A method for continuous detection of compaction quality in filling projects based on momentum

Abstract

A momentum-based continuous monitoring method for compaction quality in filling projects collects vertical vibration signals generated when the vibrating wheel of a compactor interacts with the soil compaction layer. It captures the peak velocity within each cycle, calculates the ultimate compaction momentum of the vibrating wheel within each cycle, and then calculates the momentum-type compaction quality continuous monitoring index, i.e., the momentum compaction value (MCV). Here, k represents the calibration coefficient of the filling material, and represents the average value of the ultimate compaction momentum of the vertical vibration signal over N cycles. Combined with spatial location information, a spatiotemporal compaction quality distribution map of the compaction area is generated. This invention features accuracy, continuity, real-time performance, low discreteness, and low cost. Integration with intelligent decision-making and machine group collaboration functions can achieve intelligent collaborative operation of a compactor group.

Description

Technical Field

[0001] This invention belongs to the field of intelligent construction technology in civil engineering and can be used for online detection of compaction quality. In particular, it relates to a method for continuous detection of compaction quality in filling projects based on momentum. Background Technology

[0002] The compaction quality of filling materials is an important indicator in filling engineering construction. According to different testing methods, testing equipment and technology can be divided into direct methods and indirect methods. Direct methods are based on sampling inspection, which can only reflect the compaction status of certain points and cannot reflect the compaction quality of the entire working face. Moreover, it is a destructive testing method.

[0003] Indirect methods indirectly reflect the filling quality of fill materials, overcoming the shortcomings of direct methods, and are currently a major research direction. Typical examples include continuous compaction control technology, roller-integrated compaction monitoring technology, and intelligent compaction technology. Correspondingly, the proposed continuous compaction quality detection indicators mainly include CMV, MDP, and K. s E vib And THD, etc. However, indirect methods have low detection accuracy and expensive detection equipment.

[0004] Patent CN115162309A proposes an integrated compaction momentum detection system and method for contact roller compactors. This system utilizes vibration sensors to acquire vertical vibration signals generated when the roller's vibrating wheel interacts with the soil compaction layer. The vertical vibration signal is filtered and its peak velocity is captured to calculate the ultimate compaction momentum of the vibrating wheel, thereby calculating the momentum compaction value. This momentum compaction value reflects the real-time compaction degree of the current filling compaction area. Combined with the roller's location, a spatiotemporal compaction degree index distribution map of the compaction area during on-site filling compaction operations is generated. This system is applicable to both continuous compaction quality detection of different types of fill materials and continuous compaction quality control of filling projects.

[0005] However, in this patent, its momentum compaction value I p The peak value P of the vertical vibration momentum within a single cycle c The qualified compaction vibration momentum P determined by the rolling test q The ratio is obtained as follows:

[0006] However, for a specific type of fill material, on the one hand, P q It is a fixed value, therefore the momentum compaction value I p The size is determined solely by P c It is clear that, due to the complexity and anisotropy of soil properties, using the peak value of a single cycle to characterize soil compaction in the same strip will inevitably result in a relatively large error.

[0007] On the other hand, P qIt requires a large number of on-site compaction tests to determine, which is obviously not conducive to improving the efficiency of on-site compaction operations.

[0008] In addition, calculate I p The lack of calibration for the type of fill material at the time will also affect I. p The characterization effect of compaction quality of different types of fill materials.

[0009] Therefore, it is necessary to further improve the continuous testing method for compaction quality in filling projects, addressing the aforementioned shortcomings of this patent. Summary of the Invention

[0010] In order to overcome the shortcomings of the existing technology, especially the patent CN115162309A, the purpose of this invention is to provide a momentum-based continuous detection method for compaction quality of filling projects, so as to further improve the characterization accuracy and characterization effect of momentum-based continuous detection index for compaction quality.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] A method for continuous detection of compaction quality in filling projects based on momentum includes the following steps:

[0013] Step 1: Collect the vertical vibration signal generated when the vibratory wheel of the roller interacts with the soil compaction layer, and at the same time collect the spatial position information related to the compaction position;

[0014] Step 2: For the vertical vibration signal, capture the peak velocity within each cycle, calculate the ultimate compaction momentum of the vibrating wheel within each cycle, and then calculate the momentum-type compaction quality continuous detection index, i.e., the momentum compaction value (MCV). The formula for calculating MCV is as follows:

[0015]

[0016] Where k represents the calibration coefficient of the filling material; This represents the average value of the ultimate compaction momentum of the vertical vibration signal over N cycles;

[0017]

[0018]

[0019] Let P(t) represent the limiting compaction momentum of the i-th cycle, which is obtained from the vertical vibration momentum P(t), where P(t) = mv(t), i = 1, 2, ..., N, m is the mass of the vibrating wheel, and v(t) is the vertical vibration velocity of the vibrating wheel. This represents the maximum vertical velocity of the vibrating wheel during the contact process between the vibrating wheel and the soil compaction layer in the i-th cycle, i.e., the peak value of v(t) in a single cycle;

[0020] Step 3: Combine the spatial location information to generate a spatiotemporal compaction quality distribution map of the rolling area.

