Dynamic detection method of highway subgrade settlement using wireless transmission technology

Abstract

The present invention relates to the field of highway engineering monitoring technology, and discloses a method for dynamic detection of highway roadbed settlement using wireless transmission technology. The method first accesses the technical documentation of the monitoring equipment to obtain parameters, records high-frequency settlement characteristics when the monitoring is triggered, and comprehensively selects whether to perform multi-dimensional dynamic detection. If performed, the transmission advantage value of the monitoring equipment is calculated, the wireless signal transmission status is detected, and the roadbed combination is segmented and the detection parts are screened accordingly. The settlement displacement and signal delay of each part are detected in real time, the influencing characteristics of the settlement fluctuation are analyzed and the influencing factors are calculated, and the signal transmission timeliness is monitored. Each part is scored in combination with the influencing factors, the scoring weight is determined according to the signal attenuation value and the transmission limit distance value, and the settlement dynamic detection score is comprehensively calculated. This method combines the performance of the monitoring equipment with the actual situation of the roadbed, and improves the scientific nature and adaptability of the detection.

Description

Technical Field

[0001] The invention relates to the technical field of highway engineering monitoring, in particular to a method for dynamic detection of highway roadbed settlement using wireless transmission technology. Background Art

[0002] As the foundational structure of road construction, the stability of highway subgrades is directly related to the road's service life and traffic safety. During highway operation, subgrade settlement is prone to occur due to a variety of factors, including changing geological conditions, repeated traffic loads, groundwater infiltration, and erosion from the natural environment. If this settlement is not monitored and warned of promptly, it can cause serious problems such as pavement cracking and collapse, increasing maintenance costs and posing a significant threat to driving safety. Therefore, efficient and accurate dynamic monitoring of highway subgrade settlement has long been a research priority in the field of road engineering.

[0003] Traditional methods for detecting roadbed settlement rely heavily on manual inspections and fixed-point monitoring. Manual inspections primarily determine the condition of the roadbed by observing changes in the road surface's appearance. This is not only inefficient but also heavily influenced by human experience, making it difficult to capture minute settlement changes, let alone achieve real-time dynamic monitoring. Fixed-point monitoring typically uses total stations, levels, and other equipment to conduct regular measurements at preset locations. While this improves accuracy, it suffers from limited monitoring point coverage and long data collection intervals, making it difficult to reflect the dynamic distribution characteristics of the roadbed's overall settlement. Especially for long-distance highway sections, fixed-point monitoring requires the deployment of a large number of monitoring devices, which is not only costly but also presents the challenge of difficult equipment maintenance.

[0004] With the development of sensing and wireless communication technologies, settlement monitoring systems based on wireless transmission have gradually been applied to roadbed monitoring. These systems deploy sensors at different locations on the roadbed and use wireless signal transmission to collect settlement data in real time, which, to a certain extent, improves the continuity and timeliness of monitoring. However, existing wireless transmission monitoring methods still have many shortcomings. On the one hand, wireless signals are easily affected by factors such as soil moisture, electromagnetic interference, and terrain obstruction in complex roadbed environments, resulting in unstable signal transmission, frequent data loss or delays, and seriously affecting the reliability of monitoring data. On the other hand, existing technologies often use a uniform distribution or random selection method when deploying monitoring points, without considering the different impacts of different roadbed locations on settlement fluctuations. This leads to irrational allocation of monitoring resources, insufficient monitoring accuracy in some key areas, and redundant monitoring in non-key areas, resulting in wasted resources.

[0005] At the same time, existing detection methods often focus on simply recording settlement and displacement during data processing, lacking in-depth analysis of the correlation between signal transmission status and settlement characteristics. For example, when a monitoring point experiences signal delay or attenuation, it is difficult to determine whether it is caused by equipment failure or structural changes caused by roadbed settlement, which leads to significant uncertainty in the interpretation of monitoring data. Furthermore, when assessing the overall settlement status of the roadbed, traditional methods often rely solely on a single settlement and displacement indicator, ignoring the impact of factors such as signal transmission timeliness and attenuation characteristics on the test results. This results in an incomplete assessment and is difficult to meet the dynamic detection needs of complex roadbed environments.

[0006] In actual applications, different types of monitoring equipment exhibit significant differences in wireless transmission performance, with varying signal coverage, transmission rates, and anti-interference capabilities. Failure to design a targeted monitoring plan based on equipment parameters and the actual roadbed conditions can easily lead to blind spots or data distortion. For example, on mountainous roads with complex geological conditions, wireless signal propagation paths are easily blocked by terrain. Using inappropriate equipment or deployment methods can prevent the effective transmission of large amounts of monitoring data, severely impacting detection effectiveness. Therefore, combining the wireless transmission characteristics of monitoring equipment to achieve accurate segmented detection and dynamic assessment of roadbed settlement has become a pressing technical challenge. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for dynamic detection of highway subgrade settlement using wireless transmission technology to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides a method for dynamic detection of highway subgrade settlement using wireless transmission technology, the method comprising:

[0009] S1: Access the technical documentation of the monitoring equipment to obtain the monitoring equipment parameters. When the highway subgrade settlement monitoring is triggered, record the high-frequency settlement characteristics. Combine the monitoring equipment parameters and high-frequency settlement characteristics to select whether to perform multi-dimensional dynamic detection.

[0010] S2: Perform multi-dimensional dynamic detection by calculating the transmission advantage value of the current monitoring equipment, detecting the wireless signal transmission status in the monitoring equipment, and combining the wireless signal transmission status and the transmission advantage value to divide the highway subgrade into sections and select multiple groups of detection locations;

[0011] S3: Real-time detection of settlement displacement and signal delay at each detection location, analysis of the impact of each detection location on settlement fluctuations in the highway subgrade and calculation of the impact factor, and monitoring of signal transmission timeliness at each detection location;

[0012] S4: Score the position of each marked detection part based on the signal transmission time efficiency and the influence factor of the corresponding position on the settlement fluctuation. Collect the signal attenuation value of each marked detection part and the limit distance value during transmission to obtain the score weight of each detection part. Combine the position score and score weight of each marked detection part to calculate the dynamic detection score of the settlement of the highway subgrade under the current monitoring equipment.

