A strong compaction method for embankment

Abstract

The present invention relates to the technical field of dynamic compaction data processing, and in particular to a dynamic compaction method for embankments. The method comprises the following steps: in the process of embankment compaction, collecting embankment basic data and embankment position data, wherein the embankment basic data includes embankment terrain data and rammer position data; acquiring embankment compaction path data, and performing embankment dynamic compaction position determination operations according to the embankment compaction path data, the embankment basic data and the embankment position data, thereby obtaining embankment dynamic compaction position in-place data; performing dynamic compaction position warning operations according to the embankment dynamic compaction position in-place data; acquiring embankment dynamic compaction target data, and obtaining rammer control parameter data according to the embankment dynamic compaction target data and the embankment basic data, so as to perform rammer compaction operations; acquiring embankment pressure data and compaction vibration data, so as to obtain compaction operation evaluation data. The present invention improves the accuracy, efficiency and quality of compaction operations.

Description

Technical Field

[0001] The invention relates to the technical field of dynamic compaction data processing, and in particular to a dynamic compaction method for an embankment. Background Art

[0002] The tamping compaction method of embankments is an engineering technology used to strengthen the soil foundation, which is usually used in roads, bridges and other infrastructure projects. The conventional method uses large mechanical equipment (such as vibratory rollers or heavy vibratory hammers) to vibrate or impact the soil at a high frequency and high amplitude, so that the soil particles are rearranged and tightly packed, thereby increasing its density and bearing capacity. The conventional tamping compaction method of embankments often uses manual operation, lifting the tamping hammer based on experience, and controlling the energy by the lifting height. In conventional operations, this often leads to poor control effects. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a strong tamping compaction method for embankments to solve at least one of the above technical problems.

[0004] The present application provides a method for compacting an embankment, the method comprising:

[0005] S1. During the embankment compaction process, collecting embankment basic data and embankment position data, wherein the embankment basic data includes embankment topographic data and rammer position data;

[0006] S2, obtaining embankment compaction path data, and performing embankment compaction position determination operation according to the embankment compaction path data, the embankment basic data and the embankment position data, so as to obtain embankment compaction position in-place data, wherein the path compaction position in-place data includes in-place success data and in-place failure data;

[0007] S3, when it is determined that the embankment strong compaction position in-place data is the in-place failure data, performing a strong compaction position warning operation according to the embankment strong compaction position in-place data;

[0008] S4, when it is determined that the embankment compaction position in-place data is the successful in-place data, the embankment compaction target data is obtained, and the ramming hammer control parameter data is calculated according to the embankment compaction target data and the embankment basic data to obtain the ramming hammer control parameter data for performing the ramming hammer compaction operation;

[0009] S5. During the tamping process with the rammer, embankment pressure data and tamping vibration data are obtained, and a tamping operation evaluation is performed based on the embankment pressure data and the tamping vibration data to obtain tamping operation evaluation data to adjust the tamping operation with the rammer.

[0010] In the present invention, by collecting embankment basic data and embankment position data, and performing strong tamping position determination operations according to the embankment compaction path data, accurate tamping position control can be achieved, ensuring that the tamping hammer is tamped at the correct position, thereby improving the tamping efficiency and uniformity. By acquiring embankment pressure data and tamping vibration data, real-time monitoring and evaluation are performed during the tamping process of the tamping hammer, and poor tamping effects or problems can be discovered in time, so that adjustment operations can be performed to improve the tamping quality. The computer control system is used to calculate the tamping hammer control parameters, which reduces human errors and subjective judgments and improves the accuracy and consistency of tamping. It reduces dependence on workers, saves human resources, and reduces the labor intensity of tamping operations. By real-time monitoring and evaluation of tamping operations, and adjusting according to data, the tamping quality and efficiency can be improved, ensuring that the embankment tamping meets the specified standards and requirements. The present invention improves the accuracy, efficiency and quality of tamping operations through intelligent data collection and control, which helps to reduce engineering costs and improve the sustainability of the project.

[0011] Optionally, during the process of embankment compaction, collecting embankment foundation data and embankment position data includes:

[0012] During the embankment compaction process, the embankment is scanned by radar laser through terrain sensors to obtain embankment terrain data;

[0013] The rammer position is collected by a position sensor preset on the rammer to obtain rammer position data, wherein the rammer position data includes rammer positioning position data and rammer motion trajectory data.

[0014] In the present invention, the terrain data of the embankment can be collected with high precision by performing radar laser scanning through the terrain sensor, which helps to more accurately understand the terrain features of the embankment, including elevation, slope, curvature, etc., thereby providing accurate basic data for the compaction operation. The position sensor preset on the rammer can collect the position data and motion trajectory data of the rammer in real time, so that the position and state of the rammer can be monitored in real time, which helps to ensure the accurate position of the rammer during the compaction process and improve the accuracy of the compaction operation. Combined with the embankment terrain data and the rammer position data, accurate compaction position control can be achieved. The positioning position data and motion trajectory data of the rammer can help determine the actual position of the rammer, thereby ensuring that the rammer is compacted at the correct position. The use of automated terrain sensors and position sensors reduces human errors and subjective judgments, and improves the accuracy and consistency of data collection. Through accurate terrain data and rammer position data, the lifting height and tamping position of the rammer can be better controlled, thereby improving the compaction efficiency and uniformity, and reducing waste and unnecessary energy consumption. The data collected in real time allows real-time monitoring and evaluation of compaction operations, and adjustments can be made based on the data to improve compaction quality and efficiency.

[0015] Optionally, the acquiring of embankment compaction path data, and performing embankment compaction position determination operation according to the embankment compaction path data, the embankment foundation data and the embankment position data, thereby obtaining embankment compaction position in-situ data, includes:

[0016] S21, obtaining embankment compaction path data;

[0017] S22, performing compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data;

[0018] S23, predicting the position of the rammer according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data;

[0019] S24, performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data;

[0020] S25. Perform real-time embankment compaction position determination processing according to the optimized planning data to obtain embankment compaction position in-situ data.

[0021] In the present invention, by obtaining the embankment compaction path data and performing compaction route planning according to the embankment basic data, the compaction path can be efficiently determined, obstacles can be avoided, and the embankment terrain characteristics can be considered, which is helpful to optimize the path of the compaction operation and improve the compaction efficiency. The rammer position prediction is performed through the rammer position data and the compaction embankment planning data, and the position of the rammer can be accurately predicted to ensure that the rammer moves correctly on the compaction path. By adopting dynamic planning optimization processing, the operating parameters of the rammer, such as the lifting height and energy demand, can be adjusted in real time according to the real-time rammer position prediction data, which helps to optimize the compaction operation in real time and ensure the best compaction effect. By optimizing the planning data and real-time position determination processing, it can be ensured that the rammer is compacted in the best way on the compaction path, and the compaction quality and uniformity are improved. By optimizing the planning, unnecessary energy consumption can be reduced, the energy utilization of the rammer can be made more efficient, and the energy cost can be reduced. Through real-time monitoring and adjustment, poor compaction effect or problems can be discovered in time, and adjustments can be made according to the data to meet project requirements.