[0021] Compared with existing equipment and technologies for testing the compaction quality of filling projects, this invention features contact-based, continuous, real-time, accurate, low-discrepancy, and low-cost operation. It is applicable to continuous compaction quality testing and control of different types of filling materials, and can perform real-time testing of the compaction quality of the entire working area. By controlling the compaction parameters of the roller's vibrating wheel through feedback, intelligent continuous compaction can be easily achieved. Information related to the compaction process can be stored and retrieved in real-time for use by on-site roller operators and supervisors, and can also be integrated with a remote monitoring center for use by remote management personnel and owners. This not only improves construction efficiency but also ensures the economic efficiency and refined management of the project.

[0022] 1) According to the momentum compaction value (MCV) calculation formula of the present invention, for a certain fill material, the momentum compaction value (MCV) is determined by the average value of the ultimate compaction momentum over N cycles and the calibration coefficient. Compared with patent CN115162309A, it can effectively improve the characterization accuracy and characterization effect of the continuous detection index of momentum compaction quality.

[0023] 2) According to the momentum compaction value (MCV) calculation formula of the present invention, the calculation of momentum compaction value (MCV) is calibrated for the type of fill material. Compared with patent CN115162309A, it will improve the characterization effect of the continuous detection index of momentum compaction quality. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the hardware architecture of the present invention.

[0025] Figure 2 This is a schematic diagram showing the relationship between MCV and the number of rolling passes in this invention.

[0026] Figure 3 This is a schematic diagram illustrating the relationship between MCV and compaction degree in this invention.

[0027] Figure 4 In the comparison I p A diagram showing the relationship between the number of compaction passes and the number of rolling passes.

[0028] Figure 5 In the comparison I p A schematic diagram showing the correlation between compaction degree and density. Detailed Implementation

[0029] The present invention will be further described in detail below with reference to the principles and embodiments of the detection system and method. The following embodiments and figures are used to illustrate the specific implementation process of the momentum-based continuous detection method for compaction quality of filling projects proposed in this invention, but are not intended to limit the scope of the invention.

[0030] This invention relates to a momentum-based method for continuous monitoring of compaction quality in embankment construction projects. It is used with a compaction roller for on-site compaction operations, but can also be applied to other similar equipment. The steps are as follows:

[0031] Step 1: Collect the vertical vibration signal generated when the vibratory wheel of the roller interacts with the soil compaction layer, and at the same time collect the spatial position information related to the compaction position;

[0032] Step 2: For the vertical vibration signal, capture the peak velocity in each cycle, calculate the ultimate compaction momentum of the vibrating wheel in each cycle, and then calculate the momentum-type compaction quality continuous detection index, namely the momentum compaction value MCV.

[0033] Step 3: Combine spatial location information to generate a spatiotemporal compaction quality distribution map of the rolling area.

[0034] Similar to or the same as patent CN115162309A, such as Figure 1 As shown in the figure, the dashed box indicates the detection system 10, which mainly includes a vibration sensor 1, a data acquisition instrument 2, an industrial control computer 3, a GPS / BDS receiver 4, an onboard display 5, and a power supply system 9. The figure also shows a vibrating wheel 6, a frame 7, and a compactor 8. When the compactor compacts the fill layer, this invention utilizes the vibration sensor 1 to collect the vertical vibration signal generated by the interaction between the compactor's vibrating wheel and the fill layer in real time. The GPS / BDS receiver 4 collects the current spatial position information of the compactor in real time, and performs signal conditioning on these signals. The signals are then transmitted via data cable to the industrial control computer 3 for further analysis and processing to obtain the real-time momentum-type continuous compaction quality detection index, namely the momentum compaction value (MCV). This value, along with the spatial position information, is displayed on the onboard display 5 as a spatiotemporal compaction quality distribution map of the compaction area.

[0035] Among them, vibration sensor 1 is used to acquire the vertical vibration signal generated when the vibrating wheel of the roller interacts with the filling layer during the compaction of the filling layer. The vibration signal can be divided into vibration velocity signal and vibration acceleration signal according to the type of vibration sensor. Vibration sensor (1) is fixed on a fixed support, which is directly connected to the vibrating wheel of the roller.

[0036] GPS / BDS receiver 4 is used to provide spatial signals related to the compaction location.