[0013] Preferably, the monitoring device parameters include an anti-interference threshold of the monitoring device signal. By obtaining the device sensitivity, transmission bandwidth, and device deployment density, a signal stability model is established, and the transmission attenuation of the monitoring device is determined and accumulated with the initial strength to obtain the anti-interference threshold of the monitoring device signal.

[0014] By setting the standard monitoring test section and monitoring frequency parameters, the tested section is repeatedly monitored within the set duration, and the total number of monitoring times is recorded to obtain high-frequency settlement characteristics;

[0015] The anti-interference threshold and high-frequency settlement characteristics of the monitoring equipment signal are compared with the corresponding reasonable thresholds to obtain the reasonable results of the current monitoring equipment deployment. If the judgment results all indicate that the current monitoring equipment deployment is unreasonable, the dynamic detection of highway subgrade settlement in the current monitoring equipment is defaulted to a low score evaluation. Otherwise, multi-dimensional dynamic detection of the highway subgrade in the current monitoring equipment is performed.

[0016] Preferably, the anti-interference threshold and high-frequency sedimentation characteristics of the monitoring equipment signal are standardized, and the transmission advantage value is obtained using the geometric mean method;

[0017] By setting up a miniature signal sensor on the signal transmission path of the monitoring equipment, measuring the signal strength and stability changes, detecting the wireless signal transmission status in the monitoring equipment, and introducing an effective transmission judgment model based on signal propagation, the signal flux per unit time is obtained.

[0018] Preferably, the transmission advantage value and the signal flux per unit time are normalized and substituted into the logistic regression model to obtain the site screening embedding value;

[0019] Combine and segment the highway subgrade to obtain various parts of the highway subgrade, and perform comprehensive calculations based on the transmission demand index of the parts and the historical failure probability of the parts;

[0020] Based on the signal transmission parameters of the part, the expected signal loss value per unit time of the part is calculated, and the minimum transmission bandwidth required is inferred by combining the signal transmission model to obtain the part transmission demand index;

[0021] By retrieving historical data of highway subgrade, extracting failure events of each structural part, and statistically analyzing each part separately, the statistical number of failures of the part in the reference period is calculated by ratio with the number of operating cycles to obtain the historical failure probability of the part.

[0022] Preferably, the site transmission demand index and the site historical failure probability are standardized and substituted into the comprehensive risk assessment model to determine the site activity value corresponding to each site of the highway subgrade;

[0023] The activity values ​​of the parts are sorted from small to large according to their numerical values. The part screening embedding value is compared with the preset multiple grouping thresholds through the preset multiple grouping thresholds. The set of detection parts whose grouping thresholds are less than or equal to the current embedding value is selected. Combined with the activity value sorting results, all detection parts whose grouping thresholds are located before the current embedding value in the sorting are selected at the same time.

[0024] Preferably, the settlement displacement and signal delay of each detection part are detected in real time by an embedded displacement sensor array and an infrared ranging or laser positioning array;

[0025] By collecting the displacement change sequence within a unit time, setting the sampling period, obtaining continuous displacement sampling values, and calculating based on the difference and variation analysis model, the settlement displacement is obtained;

[0026] By detecting the signal delay change of each detection part within a unit time, setting the detection cycle, obtaining a continuous delay sequence, subtracting the delay value at the start time of the signal delay from the delay value at the end time of the signal delay, and calculating the ratio with the corresponding detection time interval to obtain the signal delay;

[0027] The settlement displacement and signal delay were normalized and substituted into the exponential synergy index model to obtain the influencing factors of settlement fluctuation.

[0028] Preferably, the influence factor of the sedimentation fluctuation in each detection part is compared with a preset influence threshold. If the influence factor of the sedimentation fluctuation is greater than or equal to the influence threshold, the influence feature of the sedimentation fluctuation is judged to be high-impact, and the corresponding detection part is recorded as low-scoring and screened out. Conversely, if the influence factor of the sedimentation fluctuation is less than the influence threshold, the influence feature of the sedimentation fluctuation is judged to be low-impact, and the corresponding detection part is retained and marked.

[0029] When the highway subgrade settlement monitoring is triggered, the signal transmission time efficiency of the monitoring mark detection part is monitored, the signal sending and receiving time of each mark detection part at the moment the highway subgrade monitoring is triggered is obtained, and the signal transmission time efficiency is obtained based on the timestamp difference calculation.

[0030] Preferably, the signal transmission time efficiency of each marker detection site is combined with the influence factor of the corresponding site on the sedimentation fluctuation, and then substituted into the parabolic nonlinear fusion model to obtain the score of each marker detection site.

[0031] Preferably, a stable low-power signal is applied to each marker detection site and its received strength is measured, and the signal attenuation value of each marker detection site is calculated based on the signal attenuation law;

[0032] Combined with the material characteristics of the monitoring equipment, a signal attenuation model is established to set the maximum allowable attenuation and calculate the transmission limit distance value of each marker detection part;

[0033] The signal attenuation value of each marked detection site and the limit distance value during transmission are standardized and substituted into the exponential entropy bias model to obtain the scoring weight of each detection site.