[0022] Optionally, performing compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data includes:

[0023] S221, constructing a three-dimensional model according to the embankment terrain data in the embankment position data to obtain a three-dimensional terrain embankment terrain model;

[0024] S222, turning on the laser radar to scan obstacles and obtaining obstacle data, wherein the obstacle data includes obstacle specification data and obstacle position data;

[0025] S223, adding the obstacle data to the three-dimensional terrain embankment terrain model, thereby obtaining a three-dimensional terrain obstacle model;

[0026] S224, gridding the three-dimensional terrain obstacle model to obtain a terrain obstacle grid model;

[0027] S225. Calculate an obstacle avoidance path based on the terrain obstacle grid model to obtain compacted embankment planning data.

[0028] In the present invention, by constructing a three-dimensional model according to the terrain data in the embankment position data, a three-dimensional terrain model of the embankment can be accurately established, and accurate terrain feature information, including elevation, slope, curvature, etc., is provided, providing high-quality basic data for the embankment compaction operation. By turning on the laser radar to scan obstacles and obtain obstacle data, the specification data and location data of the obstacles can be effectively detected and obtained. The obstacle data is added to the three-dimensional terrain embankment terrain model, and a comprehensive three-dimensional model containing terrain and obstacle information can be created, which is helpful for better planning of the compaction path. By gridding the three-dimensional terrain obstacle model and calculating the obstacle avoidance path according to the terrain obstacle gridding model, the compaction path that avoids obstacles can be accurately calculated, which helps to ensure that the compaction operation can safely bypass obstacles when encountering obstacles to avoid potential collisions or damage. Through accurate terrain models, obstacle data and obstacle avoidance path calculations, the compaction path can be optimized, the compaction efficiency and quality can be improved, and the rammer can be compacted along a more reasonable and safe path, reducing energy waste and errors. Through effective obstacle detection and avoidance, the risks in the compaction operation can be reduced, and potential accidents and damage can be reduced.

[0029] Optionally, the performing rammer position prediction according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data includes:

[0030] S231, performing rammer distribution processing according to the compacted embankment planning data and the terrain obstacle grid model to obtain rammer distribution processing data;

[0031] S232, obtaining rammer parameter data;

[0032] S233, performing dynamic simulation according to the rammer parameter data to obtain rammer dynamic simulation data;

[0033] S234, performing position prediction based on the rammer dynamics simulation data, the compacted embankment planning data and the rammer distribution processing data through a preset rammer motion time window data to obtain rammer position prediction data.

[0034] In the present invention, by performing rammer distribution processing according to compaction embankment planning data and terrain obstacle gridding model, obtaining rammer parameter data, performing dynamic simulation, and combining rammer dynamic simulation data, compaction embankment planning data and rammer distribution processing data, the position of the rammer can be accurately predicted, which helps to ensure that the rammer compacts according to the expected path and improves the accuracy of compaction. Through dynamic simulation, the dynamic characteristics of the rammer, including speed, acceleration, etc., can be considered to more accurately predict the position of the rammer, which helps to avoid the rammer moving too fast or too slow, and improves the uniformity and quality of compaction. Through the rammer distribution processing, the situation of multiple rammers compacting at the same time can be considered, ensuring the coordinated work and uniform distribution between the rammers, which helps to improve the efficiency and consistency of compaction. Through the preset rammer movement time window data, the movement of the rammer within a specific time can be limited, ensuring that the rammer compacts at a reasonable speed, which helps to optimize the movement trajectory of the rammer and reduce energy waste and errors. By accurately predicting the rammer position, taking into account the rammer dynamics and rammer distribution processing, the compaction efficiency and compaction quality can be improved. The rammer can compact along the optimal path and speed, reducing errors and waste.

[0035] Optionally, the performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data includes:

[0036] Calculate compaction efficiency cost according to the rammer position prediction data to obtain compaction efficiency cost data;

[0037] Performing uniformity calculation according to the rammer position prediction data to obtain uniformity data;

[0038] The compacted embankment planning data is optimized through strategy iteration according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data.

[0039] In the present invention, by calculating the compaction efficiency cost according to the rammer position prediction data, the compaction efficiency of each rammer position can be determined. The strategy iteration optimization process of optimizing the planning data aims to maximize the compaction efficiency and ensure that the rammer at each compaction point can work in the most efficient way, thereby reducing the compaction time and energy consumption. The uniformity calculation is performed through the rammer position prediction data, and the uniformity of the compaction operation can be evaluated. The strategy iteration optimization process aims to improve the uniformity and ensure that the compaction effect of the rammer at different locations is evenly distributed, thereby reducing the unevenness of the embankment. By comprehensively considering the compaction efficiency cost data and the uniformity data, the strategy iteration optimization can find a balance point, which can not only improve the compaction efficiency, but also improve the uniformity, which helps to optimize the compaction planning, so as to compact the embankment in a short time and ensure the quality of compaction. By optimizing the planning data, the energy consumption of the rammer can be reduced, the fuel cost can be reduced, and at the same time, by reducing the compaction time and improving the uniformity, the cost of manual maintenance and repair can also be reduced. By maximizing efficiency and uniformity, the present invention overcomes the limitations of manual experience through numerical processing and ensures the quality of compaction work, while traditional methods usually rely on experience and rules to determine the compaction position and path, so the accuracy is limited.

[0040] Optionally, the calculating the compaction efficiency cost according to the rammer position prediction data to obtain the compaction efficiency cost data includes:

[0041] Performing time efficiency cost calculation according to the rammer position prediction data to obtain time efficiency cost data;

[0042] Calculate the energy efficiency cost according to the rammer position prediction data to obtain energy efficiency cost data;

[0043] The time efficiency cost data and the energy efficiency cost data are integrated to obtain compaction efficiency cost data.

[0044] In the present invention, by separately calculating the time efficiency cost and the energy efficiency cost, the efficiency of the compaction operation can be evaluated more accurately. The time efficiency cost takes into account the time required for the rammer to complete the compaction task, while the energy efficiency cost takes into account the energy consumed by the rammer to complete the compaction task. Taking these two factors into consideration helps to more accurately evaluate the compaction efficiency. By integrating the time efficiency cost data and the energy efficiency cost data, different evaluation indicators can be customized for different projects and needs. For example, for some projects, time efficiency is more critical, while for other projects, energy saving is more important, and the weights can be adjusted to meet different project needs. After the compaction efficiency cost data is generated, it can be used for decision support. The system can formulate an optimization strategy for the compaction operation based on this data to shorten the compaction time or reduce energy consumption as much as possible while ensuring quality, which helps to reduce costs and improve efficiency.

[0045] Optionally, performing strategy iteration optimization on the compacted embankment planning data according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data includes:

[0046] Acquire standard compacted embankment planning data, and construct a reinforcement learning agent according to the standard compacted embankment planning data to obtain a reinforcement learning agent;

[0047] Performing deep deterministic policy gradient calculation through a reinforcement learning agent according to the compaction efficiency cost data and the uniformity data to obtain optimized reward data;

[0048] The compacted embankment planning data is iteratively optimized according to the optimization reward data to obtain optimized planning data.

[0049] In the present invention, the reinforcement learning agent can perform deep deterministic policy gradient calculation based on the compaction efficiency cost data and uniformity data, so that the optimization process is adaptive, and the system can dynamically adjust the optimization strategy according to different compaction conditions and requirements to maximize efficiency and uniformity. The reinforcement learning agent can make decisions in real-time compaction operations, adjust the operating parameters of the rammer according to the current situation and data, and help to make optimization decisions in time during the compaction process to adapt to the changing engineering conditions. Through deep deterministic policy gradient calculation, the reinforcement learning agent can generate optimization reward data, which reflects the strategy for achieving the best effect in the compaction operation. The iterative optimization process aims to maximize the reward and ensure that the compaction operation reaches the best level in terms of efficiency and uniformity. The automated decision-making ability of the reinforcement learning agent reduces the need for human intervention. The system can adjust the operation of the rammer according to the agent's suggestions without continuous manual supervision and adjustment. Through adaptive optimization and real-time decision support, it helps to improve the sustainability of the project, maximize the compaction efficiency and uniformity, reduce resource waste, and thus reduce the environmental and economic costs of the project.

