Method and system for edge compaction detection of subgrade transition section

Abstract

The application provides a roadbed transition section edge compaction detection method and system, and relates to the technical field of roadbed compaction detection.The method comprises the following steps: setting a detection point layout rule based on a compaction correlation scale and a sensor monitoring scale; after locating a compaction blind area in the roadbed transition section, carrying out grid compaction sensor array layout; performing hierarchical collection of multi-modal compaction data to obtain a fusion compaction parameter array; after the edge compaction detection cloud receives the fusion compaction parameter array, tracing back a cross-layer time sequence correlation array mapping calculation distributed compaction degree deviation node; solving a supplementary compaction control sequence to perform targeted correction; and performing a supplementary compaction control closed-loop iteration according to re-measured data.The application solves the technical problem that the prior art only relies on limited detection points for sampling inspection, cannot comprehensively cover the local compaction blind area and uneven area of the roadbed transition section, causes incomplete detection, and thus affects the safety and service life of the road.

Description

Technical Field

[0001] This invention relates to the field of roadbed compaction testing technology, specifically to a method and system for testing edge compaction of roadbed transition sections. Background Technology

[0002] The roadbed transition section refers to the area where soft soil layers transition to higher-strength soil layers during road or railway construction. Due to the change in foundation soil properties, the compaction requirements in this section are extremely high, and the compaction quality directly affects the roadbed's bearing capacity, stability, and long-term performance. Traditional compaction testing methods often rely on discrete testing points. These testing points are often not densely or evenly distributed, resulting in blind spots in some areas. The existence of blind spots means that the compaction quality in some areas is not detected in time, leading to incomplete quality testing. This may result in some substandard compaction areas being missed. Missed compaction defects can affect the long-term stability of the roadbed and even lead to problems such as settlement and cracking during use, affecting the road's safety and service life. Summary of the Invention

[0003] This application provides a method and system for testing the edge compaction of roadbed transition sections, aiming to solve the technical problem that existing technologies rely on limited testing points for sampling inspection, which cannot fully cover the local compaction blind spots and uneven areas of the roadbed transition sections, resulting in incomplete testing and thus affecting the safety and service life of the road.

[0004] The first aspect of this application discloses a method for edge compaction detection of roadbed transition sections. The method includes: setting detection point layout rules based on compaction correlation scales and sensor monitoring scales; locating compaction blind zones in the roadbed transition section and then deploying a gridded compaction sensor array for the blind zones according to the detection point layout rules; driving the gridded compaction sensor array to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array; transmitting the fused compaction parameter array in parallel to an edge compaction detection cloud via a wireless communication link; receiving the fused compaction parameter array and then calculating the distributed compaction deviation nodes of the fused compaction parameter array by backtracking the cross-layer temporal correlation array mapping; solving the supplementary compaction control sequence based on the distributed compaction deviation nodes to perform targeted correction of the compaction blind zones; and performing a closed-loop iteration of supplementary compaction control based on the retest data returned by the gridded compaction sensor array.

[0005] The second aspect of this application discloses a system for detecting edge compaction in roadbed transition sections. This system is used in the aforementioned method for detecting edge compaction in roadbed transition sections. The system includes: a layout rule setting module for setting detection point layout rules based on compaction correlation scales and sensor monitoring scales; a sensor array layout module for, after locating compaction blind zones in the roadbed transition section, deploying a gridded compaction sensor array for the compaction blind zones according to the detection point layout rules; and a compaction data acquisition module for driving the gridded compaction sensor array to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array; and a compaction parameter generation module. The system includes a transmission module for transmitting the fused compaction parameter array in parallel to the edge compaction detection cloud via a wireless communication link; a deviation node calculation module for calculating the distributed compaction deviation nodes of the fused compaction parameter array by backtracking the cross-layer time-series correlation array mapping after receiving the fused compaction parameter array; a targeted correction module for performing targeted correction of the compaction blind zone based on the compensation control sequence obtained from the distributed compaction deviation nodes; and a closed-loop iteration module for performing closed-loop iteration of compensation control based on the retest data returned by the gridded compaction sensor array.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By setting rules for the placement of monitoring points, a reasonable distribution of these points can be ensured, avoiding blind spots and ensuring comprehensive coverage of the compaction quality throughout the entire roadbed transition section. After locating compaction blind spots in the roadbed transition section, a gridded sensor array is used for supplementary coverage. A well-planned sensor array effectively eliminates potential blind spots, enabling all-round, comprehensive monitoring of compaction quality. Multimodal data acquisition generates a fused compaction parameter array. This data is acquired in real-time, possessing high accuracy and rich dimensions, comprehensively reflecting various variables during the compaction process. Real-time data from each monitoring point is transmitted in parallel to the cloud via a wireless communication link. Wireless transmission not only improves data transmission efficiency but also reduces the complexity of on-site wiring, making the entire system more flexible and convenient to deploy. After receiving the data in the cloud… Cross-layer time-series correlation analysis is performed to backtrack compaction data and calculate distributed compaction deviation nodes. This process, through analysis of time-series data, can accurately identify areas with uneven compaction and precisely locate these deviation areas. Based on the located distributed compaction deviation nodes, a compaction control sequence is calculated, and compaction commands are pushed to the compaction equipment. The compaction equipment makes targeted corrections according to the commands to ensure that the compaction degree of the subgrade after compaction meets the design standard. Through automated compaction control sequences, manual intervention can be reduced, improving the accuracy and efficiency of compaction work. By providing real-time feedback on the retest data after compaction, closed-loop control is executed for iterative correction until the compaction quality meets the standard. Closed-loop control ensures the real-time nature and dynamic optimization of the compaction process, ensuring the controllability and efficiency of compaction quality.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the method for edge compaction testing of the roadbed transition section provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the edge compaction detection system for the roadbed transition section provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: 10 for layout rule setting module, 20 for sensor array layout module, 30 for compaction data acquisition module, 40 for compaction parameter transmission module, 50 for deviation node calculation module, 60 for target correction module, and 70 for closed-loop iteration module. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1 As shown in the embodiment of this application, a method for detecting edge compaction of roadbed transition sections is provided, the method comprising:

[0014] The rules for setting up detection points are based on the compaction correlation scale and the sensor monitoring scale.

[0015] The compaction correlation scale refers to the range of influence during the compaction process. For example, if the compaction degree deviation in a certain area is large, it will affect a certain surrounding area. Therefore, it is necessary to define the influence range of compaction defects, which is determined based on factors such as the diffusion range of compaction defects and the size of the affected area. The sensor monitoring scale refers to the range and accuracy that sensors can effectively monitor. For example, the effective measurement range of a laser sensor, the effective distance of a nuclear density meter, and the positioning accuracy of radar all affect the density and distribution pattern of detection points. Based on the combination of these two factors, the number, spacing, and coverage of detection points are set to ensure the accuracy and comprehensiveness of the detection.

[0016] After locating the compaction blind zone in the roadbed transition section, a gridded compaction sensor array is deployed for the compaction blind zone according to the detection point layout rules.

[0017] Through data analysis and on-site investigation, the locations of compaction blind zones were identified. These blind zones are areas that fail to meet testing standards due to uneven compaction or incomplete coverage by compaction equipment. A grid was created, with multiple sensors arranged in each grid. The grid size and sensor density were determined according to the detection point layout rules. The compaction sensor array includes various sensors, such as nuclear density meters, laser sensors, and radar, each with different measurement methods and accuracies.

[0018] The gridded compaction sensor array is driven to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array.

[0019] Data is collected simultaneously using multiple sensors. A nuclear density meter is used to measure compaction degree, a laser sensor is used to measure the elevation of the terrain, and radar is used to locate and determine the position of each detection point. Different sensors collect data from different levels, and finally these data are integrated into a fused compaction parameter array. This array contains the measurement results of all sensors and represents the compaction status at various locations of the roadbed.

[0020] The fused compaction parameter array is transmitted in parallel to the edge compaction detection cloud via a wireless communication link.

[0021] The collected fusion compaction parameter array is uploaded to the edge compaction detection cloud in real time via wireless communication links, such as Wi-Fi, LTE, and 5G. Using wireless communication links enables remote real-time data transmission, avoids manual intervention, and improves data transmission efficiency. To further improve data transmission efficiency, data collected by multiple sensors is transmitted to the cloud simultaneously, which reduces latency and improves response speed.

[0022] After receiving the fused compaction parameter array, the edge compaction detection cloud backtracks the cross-layer time-series correlation array mapping to calculate the distributed compaction deviation nodes of the fused compaction parameter array.

[0023] Data acquisition is multi-layered and multi-modal, therefore, it is necessary to analyze the data by backtracking cross-layer temporal correlation. Cross-layer temporal correlation refers to combining data from different levels and analyzing their temporal and spatial correlations. The backtracking process involves analyzing the compaction data at each detection point to identify temporal and spatial deviations during the compaction process. After backtracking, all compaction data are mapped into a distributed model, and the compaction deviation at each detection point is calculated. The resulting distributed compaction deviation nodes represent the areas of insufficient compaction.

[0024] The compaction blind zone is targeted and corrected based on the compaction deviation node solution of the compaction control sequence.

[0025] Based on the distributed compaction deviation nodes, the specific strategy for additional compaction in each deviation area is calculated. The additional compaction control sequence includes information such as the intensity, time, and location of the additional compaction. This process calculates the additional compaction parameters based on the differences in compaction degree, specific construction requirements, and the working capacity of the compaction equipment. For example, areas with low compaction degree need to be compacted at certain points, or the number of compaction passes needs to be increased, or the pressure of the compaction equipment needs to be changed. The additional compaction control sequence is directly sent to the compaction equipment to guide it to perform targeted correction. Targeted correction means that the additional compaction equipment performs precise additional compaction near the identified deviation nodes, thereby ensuring that the compaction degree of these blind areas meets the requirements.

[0026] The edge compaction detection cloud performs closed-loop iteration of pressure compensation control based on the retest data returned by the gridded compaction sensor array.

[0027] After the compaction operation is completed, the compacted area is re-detected using a gridded compaction sensor array, and the resulting re-measurement data is transmitted back to the edge compaction detection cloud platform. This re-measurement data provides compaction degree information after compaction. Based on this data, the compaction effect is assessed. If some areas still do not meet the standard after compaction, a new compaction control sequence is automatically generated, and the compaction operation continues. This process iterates continuously until all deviation areas are corrected and the design standard is met. Closed-loop iteration means that the strategy is continuously adjusted based on real-time data to ensure that the compaction quality meets the requirements. This automated compaction and re-measurement process effectively improves construction quality and saves on the cost of manual intervention.

