A method and system for pressure compensation construction of an unmanned road roller

Abstract

The application discloses a kind of unmanned road roller pressure compensation construction method and system, comprising: in construction site, for real-time data synchronization, communication network is laid out, compaction quality management platform is deployed;Determine rolling area and rolling path, design operation parameter, the area of rolling according to design number uses preset final pressure operation parameter, implement conventional rolling;Real-time acquisition construction state data in conventional rolling process, evaluate current compaction effect;According to the evaluation result, adjust positioning reference and position registration strategy, obtain adjusted positioning information, identify weak area according to two-dimensional compaction degree nephogram and adjusted positioning information, determine pressure compensation number of passes and pressure compensation priority, generate pressure compensation task;Iterative execution until reaching preset termination condition.The application can improve the continuity of subgrade rolling operation, reduce manual intervention and operation connection delay, improve the pertinence and resource utilization efficiency of pressure compensation operation.

Description

Technical Field

[0001] This invention relates to the field of unmanned road roller technology, and in particular to a method and system for compaction construction using an unmanned road roller. Background Technology

[0002] Unmanned road rollers and their supporting intelligent compaction technology belong to the technical field of engineering machinery automation and intelligent construction. With the maturity of high-precision satellite navigation such as GNSS / BeiDou, inertial navigation, lidar and multi-source sensor fusion, IoT communication and cloud data processing capabilities, road rollers are gradually evolving from the traditional mode of relying on manual operation to unmanned, data-driven autonomous operation. Current technological development shows a trend of evolution from single-machine automation to machine group collaboration, and from passive monitoring to closed-loop intelligent control: by collecting indicators such as compaction degree, temperature, and smoothness in real time through onboard sensors and generating a full-domain compaction cloud map, combined with high-precision trajectory control, the construction trajectory error can be controlled at the centimeter level, and the commonly seen level of about 2 to 3 cm in engineering practice. This provides a data foundation for accurately identifying weak areas and implementing targeted compaction, and gradually realizes large-scale application scenarios such as "one person controlling multiple machines" and continuous construction around the clock, which is especially suitable for the engineering construction needs of plateau, cold and sparsely populated areas.

[0003] Nevertheless, existing technologies still face several objective technical challenges in practical engineering applications that hinder the widespread adoption and quality assurance of unmanned compaction:

[0004] (1) There is no sufficient closed-loop collaboration between continuous compaction quality detection and unmanned compaction operation. Detection is mainly based on post-event sampling or offline analysis, which leads to the reliance on manual interpretation for compaction decision and the existence of positioning deviation and response delay, thus making it impossible to achieve an efficient and continuous automatic compaction process.

[0005] (2) The evaluation of compaction quality and the setting of additional compaction parameters mostly rely on empirical or fixed-value strategies, lacking a dynamic parameterized adjustment mechanism for weak areas, which can easily lead to over-compaction or under-compaction, affecting construction costs and project safety.

[0006] (4) In addition, in complex field environments such as dynamic obstacles, unstructured terrain, signal blockage, rain, fog and dust, the sensor's recognition ability and positioning reliability decrease, resulting in insufficient spatiotemporal alignment between the detection data and the actual working position, thereby reducing the accuracy of pressure compensation decision.

[0007] (5) Finally, the current pressure replenishment path planning and start-stop strategies are mostly based on fixed rules and lack the ability to deal with special working conditions such as local unevenness and material segregation. It is difficult to ensure that there is no pressure leakage or overpressure or damage to the surface flatness under all working conditions. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide an unmanned road roller compaction construction method and system. By integrating continuous compaction detection and intelligent command system, and based on the establishment of a preliminary compaction layer and provision of an initial quality field model by conventional rolling, an intelligent compaction process based on high-precision positioning and real-time compaction data (CEV value compaction cloud map) is introduced to form a precise closed-loop quality control of detection, decision-making and execution. This improves the compaction qualification rate from more than 80% after conventional rolling to more than 95%, achieving comprehensive optimization of quality uniformity, operation efficiency and resource saving.

[0009] The embodiments of the present invention are implemented as follows:

[0010] A method for compaction construction using an unmanned road roller, comprising:

[0011] S100 deploys a communication network at the construction site for real-time data synchronization and a compaction quality management platform on local servers and in the cloud.

[0012] S200: Determine the compaction area and compaction path, design the operating parameters for conventional compaction, implement conventional compaction, and establish a preliminary compacted layer.

[0013] S300 uses preset final compaction parameters to finish compacting areas that have been compacted according to the designed number of times.

[0014] S400, at the end of conventional compaction, continuous compaction quality monitoring is used to collect subgrade compaction state data. Based on the subgrade compaction state data, a two-dimensional compaction degree cloud map is generated to evaluate the current compaction operation parameters and compaction effect.