[0050] Optionally, the acquiring of embankment compaction target data, and calculating rammer control parameters according to the embankment compaction target data and the embankment basic data to obtain rammer control parameter data, includes:

[0051] Obtain target data for embankment compaction;

[0052] Perform compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data;

[0053] The rammer control parameter is calculated according to the compaction depth data and the compaction layer bearing capacity data to obtain the rammer control parameter data.

[0054] In the present invention, by acquiring the target data of embankment compaction, the system can accurately understand the compaction depth and compaction layer bearing capacity that need to be achieved, thereby ensuring that the compaction operation of the rammer is consistent with the project requirements, helping to avoid excessive or insufficient compaction, and improving the quality and reliability of the project. Based on the compaction depth data and the compaction layer bearing capacity data, the system can calculate the optimal rammer control parameters, including ramming energy, ramming frequency, etc., to ensure that the operation of the rammer can minimize resource waste and energy consumption while achieving the compaction target. Through precise compaction targets and optimal control parameters, the compaction operation of the rammer can be carried out more efficiently, which means that the project completion time will be shortened, the project cost will be reduced, and the efficiency will be improved. Accurate compaction targets and control parameters help ensure that the quality and bearing capacity of the compacted layer meet the design requirements, and help improve the sustainability and long-term stability of the project.

[0055] Optionally, performing compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data includes:

[0056] Perform compaction depth processing according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data;

[0057] Extract soil material according to the rammer position data to obtain soil material data;

[0058] Perform soil mechanical parameter simulation according to the soil material data to obtain soil mechanical parameter simulation data;

[0059] The compacted layer bearing capacity is calculated according to the compaction depth data and the soil mechanical parameter simulation data to obtain the compacted layer bearing capacity data.

[0060] In the present invention, by performing compaction depth processing according to the embankment strong compaction target data and the embankment foundation data, the system can determine the accurate compaction depth to ensure that the compaction operation is carried out in accordance with the project requirements, which helps to avoid excessive or insufficient compaction and improve the quality of the project. By extracting soil material data based on the rammer position data and simulating soil mechanical parameters, the system takes into account the physical properties of the soil, which helps to better understand the strength and bearing capacity of the soil in different regions, so as to perform compaction according to actual conditions. Based on the compaction depth data and the soil mechanical parameter simulation data, the system can calculate the bearing capacity of the compacted layer, which helps to ensure that the compacted layer has sufficient bearing capacity to meet the design requirements of the project. Accurately calculating the compaction depth and considering the soil material can reduce unnecessary compaction, thereby reducing resource waste and energy consumption. Considering soil mechanical parameters and the bearing capacity of the compacted layer helps to improve the reliability of the project and ensure that the embankment can withstand traffic and loads stably for a long time.

[0061] The purpose of the present invention is to collect embankment basic data and embankment position data, including terrain data and rammer position data, so that the system can realize real-time data collection and processing, so that immediate decisions can be made according to the current situation to ensure that the embankment compaction is carried out according to the design requirements. Using the embankment compaction path data and terrain data, the system performs complex path planning, taking into account the terrain characteristics and the location of obstacles, which helps to avoid collisions with obstacles and ensure that the rammer works in a safe position. When the embankment strong compaction position in-place data is a failure to be in place, the system will perform a strong compaction position warning operation to remind the operator to pay attention, which helps to reduce operating errors and accidents. Once the embankment strong compaction position in-place data is a successful in-place, the system obtains the embankment strong compaction target data, and calculates the rammer control parameters according to the embankment basic data, ensuring that the operation of the rammer is carried out according to the specific engineering requirements to achieve the best compaction effect. During the rammer compaction process, the system not only collects embankment pressure data and compaction vibration data, but also performs complex compaction operation evaluation, which helps to monitor the compaction effect in real time and make adjustments based on the data to ensure the uniformity and quality of the embankment compaction. Through more accurate compaction position and parameter control, the system can reduce unnecessary compaction, thereby reducing resource consumption and environmental impact. The present invention realizes automated and intelligent embankment compaction, reduces the need for human intervention, and improves the efficiency and consistency of operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0063] Figure 1 A flowchart showing the steps of a method for compacting an embankment according to an embodiment of the present invention is provided;

[0064] Figure 2 A flowchart showing the steps of a method for acquiring embankment compaction position in-situ data according to an embodiment;

[0065] Figure 3 A flowchart showing the steps of a compaction route planning method according to an embodiment is shown;

[0066] Figure 4 A flowchart showing the steps of a method for predicting the position of a rammer according to an embodiment is shown;

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0071] See also Figures 1 to 4 The present application provides a method for compacting an embankment, the method comprising:

[0072] S1. During the embankment compaction process, collecting embankment basic data and embankment position data, wherein the embankment basic data includes embankment topographic data and rammer position data;

[0073] Specifically, a laser radar sensor is used to scan the embankment to obtain the embankment terrain data, including ground elevation, slope, curvature, etc. A position sensor is installed on the rammer to obtain the rammer position data, including three-dimensional coordinates and motion trajectory, through GPS or inertial measurement unit (IMU).

[0074] S2, obtaining embankment compaction path data, and performing embankment compaction position determination operation according to the embankment compaction path data, the embankment basic data and the embankment position data, so as to obtain embankment compaction position in-place data, wherein the path compaction position in-place data includes in-place success data and in-place failure data;

[0075] Specifically, based on the embankment terrain data, an algorithm is used to calculate the expected embankment compaction path data, such as using terrain analysis and path planning algorithms. The embankment position data is combined with the compaction path data to determine the tamping position data of the tamping hammer, and the tamping hammer position needs to be calibrated to match the path.

[0076] S3, when it is determined that the embankment strong compaction position in-place data is the in-place failure data, performing a strong compaction position warning operation according to the embankment strong compaction position in-place data;

[0077] Specifically, if the embankment compaction position data indicates that the position of the rammer is not as expected or is unsafe, the system can issue a warning, such as using a visual or sound signal to implement a warning operation, to remind the operator to take measures.

[0078] S4, when it is determined that the embankment compaction position in-place data is the successful in-place data, the embankment compaction target data is obtained, and the ramming hammer control parameter data is calculated according to the embankment compaction target data and the embankment basic data to obtain the ramming hammer control parameter data for performing the ramming hammer compaction operation;

[0079] Specifically, the target data of embankment compaction is obtained from the engineering design, including information such as the depth of the compacted layer and material requirements. According to the embankment basic data and the target data of compaction, the control parameters of the rammer, such as the ramming frequency and the rammer pressure, are calculated. Ramming frequency: The ramming frequency is calculated according to the properties of the soil and the required compaction depth. For example, if the embankment needs to be compacted at a depth of 2 meters, the frequency can be determined according to the soil type and engineering specifications. If the soil is loose, the frequency is higher, and if the soil is dense, the frequency is lower. Rammer pressure: According to the embankment topographic data, the properties and changes of the soil under the rammer are determined, and the required rammer pressure is determined to ensure that the compacted layer reaches the required density and bearing capacity. The rammer pressure is usually adjusted by a hydraulic system. Ramming speed: According to the compaction depth and the required compaction efficiency, the compaction speed is determined. The speed should enable the rammer to compact effectively without damaging the quality of the embankment.