[0028] Furthermore, based on the compaction correlation scale and sensor monitoring scale, the method sets the rules for the layout of detection points, and includes:

[0029] The compaction quality verification zone is defined based on the influence range of compaction defects; the rule envelope of the compaction quality verification zone is constructed as the compaction correlation scale; the effective distance for elevation measurement of the laser sensor, the effective distance for compaction detection of the nuclear density meter, and the effective distance for positioning of the lateral alarm radar are obtained interactively; the rectangular envelope intersection of the effective distance for elevation measurement, the effective distance for compaction detection, the effective distance for positioning, and the compaction correlation scale is solved to output the detection point layout rules.

[0030] Compaction defects are not merely localized; they can affect surrounding areas. For example, uneven compaction in one area can cause the foundation within a certain radius to fail to meet design standards. Therefore, the diffusion of compaction defects must be considered. Based on the extent of their impact, a compaction quality check zone is delineated. This check zone is the area requiring further inspection and control of compaction, including areas where insufficient compaction has been identified and the affected surrounding areas. The goal is to ensure that all potential compaction defects are corrected promptly. The delineation of the compaction quality check zone is accomplished through spatial data analysis. Based on compaction data, topographic features, and other information, areas with compaction problems are identified, providing a basis for subsequent monitoring and recompaction.

[0031] The regular envelope is a theoretical range used to describe the size and shape of the compaction quality check zone, as well as the standards for compaction defects within that zone. For example, some areas require stricter compaction control, while the standards for other areas can be slightly more lenient. The regular envelope can be adjusted based on factors such as the type of foundation, construction standards, and equipment capabilities. Using the regular envelope as a compaction correlation scale provides a reference standard for subsequent inspection and correction.

[0032] Laser sensors are used to measure changes in ground elevation, acquiring effective topographic data to help analyze roadbed height, flatness, etc. The effective elevation measurement distance refers to the maximum distance at which the laser sensor can operate stably and effectively while maintaining measurement accuracy. Nuclear density meters are used to measure the compaction degree of soil or roadbed. The effective compaction detection distance determines the maximum depth or measurement range at which the nuclear density meter can accurately detect compaction. Lateral alarm radar is used to precisely locate compaction points, monitoring the entire area and marking specific detection points. The effective positioning distance refers to the maximum range of location coordinates it can accurately acquire.

[0033] Each sensor has a different effective measurement range, but their effective coverage areas overlap. By solving for the intersection of these areas, we ensure that each detection point covers the most critical area while avoiding redundant areas. Based on the result of the rectangular envelope intersection, we finally output a reasonable detection point placement rule, clarifying where each sensor should be placed to ensure blind-spot-free coverage.

[0034] Furthermore, after receiving the fused compaction parameter array, the edge compaction detection cloud calculates the distributed compaction deviation nodes of the fused compaction parameter array by tracing back the cross-layer time-series correlation array mapping. The method includes:

[0035] The spatial propagation verification of compaction defects is performed on the fused compaction parameter array, and the compaction defect diffusion vector and isolated defect point set are output. Using the compaction defect diffusion vector and isolated defect point set as weighting constraint factors, the compaction deviation multi-parameter coupling decision is performed on the backtracking cross-layer temporal correlation array and the fused compaction parameter array, and the distributed compaction degree deviation node is output.

[0036] By traversing the fused compaction parameter array using a set multimodal threshold, compaction defect points are identified throughout the detection area. These defect points do not meet the preset criteria and belong to the set of isolated defect points. The method verifies whether these isolated defect point sets may cause a larger-scale compaction problem. If the isolated defects are spatially conductive, they will cause the compaction defects to spread. Therefore, the propagation path of these defects is further tracked, and compaction defect diffusion vectors are output. These vectors show the direction and range of defect expansion.

[0037] The backtracking cross-layer time-series correlation array is compared with the fused compaction parameter array to perform cross-layer compaction deviation detection, confirming the compaction degree changes at different time points and locations, and locating the initial deviation nodes, which are areas of insufficient compaction. After identifying the initial deviation nodes, the set of isolated defect points is projected onto these initial deviation nodes for deviation compensation, outputting corrected distributed compaction degree deviation nodes. These nodes show the compaction degree deviation of the entire region, providing data support for the next compaction command.

[0038] Furthermore, the method involves verifying the spatial propagation of compaction defects on the fused compaction parameter array, outputting a compaction defect diffusion vector and a set of isolated defect points, and includes:

[0039] Based on a preset multimodal threshold, the fused compaction parameter array is traversed to locate the set of isolated defect points. After removing the set of isolated defect points from the fused compaction parameter array, the compaction gradient of adjacent detection points is solved to locate the compaction defect diffusion region. The adjacent detection point dual-thread correlation gradient is solved for the compaction defect diffusion region to output the defect coupling factor, wherein the dual-thread correlation gradient includes the moisture content gradient and the layer thickness gradient. The compaction defect diffusion vector is generated according to the spatial continuity and induction type of the defect coupling factor.

[0040] Multimodal thresholds refer to a set of thresholds for different parameters, which are applicable to various data such as compaction degree, moisture content, and layer thickness. The process iterates through the array of compaction parameters, evaluating each data point to determine if it meets the preset multimodal thresholds. If a parameter at a point exceeds the set threshold range, that point is marked as an isolated defect. Isolated defect points do not represent widespread compaction problems; they may simply be occasional fluctuations in the data. The set of isolated defect points is the collection of all individual points judged to deviate from the standard.