[0034] Preferably, the settlement dynamic detection score of the highway subgrade under the current monitoring equipment is obtained by comprehensively summing the site scores and score weights of each marked detection site through weighted summation.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] This method obtains parameters by accessing the technical documentation of monitoring equipment and, based on high-frequency settlement characteristics, determines whether to conduct multi-dimensional dynamic testing, thus enabling flexible adjustment of monitoring strategies. This approach allows for targeted selection of detection modes based on the performance differences of different monitoring equipment and the actual roadbed settlement conditions, avoiding the resource waste or insufficient detection issues associated with the traditional fixed monitoring mode. For example, on sections of road with relatively stable settlement, the detection dimensions can be reduced to lower the equipment's operating load; whereas, in areas with large settlement fluctuations, multi-dimensional dynamic testing is initiated to ensure that subtle settlement changes are captured, making the detection process more tailored to actual needs.

[0037] In the selection of detection locations, this method innovatively introduces the transmission advantage value and the wireless signal transmission status as the basis for segmentation, and screens out multiple groups of detection locations through combined segmentation. This process fully considers the correlation between the wireless transmission characteristics of the monitoring equipment and the roadbed structure, making the distribution of detection locations more scientific and reasonable. The traditional method of evenly distributing monitoring points often results in data collection failures in areas with poor signal transmission conditions, while there is redundancy in areas with good signals but less impact from settlement. By comprehensively evaluating the wireless signal transmission status and transmission advantage value, this method can concentrate the detection locations in areas that are more valuable for settlement monitoring, while avoiding signal transmission blind spots, thereby improving the effectiveness of detection data and optimizing the allocation of monitoring resources.

[0038] During the real-time detection process, this method not only focuses on settlement displacement and signal delay, but also deeply analyzes the impact characteristics of each detection part on settlement fluctuations and calculates the impact factors, while monitoring the signal transmission timeliness. This multi-parameter collaborative analysis method breaks the limitation of traditional methods that only focus on a single indicator of settlement displacement, and establishes a relationship between settlement characteristics and signal transmission performance. By clarifying the impact factors of each part on settlement fluctuations, it is possible to accurately identify key monitoring areas in the roadbed. Small settlement changes in these areas may cause instability in the overall structure and require special attention. Monitoring the signal transmission timeliness can promptly detect changes in the signal transmission path caused by roadbed settlement, avoid misjudging equipment failures as settlement anomalies, and improve the accuracy of interpretation of detection data.

[0039] In the detection and scoring stage, this method forms a comprehensive dynamic detection score for settlement through the comprehensive calculation of location scores and scoring weights. Parameters such as signal attenuation value and extreme distance value are introduced into the scoring process, fully considering the differences in signal transmission between different detection locations and the degree of influence on settlement fluctuations, so that the final scoring results can truly reflect the overall settlement status of the roadbed and the operating efficiency of the monitoring system. Traditional methods for evaluating the settlement status of the roadbed often rely on a single displacement threshold, making it difficult to quantify the correlation between settlements in different areas and the reliability of the monitoring system. The scoring mechanism of this method can organically combine signal transmission performance with settlement characteristics, providing a more comprehensive quantitative basis for roadbed status assessment, helping engineers to more accurately judge roadbed stability and formulate targeted maintenance measures.

[0040] This method is adaptable to different types of monitoring equipment and complex roadbed environments. By dynamically adjusting detection strategies, optimizing detection location distribution, and integrating multi-dimensional parameter evaluation, it enables effective settlement monitoring even in areas with complex geological conditions and difficult signal transmission. Whether in terrain-obstructed areas on mountain roads or in the electromagnetic interference environments of urban roads, this method ensures the continuity and reliability of detection data by analyzing signal transmission status and settlement impact characteristics, providing a more flexible and efficient solution for dynamic monitoring of highway roadbed settlement. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a working principle diagram of the method for dynamic detection of highway subgrade settlement using wireless transmission technology according to the present invention;

[0042] Figure 2 Flowchart for monitoring equipment parameters and high-frequency sedimentation feature processing;

[0043] Figure 3 A flow chart showing the detection of transmission advantage value and wireless signal transmission status;

[0044] Figure 4Flowchart for testing site screening and transmission requirement calculation;

[0045] Figure 5 This is a flow chart of settlement displacement and signal delay detection. DETAILED DESCRIPTION

[0046] 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 creative efforts are within the scope of protection of the present invention.

[0047] See also Figure 1-Figure 5 The present invention provides a method for dynamic detection of highway subgrade settlement using wireless transmission technology, and the specific implementation steps are as follows:

[0048] S1: Access the monitoring equipment's technical documentation to obtain the equipment's parameters. When the roadbed settlement monitoring is triggered, record the high-frequency settlement characteristics. Combine these characteristics with the monitoring equipment parameters to determine whether to perform multi-dimensional dynamic detection. The monitoring equipment parameters include the signal anti-interference threshold of the monitoring equipment. By performing relevant operations to obtain the high-frequency settlement characteristics, the two are compared with the corresponding reasonable thresholds to determine whether the current monitoring equipment deployment is reasonable and whether to perform multi-dimensional dynamic detection.

[0049] S2: If multi-dimensional dynamic detection is performed, the transmission advantage value of the current monitoring equipment is calculated, the wireless signal transmission status within the monitoring equipment is detected, and the wireless signal transmission status and transmission advantage value are comprehensively considered to combine and segment the highway subgrade and screen out multiple groups of detection locations. Specifically, the anti-interference threshold and high-frequency sedimentation characteristics of the monitoring equipment signal are first standardized, and the transmission advantage value is obtained using the geometric mean method. At the same time, the signal strength and stability changes are measured by micro-signal sensors installed on the signal transmission path. An effective transmission judgment model is introduced to obtain the signal flux per unit time. After the transmission advantage value and signal flux are standardized, they are inserted into the logistic regression model to obtain the location screening embedded value. The highway subgrade is then combined and segmented, and the detection locations are screened based on the location transmission demand index and the location historical failure probability.