[0080] S5. During the tamping process with the rammer, embankment pressure data and tamping vibration data are obtained, and a tamping operation evaluation is performed based on the embankment pressure data and the tamping vibration data to obtain tamping operation evaluation data to adjust the tamping operation with the rammer.

[0081] Specifically, during the tamping process, the embankment pressure data and tamping vibration data are continuously collected to evaluate the effect of the tamping operation. Based on the real-time data, the system can adjust the control parameters of the tamping hammer to ensure that the embankment tamping meets the design requirements, with good uniformity and moderate pressure.

[0082] Specifically, embankment pressure data analysis: Analyze the embankment pressure data to determine the pressure applied by the rammer to the embankment. If the pressure is too high, the embankment material is over-compressed, and the pressure needs to be reduced appropriately.

[0083] Analysis of compaction vibration data: Analyze the compaction vibration data to determine the vibration frequency and amplitude of the compaction. These data can be used to evaluate the uniformity of compaction. If the compaction is uneven, the position of the rammer or the vibration parameters need to be adjusted. Compaction operation evaluation: Based on the real-time analysis data, the system generates compaction operation evaluation data, including the uniformity of embankment compaction, pressure distribution, vibration effect, etc. These evaluation data can be used to determine whether the compaction meets the design requirements. Adjust the rammer control parameters: If the real-time evaluation data shows that the compaction is uneven or the pressure is too high, the system can adjust the control parameters of the rammer based on the feedback information. For example: Adjustment of rammer position: If the compaction effect of a certain area is not good, the system can adjust the position of the rammer to compact the area more evenly. Vibration parameter adjustment: If the vibration effect is not ideal, the vibration frequency and vibration amplitude can be adjusted to improve the uniformity of compaction.

[0084] In the present invention, by collecting embankment basic data and embankment position data, and performing strong tamping position determination operations according to the embankment compaction path data, accurate tamping position control can be achieved, ensuring that the tamping hammer is tamped at the correct position, thereby improving the tamping efficiency and uniformity. By acquiring embankment pressure data and tamping vibration data, real-time monitoring and evaluation are performed during the tamping process of the tamping hammer, and poor tamping effects or problems can be discovered in time, so that adjustment operations can be performed to improve the tamping quality. The computer control system is used to calculate the tamping hammer control parameters, which reduces human errors and subjective judgments and improves the accuracy and consistency of tamping. It reduces dependence on workers, saves human resources, and reduces the labor intensity of tamping operations. By real-time monitoring and evaluation of tamping operations, and adjusting according to data, the tamping quality and efficiency can be improved, ensuring that the embankment tamping meets the specified standards and requirements. The present invention improves the accuracy, efficiency and quality of tamping operations through intelligent data collection and control, which helps to reduce engineering costs and improve the sustainability of the project.

[0085] Optionally, during the process of embankment compaction, collecting embankment foundation data and embankment position data includes:

[0086] During the embankment compaction process, the embankment is scanned by radar laser through terrain sensors to obtain embankment terrain data;

[0087] Specifically, a terrain sensor, such as a laser radar or LiDAR sensor, is installed on a vehicle or equipment. The laser radar is used to scan the embankment ground and the returned laser beam data is recorded. By synchronizing the sensor position with the position of the rammer, the position of each laser beam and the position of the rammer as well as the scanning time are recorded. The laser beam data is processed to generate embankment terrain data, including road surface height, slope, curvature and other information.

[0088] The rammer position is collected by a position sensor preset on the rammer to obtain rammer position data, wherein the rammer position data includes rammer positioning position data and rammer motion trajectory data.

[0089] Specifically, a position sensor is installed on the rammer, which can be a GPS receiver, an inertial measurement unit (IMU) or other positioning device. The three-dimensional coordinates of the rammer are recorded in real time, including the position information of the X, Y and Z axes. The motion trajectory data of the rammer is recorded, including speed, direction and acceleration. The recording time of the rammer position data is synchronized with the recording time of the terrain sensor for subsequent data association.

[0090] In the present invention, the terrain data of the embankment can be collected with high precision by performing radar laser scanning through the terrain sensor, which helps to more accurately understand the terrain features of the embankment, including elevation, slope, curvature, etc., thereby providing accurate basic data for the compaction operation. The position sensor preset on the rammer can collect the position data and motion trajectory data of the rammer in real time, so that the position and state of the rammer can be monitored in real time, which helps to ensure the accurate position of the rammer during the compaction process and improve the accuracy of the compaction operation. Combined with the embankment terrain data and the rammer position data, accurate compaction position control can be achieved. The positioning position data and motion trajectory data of the rammer can help determine the actual position of the rammer, thereby ensuring that the rammer is compacted at the correct position. The use of automated terrain sensors and position sensors reduces human errors and subjective judgments, and improves the accuracy and consistency of data collection. Through accurate terrain data and rammer position data, the lifting height and tamping position of the rammer can be better controlled, thereby improving the compaction efficiency and uniformity, and reducing waste and unnecessary energy consumption. The data collected in real time allows real-time monitoring and evaluation of compaction operations, and adjustments can be made based on the data to improve compaction quality and efficiency.

[0091] Optionally, the acquiring of embankment compaction path data, and performing embankment compaction position determination operation according to the embankment compaction path data, the embankment foundation data and the embankment position data, thereby obtaining embankment compaction position in-situ data, includes:

[0092] S21, obtaining embankment compaction path data;

[0093] Specifically, sensors on vehicles or equipment are deployed, such as GPS, laser rangefinders, or ground scanners. The position coordinates of the vehicles or equipment are recorded in real time to obtain the current position data of the embankment. According to the requirements of the embankment compaction task, the topographic data of the embankment, such as height, slope, curvature, etc., can be collected.

[0094] S22, performing compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data;

[0095] Specifically, the acquired embankment location data and terrain data are used to construct an embankment terrain model, which is usually expressed in three-dimensional form. Terrain obstacle scanning is performed to obtain the location and specification data of obstacles, which are added to the embankment terrain model to generate a three-dimensional terrain obstacle model containing obstacle information. The three-dimensional terrain obstacle model is gridded to divide the terrain into regular grid cells for path planning. A path planning algorithm (such as the A* algorithm or the Dijkstra algorithm) is used to calculate the optimal compaction path on the gridded terrain to generate compacted embankment planning data.

[0096] S23, predicting the position of the rammer according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data;

[0097] Specifically, the actual position of the rammer is recorded by the position sensor of the rammer. According to the compacted embankment planning data and terrain information, the dynamic prediction of the rammer position is performed, taking into account the speed, acceleration and terrain changes of the rammer. According to the predicted rammer position, the rammer position prediction data is generated, including the X, Y, Z coordinates and motion trajectory information of the rammer.

[0098] S24, performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data;

[0099] Specifically, the compaction efficiency cost and uniformity cost are calculated using the rammer position prediction data, and these two factors can be balanced according to different weights. Using a dynamic programming algorithm, such as a value iteration-based method, the compaction embankment planning data is optimized according to the cost function to maximize the compaction efficiency and uniformity.