[0015] Based on the evaluation results, the S500 adjusts the positioning reference and location registration strategy to obtain the adjusted positioning information.

[0016] S600: Based on the two-dimensional compaction cloud map and the adjusted positioning information, identify weak areas, determine the number of compaction passes and compaction priority according to the degree of weakness, and generate a compaction task.

[0017] S700, iteratively execute the pressure replenishment task until the preset termination condition is met.

[0018] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction construction method, step S100, which involves deploying a communication network at the construction site for real-time data synchronization and deploying a compaction quality management platform on a local server and in the cloud, includes:

[0019] S110 establishes a GNSS differential base station at the construction site and accesses RTK services.

[0020] S120 sets up a local area network at the construction site and establishes a two-way synchronization channel with the cloud through a local server.

[0021] The S130 deploys a compaction quality management platform on both local servers and in the cloud.

[0022] In a preferred embodiment of the present invention, in the above-mentioned unmanned roller compaction construction method, step S200, which involves determining the compaction area and compaction path, designing conventional compaction operation parameters, implementing conventional compaction, and establishing a preliminary compacted layer, includes:

[0023] S210, based on the GNSS system carried by the unmanned road roller, sets the boundary of the compaction area in the digital construction model and generates preliminary trajectory planning;

[0024] S220, based on the type of fill material and the designed soil layer thickness, presets the vibration parameters and number of cycles for conventional compaction.

[0025] The S230 unmanned road roller adjusts the vibration frequency, vibration amplitude, and travel speed according to the soil resistance and vibration parameters to perform initial compaction and secondary compaction on the construction section until the preset number of times is completed, thus establishing a preliminary compacted layer.

[0026] S240 outputs construction status data for the conventional compaction stage.

[0027] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction method, step S300, which involves performing conventional compaction finishing on the area that has already been compacted according to the designed number of times, using preset conventional compaction end parameters, includes:

[0028] S310, in the rolling area where the designed number of rolling cycles has been completed, conventional rolling is performed to finish the rolling according to the preset final compaction parameters. The final compaction parameters include low-frequency vibration, slow and uniform speed of movement, and minimum thickening rate, so as to further smooth the road surface and improve the surface compaction with minimal disturbance.

[0029] S320: During the final compaction process, the road surface smoothness and slope are monitored in real time, and the vibration mode and the final compaction parameters are adjusted according to the monitoring results.

[0030] S330 outputs construction status data for the final pressure stage.

[0031] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction construction method, step S400, which involves real-time acquisition of construction status data, generating a two-dimensional compaction degree cloud map based on the construction status data, and evaluating the current rolling operation parameters and compaction effect, includes:

[0032] The S410 collects construction status data in real time during the conventional compaction stage and the final compaction stage, and synchronizes it to the local server and cloud platform through the communication network deployed at the construction site.

[0033] S420 performs data synchronization and preprocessing on the collected multi-source construction status data to obtain cleaned data.

[0034] S430, based on the cleaned data, calculate the compaction index, associate the sampling points of each unmanned road roller with GNSS coordinates, and generate a two-dimensional compaction cloud map in the form of a two-dimensional point cloud in real time.

[0035] S440, based on the two-dimensional compaction cloud map, the construction area is assessed for quality. The assessment results include the percentage of qualified area, the location of weak areas, the severity level, and statistical indicators.

[0036] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction construction method, in S410, the construction status data includes at least one of the following: compaction trajectory, position coordinates, vibration feedback, acceleration signal, travel speed, number of compaction passes, and soil layer thickness.

[0037] In S420, the data synchronization includes clock synchronization and timestamp alignment, and the data preprocessing includes data format standardization, filtering and noise reduction, and establishing a mapping relationship between the compaction trajectory and sensor data.

[0038] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction construction method, in S430, the compaction index is calculated based on the vibration compaction energy value (CEV), and the calculation method includes:

[0039] S431, real-time acquisition of the vertical acceleration signal a(t) of the vibrating wheel;

[0040] S432, Perform empirical mode decomposition on the vertical acceleration signal a(t) to obtain the intrinsic mode function components;

[0041] S433, Perform Hibert transform on each of the intrinsic mode function components to calculate the instantaneous frequency and energy distribution;

[0042] S434, by integrating the Hibert energy spectrum in the time-frequency domain, the total energy E of the vibration signal is obtained. total CEV, as a continuous performance indicator for compaction quality, is calculated using the following formula: ,in, Let be the Hibert energy spectrum of the i-th intrinsic mode function component.

