, during the monitoring period, road surface vibration amplitude data is collected and a corresponding time series is established. By analyzing the fluctuations of road surface vibration amplitude within the time series, the impact of abnormal fluctuations of road surface vibration amplitude on road safety engineering is determined, specifically including:

[0107] Acceleration sensors are installed on the road surface to collect road vibration amplitude data;

[0108] Set the sampling frequency

[0109] (e.g. 100 Hz), set the sampling interval (e.g. every minute);

[0110] Collect vibration amplitude data in time series;

[0111] The collected vibration amplitude data is transmitted to the central data processing platform through the Internet of Things for subsequent analysis of the vibration amplitude data;

[0112] Use a bandpass filter to remove ambient noise and high-frequency interference signals;

[0113] The vibration amplitude data of each acquisition point is formed into a time series;

[0114] Eliminate the DC component in the time series to obtain a zero-mean time series;

[0115] The calculation expression of the zero-mean time series is:

[0116] ;

[0117] Where, Indicates the number of acceleration sensors, is a positive integer greater than 0, Indicates the time point of collection, is a positive integer greater than 0, Indicates the The accelerometer The zero mean value of the vibration amplitude data collected at each time point, Indicates the The accelerometer The vibration amplitude data collected at each time point, Indicates the The average value of the vibration amplitude data collected by the acceleration sensors;

[0118] Perform discrete Fourier transform on the zero-mean sequence to obtain frequency domain components, and calculate the Fourier coefficients corresponding to each frequency domain component;

[0119] The calculation expression of the Fourier coefficient is:

[0120] ;

[0121] Where, Indicates the maximum number of acquisition time points, represents the number of frequency components, represents the imaginary unit, Indicates the Accelerometer The Fourier coefficients of the frequency components, Indicates the frequency components, Represents the logarithm of the base of a natural number;

[0122] Calculate the vibration amplitude data in frequency components The power spectral density of

[0123] The calculation expression of the power spectrum density is:

[0124] ;

[0125] Where, Indicates the The accelerometer The power spectral density of the frequency components;

[0126] Calculate the cumulative value of the power spectrum within the frequency range to obtain the cumulative power spectrum;

[0127] The calculation expression of the cumulative power spectrum is:

[0128] ;

[0129] Where, Indicates the The accelerometer The cumulative power spectrum of the frequency components;

[0130] Calculate the vibration fluctuation index based on the cumulative power spectrum;

[0131] The calculation expression of the vibration fluctuation index is:

[0132] ;

[0133] Where, Indicates the The vibration fluctuation index of the vibration amplitude data collected by the acceleration sensor, Indicates the An accelerometer The maximum power spectral density among the power spectral densities of frequency components;

[0134] Obtain the vibration fluctuation index of all acceleration sensors on the same monitored road surface, and calculate the average of all vibration fluctuation indexes to obtain the comprehensive vibration fluctuation index;

[0135] comparing the comprehensive vibration fluctuation index with a preset threshold;

[0136] If the comprehensive vibration fluctuation index is greater than or equal to the preset threshold, it means that the vibration fluctuation level of the corresponding road section is high, and the impact on road safety is high;

[0137] If the comprehensive vibration fluctuation index is less than the preset threshold, it means that the vibration fluctuation level of the corresponding road section is low and the impact on road safety is low;

[0138] It should be noted that the comprehensive vibration fluctuation index reflects the vibration condition of the road surface, and when the value of the comprehensive vibration fluctuation index is larger, the corresponding road surface vibration fluctuation degree is higher, and the impact on road safety is greater.

[0139] In S3, the monitoring area is divided into several monitoring sections. The abnormal fluctuations of road surface moisture and road surface vibration amplitude in each monitoring section are comprehensively analyzed to evaluate the safety of each monitoring section, including:

[0140] Divide the monitoring area into several monitoring sections;

[0141] Obtain the global impact index and comprehensive vibration fluctuation index of each monitored road section, normalize the global impact index and comprehensive vibration fluctuation index of each monitored road section, and calculate the safety factor of each road section;

[0142] The calculation expression of the safety factor is:

[0143] ;

[0144] Where, Indicates the number of monitored sections, represents the safety factor of the h-th road section, represents the comprehensive vibration fluctuation index, represents the global impact index, and is the preset scale factor, and and Both are greater than 0.

[0145] It should be noted that the global impact index and comprehensive vibration fluctuation index of each road section are inversely proportional to the safety factor of the corresponding road section. The larger the global impact index and comprehensive vibration fluctuation index, the smaller the corresponding safety factor and the lower the safety of the corresponding road section.