[0100] S25. Perform real-time embankment compaction position determination processing according to the optimized planning data to obtain embankment compaction position in-situ data.

[0101] Specifically, during the real-time compaction process, the target position and motion trajectory of the rammer are determined based on the optimized planning data to ensure that the rammer operates in the optimal path and position during the compaction process. The embankment pressure data and compaction vibration data are recorded in real time for compaction operation evaluation. Based on the actual position and motion trajectory of the rammer, the embankment compaction position data is generated, which includes the X, Y, and Z coordinates of the rammer and the information on success or failure of the placement.

[0102] In the present invention, by obtaining the embankment compaction path data and performing compaction route planning according to the embankment basic data, the compaction path can be efficiently determined, obstacles can be avoided, and the embankment terrain characteristics can be considered, which is helpful to optimize the path of the compaction operation and improve the compaction efficiency. The rammer position prediction is performed through the rammer position data and the compaction embankment planning data, and the position of the rammer can be accurately predicted to ensure that the rammer moves correctly on the compaction path. By adopting dynamic planning optimization processing, the operating parameters of the rammer, such as the lifting height and energy demand, can be adjusted in real time according to the real-time rammer position prediction data, which helps to optimize the compaction operation in real time and ensure the best compaction effect. By optimizing the planning data and real-time position determination processing, it can be ensured that the rammer is compacted in the best way on the compaction path, and the compaction quality and uniformity are improved. By optimizing the planning, unnecessary energy consumption can be reduced, the energy utilization of the rammer can be made more efficient, and the energy cost can be reduced. Through real-time monitoring and adjustment, poor compaction effect or problems can be discovered in time, and adjustments can be made according to the data to meet project requirements.

[0103] Optionally, performing compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data includes:

[0104] S221, constructing a three-dimensional model according to the embankment terrain data in the embankment position data to obtain a three-dimensional terrain embankment terrain model;

[0105] Specifically, terrain sensors are used to collect embankment terrain data, including ground height information, slope, curvature, etc. The collected data is used to construct a three-dimensional terrain model of the embankment, which is usually achieved using mathematical modeling and computer graphics technology. The three-dimensional terrain model should include the geometric shape and terrain features of the embankment for subsequent path planning and obstacle scanning.

[0106] S222, turning on the laser radar to scan obstacles and obtaining obstacle data, wherein the obstacle data includes obstacle specification data and obstacle position data;

[0107] Specifically, LiDAR is a sensor that can be used to quickly and accurately scan the surrounding environment. LiDAR sensors are mounted on rammers or equipment to perform real-time environmental scanning. LiDAR sends a laser beam and measures the time and distance it reflects back to determine the location and specification data of surrounding obstacles. Obstacle data includes information such as the size, height, and shape of the obstacle.

[0108] S223, adding the obstacle data to the three-dimensional terrain embankment terrain model, thereby obtaining a three-dimensional terrain obstacle model;

[0109] Specifically, the obstacle data is integrated with the 3D terrain embankment terrain model to represent the location and shape of the obstacle in the model. The geometric shape of the obstacle can be intersected with the terrain model to determine the impact range of the obstacle. Finally, a complete 3D terrain obstacle model containing embankment terrain and obstacle information is generated.

[0110] S224, gridding the three-dimensional terrain obstacle model to obtain a terrain obstacle grid model;

[0111] Specifically, the 3D terrain obstacle model is converted into a discrete grid representation for path planning. The gridding process maps terrain and obstacle information onto a regular grid of cells, usually represented in the form of a 2D or 3D grid. Ensure that the gridded model is accurate enough to accurately consider the effects of terrain and obstacles in path planning.

[0112] S225. Calculate an obstacle avoidance path based on the terrain obstacle grid model to obtain compacted embankment planning data.

[0113] Specifically, an obstacle avoidance path planning algorithm, such as the A algorithm, the Dijkstra algorithm, or the RRT algorithm, is used to calculate the optimal path for compacting the embankment based on a terrain obstacle grid model. Factors such as terrain, obstacles, and rammer size are considered to ensure that the generated path is safe and avoids obstacles. Finally, the compaction embankment planning data is obtained, including the coordinate points, direction, curvature, and other information of the path, which is used to guide the movement and compaction operation of the rammer on the embankment.

[0114] In the present invention, by constructing a three-dimensional model according to the terrain data in the embankment position data, a three-dimensional terrain model of the embankment can be accurately established, and accurate terrain feature information, including elevation, slope, curvature, etc., is provided, providing high-quality basic data for the embankment compaction operation. By turning on the laser radar to scan obstacles and obtain obstacle data, the specification data and location data of the obstacles can be effectively detected and obtained. The obstacle data is added to the three-dimensional terrain embankment terrain model, and a comprehensive three-dimensional model containing terrain and obstacle information can be created, which is helpful for better planning of the compaction path. By gridding the three-dimensional terrain obstacle model and calculating the obstacle avoidance path according to the terrain obstacle gridding model, the compaction path that avoids obstacles can be accurately calculated, which helps to ensure that the compaction operation can safely bypass obstacles when encountering obstacles to avoid potential collisions or damage. Through accurate terrain models, obstacle data and obstacle avoidance path calculations, the compaction path can be optimized, the compaction efficiency and quality can be improved, and the rammer can be compacted along a more reasonable and safe path, reducing energy waste and errors. Through effective obstacle detection and avoidance, the risks in the compaction operation can be reduced, and potential accidents and damage can be reduced.

[0115] Optionally, the performing rammer position prediction according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data includes:

[0116] S231, performing rammer distribution processing according to the compacted embankment planning data and the terrain obstacle grid model to obtain rammer distribution processing data;

[0117] Specifically, the compaction embankment planning data is used to determine the movement path and target points of the rammer. Combined with the terrain obstacle gridding model, obstacles on the path are checked, potential collision risks are identified, and path adjustments are made to avoid collisions. Based on these analyses, rammer distribution processing data is generated, including safe rammer paths and target points.

[0118] S232, obtaining rammer parameter data;

[0119] Specifically, the rammer parameter data includes the physical characteristics of the rammer, such as weight, size, vibration frequency, amplitude, etc.

[0120] S233, performing dynamic simulation according to the rammer parameter data to obtain rammer dynamic simulation data;

[0121] Specifically, the dynamic model of the rammer is established using the rammer parameter data, usually using physical equations and numerical simulation methods. The dynamic simulation considers the physical processes of the rammer such as movement, vibration, and collision to simulate the behavior of the rammer during the compaction process. The simulation can provide time series data on parameters such as the rammer position, speed, amplitude, and the interaction information between the rammer and the embankment.

[0122] S234, performing position prediction based on the rammer dynamics simulation data, the compacted embankment planning data and the rammer distribution processing data through a preset rammer motion time window data to obtain rammer position prediction data.

[0123] Specifically, a rammer position prediction algorithm is developed by combining rammer dynamics simulation data, compaction embankment planning data, and rammer distribution processing data. The position, speed, and amplitude of the rammer are predicted within a preset rammer motion time window. The position prediction data can be used to adjust the control parameters of the rammer to ensure that the rammer performs as planned during the compaction operation and can be adjusted in real time according to actual conditions.