[0041] Isolated defect point sets are removed from the fused compaction parameter array. Based on the remaining compaction data, the compaction gradient between adjacent detection points is calculated. The compaction gradient represents the rate of change in compaction degree between adjacent points. If the compaction degree change between two detection points is large, then there is a problem of uneven compaction between these two points. By analyzing the compaction gradient between adjacent points, the diffusion areas of compaction defects are identified. If the compaction degree of a certain area has obvious spatial fluctuations or differences, then this area may be the source of uneven compaction, and the defect has a tendency to spread. These diffusion areas are located and marked as key areas for subsequent compaction and repair.

[0042] Further dual-threaded correlation gradient calculation is performed on adjacent detection points in the compaction defect diffusion area. This step integrates the moisture content gradient and the layer thickness gradient. Moisture content directly affects the compaction effect during the compaction process; excessively high moisture content increases compaction difficulty, leading to insufficient compaction, while excessively low moisture content results in uneven soil compaction. The layer thickness of the soil affects the compaction effect; uneven thickness can lead to insufficient compaction, especially in the roadbed transition zone where thickness variations are significant, making compaction defects more pronounced. Through dual-threaded analysis, a defect coupling factor is output, reflecting how these two factors jointly influence the diffusion of compaction defects.

[0043] By analyzing the spatial distribution of defect coupling factors, the pattern of defect diffusion can be determined. For example, if the values ​​of defect coupling factors exhibit spatial continuity, such as regional diffusion, it indicates common problems in certain areas, such as insufficient compaction due to soil characteristics. The induction type of compaction defects is a comprehensive result of multiple factors, including soil type, moisture content, and compaction equipment settings. By analyzing defect coupling factors and spatial continuity, the induction type of defects can be inferred. For example, if fluctuations in moisture content and thickness gradient are related to a specific soil type, it may be a compaction problem caused by the soil's inherent characteristics. Based on the spatial distribution and induction type of defect coupling factors, a compaction defect diffusion vector is generated. This vector accurately describes the diffusion direction and intensity of defects in space and their relationship with other factors, providing an important basis for the formulation of subsequent compaction control and remediation schemes.

[0044] Furthermore, the multimodal thresholds include compaction degree threshold, moisture content threshold, compaction frequency threshold, and layer thickness threshold. Each fused compaction parameter in the fused compaction parameter array includes the average of K parallel compaction measurements, real-time moisture content, real-time rolling frequency, and real-time layer thickness.

[0045] The compaction threshold sets a reasonable range for compaction degree; below this value indicates insufficient compaction, which may lead to roadbed instability. The moisture content threshold defines the upper and lower limits of moisture content; soil exceeding this range will affect the compaction effect. The compaction frequency threshold specifies the number of compaction cycles; soil compaction should be carried out at a certain number of cycles to ensure quality; below this number may lead to insufficient compaction. The layer thickness threshold sets the thickness range of different soil layers; exceeding this range may lead to uneven compaction.

[0046] The mean of K parallel compaction measurements refers to the average value obtained by performing multiple compaction measurements at each test point to reduce random errors; K is a positive integer. Real-time moisture content is the current real-time moisture content of the soil, which affects the degree of compaction. Real-time rolling number is the number of times the current soil layer is rolled, which determines the compaction effect. Real-time layer thickness represents the real-time thickness of the soil layer; uneven thickness may lead to uneven compaction.

[0047] Furthermore, the method involves performing a dual-threaded correlated gradient calculation on adjacent detection points in the compaction defect diffusion region to output the defect coupling factor.

[0048] Using the compaction defect diffusion area as the spatial input range, the moisture content parameter subarray and the layer thickness parameter subarray are retrieved from the fused compaction parameter array. The moisture content gradient of adjacent detection points in the moisture content parameter subarray is calculated to obtain a moisture content gradient distribution map. A preset moisture content gradient threshold is used to traverse the moisture content gradient distribution map to locate areas where the moisture content gradient exceeds the standard. The layer thickness gradient of adjacent detection points in the layer thickness parameter subarray is calculated to obtain a thickness gradient distribution map. A preset thickness gradient threshold is used to traverse the thickness gradient distribution map to locate areas where the thickness gradient exceeds the standard. The areas where the moisture content gradient exceeds the standard and the areas where the thickness gradient exceeds the standard are spatially overlapped to identify defects, and the defect coupling factor is output.

[0049] The compaction defect diffusion area identified above is used as the spatial input range for analysis, meaning that only relevant data within these areas are considered, avoiding unnecessary calculations for irrelevant areas. The moisture content parameter subarray records moisture content data at different locations within the diffusion area, reflecting the soil's wetness; the layer thickness parameter subarray records layer thickness data within the diffusion area, reflecting variations in soil layer thickness.

[0050] The extracted moisture content parameter subarray is calculated to determine the moisture content change between each pair of adjacent detection points. The moisture content gradient is the rate of moisture content change between adjacent detection points, expressed as the ratio of the moisture content difference between two points to their distance. If the moisture content gradient in a certain area is large, it indicates that the moisture content change in that area is relatively drastic, and there is a potential compaction problem. By calculating the moisture content gradient of all adjacent points, a moisture content gradient distribution map is generated, which shows the moisture content change between each detection point.

[0051] To determine which areas have excessively large moisture content variations, a moisture content gradient threshold is set. This threshold is determined based on actual conditions and engineering requirements, and is used to judge whether the moisture content gradient exceeds the normal range. The entire moisture content gradient distribution map is traversed, and for each detection point, its moisture content gradient is compared with the moisture content gradient threshold. When the gradient exceeds the threshold, the point and its surrounding area are marked as areas with excessive moisture content gradients.