[0050] S3: Real-time detection of settlement displacement and signal delay at each detection point. Analysis of the impact of each detection point on settlement fluctuations in the highway subgrade is performed, and the impact factor is calculated. The signal transmission time efficiency of each detection point is monitored. Detection is performed using an embedded displacement sensor array and an infrared ranging or laser positioning array. The displacement change sequence and signal delay change are collected. Settlement displacement and signal delay are calculated and standardized, then incorporated into the index synergy index model to obtain the impact factor. Simultaneously, when settlement monitoring is triggered, the signal transmission and reception times are obtained to calculate the transmission time efficiency.

[0051] S4: The signal transmission time efficiency of each marked detection location is combined with the influence factor of the corresponding location on the settlement fluctuation to perform a location score. The signal attenuation value and the transmission limit distance value of each marked detection location are collected to obtain the scoring weight of each detection location. The location score of each marked detection location and the scoring weight are combined to calculate the dynamic detection score of the settlement of the highway subgrade under the current monitoring equipment. The signal transmission time efficiency and the influence factor are normalized and then applied to the parabolic nonlinear fusion model to obtain the location score. The signal attenuation value is calculated by applying a stable low-power signal to measure the receiving strength. The maximum allowable attenuation value is set in a model based on the material characteristics of the equipment to calculate the limit distance value. After normalization, the exponential entropy bias model is applied to obtain the scoring weight. Finally, the weighted summation is used to obtain the detection score.

[0052] Example 1: This example mainly involves the acquisition of monitoring equipment parameters, the recording of high-frequency sedimentation characteristics, and the multi-dimensional dynamic detection selection and judgment based on the two. The specific implementation method is as follows:

[0053] Access the technical documentation for the monitoring device and obtain its parameters. These parameters include the device's signal interference threshold. To determine this threshold, you first need to obtain data on the device's sensitivity, transmission bandwidth, and device deployment density. Based on this data, build a signal stability model. This model determines the device's transmission attenuation. The attenuation is then added to the initial strength to determine the device's signal interference threshold.

[0054] When highway subgrade settlement monitoring is triggered, high-frequency settlement signatures need to be recorded. This involves setting a standard monitoring test section and monitoring frequency parameters. Repeated monitoring is performed on the tested section for a set duration, with the total number of monitoring times recorded in detail. This allows for the generation of high-frequency settlement signatures.

[0055] The decision to conduct multi-dimensional dynamic monitoring is based on comprehensive considerations of monitoring equipment parameters and high-frequency settlement characteristics. Specifically, the monitoring equipment's signal anti-interference threshold and high-frequency settlement characteristics are compared with corresponding reasonable thresholds. These reasonable thresholds are pre-set based on the actual needs of highway subgrade settlement monitoring and relevant standards. This comparison determines the appropriateness of the current monitoring equipment deployment.

[0056] If the judgment results show that the anti-interference threshold of the monitoring equipment signal and the high-frequency settlement characteristics do not meet the corresponding reasonable thresholds, that is, both indicate that the current monitoring equipment deployment is unreasonable, then in this case, the dynamic detection of highway subgrade settlement in the current monitoring equipment is defaulted to a low score evaluation, and multi-dimensional dynamic detection will no longer be performed.

[0057] On the contrary, if the judgment results do not all indicate that the current monitoring equipment deployment is unreasonable, that is, at least one parameter meets the reasonable threshold, then it is necessary to perform multi-dimensional dynamic detection of the highway subgrade in the current monitoring equipment.

[0058] Throughout the implementation process, every step must be strictly followed according to the prescribed operating procedures to ensure accurate and reliable data, thus providing an effective basis for subsequent judgment and testing. For example, when obtaining equipment sensitivity, transmission bandwidth, and equipment deployment density, ensuring data accuracy directly affects the establishment of signal stability models and the calculation of monitoring equipment signal anti-interference thresholds. When setting standard monitoring test sections and monitoring frequency parameters, the actual conditions of the highway subgrade and monitoring requirements must be fully considered to ensure the effectiveness of repeated monitoring operations and the accuracy of high-frequency settlement characteristics.

[0059] When making comparisons and judgments, we must strictly follow pre-set reasonable thresholds and avoid subjective arbitrariness. Only in this way can we accurately judge whether the current monitoring equipment deployment is reasonable and correctly decide whether to conduct multi-dimensional dynamic detection.

[0060] Example 2: This example mainly focuses on the calculation of transmission advantage value during multi-dimensional dynamic detection, the detection of wireless signal transmission status, and the preliminary screening of detection locations. The specific implementation method is as follows:

[0061] Once multi-dimensional dynamic testing is determined, the transmission advantage value (TAD) for the current monitoring device must be calculated. This involves normalizing the previously acquired anti-interference threshold and high-frequency dropout characteristics of the monitoring device's signal. This standardization process ensures comparability of data across different dimensions. The specific processing method depends on the data characteristics and testing requirements. After normalization, the geometric mean method is used to calculate the TAD value for the two standardized data sets.

[0062] It is necessary to monitor the wireless signal transmission status within the monitoring device. This is achieved by placing miniature signal sensors along the monitoring device's signal transmission path. These sensors measure changes in signal strength and stability. These miniature signal sensors can detect dynamic changes in the signal during transmission in real time and transmit the collected data to the processing system. Simultaneously, an effective transmission judgment model based on signal propagation theory is introduced to analyze and process the collected signal strength and stability change data to derive the signal flux per unit time. Based on the fundamental principles of signal propagation, this model can accurately determine the signal transmission volume per unit time.

[0063] After obtaining the transmission advantage value and signal flux per unit time, these two data points need to be normalized. These normalized data are then substituted into a logistic regression model to calculate the site screening embedding value. The logistic regression model is a commonly used statistical analysis model that can effectively classify and predict data. Here, it is used to calculate the site screening embedding value, providing a basis for subsequent detection site selection.

[0064] The highway subgrade must be segmented to identify its various locations. This segmentation method can be determined based on the subgrade's structural characteristics and testing requirements, ensuring the subgrade is rationally divided into multiple independent locations. After identifying each location, a comprehensive calculation based on the location's transmission demand index and historical failure probability is performed to select suitable locations for testing.