[0124] In the present invention, by performing rammer distribution processing according to compaction embankment planning data and terrain obstacle gridding model, obtaining rammer parameter data, performing dynamic simulation, and combining rammer dynamic simulation data, compaction embankment planning data and rammer distribution processing data, the position of the rammer can be accurately predicted, which helps to ensure that the rammer compacts according to the expected path and improves the accuracy of compaction. Through dynamic simulation, the dynamic characteristics of the rammer, including speed, acceleration, etc., can be considered to more accurately predict the position of the rammer, which helps to avoid the rammer moving too fast or too slow, and improves the uniformity and quality of compaction. Through the rammer distribution processing, the situation of multiple rammers compacting at the same time can be considered, ensuring the coordinated work and uniform distribution between the rammers, which helps to improve the efficiency and consistency of compaction. Through the preset rammer movement time window data, the movement of the rammer within a specific time can be limited, ensuring that the rammer compacts at a reasonable speed, which helps to optimize the movement trajectory of the rammer and reduce energy waste and errors. By accurately predicting the rammer position, taking into account the rammer dynamics and rammer distribution processing, the compaction efficiency and compaction quality can be improved. The rammer can compact along the optimal path and speed, reducing errors and waste.

[0125] Optionally, the performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data includes:

[0126] Calculate compaction efficiency cost according to the rammer position prediction data to obtain compaction efficiency cost data;

[0127] Specifically, the tamping efficiency cost is calculated using the tamping hammer position prediction data. The tamping efficiency can usually be evaluated based on the tamping hammer's working speed, vibration frequency, amplitude and other parameters. Different efficiency calculation methods can be used, for example, the tamping efficiency can be estimated based on the tamping depth and time per unit area. The tamping efficiency cost data reflects the efficiency of the tamping hammer at different positions and time points, and can be used for optimization planning.

[0128] Performing uniformity calculation according to the rammer position prediction data to obtain uniformity data;

[0129] Specifically, the uniformity of the embankment is calculated using the predicted rammer position data. Uniformity generally refers to the consistency of the compaction degree or density of different parts of the embankment. The uniformity calculation can be based on factors such as the vibration mode, working speed and position distribution of the rammers. Uniformity data can be obtained by analyzing the compaction degree distribution of the embankment and used to evaluate the quality of the embankment.

[0130] The compacted embankment planning data is optimized through strategy iteration according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data.

[0131] Specifically, the strategy iteration optimization algorithm is formulated using the compaction efficiency cost data and uniformity data. Different optimization methods can be used, such as genetic algorithm, simulated annealing algorithm, deep reinforcement learning, etc., according to the specific situation. The goal of optimization is to adjust the compaction embankment planning data to find the best balance between efficiency and uniformity. Through iterative optimization, new optimized planning data are obtained, which can be used to guide the actual operation of the rammer to obtain more efficient embankment compaction.

[0132] In the present invention, by calculating the compaction efficiency cost according to the rammer position prediction data, the compaction efficiency of each rammer position can be determined. The strategy iteration optimization process of optimizing the planning data aims to maximize the compaction efficiency and ensure that the rammer at each compaction point can work in the most efficient way, thereby reducing the compaction time and energy consumption. The uniformity calculation is performed through the rammer position prediction data, and the uniformity of the compaction operation can be evaluated. The strategy iteration optimization process aims to improve the uniformity and ensure that the compaction effect of the rammer at different locations is evenly distributed, thereby reducing the unevenness of the embankment. By comprehensively considering the compaction efficiency cost data and the uniformity data, the strategy iteration optimization can find a balance point, which can not only improve the compaction efficiency, but also improve the uniformity, which helps to optimize the compaction planning, so as to compact the embankment in a short time and ensure the quality of compaction. By optimizing the planning data, the energy consumption of the rammer can be reduced, the fuel cost can be reduced, and at the same time, by reducing the compaction time and improving the uniformity, the cost of manual maintenance and repair can also be reduced. By maximizing efficiency and uniformity, the present invention overcomes the limitations of manual experience through numerical processing and ensures the quality of compaction work, while traditional methods usually rely on experience and rules to determine the compaction position and path, so the accuracy is limited.

[0133] Optionally, the calculating the compaction efficiency cost according to the rammer position prediction data to obtain the compaction efficiency cost data includes:

[0134] Performing time efficiency cost calculation according to the rammer position prediction data to obtain time efficiency cost data;

[0135] Specifically, the time efficiency cost of the rammer at different positions and time points is calculated using the rammer position prediction data. The time efficiency cost can usually be calculated based on the time required for the rammer to work at different positions. For example, the compaction time on different paths can be considered when deeply optimizing the planning data. The time efficiency cost data is used to measure the time efficiency of the rammer's work to determine the optimal compaction path.

[0136] Calculate the energy efficiency cost according to the rammer position prediction data to obtain energy efficiency cost data;

[0137] Specifically, the energy efficiency cost of the rammer at different positions and time points is calculated using the rammer position prediction data. The energy efficiency cost can usually be calculated based on the energy consumption required for the rammer to work at different positions, taking into account parameters such as the vibration and compaction force of the rammer. The energy efficiency cost data is used to measure the energy efficiency of the rammer work to determine the optimal compaction path.

[0138] The time efficiency cost data and the energy efficiency cost data are integrated to obtain compaction efficiency cost data.

[0139] Specifically, the time efficiency cost data and energy efficiency cost data are integrated to comprehensively evaluate the compaction efficiency cost at different locations and time points. Different integration methods, such as weighted average, can be used to weigh time and energy efficiency according to specific needs. The compaction efficiency cost data reflects the efficiency of the rammer under different working conditions and can be used for optimization planning to improve the efficiency of compaction operations.

[0140] In the present invention, by separately calculating the time efficiency cost and the energy efficiency cost, the efficiency of the compaction operation can be evaluated more accurately. The time efficiency cost takes into account the time required for the rammer to complete the compaction task, while the energy efficiency cost takes into account the energy consumed by the rammer to complete the compaction task. Taking these two factors into consideration helps to more accurately evaluate the compaction efficiency. By integrating the time efficiency cost data and the energy efficiency cost data, different evaluation indicators can be customized for different projects and needs. For example, for some projects, time efficiency is more critical, while for other projects, energy saving is more important, and the weights can be adjusted to meet different project needs. After the compaction efficiency cost data is generated, it can be used for decision support. The system can formulate an optimization strategy for the compaction operation based on this data to shorten the compaction time or reduce energy consumption as much as possible while ensuring quality, which helps to reduce costs and improve efficiency.

[0141] Optionally, performing strategy iteration optimization on the compacted embankment planning data according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data includes:

[0142] Acquire standard compacted embankment planning data, and construct a reinforcement learning agent according to the standard compacted embankment planning data to obtain a reinforcement learning agent;

[0143] Specifically, standard compaction embankment planning data is collected, which can be based on past compaction operation experience or previous planning results. A reinforcement learning agent is built, usually a deep reinforcement learning model such as a deep Q network (DQN) or a deterministic policy gradient (DDPG) model. The agent will be used to learn and optimize the compaction embankment planning.

[0144] Performing deep deterministic policy gradient calculation through a reinforcement learning agent according to the compaction efficiency cost data and the uniformity data to obtain optimized reward data;

[0145] Specifically, a reinforcement learning agent is used to calculate the deep deterministic policy gradient by inputting the tamping efficiency cost data and uniformity data. The deep deterministic policy gradient is used to guide the agent to select the best tamping path and behavior to maximize the reward function, where the reward function is usually composed of the tamping efficiency and uniformity data. The calculated optimized reward data will be used for iterative optimization in subsequent steps.