[0052] Similar to moisture content gradient calculation, the thickness change rate is calculated by comparing the layer thickness difference between each pair of adjacent detection points. The resulting layer thickness gradient reflects the spatial uniformity of soil layer thickness. An excessively large thickness gradient can lead to uneven compaction or insufficient compaction. Mapping all calculated thickness gradients spatially creates a thickness gradient distribution map.

[0053] A thickness gradient threshold is set based on design standards, construction specifications, or experience data to determine whether the variation in soil layer thickness is excessive. The thickness gradient distribution map is traversed point by point, and the thickness gradient of each detection point is compared with the threshold. When the gradient exceeds the threshold, the point and its surrounding area are marked as areas with excessive thickness gradient.

[0054] Spatial overlap analysis was performed on the areas where the moisture content gradient exceeded the standard and the areas where the thickness gradient exceeded the standard. The spatially overlapping areas were marked and identified as compaction defect areas. The coupling information of moisture content and layer thickness was integrated, and the defect was classified and identified according to the defect type, such as being caused by a single factor or a coupling factor. A defect coupling factor was generated, which not only indicates the spatial location of the defect, but also reflects the cause and intensity of the defect.

[0055] Furthermore, the method for generating the compaction defect diffusion vector based on the spatial continuity and induction type of the defect coupling factor includes:

[0056] Based on the preset defect point spacing, the spatial continuity of adjacent defect points is determined for the defect coupling factor, and the continuous coordinate sets of single moisture content induced defects, single thickness unevenness defects, and coupled defects are output. The induction type labels of the continuous coordinate sets of single moisture content induced defects, single thickness unevenness defects, and coupled defects are associated to generate the compaction defect diffusion vector.

[0057] The preset defect point spacing is set according to the actual engineering requirements. It defines the maximum distance between two defect points. Defect points that exceed this distance are not adjacent defects and are judged as independent defects. By detecting the defect point spacing, it is determined which defects are adjacent, and by analyzing the spatial layout of defects, it is identified whether there are continuous defect areas.

[0058] Moisture content-induced defects in continuous coordinate sets are continuous defect areas caused by moisture content issues, such as excessive or insufficient moisture. Uneven compaction within these defect areas, as well as insufficient or excessive soil moisture, will affect the compaction effect. Thickness unevenness defects in continuous coordinate sets are caused by uneven soil layer thickness, manifesting as localized weak areas in the soil layer, leading to uneven compaction. Coupled defects in continuous coordinate sets are caused by the combined effects of multiple factors such as moisture content and thickness unevenness, and are the most complex type of defect in the compaction process.

[0059] Each defect area is labeled with an induction type tag, which describes which factor or combination thereof caused the defect. The resulting compaction defect diffusion vector contains information about the spatial expansion of the defect and the type attribute of the defect, providing a basis for subsequent repair and compaction schemes.

[0060] Furthermore, using the compaction defect diffusion vector and the set of isolated defect points as weighting constraint factors, a multi-parameter coupled decision on compaction deviation is performed on the retrospective cross-layer temporal correlation array and the fused compaction parameter array to output the distributed compaction degree deviation nodes. The method includes:

[0061] The backtracking cross-layer temporal correlation array and the fused compaction parameter array are mapped and compared to perform cross-layer compaction deviation detection and locate the initial distributed deviation node. The isolated defect point set is projected onto the initial distributed deviation node to perform explicit deviation compensation and generate explicit compensation deviation node. After spatially aligning the compaction defect diffusion vector and the explicit compensation deviation node, the implicit deviation of the explicit compensation deviation node is corrected using the compaction defect diffusion vector, and the distributed compaction degree deviation node is output.

[0062] By comparing and retrospectively analyzing cross-layer time-series correlation arrays and fused compaction parameter arrays, compaction data at multiple levels, such as different compaction depths and time periods, were compared. Based on this comparison, the compaction effects between different levels were analyzed to check for significant uneven compaction or compaction deviations. The sources of these deviations could include different soil layers, insufficient compaction passes, or improper equipment settings. The detection process identified initial distributed deviation nodes at different levels, marking locations with substandard compaction or significant deviations, which are key areas for subsequent compensation and remediation.

[0063] Projecting the set of isolated defect points onto the initially distributed deviation nodes means treating isolated defects as factors that may cause deviations throughout the area and associating their locations with other deviation nodes. This projection helps determine whether isolated defect points are localized problems or systemic problems occurring over a wider area. Explicit deviation compensation is then applied to these deviation nodes. Explicit deviation compensation refers to direct and explicit corrective measures that can be addressed through simple adjustments or additional compaction, such as increasing the number of compaction passes or adjusting the parameters of the compaction equipment. After compensation, explicitly compensated deviation nodes are generated, marking the corrective results.

[0064] Spatial alignment matches the compaction defect diffusion vector with explicit compensation deviation nodes. The diffusion vector represents the spatial expansion trend of defects, while the explicit compensation deviation nodes mark the resulting positions after compensation. The spatial alignment process helps determine whether the compensated defects have been completely corrected and whether any untreated areas remain. Implicit deviations refer to compaction problems that are not easily resolved through direct compaction operations, such as those caused by uneven moisture content or uneven soil density. These implicit deviations are identified through the compaction defect diffusion vector, and corresponding corrective measures are taken, such as adjusting compaction strategies and optimizing compaction equipment. Finally, after implicit compensation correction, the final distributed compaction deviation nodes are generated. These nodes represent the final state of the compaction process, including all corrective results after explicit and implicit compensation.