[0065] The location transmission demand index is calculated by calculating the expected signal loss per unit time at that location based on signal transmission parameters at that location, such as frequency, strength, and bandwidth. Then, using the signal transmission model, the minimum required transmission bandwidth is inferred to obtain the location transmission demand index. The signal transmission model is developed based on basic signal transmission principles and actual transmission conditions, accurately describing signal loss and bandwidth requirements during transmission.

[0066] The method for calculating the historical failure probability of a component is to retrieve historical highway subgrade data, extract failure events for each structural component, and perform a statistical analysis for each component. The statistical number of failures for that component within a reference period is then calculated by comparing the number of operating cycles to the calculated historical failure probability. The reference period can be determined based on the service life and maintenance records of the highway subgrade, ensuring that the calculated historical failure probability is of reference value.

[0067] After calculating the transmission demand index and the historical failure probability of each location, a comprehensive analysis of these two indicators is required to determine the importance and potential risks of each location, providing a more comprehensive basis for the subsequent screening of test locations. Throughout the entire process, each step must be strictly carried out according to the prescribed methods and procedures to ensure the accuracy and reliability of the calculated data, thereby enabling the selection of appropriate test locations and providing effective support for subsequent dynamic settlement testing.

[0068] When setting up miniature signal sensors, ensure that they are positioned to accurately detect signal changes along the signal transmission path to avoid inaccurate data collection due to improper sensor placement. When introducing an effective transmission judgment model, the model should be appropriately adjusted and optimized based on actual signal transmission conditions to ensure that it accurately calculates signal flux per unit time. When calculating location transmission demand indicators and location historical failure probabilities, ensure that the data used is reliable and the calculation method is correct, so that the resulting indicators accurately reflect the actual conditions of each location.

[0069] Example 3: This example mainly involves determining the part activity value based on the part transmission demand index and the part historical failure probability, and combining the part screening embedded value and the grouping threshold to screen the detection part. The specific implementation method is as follows:

[0070] After completing the segmentation of the highway subgrade components and calculating the component transmission demand index and component historical failure probability, these two indicators need to be standardized. Standardization eliminates dimensional differences between the indicators, enabling comparison and calculation on the same scale. This can be accomplished using common normalization or standardization formulas, such as mapping the data to the [0, 1] interval or converting it to a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0071] The standardized location transmission demand index and location historical failure probability are substituted into the comprehensive risk assessment model for calculation to determine the location activity value corresponding to each location of the highway subgrade. The comprehensive risk assessment model is a model based on a comprehensive evaluation of multiple indicators. It can comprehensively consider location transmission demand and historical failure situations to quantitatively assess the activity level of a location. Here, it is assumed that the expression of the comprehensive risk assessment model is:

[0072] ;

[0073] in, Indicates the activity value of the part; It represents the normalized position transmission demand index; represents the normalized historical failure probability of a part; and is the weight coefficient of the model, and . Weight coefficient and The determination of can be set according to the actual needs of highway subgrade settlement monitoring and expert experience. For example, if more attention is paid to the transmission needs of the parts, A larger weight is given to Larger weight.

[0074] After obtaining the activity values ​​for each body part, you need to sort them from smallest to largest. This step helps you intuitively understand the relative size of the activity values ​​for each body part, providing a basis for subsequent grouping and screening.

[0075] At the same time, multiple grouping thresholds are pre-set. These grouping thresholds are determined based on factors such as the structural characteristics of the highway subgrade, historical monitoring data, and engineering experience. They are used to divide parts into different groups according to the size of their activity values.

[0076] Next, the site selection embedding values ​​calculated using the logistic regression model are compared with multiple preset grouping thresholds. Specifically, each grouping threshold is checked one by one to see if it is less than or equal to the current site selection embedding value. The set of detection sites corresponding to all grouping thresholds that meet this condition is then selected.

[0077] In addition, it is necessary to combine the sorting results of the active values ​​and select all the detection parts whose grouping thresholds are before the current embedded value in the sorting. For example, assuming that the grouping threshold corresponding to the part screening embedded value is , in the sorting, all grouping thresholds are less than or equal to The position of these grouping thresholds in the sorting All previous parts will be selected into the detection part set.

[0078] When performing standardization, it is important to ensure consistency and accuracy in the processing method to avoid inaccurate calculations of site activity values ​​due to errors in the standardization process. For example, when standardizing the site transmission demand indicator, it is important to accurately obtain the maximum and minimum values, mean, standard deviation, and other parameters of the indicator to ensure that the standardized data truly reflects the relative magnitude of site transmission demand.

[0079] In determining the weight coefficient of the comprehensive risk assessment model and When evaluating the activity value of a site, the actual conditions of the highway subgrade must be fully considered. Expert meetings and analysis of historical monitoring data can be used to determine appropriate weight distribution so that the site activity value accurately reflects the site's overall risk and activity level.

[0080] When setting the grouping threshold, it is important to consider factors such as the sensitivity of different roadbed structural components to settlement, their importance, and their historical failure history. For example, for critical structural components such as bridge junctions and high-fill sections, a lower grouping threshold may be required to ensure that these components are prioritized for inspection.

[0081] Example 4: This example mainly focuses on the real-time detection of the sedimentation displacement and signal delay of each detection site, the calculation of the influencing factors, and the marking and screening of the detection sites. The specific implementation method is as follows:

[0082] Take a section of a four-lane, two-way highway roadbed as an example. This section includes fill sections, excavation sections, and bridge transition sections. Dynamic settlement monitoring first requires real-time detection of settlement displacement and signal delay at each test site. This involves employing an array of embedded displacement sensors and an infrared ranging or laser positioning array as detection tools. For example, in the fill section, a set of embedded displacement sensors is deployed every 50 meters. Each set contains multiple displacement sensors distributed along the depth of the roadbed to monitor settlement in different soil layers. Simultaneously, infrared ranging or laser positioning equipment is deployed at corresponding locations to assist in detecting displacement changes on the roadbed surface.