[0043] In a preferred embodiment of the present invention, in S440 of the above-mentioned unmanned road roller compaction construction method, the quality assessment of the construction area is performed based on the two-dimensional compaction degree cloud map, and the assessment process adopts the following quantitative model:

[0044] The goal of the conventional compaction stage is to establish a preliminary compacted layer, and its construction quality is evaluated by the overall compaction pass rate.

[0045] ,in, To improve the overall compaction pass rate, This represents the compaction degree value at point P after conventional rolling. This is an indicator function; its value is 1 when the condition is true, and 0 otherwise. For the region The area.

[0046] In a preferred embodiment of the present invention, in the above-described unmanned road roller compaction method, step S500, adjusting the positioning reference and position registration strategy based on the evaluation results to obtain the adjusted positioning information includes:

[0047] S510, compare the location of the weak area in the evaluation result with the field survey data, RTK differential benchmark and historical trajectory data, quantify the systematic offset and local deviation of the detection data in the whole domain, form a position deviation field, and determine the offset vector and confidence index that need to be corrected.

[0048] S520, based on the position deviation field and the equipment structure parameters of the unmanned road roller, the positioning reference is calibrated to the center of the roller wheel of the unmanned road roller. In the positioning calculation, a rigid transformation matrix from the wheel center to the receiving antenna is introduced to eliminate the geometric error between the vehicle body and the wheel center. At the same time, carrier phase differential RTK or corresponding centimeter-level real-time dynamic positioning calculation is used to improve the registration accuracy. The target registration accuracy is preferably less than 0.1m.

[0049] S530: Based on the calibration results of the position deviation field and the positioning reference, coordinate registration is performed on the local server and cloud platform to generate a standardized set of coordinate correction parameters, obtain the adjusted positioning information, and mark the abnormal points or low confidence sections with verification labels.

[0050] In a preferred embodiment of the present invention, in the above-mentioned unmanned road roller compaction construction method, step S600, which involves identifying weak areas based on the two-dimensional compaction cloud map and adjusted positioning information, determining the number of compaction passes and compaction priority according to the degree of weakness, and generating a compaction task, includes:

[0051] S610, on the cloud platform, based on the two-dimensional compaction cloud map, weak areas where compaction has not met the standard are identified, and a supplementary compaction process is carried out.

[0052] S620, based on the location and level of each weak area, determine the initial number of pressure reinforcement passes and the pressure reinforcement priority, and generate a task list, wherein the task list includes at least one of the following: weak area boundary coordinates, pressure reinforcement priority, initial number of pressure reinforcement passes, pre-vibration start distance, and minimum coverage width.

[0053] S630, based on the task list, plans a back-and-forth compaction path according to the principle of advancing lane by lane. After the current lane is compacted to a qualified standard, it switches to the adjacent lane to continue compacting the weak area.

[0054] S640 sets process control constraints, including prohibiting the occurrence of uncompacted areas exceeding 1m in a row, ensuring that the minimum interval between vibration on / off is not less than 10m, maintaining a safe distance of not less than 1m between compaction operations and the roadbed edge, and ensuring that the minimum coverage width for a single compaction operation is not less than 1m.

[0055] S650, generate compression task.

[0056] In a preferred embodiment of the present invention, in the above-described unmanned road roller compaction method, step S700, the iterative execution of the compaction task until a preset termination condition is met, includes:

[0057] S710, the compaction task is sent to the unmanned road roller.

[0058] S720: Receives real-time compaction feedback data from the unmanned road roller; generates a re-inspection cloud map based on the feedback data; and, based on the re-inspection cloud map, determines the compaction effect as qualified for each lane. The determination method is as follows: assuming the current compaction lane is... Its area is The compaction degree value of any point P within the lane after additional compaction is Define the lane compaction pass rate ,in, This is an indicator function.

[0059] S730, when If the lane pressure is not met, the lane pressure is determined to be qualified; if not, the re-inspection cloud map and the current positioning information are used as new inputs to iteratively generate a pressure replenishment task until the predetermined qualified threshold is reached.

[0060] An unmanned road roller compaction system, comprising:

[0061] The communication module is used to deploy a communication network for real-time data synchronization at the construction site, and to deploy a compaction quality management platform on a local server and in the cloud.

[0062] The conventional compaction module is used to determine the compaction area and compaction path, design the conventional compaction operation parameters, implement conventional compaction, and establish a preliminary compacted layer.

[0063] The final compaction module is used to perform final compaction on areas that have been compacted according to the designed number of times, using preset final compaction operation parameters.

[0064] The data evaluation module is used to collect construction status data in real time, generate a two-dimensional compaction degree cloud map based on the construction status data, and evaluate the current rolling operation parameters and compaction effect.

[0065] The positioning adjustment module is used to adjust the positioning benchmark and location registration strategy based on the evaluation results to obtain the adjusted positioning information.