[0146] In S4, each road section is marked according to the evaluation results and divided into high-risk sections and low-risk sections, including:

[0147] Comparing the safety factor of each road segment with a preset safety factor threshold;

[0148] If the safety factor of each road section is greater than or equal to the preset safety factor threshold, it means that the safety of the corresponding road section is high and is recorded as a low-risk road section;

[0149] If the safety factor of each road section is less than the preset safety factor threshold, it means that the safety of the corresponding road section is low and is recorded as a high-risk road section.

[0150] In S5, the monitoring frequency will be increased for low-risk sections, and real-time warnings, emergency responses, dynamic traffic signal adjustments, traffic warnings, driver training, and long-term maintenance plans will be implemented for high-risk sections to ensure road safety and longevity. Specifically, the following measures will be implemented:

[0151] For road sections marked as low-risk, the sampling frequency of humidity, vibration and other monitoring parameters of low-risk sections will be increased. By optimizing the sampling frequency, the changes in the road sections can be monitored in real time.

[0152] Regularly inspect and calibrate humidity sensors, vibration sensors, and other equipment to ensure the accuracy of data collection; deploy backup sensors to prevent data interruptions due to hardware failures.

[0153] By analyzing historical data of low-risk road sections and combining road conditions and traffic flow forecasts, long-term maintenance cycles can be intelligently planned;

[0154] For example, automatically schedule regular maintenance operations, especially during bad weather seasons or around high-traffic holidays;

[0155] Information display boards are installed on low-risk sections of road to publish real-time road conditions and remind drivers to pay attention to slippery roads, weather changes, etc.

[0156] For high-risk road sections, the system will automatically initiate an early warning;

[0157] By leveraging IoT technology, real-time data is linked to traffic lights, road sign screens, and traffic broadcast systems. When an anomaly occurs, the system will quickly issue warning information to drivers through variable message signs, traffic lights, and mobile phone apps, including:

[0158] When the road surface is slippery, the signal light cycle is automatically adjusted to reduce traffic flow and avoid congestion;

[0159] Publish road warning information (such as "road flooding, slow down") to the smart devices of nearby drivers;

[0160] Based on real-time monitoring of road conditions and traffic flow data, the Intelligent Transportation System (ITS) can dynamically adjust traffic lights on high-risk sections of road. For example, during accident-prone periods or inclement weather, it can extend red light duration to reduce traffic flow.

[0161] Big data analysis is used to predict traffic flow changes on high-risk roads and to adjust traffic flow in advance. For example, during the rainy season or during specific periods (such as morning and evening rush hours), a real-time traffic flow dispatch system can be used to guide vehicles around high-risk roads to reduce the risk of accidents.

[0162] See also Figure 2 As shown, a road construction safety monitoring system includes:

[0163] A data acquisition module, which is used to collect road surface moisture data and road surface vibration amplitude data within a monitoring period;

[0164] A road surface moisture assessment module is used to collect road surface moisture data within a monitoring period and establish a corresponding time series. Based on the road surface moisture fluctuation amplitude within the time series, the module determines the impact of abnormal road surface moisture fluctuations on road safety engineering within the monitoring period.

[0165] a pavement vibration amplitude assessment module, which is used to collect pavement vibration amplitude data and establish a corresponding time series. By analyzing the pavement vibration amplitude fluctuations within the time series, the module determines the impact of abnormal pavement vibration amplitude fluctuations on road safety engineering;

[0166] A comprehensive evaluation module, which is used to divide the monitoring area into several monitoring sections, and comprehensively analyze the abnormal fluctuations of road surface moisture and abnormal fluctuations of road surface vibration amplitude in each monitoring section to evaluate the safety of each monitoring section;

[0167] A risk section classification module, which marks each road section according to the assessment results and divides it into high-risk sections and low-risk sections;

[0168] An early warning response module strengthens the monitoring frequency for low-risk sections, and provides real-time early warnings, emergency responses, dynamic adjustment of traffic signals, issuance of traffic reminders and maintenance plans for high-risk sections to ensure the safety and service life of the roads.