[0083] When detecting settlement displacement, it is necessary to collect a sequence of displacement changes within a unit time. Taking one minute as a unit time, the sampling period is set to 10 seconds, that is, a displacement sampling value is obtained every 10 seconds, so that 6 consecutive displacement sampling values ​​can be obtained within one minute. These sampling values ​​are calculated based on the difference and variation analysis model to obtain the settlement displacement. Specifically, by calculating the difference (difference) between adjacent sampling values, the displacement change trend and amplitude are analyzed, and then combined with the variation analysis model, the stability and significance of the displacement change are evaluated, thereby determining the settlement displacement of the detection part within a unit time.

[0084] Taking a test site in a fill section as an example, assume the displacement value collected in the first 10 seconds is 0.2mm, the second 10 seconds is 0.3mm, the third 10 seconds is 0.25mm, the fourth 10 seconds is 0.4mm, the fifth 10 seconds is 0.35mm, and the sixth 10 seconds is 0.3mm. Through differential calculation, the adjacent differences are 0.1mm, -0.05mm, 0.15mm, -0.05mm, and -0.05mm, respectively. These differences are then subjected to variation analysis, calculating their variance and standard deviation to determine the fluctuation range of the displacement change. Ultimately, the settlement displacement of the test site within this minute is determined to be approximately 0.3mm.

[0085] When testing signal delay, it's necessary to measure the change in signal delay per unit time at each test location. Again, using one minute as the unit time, set the test cycle to 15 seconds. This means acquiring signal delay data every 15 seconds, resulting in four consecutive delay sequences within one minute. Subtract the delay value at the start of the signal delay from the delay value at the end of the signal delay, and then calculate the ratio by the corresponding test interval to determine the signal delay.

[0086] For example, the signal delay for a given detection location in the first 15 seconds starts at 5ms and ends at 5.5ms; the second 15 seconds starts at 5.5ms and ends at 6ms; the third 15 seconds starts at 6ms and ends at 6.8ms; and the fourth 15 seconds starts at 6.8ms and ends at 7.2ms. If the detection interval is 15 seconds, then the signal delay for the first time period is (5.5-5) / 15 = 0.033ms / s; the second is (6-5.5) / 15 = 0.033ms / s; the third is (6.8-6) / 15 = 0.053ms / s; and the fourth is (7.2-6.8) / 15 = 0.027ms / s. Combining the signal delays for these four time periods gives an average signal delay of approximately 0.037ms / s for that detection location over the entire minute.

[0087] After obtaining the settlement displacement and signal delay, these two data need to be normalized to eliminate the influence of dimension. After normalization, they are substituted into the exponential synergy index model to obtain the influencing factor of settlement fluctuation. The exponential synergy index model comprehensively considers the synergistic relationship between settlement displacement and signal delay, quantifying the influence of the detection location on settlement fluctuation.

[0088] Assuming that after standardization, the normalized value of the settlement displacement of a certain detection part is 0.6, and the normalized value of the signal delay is 0.4, after substituting them into the index synergy index model, the settlement fluctuation influence factor of this part is obtained to be 0.52.

[0089] Next, the influence factor of the settlement fluctuation in each detection part is compared with the preset influence threshold. The preset influence threshold is determined according to the design standard of the highway subgrade and historical monitoring data, for example, it is set to 0.5. If the settlement fluctuation influence factor of a certain detection part is greater than or equal to 0.5, such as 0.52 in the above example, the settlement fluctuation influence characteristic of the part is judged to be high impact, and the detection part is recorded as a low score evaluation and screened out, and no subsequent signal transmission timeliness monitoring is performed on it. If the influence factor of a certain detection part is less than 0.5, such as 0.45, then its influence characteristic is judged to be low impact, and the part is retained and marked for subsequent monitoring of its signal transmission timeliness.

[0090] When a roadbed settlement monitoring triggers, for example, if a deployed settlement warning sensor detects that the settlement of a certain section of the roadbed exceeds a preset warning value, the signal transmission efficiency of the marker detection location needs to be monitored. Specifically, this involves obtaining the signal transmission and reception times of each marker detection location at the moment the roadbed monitoring is triggered and calculating the signal transmission efficiency based on the timestamp difference.

[0091] Taking a marked detection location as an example, at the moment the settlement monitoring is triggered, the timestamp of the signal sending moment is T1 = 10:00:00.000 on July 7, 2025, and the timestamp of the signal receiving moment is T2 = 10:00:00.020 on July 7, 2025. Therefore, the signal transmission time efficiency is T2-T1=20ms.

[0092] Throughout the implementation process, the deployment of detection equipment must be carefully planned based on the actual structure and geological conditions of the highway subgrade. For different road sections, such as fill sections, cut sections, and bridge transitions, the density and location of sensors and ranging equipment must be adjusted to ensure comprehensive and accurate detection of settlement displacement and signal delays at all locations.

[0093] When collecting displacement change sequences and signal delay changes, the set sampling and detection periods must be strictly followed to ensure data continuity and accuracy. After data collection, the data processing process must be rigorous, and standardization and model calculations must be carried out according to established methods to avoid the introduction of human errors.

[0094] By real-time monitoring of settlement displacement and signal delay at each detection location, as well as calculating influencing factors, we can accurately identify and eliminate locations with significant impact on settlement fluctuations, while retaining less influential locations for focused monitoring. This helps improve the efficiency and pertinence of dynamic monitoring of highway subgrade settlement, providing reliable monitoring data for safe highway subgrade operations.