[0066] The compaction module is used to identify weak areas based on the two-dimensional compaction cloud map and the adjusted positioning information, determine the number of compaction passes and compaction priority according to the degree of weakness, and generate a compaction task.

[0067] The pressure compensation feedback module is used to iteratively execute the pressure compensation task until a preset termination condition is met.

[0068] The beneficial effects of the embodiments of the present invention are:

[0069] This invention constructs a two-stage compaction system combining conventional rolling and intelligent supplementary compaction, transforming compaction operations from experience-driven to data-driven. Conventional rolling establishes a preliminary stable quality field and ensures an initial pass rate, with ≥80% being preferred. Intelligent supplementary compaction targets weak areas, ultimately increasing the overall pass rate to a preferred ≥95%. This two-stage process, through layered objectives and graded treatment, directly addresses the technical shortcomings of traditional single-pass rolling, such as uneven coverage and the blind nature of subsequent supplementary compaction, thereby significantly improving the consistency and reliability of roadbed compaction.

[0070] This invention proposes and employs a high-spatiotemporal registration detection and positioning combination. Using the wheel center as the positioning reference and combining RTK or carrier phase differential to achieve centimeter-level positioning accuracy, it uses vibration energy-based CEV as a real-time compaction index and performs high-frequency sampling at an optimized sampling interval to achieve a precise one-to-one correspondence between sensor data and the actual working component position. This solves the problem of spatiotemporal mismatch between existing detection and operation methods, significantly improving the positioning accuracy of weak area identification and the executability of compaction commands.

[0071] This invention achieves a cloud-based collaborative closed-loop compaction control system encompassing detection, analysis, operation, and verification. The system automatically identifies weak areas from compaction cloud maps, generates compaction tasks based on defect severity, distributes these tasks to unmanned rollers for compaction, and transmits re-inspection data in real time until the compaction is deemed satisfactory or triggers manual review. This closed-loop mechanism transforms compaction from post-construction manual intervention to real-time automatic iteration, reducing both human error and the average number of unnecessary compaction passes, thereby achieving the technical effects of improving quality and reducing costs.

[0072] This invention constructs a bidirectional synchronous architecture between a low-latency local area network on-site and a local server-cloud platform, ensuring real-time aggregation, visualization, and control distribution of multi-source data such as positioning, vibration, trajectory, and mapping. It also supports historical playback of trajectory and vibration data, archiving of quality records, and subsequent model training. This data platform not only solves the decision-making lag problems caused by wireless lag and data latency but also provides a technical foundation for traceability of the construction process, identification of quality responsibility, and continuous optimization.

[0073] This invention enhances adaptability and efficiency under complex site conditions and machine group collaboration: by limiting the pressure replenishment strategy to single-lane sequential advancement and process control constraints, such as pre-vibration initiation, minimum interval, and edge safety distance, and performing task partitioning and priority scheduling in the cloud, it can maintain operational continuity and safety under special working conditions such as obstacles, unstructured terrain, or weakened signals. At the same time, it enables one person to control multiple machines in collaborative operation, significantly reducing manual intervention and process connection waiting time, and meeting the market demand for unmanned construction in harsh environments such as high altitude and cold regions. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart of the unmanned road roller compaction construction method of the present invention.

[0076] Figure 2 This is a conventional compaction path diagram for the unmanned roller compaction construction method of the present invention.

[0077] Figure 3 This is an intelligent compaction path diagram for the unmanned roller compaction construction method of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0079] Please refer to Figure 1The first embodiment of the present invention provides a method for compaction construction using an unmanned road roller, comprising: S100, deploying a communication network for real-time data synchronization at the construction site, and deploying a compaction quality management platform on a local server and in the cloud; S200, determining the compaction area and compaction path, designing conventional compaction operation parameters, implementing conventional compaction, and establishing a preliminary compaction layer; S300, performing final compaction on the area that has been compacted according to the designed number of times using preset final compaction operation parameters, including low-frequency vibration and uniform speed travel; S400, completing the conventional compaction... At the end of the process, continuous compaction quality monitoring is used to collect subgrade compaction status data. Based on the subgrade compaction status data, a two-dimensional compaction degree cloud map is generated to evaluate the current rolling operation parameters and compaction effect. In S500, according to the evaluation results, the positioning benchmark and position registration strategy are adjusted to obtain the adjusted positioning information. In S600, weak areas are identified based on the two-dimensional compaction degree cloud map and the adjusted positioning information. The number of additional compaction passes and the additional compaction priority are determined according to the degree of weakness, and an additional compaction task is generated. In S700, the additional compaction task is executed iteratively until the preset termination condition is reached.