[0169] The working principle of the present invention is as follows: by collecting road moisture and vibration amplitude data at high frequency and establishing a time series within the monitoring period, the fluctuation amplitude of humidity and vibration data and its impact on road safety are analyzed using filtering and Fourier transform techniques; the method first divides the monitoring area into multiple sections, and evaluates the safety of each section by comprehensively analyzing the abnormal fluctuations in humidity and vibration, and accordingly divides the sections into high-risk and low-risk categories; for low-risk sections, measures such as increasing the monitoring frequency and regularly inspecting and maintaining sensors are taken; and for high-risk sections, strategies such as real-time early warning, emergency response, dynamic adjustment of traffic signals, issuance of traffic prompts, driver training and formulation of long-term maintenance plans are implemented to ensure road safety and extend its service life; the entire system consists of multiple modules for data collection, road moisture assessment, road vibration amplitude assessment, comprehensive assessment, risk section division and early warning response. Through Internet of Things technology and big data analysis, precise and intelligent management of urban road traffic is achieved.

[0170] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0171] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0172] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0173] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0174] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A road construction safety monitoring method, characterized in that: The following steps are involved: S1: During a monitoring period, road surface moisture data is collected and a corresponding time series is established. Based on the road surface moisture fluctuation amplitude within the time series, a global impact index is calculated to determine the impact of abnormal road surface moisture fluctuations on road safety engineering during the monitoring period. The process of obtaining the global impact index specifically includes: Generate humidity time series for each monitoring point; Decompose the time series into trend component, seasonal component and random component, and calculate the filtered humidity data; The expression of the filtered humidity data is: ； Where, Indicates the number of sensors, =1,2,......,m, Indicates the A humidity sensor at time The filtered humidity data, represents the trend component of the humidity time series, represents the seasonal component of the humidity time series, represents the random component of the humidity time series; Calculate the fluctuation range, the calculation expression is: ； Where, represents the fluctuation amplitude of the random component of humidity, represents the random component of the maximum humidity time series, represents the random component of the minimum humidity time series; The fluctuation amplitude obtained by each sensor is combined to calculate the global impact index; The calculation expression of the global impact index is: ； Where, Indicates the maximum number of humidity sensors, represents the global impact index, is the preset weight factor; S2: During the monitoring period, road surface vibration amplitude data is collected and a corresponding time series is established. By analyzing the fluctuation of the road surface vibration amplitude within the time series, a comprehensive vibration fluctuation index is calculated, and the degree of impact of abnormal fluctuations in the road surface vibration amplitude on road safety engineering is determined. The process of obtaining the comprehensive vibration fluctuation index specifically includes: Collect vibration amplitude data in time series; The collected vibration amplitude data is transmitted to the central data processing platform via the Internet of Things; Use a bandpass filter to remove ambient noise and high-frequency interference signals; The vibration amplitude data of each acquisition point is formed into a time series; Eliminate the DC component in the time series to obtain a zero-mean time series; The calculation expression of the zero-mean time series is: ； Where, Indicates the number of acceleration sensors, is a positive integer greater than 0, Indicates the time point of collection, is a positive integer greater than 0, Indicates the The accelerometer The zero mean value of the vibration amplitude data collected at each time point, Indicates the The accelerometer The vibration amplitude data collected at each time point, Indicates the The average value of the vibration amplitude data collected by the acceleration sensors; Perform discrete Fourier transform on the zero-mean sequence to obtain frequency domain components, and calculate the Fourier coefficients corresponding to each frequency domain component; The calculation expression of the Fourier coefficient is: ； Where, Indicates the maximum number of acquisition time points, represents the number of frequency components, represents the imaginary unit, Indicates the Accelerometer The Fourier coefficients of the frequency components, Indicates the frequency components, Represents the logarithm of the base of a natural number; Calculate the vibration amplitude data in frequency components The power spectral density of The calculation expression of the power spectrum density is: ； Where, Indicates the The accelerometer The power spectral density of the frequency components; Calculate the cumulative value of the power spectrum within the frequency range to obtain the cumulative power spectrum; The calculation expression of the cumulative power spectrum is: ； Where, Indicates the The accelerometer The cumulative power spectrum of the frequency components; Calculate the vibration fluctuation index based on the cumulative power spectrum; The calculation expression of the vibration fluctuation index is: ； Where, Indicates the The vibration fluctuation index of the vibration amplitude data collected by the acceleration sensor, Indicates the An accelerometer The maximum power spectral density among the power spectral densities of frequency components; Obtain the vibration fluctuation index of all acceleration sensors on the same monitored road surface, and calculate the average of all vibration fluctuation indexes to obtain a comprehensive vibration fluctuation index; S3: Divide the monitoring area into several monitoring sections, conduct a comprehensive analysis of the abnormal fluctuations in road surface moisture and road surface vibration amplitude in each monitoring section, calculate the safety factor, and evaluate the safety of each monitoring section; S4: Based on the assessment results, each road section is marked and divided into high-risk sections and low-risk sections; S5: For low-risk sections, increase the monitoring frequency; for high-risk sections, provide real-time warnings, emergency responses, dynamically adjust traffic signals, and issue traffic reminders and maintenance plans.