[0065] Furthermore, the method of performing targeted correction of the compaction blind zone based on the compaction deviation node solution of the compaction control sequence includes:

[0066] Extract the first defect type and first defect parameter of the first compaction deviation node; match the first compensation parameter package in the process test database based on the first defect type and first defect parameter; output the distributed compensation parameter package of the distributed compaction deviation node by analogy; perform adjacent compensation parameter aggregation on the distributed compensation parameter package to output the regional compensation instruction set; deconstruct the regional compensation instruction set with the compaction direction of the compaction blind zone as a constraint, and output the compensation control sequence.

[0067] Randomly select one of the distributed compaction deviation nodes as the first compaction deviation node and use it as the current analysis object. Extract the first defect type of the first compaction deviation node, such as insufficient compaction or abnormal moisture content. Extract the first defect parameters of the first compaction deviation node, such as specific compaction value, moisture content, and layer thickness.

[0068] The process test database contains historical test data and compaction construction experience. In this database, each defect type and defect parameter has a corresponding compensation scheme. Based on the first defect type and the first defect parameter, the database is searched for a suitable first compaction parameter package. This compaction parameter package refers to a set of compaction correction measures, such as increasing the number of compaction passes, adjusting the compaction strength of the compaction equipment, and adjusting the operating speed and compaction direction of the compaction equipment.

[0069] After obtaining the compaction scheme for the first deviation node, the process is repeated to analyze all nodes in the distributed compaction deviation nodes and obtain distributed compaction parameter packages. These compaction parameter packages are obtained by matching the specific defect type and parameters of each node.

[0070] When multiple adjacent compaction deviation nodes require correction, these nodes may have similar compaction requirements. Aggregating the compaction parameters of these adjacent nodes aims to reduce redundant operations and avoid performing the same or excessive compaction operations in adjacent areas, thereby improving construction efficiency and saving resources. The aggregated compaction parameters form a regionalized compaction instruction set, which contains the compaction measures to be performed in a specific area.

[0071] In certain areas, especially compaction blind spots, specific compaction direction requirements exist. Using the compaction direction of these blind spots as a constraint ensures that the supplementary compaction process does not affect the construction quality of other areas, or avoids uneven compaction caused by improper rolling direction. Deconstruction refers to refining the regionalized supplementary compaction instruction set into specific operational steps and adjusting them according to the actual conditions of the construction site. For example, some areas require more frequent rolling, while in other areas only local adjustments to the compaction equipment settings are needed. Ultimately, specific supplementary compaction control sequences are output, containing detailed instructions for each operational step.

[0072] Example 2, based on the same inventive concept as the edge compaction detection method for the roadbed transition section in the aforementioned examples, such as... Figure 2 As shown in the embodiment of this application, a system for detecting edge compaction of roadbed transition sections is provided. The system includes:

[0073] The system includes a layout rule setting module 10, used to set the detection point layout rules based on the compaction correlation scale and sensor monitoring scale; a sensor array layout module 20, used to locate the compaction blind zone in the roadbed transition section and then, according to the detection point layout rules, lay out a gridded compaction sensor array for the compaction blind zone; a compaction data acquisition module 30, used to drive the gridded compaction sensor array to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array; and a compaction parameter transmission module 40, used to transmit the fused compaction parameter array in parallel via a wireless communication link. The edge compaction detection cloud platform receives the fused compaction parameter array and then backtracks to calculate the distributed compaction deviation nodes of the fused compaction parameter array via cross-layer time-series correlation array mapping. A targeted correction module 60 is used to solve the compaction control sequence based on the distributed compaction deviation nodes to perform targeted correction of the compaction blind zone. A closed-loop iteration module 70 is used by the edge compaction detection cloud platform to perform closed-loop iteration of compaction control based on the retest data returned by the gridded compaction sensor array.

[0074] Furthermore, the deployment rule setting module 10 is used to perform the following operation steps:

[0075] The compaction quality verification zone is defined based on the influence range of compaction defects; the rule envelope of the compaction quality verification zone is constructed as the compaction correlation scale; the effective distance for elevation measurement of the laser sensor, the effective distance for compaction detection of the nuclear density meter, and the effective distance for positioning of the lateral alarm radar are obtained interactively; the rectangular envelope intersection of the effective distance for elevation measurement, the effective distance for compaction detection, the effective distance for positioning, and the compaction correlation scale is solved to output the detection point layout rules.

[0076] Furthermore, the deviation node calculation module 50 is used to perform the following operation steps:

[0077] The spatial propagation verification of compaction defects is performed on the fused compaction parameter array, and the compaction defect diffusion vector and isolated defect point set are output. Using the compaction defect diffusion vector and isolated defect point set as weighting constraint factors, the compaction deviation multi-parameter coupling decision is performed on the backtracking cross-layer temporal correlation array and the fused compaction parameter array, and the distributed compaction degree deviation node is output.