[0080] In S100, the deployment of a communication network for real-time data synchronization at the construction site and the deployment of a compaction quality management platform on a local server and in the cloud include: S110, establishing a GNSS differential base station at the construction site and accessing RTK services; S120, deploying a local area network at the construction site and establishing a two-way synchronization channel between the local server and the cloud; S130, deploying the compaction quality management platform on both the local server and the cloud, with the platform having functions such as real-time data reception, CEV calculation and two-dimensional compaction degree cloud map generation, visualization display, rolling trajectory and vibration history playback, weak area identification algorithm, supplementary compaction path or parameter planning and distribution, task archiving and quality recording.

[0081] Among them, S200 refers to Figure 1The process of determining the compaction area and compaction path, designing conventional compaction operation parameters, implementing conventional compaction, and establishing a preliminary compacted layer includes: S210, based on the GNSS system mounted on the unmanned roller, setting the compaction area boundary in the digital construction model, generating a preliminary trajectory plan for a "V" shaped compaction path with full coverage, wherein the error of the area boundary is controlled within 0.3m to 0.5m, the trajectory overlap width is not less than 0.3m, and the trajectory preferably adopts a V or reciprocating pattern to improve compaction uniformity and reduce the frequency of mechanical turning; S220, according to the filler type and The design soil layer thickness is set, and the vibration parameters and number of conventional compaction cycles are preset. The vibration parameters include the number of strong and weak vibrations, vibration frequency, vibration amplitude, and travel speed. The vibration parameters aim to achieve deep compaction (strong vibration) followed by final inspection (weak vibration). The vibration parameters are written into the work task. S230: The unmanned road roller adjusts the vibration frequency, vibration amplitude, and travel speed according to the soil resistance and vibration parameters, and performs initial compaction and secondary compaction on the construction section until the preset number of cycles is completed to establish a preliminary compacted layer. S240: The construction status data of the conventional compaction stage is output.

[0082] In S300, the step of performing conventional compaction finishing on the area that has been compacted according to the designed number of times includes: S310, performing conventional compaction finishing on the area that has been compacted according to the designed number of times, wherein the final compaction parameters include low-frequency vibration, slow and uniform speed of travel, and minimum thickening rate, to further smooth the road surface and improve the surface compaction with minimal disturbance; S320, during the final compaction process, monitoring the road surface smoothness and slope in real time, and adjusting the vibration mode and the final compaction parameters according to the monitoring results; S330, outputting the construction status data of the final compaction stage.

[0083] In step S400, the real-time acquisition of construction status data, the generation of a two-dimensional compaction cloud map based on the construction status data, and the evaluation of current rolling operation parameters and compaction effect include: S410, real-time acquisition of construction status data during the conventional rolling stage and the final compaction stage, and synchronization to the local server and cloud platform through the communication network deployed at the construction site; S420, data synchronization and data preprocessing of the acquired multi-source construction status data to obtain cleaned data; S430, calculation of compaction index based on the cleaned data, association of the sampling points of each unmanned roller with GNSS coordinates, and real-time generation of a two-dimensional compaction cloud map presented in the form of a two-dimensional point cloud; S440, quality assessment of the construction area based on the two-dimensional compaction cloud map, the assessment results including the percentage of qualified area, the location of weak areas, the severity level, and statistical indicators, wherein the percentage of qualified area is compared with a preset threshold, and the primary threshold for initiating supplementary compaction after conventional rolling is preferably ≥80%.

[0084] In S410, the construction status data includes at least one of the following: compaction trajectory, (GNSS / RTK) position coordinates, vibration feedback (vibration amplitude / frequency), acceleration signal, driving speed, number of compaction passes, and soil layer thickness; in S420, the data synchronization includes clock synchronization and timestamp alignment, and the data preprocessing includes data format standardization, filtering and noise reduction, and establishing a mapping relationship between the compaction trajectory and sensor data.

[0085] In step S430, the compaction index is calculated based on the vibration compaction energy value CEV. The calculation method includes: S431, real-time acquisition of the vertical acceleration signal a(t) of the vibrating wheel; S432, empirical mode decomposition of the vertical acceleration signal a(t) to obtain intrinsic mode function components; S433, Hibert transform of each intrinsic mode function component to calculate the instantaneous frequency and energy distribution; S434, obtaining the total energy E of the vibration signal by integrating the Hibert energy spectrum in the time-frequency domain. total CEV, as a continuous performance indicator for compaction quality, is calculated using the following formula: ,in, Let be the Hibert energy spectrum of the i-th intrinsic mode function component.

[0086] In S440, the quality of the construction area is assessed based on the two-dimensional compaction cloud map. The assessment process uses the following quantitative model: The goal of the conventional rolling stage is to establish a preliminary compacted layer, and its construction quality is evaluated by the overall compaction pass rate. ,in, To improve the overall compaction pass rate, This represents the compaction degree value at point P after conventional rolling. This is an indicator function; its value is 1 when the condition is true, and 0 otherwise. For the region The area.