[0095] Example 5: This example mainly involves the calculation of the site score of the marked detection site, the acquisition of the score weight, and the comprehensive calculation process of the dynamic detection score of the highway subgrade settlement. The specific implementation method is as follows:

[0096] After calculating the sedimentation fluctuation influence factors for each test site and marking and eliminating the test sites, it is necessary to assign a site score to each marked test site. The site score calculation requires combining the signal transmission efficiency of each marked test site and the influence factor of the corresponding site on sedimentation fluctuation.

[0097] Obtain the signal transmission efficiency for each marker detection location. For example, when a highway subgrade settlement monitoring is triggered, record the timestamps of the signal transmission and reception at each marker detection location and calculate the timestamp difference to obtain the signal transmission efficiency. For example, if a marker detection location has a signal transmission timestamp of 14:30:25.123 on July 7, 2025, and a reception timestamp of 14:30:25.156 on July 7, 2025, when monitoring is triggered, the signal transmission efficiency for this location is 33 milliseconds.

[0098] The corresponding sedimentation fluctuation influence factor has been calculated for each marker detection site. For example, the influence factor of another marker detection site is 0.35, indicating that this site has a low impact on sedimentation fluctuation.

[0099] The signal transmission time and corresponding impact factor for each marker detection site were normalized. Normalization is performed to make data of different dimensions comparable. This can be done by mapping the data to a specific interval, such as mapping both the signal transmission time and the impact factor to the interval [0, 1]. Assuming the maximum signal transmission time is 50 milliseconds and the minimum is 10 milliseconds, a normalized value of 33 milliseconds for a particular site is 0.575; the impact factor of 0.35 is used directly as the normalized value.

[0100] Substituting the standardized signal transmission time and impact factor into the parabolic nonlinear fusion model yields a score for each marker detection site. The parabolic nonlinear fusion model accounts for the nonlinear relationship between the two, providing a quantitative assessment of the site's overall status. For example, substituting the two standardized values ​​into the model yields a score of approximately 0.62 for this site.

[0101] After completing the site score calculation, it is necessary to collect the signal attenuation value of each marked detection site and the limit distance value during transmission, and obtain the score weight of each detection site.

[0102] The method for collecting signal attenuation values ​​is to apply a stable low-power signal to each marker detection location, measure the received strength, and calculate the signal attenuation value based on the signal attenuation law. For example, if a stable low-power signal of 10dBm is applied to a marker detection location, the signal strength measured at the receiving end is 8dBm. According to the signal attenuation law, the attenuation value is 10-8 = 2dB.

[0103] Calculating the transmission distance limit requires considering the material properties of the monitoring equipment, establishing a signal attenuation model, and setting the maximum allowable attenuation. For example, the monitoring equipment uses a certain type of wireless transmission module, whose material properties determine a maximum allowable signal attenuation of 15dB. Using the signal attenuation model, the transmission distance corresponding to 15dB of signal attenuation is calculated. This is the transmission distance limit for that marker detection location. Suppose the model calculates the maximum distance limit for a particular location to be 80 meters.

[0104] The signal attenuation and limit distance values ​​of each marker detection location are normalized. For example, if the signal attenuation range is 0-20dB, 2dB at a certain location is normalized to 0.1; if the limit distance range is 50-100 meters, 80 meters is normalized to 0.6.

[0105] Substituting the standardized signal attenuation and extreme distance values ​​into the exponential entropy bias model yields a scoring weight for each detection location. The exponential entropy bias model comprehensively considers the impact of signal attenuation and extreme distance on the importance of the detection location, determining weight distribution through entropy calculation. For example, substituting the two standardized values ​​into the model yields a scoring weight of 0.45 for this location.

[0106] The dynamic settlement detection score of the highway subgrade at the current monitoring equipment is calculated by combining the site scores and score weights of each marked detection site. The specific calculation method is weighted summation, that is, the site score of each marked detection site is multiplied by its score weight, and then all the results are added together.

[0107] Suppose a section of highway subgrade has three marked inspection locations, with location scores of 0.62, 0.75, and 0.58, respectively, and corresponding weights of 0.45, 0.35, and 0.2, respectively. The inspection scores are calculated as: 0.62 × 0.45 = 0.279, 0.75 × 0.35 = 0.2625, 0.58 × 0.2 = 0.116, and the sum is 0.279 + 0.2625 + 0.116 = 0.6575. This means that the dynamic settlement inspection score for this section of highway subgrade under the current monitoring equipment is 0.6575 (the number of decimal places can be adjusted based on actual needs).

[0108] Throughout the implementation process, when applying a stable low-power signal, it is necessary to ensure the stability and consistency of the signal power to avoid inaccurate reception strength measurements due to power fluctuations. The equipment used to measure reception strength must be calibrated to ensure the reliability of the measurement data. When establishing a signal attenuation model, it is important to fully consider the material properties of the monitoring equipment, such as antenna gain, transmit power, and receive sensitivity, as well as environmental factors around the highway subgrade, such as obstacles and electromagnetic interference, so that the model accurately reflects the signal attenuation during actual transmission.

[0109] During the standardization process, the value ranges of each parameter must be clearly defined to ensure that the standardized data reasonably reflects the relative size of the original data. When substituting the model into the calculation of site scores and score weights, the model's calculation steps must be strictly followed to avoid calculation errors. When performing the weighted summation, the scores and weights for each site must be accurately matched to ensure the accuracy of the final test score.