[0078] Furthermore, the deviation node calculation module 50 is used to perform the following operation steps:

[0079] Based on a preset multimodal threshold, the fused compaction parameter array is traversed to locate the set of isolated defect points. After removing the set of isolated defect points from the fused compaction parameter array, the compaction gradient of adjacent detection points is solved to locate the compaction defect diffusion region. The adjacent detection point dual-thread correlation gradient is solved for the compaction defect diffusion region to output the defect coupling factor, wherein the dual-thread correlation gradient includes the moisture content gradient and the layer thickness gradient. The compaction defect diffusion vector is generated according to the spatial continuity and induction type of the defect coupling factor.

[0080] Furthermore, the multimodal thresholds include compaction degree threshold, moisture content threshold, compaction frequency threshold, and layer thickness threshold. Each fused compaction parameter in the fused compaction parameter array includes the average of K parallel compaction measurements, real-time moisture content, real-time rolling frequency, and real-time layer thickness.

[0081] Furthermore, the deviation node calculation module 50 is used to perform the following operation steps:

[0082] Using the compaction defect diffusion area as the spatial input range, the moisture content parameter subarray and the layer thickness parameter subarray are retrieved from the fused compaction parameter array. The moisture content gradient of adjacent detection points in the moisture content parameter subarray is calculated to obtain a moisture content gradient distribution map. A preset moisture content gradient threshold is used to traverse the moisture content gradient distribution map to locate areas where the moisture content gradient exceeds the standard. The layer thickness gradient of adjacent detection points in the layer thickness parameter subarray is calculated to obtain a thickness gradient distribution map. A preset thickness gradient threshold is used to traverse the thickness gradient distribution map to locate areas where the thickness gradient exceeds the standard. The areas where the moisture content gradient exceeds the standard and the areas where the thickness gradient exceeds the standard are spatially overlapped to identify defects, and the defect coupling factor is output.

[0083] Furthermore, the deviation node calculation module 50 is used to perform the following operation steps:

[0084] Based on the preset defect point spacing, the spatial continuity of adjacent defect points is determined for the defect coupling factor, and the continuous coordinate sets of single moisture content induced defects, single thickness unevenness defects, and coupled defects are output. The induction type labels of the continuous coordinate sets of single moisture content induced defects, single thickness unevenness defects, and coupled defects are associated to generate the compaction defect diffusion vector.

[0085] Furthermore, the deviation node calculation module 50 is used to perform the following operation steps:

[0086] The backtracking cross-layer temporal correlation array and the fused compaction parameter array are mapped and compared to perform cross-layer compaction deviation detection and locate the initial distributed deviation node. The isolated defect point set is projected onto the initial distributed deviation node to perform explicit deviation compensation and generate explicit compensation deviation node. After spatially aligning the compaction defect diffusion vector and the explicit compensation deviation node, the implicit deviation of the explicit compensation deviation node is corrected using the compaction defect diffusion vector, and the distributed compaction degree deviation node is output.

[0087] Furthermore, the targeting correction module 60 is used to perform the following operational steps:

[0088] Extract the first defect type and first defect parameter of the first compaction deviation node; match the first compensation parameter package in the process test database based on the first defect type and first defect parameter; output the distributed compensation parameter package of the distributed compaction deviation node by analogy; perform adjacent compensation parameter aggregation on the distributed compensation parameter package to output the regional compensation instruction set; deconstruct the regional compensation instruction set with the compaction direction of the compaction blind zone as a constraint, and output the compensation control sequence.

[0089] Through the foregoing detailed description of the edge compaction test method for the roadbed transition section, those skilled in the art can clearly understand the edge compaction test system for the roadbed transition section in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for detecting edge compaction in roadbed transition sections, characterized in that, The method includes: The rules for setting up detection points are based on the compaction correlation scale and the sensor monitoring scale. After locating the compaction blind zone in the roadbed transition section, the grid-based compaction sensor array for the compaction blind zone is deployed according to the detection point layout rules. The gridded compaction sensor array is driven to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array; The fused compaction parameter array is transmitted in parallel to the edge compaction detection cloud via a wireless communication link; After receiving the fused compaction parameter array, the edge compaction detection cloud backtracks the cross-layer time-series correlation array mapping to calculate the distributed compaction deviation nodes of the fused compaction parameter array; The targeted correction of the compaction blind zone is performed based on the compaction deviation node solution of the compaction control sequence. The edge compaction detection cloud performs a closed-loop iteration of pressure compensation control based on the retest data returned by the gridded compaction sensor array; After receiving the fused compaction parameter array, the edge compaction detection cloud calculates the distributed compaction deviation nodes of the fused compaction parameter array by tracing back the cross-layer time-series correlation array mapping, including: The spatial propagation of compaction defects is verified by the fused compaction parameter array, and the compaction defect diffusion vector and isolated defect point set are output. Using the compaction defect diffusion vector and the isolated defect point set as weighting constraint factors, the compaction deviation multi-parameter coupled decision is performed on the retrospective cross-layer temporal correlation array and the fused compaction parameter array, and the distributed compaction degree deviation node is output. The step of verifying the spatial propagation of compaction defects on the fused compaction parameter array, and outputting the compaction defect diffusion vector and the set of isolated defect points, includes: The isolated defect point set is located by traversing the fused compaction parameter array based on a preset multimodal threshold. After removing the isolated defect point set from the fused compaction parameter array, the compaction gradient of adjacent detection points is solved to locate the compaction defect diffusion area; The adjacent detection points are correlated by a dual-thread gradient solution to be performed on the compaction defect diffusion area, and the defect coupling factor is output. The dual-thread correlated gradient includes the moisture content gradient and the layer thickness gradient. The compaction defect diffusion vector is generated based on the spatial continuity and induction type of the defect coupling factor. The step of generating the compaction defect diffusion vector based on the spatial continuity and induction type of the defect coupling factor includes: Based on the preset defect point spacing, the spatial continuity of adjacent defect points is determined for the defect coupling factor, and the continuous coordinate set of single moisture content-induced defects, the continuous coordinate set of single thickness unevenness defects, and the continuous coordinate set of coupled defects are output. The compaction defect diffusion vector is generated by associating the induction type labels of the continuous coordinate set of single induced defects of moisture content, the continuous coordinate set of single induced defects of thickness unevenness, and the continuous coordinate set of coupled defects. The step of using the compaction defect diffusion vector and the set of isolated defect points as weighting constraint factors to perform multi-parameter coupled decision-making on the backtracking cross-layer time-series correlation array and the fused compaction parameter array to output the distributed compaction degree deviation nodes includes: The mapping and comparison of the backtracking cross-layer temporal correlation array and the fused compaction parameter array are performed to perform cross-layer compaction deviation detection and locate the initial distributed deviation node; The isolated defect point set is projected onto the initial distributed deviation node for explicit deviation compensation, generating explicit compensation deviation nodes. After spatially aligning the compaction defect diffusion vector and the explicit compensation deviation node, the implicit deviation of the explicit compensation deviation node is corrected using the compaction defect diffusion vector, and the distributed compaction deviation node is output.