[0087] In step S500, adjusting the positioning benchmark and position registration strategy based on the evaluation results to obtain the adjusted positioning information includes: S510, comparing the weak area location in the evaluation results with UAV aerial survey or laser scanning field mapping data, RTK differential benchmark, and historical trajectory data to quantify the systematic offset and local deviation of the detection data in the entire domain, forming a position deviation field, and determining the offset vector and confidence index to be corrected; S520, based on the position deviation field and the equipment structure parameters of the unmanned road roller, calibrating the positioning benchmark to the center of the compaction wheel of the unmanned road roller, and introducing the wheel into the positioning solution. A rigid transformation matrix from the center to the receiving antenna is used to eliminate the geometric error between the vehicle body and the wheel center. At the same time, carrier phase differential RTK or corresponding centimeter-level real-time dynamic positioning calculation is used to improve the registration accuracy. The target registration accuracy is preferably less than 0.1m. S530, coordinate registration is performed on the local server and cloud platform based on the calibration results of the position deviation field and the positioning reference, including coordinate system transformation and deviation correction. A standardized coordinate correction parameter set is generated, including wheel center offset, time delay correction, RTK correction coefficient and coordinate transformation matrix, to obtain the adjusted positioning information. Anomalies or low confidence sections are marked with verification marks.

[0088] In step S600, the step of identifying weak areas based on the two-dimensional compaction cloud map and adjusted positioning information, determining the number of compaction passes and compaction priority according to the degree of weakness, and generating a compaction task includes: S610, on the cloud platform, identifying weak areas that have not met the compaction standards based on the two-dimensional compaction cloud map, and performing a compaction process; S620, determining the initial number of compaction passes and compaction priority based on the location and level of each weak area, and generating a task list, the task list including the weak area boundary coordinates, compaction priority, initial number of compaction passes, pre-vibration initiation distance (preferably about 2m before reaching the weak area), and minimum coverage width (preferably ≥1m), at least one of the following; S630, based on the task list, please refer to... Figure 3 Based on the principle of advancing lane by lane, a back-and-forth compaction path is planned. After the current lane is compacted to a qualified standard, the process switches to the adjacent lane to continue compacting the weak areas. S640 sets process control constraints, including prohibiting the occurrence of uncompacted areas exceeding 1m in a row, ensuring that the minimum interval between vibration on / off is not less than 10m, maintaining a safe distance of not less than 1m between compaction operations and the roadbed edge, and ensuring that the minimum coverage width of a single compaction operation is not less than 1m. S650 generates a compaction task.

[0089] In step S700, the iterative execution of the compaction task until a preset termination condition is met includes: S710, sending the compaction task to the unmanned road roller; S720, receiving the real-time compaction feedback data from the unmanned road roller, generating a re-inspection cloud map based on the feedback data, and judging the compaction effect on a single lane based on the re-inspection cloud map. The judgment method is to assume the current compaction lane is... Its area is The compaction degree value of any point P within the lane after additional compaction is Define the lane compaction pass rate ,in, For the indicative function;; S730, if not reached, the re-inspection cloud map and the current positioning information are used as new inputs to iteratively generate a pressure replenishment task until the predetermined qualified threshold is reached.

[0090] A second embodiment of the present invention provides an unmanned road roller compaction construction system, comprising: a communication module for deploying a communication network for real-time data synchronization at the construction site, and deploying a compaction quality management platform on a local server and in the cloud; a conventional compaction module for determining the compaction area and compaction path, designing conventional compaction operation parameters, implementing conventional compaction, and establishing a preliminary compaction layer; a final compaction module for applying preset final compaction operation parameters, including low-frequency vibration and uniform speed travel, to the area that has been compacted according to the design number of times, and performing final compaction; a data evaluation module for collecting construction status data in real time, generating a two-dimensional compaction degree cloud map based on the construction status data, and evaluating the current compaction operation parameters and compaction effect; a positioning adjustment module for adjusting the positioning benchmark and position registration strategy according to the evaluation results to obtain adjusted positioning information; a compaction module for identifying weak areas according to the two-dimensional compaction degree cloud map and the adjusted positioning information, determining the number of compaction passes and compaction priority according to the degree of weakness, and generating a compaction task; and a compaction feedback module for iteratively executing the compaction task until a preset termination condition is reached.

[0091] The computer program product of the unmanned road roller compaction construction method and device provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0092] Specifically, the storage medium can be a general-purpose storage medium, such as a portable disk or hard disk. When the computer program on the storage medium is run, it can execute the above-mentioned unmanned road roller compaction construction method, thereby improving the continuity and accuracy of compaction quality detection, reducing human intervention and operation connection delays, improving the pertinence and resource utilization efficiency of compaction operations, and meeting the engineering requirements for unmanned, continuous and traceable construction in complex environments.