[0146] The compacted embankment planning data is iteratively optimized according to the optimization reward data to obtain optimized planning data.

[0147] Specifically, the initial compaction embankment planning data is iteratively optimized using the calculated optimization reward data. Different optimization algorithms, such as gradient descent or genetic algorithms, can be used to update the compaction embankment planning to maximize compaction efficiency and uniformity. The goal of iterative optimization is to find the optimal embankment compaction plan to meet the performance indicators and improve the efficiency and quality of the compaction operation.

[0148] In the present invention, the reinforcement learning agent can perform deep deterministic policy gradient calculation based on the compaction efficiency cost data and uniformity data, so that the optimization process is adaptive, and the system can dynamically adjust the optimization strategy according to different compaction conditions and requirements to maximize efficiency and uniformity. The reinforcement learning agent can make decisions in real-time compaction operations, adjust the operating parameters of the rammer according to the current situation and data, and help to make optimization decisions in time during the compaction process to adapt to the changing engineering conditions. Through deep deterministic policy gradient calculation, the reinforcement learning agent can generate optimization reward data, which reflects the strategy for achieving the best effect in the compaction operation. The iterative optimization process aims to maximize the reward and ensure that the compaction operation reaches the best level in terms of efficiency and uniformity. The automated decision-making ability of the reinforcement learning agent reduces the need for human intervention. The system can adjust the operation of the rammer according to the agent's suggestions without continuous manual supervision and adjustment. Through adaptive optimization and real-time decision support, it helps to improve the sustainability of the project, maximize the compaction efficiency and uniformity, reduce resource waste, and thus reduce the environmental and economic costs of the project.

[0149] Optionally, the acquiring of embankment compaction target data, and calculating rammer control parameters according to the embankment compaction target data and the embankment basic data to obtain rammer control parameter data, includes:

[0150] Obtain target data for embankment compaction;

[0151] Specifically, target data for embankment compaction are collected, which generally include embankment design requirements, foundation bearing capacity requirements, compaction layer thickness requirements and other related requirements.

[0152] Perform compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data;

[0153] Specifically, the compaction depth is calculated using the embankment compaction target data and embankment foundation data, and the depth range that needs to be compacted is determined based on the embankment design requirements and soil mechanical properties. The bearing capacity of the compacted layer is calculated, and the mechanical parameters of the soil, the compaction depth and other related factors are considered, and appropriate soil mechanics calculation methods, such as the bearing capacity calculation formula, are used to estimate the bearing capacity of the compacted layer.

[0154] Calculate the compaction depth: The compaction depth is determined based on the embankment design requirements and the mechanical properties of the soil. Usually, the following factors are considered: Design requirements: First, determine the required compaction depth based on the engineering design requirements, including the overall height of the embankment, the depth requirements of the compaction layer, etc. Soil properties: Understand the mechanical parameters of the soil, such as soil type, density, shear strength, etc. These parameters are obtained through a preset database. Compaction method: Compaction can be performed using static pressure or dynamic compaction methods. Different methods require different compaction depth calculations. Groundwater level: If the groundwater level is high, the impact of groundwater also needs to be considered to determine the compaction depth. The calculation of compaction depth usually follows engineering specifications and soil mechanics principles to ensure that the embankment meets the design requirements in terms of load bearing. Calculation of the bearing capacity of the compaction layer: The bearing capacity calculation of the compaction layer is to ensure that the embankment can withstand the expected load to avoid subsidence or deformation. The calculation is usually based on appropriate soil mechanics methods, such as bearing capacity calculation formulas. The following are bearing capacity calculation methods: Strip bearing capacity calculation: The bearing capacity of the compacted layer is calculated based on the width of the embankment, the compaction depth, and soil strength parameters (such as the angular friction angle and the internal friction angle). Elastic modulus method: This method takes into account the elastic modulus of the soil and the deformation of the embankment. The bearing capacity of the compacted layer can be calculated based on the elastic modulus, the geometric properties of the embankment, and the loading conditions. Finite element analysis: For complex embankment structures and soil conditions, finite element analysis can be used to more accurately calculate the bearing capacity of the compacted layer, taking into account factors such as groundwater level and uneven load distribution. The tamping hammer control parameter calculation is performed based on the compaction depth data and the compaction layer bearing capacity data to obtain the tamping hammer control parameter data.

[0155] Specifically, based on the calculated compaction depth and compaction layer bearing capacity data, the control parameters of the rammer are determined, including compaction energy, compaction frequency, compaction amplitude, etc. The calculation of control parameters can be adjusted according to engineering experience, soil mechanics calculations and design requirements to ensure that the compaction operation meets the requirements of the embankment compaction target data.

[0156] In the present invention, by acquiring the target data of embankment compaction, the system can accurately understand the compaction depth and compaction layer bearing capacity that need to be achieved, thereby ensuring that the compaction operation of the rammer is consistent with the project requirements, helping to avoid excessive or insufficient compaction, and improving the quality and reliability of the project. Based on the compaction depth data and the compaction layer bearing capacity data, the system can calculate the optimal rammer control parameters, including ramming energy, ramming frequency, etc., to ensure that the operation of the rammer can minimize resource waste and energy consumption while achieving the compaction target. Through precise compaction targets and optimal control parameters, the compaction operation of the rammer can be carried out more efficiently, which means that the project completion time will be shortened, the project cost will be reduced, and the efficiency will be improved. Accurate compaction targets and control parameters help ensure that the quality and bearing capacity of the compacted layer meet the design requirements, and help improve the sustainability and long-term stability of the project.

[0157] Optionally, performing compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data includes:

[0158] Perform compaction depth processing according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data;

[0159] Specifically, the depth range required for compaction is determined based on the embankment compaction target data and the embankment foundation data, including the embankment design requirements and soil mechanical properties. The compaction depth data is used to determine the depth range required for construction compaction, usually in meters.

[0160] Extract soil material according to the rammer position data to obtain soil material data;

[0161] Specifically, the position of the rammer in the compaction operation is determined using the rammer position data, and the soil data is acquired by accessing a preset soil material database (such as accessing a local geological bureau public database) based on the GPS positioning position data of the location.

[0162] Perform soil mechanical parameter simulation according to the soil material data to obtain soil mechanical parameter simulation data;

[0163] Specifically, based on the determined soil material data, soil mechanical parameters are simulated or estimated, including mechanical parameters such as soil shear strength, elastic modulus, Poisson's ratio, etc. The soil mechanical parameter simulation can be verified and calibrated using soil mechanical test data.

[0164] The compacted layer bearing capacity is calculated according to the compaction depth data and the soil mechanical parameter simulation data to obtain the compacted layer bearing capacity data.

[0165] Specifically, the bearing capacity of the compacted layer is calculated using the determined compaction depth data and soil mechanics parameter simulation data, including soil mechanics calculation methods, such as bearing capacity calculation formulas. The bearing capacity calculation can help determine whether the embankment compaction operation meets the design requirements and safety standards.