[0110] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0111] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic detection of highway subgrade settlement using wireless transmission technology, characterized by: include: S1: Access the technical documentation of the monitoring equipment to obtain the monitoring equipment parameters. When the highway subgrade settlement monitoring is triggered, record the high-frequency settlement characteristics. Combine the monitoring equipment parameters and high-frequency settlement characteristics to select whether to perform multi-dimensional dynamic detection. S2: Perform multi-dimensional dynamic detection by calculating the transmission advantage value of the current monitoring equipment, detecting the wireless signal transmission status in the monitoring equipment, and combining the wireless signal transmission status and the transmission advantage value to divide the highway subgrade into sections and select multiple groups of detection locations; S3: Real-time detection of settlement displacement and signal delay at each detection location, analysis of the impact of each detection location on settlement fluctuations in the highway subgrade and calculation of the impact factor, and monitoring of signal transmission timeliness at each detection location; S4: Score the signal transmission time efficiency of each marked detection position in combination with the influence factor of the corresponding position on the settlement fluctuation. Collect the signal attenuation value and the limit distance value of each marked detection position and obtain the score weight of each detection position. Combine the position score of each marked detection position and the score weight to calculate the dynamic detection score of the settlement of the highway subgrade under the current monitoring equipment. Monitoring device parameters include the device signal anti-interference threshold. By obtaining device sensitivity, transmission bandwidth, and device deployment density, a signal stability model is established. The transmission attenuation of the monitoring device is determined and added to the initial strength to obtain the device signal anti-interference threshold. By setting the standard monitoring test section and monitoring frequency parameters, the tested section is repeatedly monitored within the set duration, and the total number of monitoring times is recorded to obtain high-frequency settlement characteristics; The anti-interference threshold and high-frequency sedimentation characteristics of the monitoring equipment signal are standardized, and the transmission advantage value is obtained using the geometric mean method.

2. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 1, characterized in that: The anti-interference threshold and high-frequency settlement characteristics of the monitoring equipment signal are compared with the corresponding reasonable thresholds to obtain the reasonable results of the current monitoring equipment deployment. If the judgment results all indicate that the current monitoring equipment deployment is unreasonable, the dynamic detection of highway subgrade settlement in the current monitoring equipment is defaulted to a low score evaluation. Otherwise, multi-dimensional dynamic detection of the highway subgrade in the current monitoring equipment is performed.

3. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 2, characterized in that: By setting up a miniature signal sensor on the signal transmission path of the monitoring equipment, measuring the signal strength and stability changes, detecting the wireless signal transmission status in the monitoring equipment, and introducing an effective transmission judgment model based on signal propagation, the signal flux per unit time is obtained.

4. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 3, characterized in that: The transmission advantage value and the signal flux per unit time were normalized and substituted into the logistic regression model to obtain the site screening embedding value; Combine and segment the highway subgrade to obtain various parts of the highway subgrade, and perform comprehensive calculations based on the transmission demand index of the parts and the historical failure probability of the parts; Based on the signal transmission parameters of the part, the expected signal loss value per unit time of the part is calculated, and the minimum transmission bandwidth required is inferred by combining the signal transmission model to obtain the part transmission demand index; By retrieving historical data of highway subgrade, extracting failure events of each structural part, and statistically analyzing each part separately, the statistical number of failures of the part in the reference period is calculated by ratio with the number of operating cycles to obtain the historical failure probability of the part.

5. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 4, characterized in that: The transmission demand index and historical failure probability of each part are standardized and substituted into the comprehensive risk assessment model to determine the corresponding activity value of each part of the highway subgrade. The activity values ​​of the parts are sorted from small to large according to their numerical values. The part screening embedding value is compared with the preset multiple grouping thresholds through the preset multiple grouping thresholds. The set of detection parts whose grouping thresholds are less than or equal to the current embedding value is selected. Combined with the activity value sorting results, all detection parts whose grouping thresholds are located before the current embedding value in the sorting are selected at the same time.

6. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 1, characterized in that: The embedded displacement sensor array and infrared ranging or laser positioning array are used to detect the settlement displacement and signal delay of each detection part in real time; By collecting the displacement change sequence within a unit time, setting the sampling period, obtaining continuous displacement sampling values, and calculating based on the difference and variation analysis model, the settlement displacement is obtained; By detecting the signal delay change of each detection part within a unit time, setting the detection cycle, obtaining a continuous delay sequence, subtracting the delay value at the start time of the signal delay from the delay value at the end time of the signal delay, and calculating the ratio with the corresponding detection time interval to obtain the signal delay; The settlement displacement and signal delay were normalized and substituted into the exponential synergy index model to obtain the influencing factors of settlement fluctuation.

7. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 6, characterized in that: The influence factor of the sedimentation fluctuation in each detection part is compared with the preset influence threshold. If the influence factor of the sedimentation fluctuation is greater than or equal to the influence threshold, the influence feature of the sedimentation fluctuation is judged to be high-impact, and the corresponding detection part is recorded as low-scoring and screened out. Conversely, if the influence factor of the sedimentation fluctuation is less than the influence threshold, the influence feature of the sedimentation fluctuation is judged to be low-impact, and the corresponding detection part is retained and marked; When the highway subgrade settlement monitoring is triggered, the signal transmission time efficiency of the monitoring mark detection part is monitored, the signal sending and receiving time of each mark detection part at the moment the highway subgrade monitoring is triggered is obtained, and the signal transmission time efficiency is obtained based on the timestamp difference calculation.

8. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 7, characterized in that: The signal transmission time efficiency of each marker detection site was combined with the influence factor of the corresponding site on sedimentation fluctuation, and then standardized and substituted into the parabolic nonlinear fusion model to obtain the score of each marker detection site.

9. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 8, characterized in that: By applying a stable low-power signal to each marker detection site and measuring its received strength, the signal attenuation value of each marker detection site is calculated based on the signal attenuation law; Combined with the material characteristics of the monitoring equipment, a signal attenuation model is established to set the maximum allowable attenuation and calculate the transmission limit distance value of each marker detection part; The signal attenuation value of each marked detection site and the limit distance value during transmission are standardized and substituted into the exponential entropy bias model to obtain the scoring weight of each detection site.

10. The method for dynamic detection of highway subgrade settlement using wireless transmission technology according to claim 9, characterized in that: The settlement dynamic detection score of the highway subgrade under the current monitoring equipment is obtained by combining the site scores and score weights of each marked detection site through weighted summation.

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