[0093] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for compaction construction using an unmanned road roller, characterized in that, include: S100 deploys a communication network at the construction site for real-time data synchronization and a compaction quality management platform on local servers and in the cloud. S200: Determine the compaction area and compaction path, design the operating parameters for conventional compaction, implement conventional compaction, and establish a preliminary compacted layer; S300: For areas that have been compacted according to the design number of times, conventional compaction is performed using preset conventional compaction end parameters. S400, at the end of conventional compaction, continuous compaction quality monitoring is used to collect subgrade compaction state data. Based on the subgrade compaction state data, a two-dimensional compaction degree cloud map is generated to evaluate the current compaction operation parameters and compaction effect. S500 adjusts the positioning reference and location registration strategy based on the evaluation results to obtain the adjusted positioning information; S600: Identify weak areas based on the two-dimensional compaction cloud map and the adjusted positioning information, determine the number of compaction passes and compaction priority according to the degree of weakness, and generate a compaction task. S700, iteratively execute the pressure replenishment task until the preset termination condition is met.

2. The unmanned road roller compaction method according to claim 1, characterized in that, In S100, the deployment of a communication network for real-time data synchronization at the construction site, and the deployment of a compaction quality management platform on a local server and in the cloud, include: S110: Establish a GNSS differential base station at the construction site and access RTK services; S120 sets up a local area network at the construction site and establishes a two-way synchronization channel with the cloud through a local server; The S130 deploys a compaction quality management platform on both local servers and in the cloud.

3. The unmanned road roller compaction method according to claim 1, characterized in that, In S200, determining the compaction area and compaction path, designing conventional compaction operating parameters, implementing conventional compaction, and establishing a preliminary compacted layer includes: S210, based on the GNSS system carried by the unmanned road roller, sets the boundary of the compaction area in the digital construction model and generates preliminary trajectory planning; S220, based on the type of fill material and the designed soil layer thickness, preset the vibration parameters and number of cycles for conventional compaction; The S230 unmanned road roller adjusts the vibration frequency, vibration amplitude and travel speed according to the soil resistance and vibration parameters to perform initial compaction and secondary compaction on the construction section until the preset number of times is completed to establish a preliminary compaction layer. S240 outputs construction status data for the conventional compaction stage.

4. The unmanned road roller compaction method according to claim 1, characterized in that, In S300, the step of performing conventional compaction finishing on the area that has been compacted according to the designed number of times includes: S310, in the compaction area where the designed number of compaction cycles has been completed, conventional compaction is performed to finish the compaction according to the preset final compaction parameters, which include low-frequency vibration, slow and uniform travel, and minimum thickening rate. S320: During the final compaction process, the road surface smoothness and slope are monitored in real time, and the vibration mode and the final compaction parameters are adjusted according to the monitoring results. S330 outputs construction status data for the final pressure stage.

5. The unmanned road roller compaction method according to claim 1, characterized in that, S400, the real-time acquisition of construction status data, the generation of a two-dimensional compaction degree cloud map based on the construction status data, and the evaluation of the current rolling operation parameters and compaction effect include: S410 collects construction status data in real time during the conventional compaction stage and the final compaction stage, and synchronizes it to the local server and cloud platform through the communication network deployed at the construction site. S420 performs data synchronization and data preprocessing on the collected multi-source construction status data to obtain cleaned data; S430, calculate the compaction index based on the cleaned data, associate the sampling point of each unmanned road roller with GNSS coordinates, and generate a two-dimensional compaction cloud map in the form of a two-dimensional point cloud in real time; S440, based on the two-dimensional compaction cloud map, the construction area is assessed for quality. The assessment results include the percentage of qualified area, the location of weak areas, the severity level, and statistical indicators.

6. The unmanned road roller compaction method according to claim 5, characterized in that, In S410, the construction status data includes at least one of the following: compaction trajectory, position coordinates, vibration feedback, acceleration signal, travel speed, number of compaction passes, and soil layer thickness. In S420, the data synchronization includes clock synchronization and timestamp alignment, and the data preprocessing includes data format standardization, filtering and noise reduction, and establishing a mapping relationship between the compaction trajectory and sensor data.