[0166] In the present invention, by performing compaction depth processing according to the embankment strong compaction target data and the embankment foundation data, the system can determine the accurate compaction depth to ensure that the compaction operation is carried out in accordance with the project requirements, which helps to avoid excessive or insufficient compaction and improve the quality of the project. By extracting soil material data based on the rammer position data and simulating soil mechanical parameters, the system takes into account the physical properties of the soil, which helps to better understand the strength and bearing capacity of the soil in different regions, so as to perform compaction according to actual conditions. Based on the compaction depth data and the soil mechanical parameter simulation data, the system can calculate the bearing capacity of the compacted layer, which helps to ensure that the compacted layer has sufficient bearing capacity to meet the design requirements of the project. Accurately calculating the compaction depth and considering the soil material can reduce unnecessary compaction, thereby reducing resource waste and energy consumption. Considering soil mechanical parameters and the bearing capacity of the compacted layer helps to improve the reliability of the project and ensure that the embankment can withstand traffic and loads stably for a long time.

[0167] The purpose of the present invention is to collect embankment basic data and embankment position data, including terrain data and rammer position data, so that the system can realize real-time data collection and processing, so that immediate decisions can be made according to the current situation to ensure that the embankment compaction is carried out according to the design requirements. Using the embankment compaction path data and terrain data, the system performs complex path planning, taking into account the terrain characteristics and the location of obstacles, which helps to avoid collisions with obstacles and ensure that the rammer works in a safe position. When the embankment strong compaction position in-place data is a failure to be in place, the system will perform a strong compaction position warning operation to remind the operator to pay attention, which helps to reduce operating errors and accidents. Once the embankment strong compaction position in-place data is a successful in-place, the system obtains the embankment strong compaction target data, and calculates the rammer control parameters according to the embankment basic data, ensuring that the operation of the rammer is carried out according to the specific engineering requirements to achieve the best compaction effect. During the rammer compaction process, the system not only collects embankment pressure data and compaction vibration data, but also performs complex compaction operation evaluation, which helps to monitor the compaction effect in real time and make adjustments based on the data to ensure the uniformity and quality of the embankment compaction. Through more accurate compaction position and parameter control, the system can reduce unnecessary compaction, thereby reducing resource consumption and environmental impact. The present invention realizes automated and intelligent embankment compaction, reduces the need for human intervention, and improves the efficiency and consistency of operations.

[0168] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0169] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for compacting an embankment, characterized in that: The method comprises: S1. During the embankment compaction process, collecting embankment basic data and embankment position data, wherein the embankment basic data includes embankment topographic data and rammer position data; S2, obtaining embankment compaction path data, and performing embankment compaction position determination operation according to the embankment compaction path data, the embankment basic data and the embankment position data, so as to obtain embankment compaction position in-place data, wherein the embankment compaction position in-place data includes in-place success data and in-place failure data; S3, when it is determined that the embankment strong compaction position in-place data is the in-place failure data, performing a strong compaction position warning operation according to the embankment strong compaction position in-place data; S4, when it is determined that the embankment compaction position in-place data is the successful in-place data, the embankment compaction target data is obtained, and the ramming hammer control parameter data is calculated according to the embankment compaction target data and the embankment basic data to obtain the ramming hammer control parameter data for performing the ramming hammer compaction operation; S5. During the tamping process, embankment pressure data and tamping vibration data are obtained, and tamping operation evaluation is performed based on the embankment pressure data and the tamping vibration data to obtain tamping operation evaluation data, so as to adjust the tamping operation of the tamping hammer; The step of obtaining the embankment compaction path data and determining the embankment compaction position according to the embankment compaction path data, the embankment foundation data and the embankment position data, thereby obtaining the embankment compaction position in-situ data, includes: Obtain embankment compaction path data; Perform compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data; Perform rammer position prediction according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data; Performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data; Perform real-time embankment compaction position determination processing according to the optimized planning data to obtain embankment compaction position in-situ data; The step of performing dynamic planning optimization processing on the compacted embankment planning data according to the rammer position prediction data to obtain optimized planning data includes: Calculate compaction efficiency cost according to the rammer position prediction data to obtain compaction efficiency cost data; Performing uniformity calculation according to the rammer position prediction data to obtain uniformity data; Performing strategy iteration optimization on the compacted embankment planning data according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data; The step of calculating the compaction efficiency cost according to the rammer position prediction data to obtain compaction efficiency cost data includes: Performing time efficiency cost calculation according to the rammer position prediction data to obtain time efficiency cost data; Calculate the energy efficiency cost according to the rammer position prediction data to obtain energy efficiency cost data; Integrate the time efficiency cost data and the energy efficiency cost data to obtain compaction efficiency cost data; The step of performing strategy iteration optimization on the compacted embankment planning data according to the compaction efficiency cost data and the uniformity data to obtain optimized planning data includes: Acquire standard compacted embankment planning data, and construct a reinforcement learning agent according to the standard compacted embankment planning data to obtain a reinforcement learning agent; Performing deep deterministic policy gradient calculation through a reinforcement learning agent according to the compaction efficiency cost data and the uniformity data to obtain optimized reward data; The compacted embankment planning data is iteratively optimized according to the optimization reward data to obtain optimized planning data.

2. The method according to claim 1, characterized in that During the embankment compaction process, the embankment basic data and embankment position data are collected, including: During the embankment compaction process, the embankment is scanned by radar laser through terrain sensors to obtain embankment terrain data; The rammer position is collected by a position sensor preset on the rammer to obtain rammer position data, wherein the rammer position data includes rammer positioning position data and rammer motion trajectory data.

3. The method according to claim 1, characterized in that The step of performing compaction route planning according to the embankment compaction path data, the embankment basic data and the embankment position data to obtain compaction embankment planning data includes: Constructing a three-dimensional model based on the embankment terrain data in the embankment position data to obtain a three-dimensional terrain embankment terrain model; Turning on the laser radar to scan obstacles and obtain obstacle data, wherein the obstacle data includes obstacle specification data and obstacle position data; Adding the obstacle data to the three-dimensional terrain embankment terrain model to obtain a three-dimensional terrain obstacle model; Performing grid processing on the three-dimensional terrain obstacle model to obtain a terrain obstacle grid model; Obstacle avoidance paths are calculated based on the terrain obstacle grid model to obtain compacted embankment planning data.

4. The method according to claim 3, characterized in that The step of performing rammer position prediction according to the rammer position data and the compacted embankment planning data to obtain rammer position prediction data includes: Performing rammer distribution processing according to the compacted embankment planning data and the terrain obstacle grid model to obtain rammer distribution processing data; Obtain rammer parameter data; Performing dynamic simulation according to the rammer parameter data to obtain rammer dynamic simulation data; According to the rammer dynamics simulation data, the compacted embankment planning data and the rammer distribution processing data, the position prediction of the rammer is performed through the preset rammer movement time window data to obtain the rammer position prediction data.

5. The method according to claim 1, characterized in that The step of obtaining the embankment compaction target data and calculating the ramming hammer control parameter according to the embankment compaction target data and the embankment basic data to obtain the ramming hammer control parameter data includes: Obtain target data for embankment compaction; Perform compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data; The rammer control parameter is calculated according to the rammer depth data and the rammer layer bearing capacity data to obtain the rammer control parameter data.

6. The method according to claim 5, characterized in that The step of performing compaction depth processing and compaction layer bearing capacity calculation according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data and compaction layer bearing capacity data includes: Perform compaction depth processing according to the embankment compaction target data and the embankment foundation data to obtain compaction depth data; Extract soil material according to the rammer position data to obtain soil material data; Perform soil mechanical parameter simulation according to the soil material data to obtain soil mechanical parameter simulation data; The compacted layer bearing capacity is calculated according to the compaction depth data and the soil mechanical parameter simulation data to obtain the compacted layer bearing capacity data.

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