2. The method for detecting edge compaction of the roadbed transition section as described in claim 1, characterized in that, The method includes setting rules for the layout of detection points based on the compaction correlation scale and the sensor monitoring scale, and includes: The compaction quality review area is defined based on the scope of impact of compaction defects; Construct the rule envelope of the compaction quality verification zone as the compaction correlation scale; The effective range for elevation measurement of the laser sensor, the effective range for compaction detection of the nuclear density meter, and the effective range for positioning of the lateral alarm radar are obtained interactively. The rectangular envelope intersection of the effective distance for elevation measurement, effective distance for compaction detection, effective distance for positioning, and compaction-related scale is solved to output the layout rules for the detection points.

3. The method for detecting edge compaction of the roadbed transition section as described in claim 1, characterized in that, The multimodal thresholds include compaction degree threshold, moisture content threshold, compaction frequency threshold, and layer thickness threshold. Each fused compaction parameter in the fused compaction parameter array includes the average of K parallel compaction measurements, real-time moisture content, real-time rolling frequency, and real-time layer thickness.

4. The method for detecting edge compaction of the roadbed transition section as described in claim 3, characterized in that, The method involves performing a dual-threaded correlated gradient calculation on adjacent detection points in the compaction defect diffusion region and outputting the defect coupling factor. Using the compaction defect diffusion area as the spatial input range, the moisture content parameter subarray and the layer thickness parameter subarray are retrieved from the fused compaction parameter array. The moisture content gradient of adjacent detection points is calculated for the moisture content parameter subarray to obtain a moisture content gradient distribution map; The moisture content gradient distribution map is traversed using a preset moisture content gradient threshold to locate areas where the moisture content gradient exceeds the standard. The thickness gradient of adjacent detection points is calculated for the layered thickness parameter subarray to obtain a thickness gradient distribution map; The thickness gradient distribution map is traversed using a preset thickness gradient threshold to locate areas where the thickness gradient exceeds the limit. The regions with excessive moisture content gradient and excessive thickness gradient are spatially overlapped to identify defects, and the defect coupling factor is output.

5. The method for detecting edge compaction of the roadbed transition section as described in claim 1, characterized in that, The method includes: Targeted correction of the compaction blind zone is performed based on the compaction deviation node solution of the compaction control sequence. Extract the first defect type and first defect parameter of the first compaction deviation node; Based on the first defect type and the first defect parameters, the first pressure compensation parameter package is matched in the process test database; Similarly, output the distributed compaction parameter package for the distributed compaction deviation node; The distributed compression parameter package is aggregated with adjacent compression parameters to output a regionalized compression instruction set; Using the compaction direction of the compaction blind zone as a constraint, the regionalized compaction instruction set is deconstructed, and the compaction control sequence is output.

6. A system for detecting edge compaction in roadbed transition sections, characterized in that, The system is used for implementing the edge compaction detection method for the roadbed transition section according to any one of claims 1-5, the system comprising: The layout rule setting module is used to set the layout rules of detection points based on the compaction correlation scale and the sensor monitoring scale. The sensor array deployment module is used to deploy a gridded compaction sensor array for the compaction blind zone according to the detection point deployment rules after locating the compaction blind zone in the roadbed transition section. The compaction data acquisition module is used to drive the gridded compaction sensor array to perform multimodal compaction data hierarchical acquisition to obtain a fused compaction parameter array; The compaction parameter sending module is used to send the fused compaction parameter array to the edge compaction detection cloud in parallel via a wireless communication link; The deviation node calculation module is used to calculate the distributed compaction deviation nodes of the fusion compaction parameter array by backtracking the cross-layer time-series correlation array mapping after the edge compaction detection cloud receives the fusion compaction parameter array; The targeted correction module is used to perform targeted correction of the compaction blind zone based on the compaction control sequence solved by the distributed compaction deviation nodes. The closed-loop iteration module is used by the edge compaction detection cloud to perform closed-loop iteration of pressure compensation control based on the retest data returned by the gridded compaction sensor array.

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