7. The unmanned road roller compaction method according to claim 5, characterized in that, In S430, the compaction index is calculated based on the vibration compaction energy value (CEV), and the calculation method includes: S431, real-time acquisition of the vertical acceleration signal a(t) of the vibrating wheel; S432, Perform empirical mode decomposition on the vertical acceleration signal a(t) to obtain the intrinsic mode function components; S433, Perform Hibert transform on each of the intrinsic mode function components to calculate the instantaneous frequency and energy distribution; S434, by integrating the Hibert energy spectrum in the time-frequency domain, the total energy E of the vibration signal is obtained. total CEV, as a continuous performance indicator for compaction quality, is calculated using the following formula: ,in, Let be the Hibert energy spectrum of the i-th intrinsic mode function component.

8. The unmanned road roller compaction method according to claim 5, characterized in that, In S440, the quality of the construction area is assessed based on the two-dimensional compaction cloud map, and the assessment formula is as follows: ,in, To improve the overall compaction pass rate, This represents the compaction degree value at point P after conventional rolling. This is an indicator function; its value is 1 when the condition is true, and 0 otherwise. For the region The area.

9. The unmanned road roller compaction method according to claim 5, characterized in that, In S500, the step of adjusting the positioning reference and location registration strategy based on the evaluation results to obtain the adjusted positioning information includes: S510, compare the location of the weak area in the evaluation result with the field survey data, RTK differential benchmark and historical trajectory data, quantify the systematic offset and local deviation of the detection data in the whole domain, form a position deviation field, and determine the offset vector and confidence index that need to be corrected. S520, based on the position deviation field and the equipment structure parameters of the unmanned road roller, calibrate the positioning reference to the center of the compaction wheel of the unmanned road roller; S530: Based on the calibration results of the position deviation field and the positioning reference, coordinate registration is performed on the local server and cloud platform to generate a standardized set of coordinate correction parameters, obtain the adjusted positioning information, and mark the abnormal points or low confidence sections with verification labels.

10. The unmanned road roller compaction method according to claim 1, characterized in that, In S600, the step of identifying weak areas based on the two-dimensional compaction cloud map and the adjusted positioning information, determining the number of compaction passes and compaction priority according to the degree of weakness, and generating a compaction task includes: S610, on the cloud platform, based on the two-dimensional compaction cloud map, weak areas where compaction has not met the standard are identified, and a supplementary compaction process is carried out; S620, based on the location of each weak area, determine the initial number of pressure reinforcement passes and the pressure reinforcement priority, and generate a task list, wherein the task list includes at least one of the following: weak area boundary coordinates, pressure reinforcement priority, initial number of pressure reinforcement passes, pre-vibration start distance, and minimum coverage width; S630, based on the task list, a back-and-forth compaction path is planned according to the principle of advancing lane by lane. After the current lane is compacted to a qualified standard, the process is switched to the adjacent lane to continue compacting the weak areas. S640 sets process control constraints, including prohibiting the occurrence of uncompacted areas exceeding 1m continuously, ensuring that the minimum interval between vibration on / off is not less than 10m, maintaining a safe distance of not less than 1m between compaction operations and the roadbed edge, and ensuring that the minimum coverage width of a single compaction operation is not less than 1m. S650, generate compression task.

11. The unmanned road roller compaction method according to claim 1, characterized in that, In S700, the iterative execution of the compression task until a preset termination condition is met includes: S710, the compaction task is sent to the unmanned road roller; S720: Receives real-time compaction feedback data from the unmanned road roller; generates a re-inspection cloud map based on the feedback data; and, based on the re-inspection cloud map, determines the compaction effect as qualified for each lane. The determination method is as follows: assuming the current compaction lane is... Its area is The compaction degree value of any point P within the lane after additional compaction is Define the lane compaction pass rate ,in, It is an indicator function; S730, when If the lane pressure is deemed acceptable, the re-inspection cloud map and the current positioning information are used as new inputs to iteratively generate a pressure replenishment task until the predetermined acceptable threshold is reached.

12. An unmanned road roller compaction system, characterized in that, include: The communication module is used to deploy a communication network for real-time data synchronization at the construction site, and to deploy a compaction quality management platform on a local server and in the cloud. The conventional compaction module is used to determine the compaction area and compaction path, design the conventional compaction operation parameters, implement conventional compaction, and establish a preliminary compacted layer. The final compaction module is used to perform final compaction on areas that have been compacted according to the designed number of times, using preset final compaction operation parameters. The data evaluation module is used to collect construction status data in real time, generate a two-dimensional compaction degree cloud map based on the construction status data, and evaluate the current rolling operation parameters and compaction effect. The positioning adjustment module is used to adjust the positioning benchmark and location registration strategy based on the evaluation results to obtain the adjusted positioning information. The compaction module is used to identify weak areas based on the two-dimensional compaction cloud map and the adjusted positioning information, determine the number of compaction passes and compaction priority according to the degree of weakness, and generate a compaction task. The pressure compensation feedback module is used to iteratively execute the pressure compensation task until a preset termination condition is met.

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