2. A road construction safety monitoring method according to claim 1, characterized in that: The determination of the degree of impact of abnormal fluctuations in road surface moisture on road safety engineering during the monitoring period specifically includes: It is determined whether the global impact index is greater than or equal to a preset global impact index threshold. If so, the humidity data of the corresponding road section has a high impact on road safety. If not, the humidity data of the corresponding road section has a low impact on road safety.

3. A road construction safety monitoring method according to claim 1, characterized in that: The determination of the degree of impact of abnormal fluctuations in road vibration amplitude on road safety engineering specifically includes: Determine whether the comprehensive vibration fluctuation index is greater than or equal to a preset threshold. If so, the abnormal fluctuation of the road surface vibration amplitude has a high impact on the road safety project. If not, the abnormal fluctuation of the road surface vibration amplitude has a low impact on the road safety project.

4. A road construction safety monitoring method according to claim 1, characterized in that: The process of obtaining the safety factor specifically includes: Divide the monitoring area into several monitoring sections; Obtain the global impact index and comprehensive vibration fluctuation index of each monitored road section, normalize the global impact index and comprehensive vibration fluctuation index of each monitored road section, and calculate the safety factor of each road section; The calculation formula of the safety factor is: ； Where, Indicates the number of monitored sections, represents the safety factor of the h-th road section, represents the comprehensive vibration fluctuation index, represents the global impact index, and is the preset scale factor, and and Both are greater than 0.

5. A road construction safety monitoring method according to claim 1, characterized in that: According to the evaluation results, each road section is marked and divided into high-risk sections and low-risk sections, including: Determine whether the safety factor of each road section is greater than or equal to the preset safety factor threshold. If so, it is recorded as a low-risk section; if not, it is recorded as a high-risk section.

6. A road construction safety monitoring method according to claim 1, characterized in that: For low-risk sections, the monitoring frequency will be increased, and for high-risk sections, real-time warnings, emergency responses, dynamic adjustments to traffic signals, and the issuance of traffic alerts and maintenance plans will be implemented. Specifically, For road sections assessed as low-risk, the sampling frequency of humidity and vibration monitoring parameters will be increased to monitor changes in the road section in real time. The accuracy and continuity of data collection will be ensured through regular inspection and calibration of sensors and deployment of backup equipment. Intelligently plan long-term maintenance plans for low-risk road sections based on historical data analysis, road conditions, and traffic flow forecasts; For high-risk road sections, an early warning mechanism will be automatically triggered, using IoT technology to link with traffic lights, road sign screens, and traffic broadcast systems; If an abnormal situation is detected, the system will issue a warning message to the driver; Based on real-time monitoring data, the intelligent transportation system can dynamically adjust traffic signal control strategies for high-risk sections of roads, use big data analysis to predict traffic flow changes, conduct traffic flow scheduling, guide vehicles to avoid high-risk areas, and reduce the probability of accidents.

7. A road construction safety monitoring system, characterized in that: A road construction safety monitoring method according to any one of claims 1 to 6, comprising: A data acquisition module, which is used to collect road surface moisture data and road surface vibration amplitude data within a monitoring period; A road surface moisture assessment module is used to collect road surface moisture data within a monitoring period and establish a corresponding time series. Based on the road surface moisture fluctuation amplitude within the time series, the module determines the impact of abnormal road surface moisture fluctuations on road safety engineering within the monitoring period. a pavement vibration amplitude assessment module, which is used to collect pavement vibration amplitude data and establish a corresponding time series. By analyzing the pavement vibration amplitude fluctuations within the time series, the module determines the impact of abnormal pavement vibration amplitude fluctuations on road safety engineering; A comprehensive evaluation module, which is used to divide the monitoring area into several monitoring sections, and comprehensively analyze the abnormal fluctuations of road surface moisture and abnormal fluctuations of road surface vibration amplitude in each monitoring section to evaluate the safety of each monitoring section; A risk section classification module, which marks each road section according to the assessment results and divides it into high-risk sections and low-risk sections; An early warning response module strengthens the monitoring frequency for low-risk sections, and provides real-time early warnings, emergency responses, dynamic adjustment of traffic signals, issuance of traffic reminders and maintenance plans for high-risk sections to ensure the safety and service life of the roads.

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