[0037] The data acquisition instrument 2 is used to synchronously acquire the vertical vibration signal of the vibrating wheel and the spatial signal related to the rolling position. It can also process the acquired signals and then transmit them to the industrial control computer 3 in real time.

[0038] The industrial control computer 3 filters and captures the peak velocity of the vertical vibration signal from the vibratory roller to obtain the ultimate compaction momentum of the vibratory roller, and then calculates the momentum-type compaction quality continuous detection index, namely the momentum compaction value (MCV). Combined with the current position coordinates of the roller, it generates and displays a spatiotemporal compaction quality distribution map of the compaction area during on-site filling and compaction operations on the onboard display 5. It can also store the momentum compaction value (MCV) and the spatial position information of the roller in real time.

[0039] The onboard display 5 displays a real-time spatiotemporal compaction quality distribution map of the compaction area during the on-site filling and compaction operation, showing areas with qualified and unqualified compaction quality, as well as other relevant information on site, such as vehicle speed, compaction trajectory, number of compaction passes, fill layer elevation, and fill layer thickness.

[0040] Power supply system 9 supplies power to all electrical units in the entire system.

[0041] For example, vibration sensor 1 and data acquisition device 2 communicate via wired communication for information transmission and power supply. The signal conditioning and processing performed by data acquisition device 2 includes amplification and filtering to convert the signal into a standard signal that the industrial control computer 3 can recognize, analyze, and store. Data acquisition device 2 and industrial control computer 3 are connected via wired communication. Data acquisition device 2 transmits the synchronously acquired spatial position signal of the compactor and the vertical vibration signal of the vibrating wheel to industrial control computer 3. Industrial control computer 3 also acquires and displays on-site information including vehicle speed, number of compaction passes, compaction trajectory, and current compaction layer elevation.

[0042] The steps for analyzing and processing the vertical vibration signals of the rollers of the rolling mill using a 3-pair industrial control computer are as follows:

[0043] First, the collected raw sound signal is filtered to eliminate outliers and remove vibration signal data for the time required for the roller to start and stop. Then, the filtered signal is divided into n equal parts according to the set value. The peak speed of each period is captured for the equally divided signal, and the peak speed data of each period can be stored in a designated location on the industrial control computer for real-time data access and online monitoring and analysis.

[0044] The main improvement of this invention lies in the use of the momentum compaction value MCV instead of the momentum compaction value I in patent CN115162309A. p In the industrial control computer 3, the calculation formula for MCV of this invention is as follows:

[0045]

[0046] Wherein, k represents the calibration coefficient of the filling material, which is a constant coefficient. It is determined according to the type of filling material in the actual project. It can usually be determined according to the particle size distribution range of the filling material. A smaller value is taken for a smaller particle size distribution range, and a larger value is taken for a larger particle size distribution range. It can also be fine-tuned based on actual measurement data. This represents the average value of the ultimate compaction momentum of the vertical vibration signal over N cycles;

[0047]

[0048]

[0049] Let P(t) represent the limiting compaction momentum of the i-th cycle, which is obtained from the vertical vibration momentum P(t), where P(t) = mv(t), i = 1, 2, ..., N, m is the mass of the vibrating wheel, and v(t) is the vertical vibration velocity of the vibrating wheel. V(t) represents the maximum vertical velocity of the vibratory wheel during the contact process between the vibratory wheel and the soil compaction layer in the i-th cycle, i.e., the peak value of v(t) in a single cycle.

[0050] When the MCV value is higher than the corresponding value of the MCV measured by the conventional point sampling method, it can be determined that the compaction quality of the current filling area meets the standard; otherwise, it is determined to be unqualified. Unqualified areas need to be promptly reported to the driver for supplementary rolling operations or auxiliary operations to improve the compaction quality.

[0051] The vertical vibration signal collected by this invention is a periodic sine wave signal. Under the same working conditions and the same number of compaction passes, the amplitude of each cycle is slightly different, but the magnitude is similar.

[0052] This invention can capture the peak velocity of a vertical vibration signal in each cycle based on an adaptive peak detection algorithm. The specific process is as follows:

[0053] 1) If the vibration sensor (1) fixed on the fixed support is a velocity sensor, then based on the adaptive peak detection algorithm, according to the difference in velocity amplitude corresponding to the three states of the vibrating wheel being stationary, statically rolling, and vibrating, the motion state is determined by the corresponding threshold interval, and then the size of the corresponding neighborhood window is determined according to the motion state, and a comparison is made within the neighborhood to realize adaptive velocity peak capture under different motion states. The steps of the adaptive peak detection algorithm are as follows:

[0054] (1) The vertical velocity signal is collected in real time and then filtered. Then, the potential peak value of the velocity is found by using a sliding window. The velocity threshold is limited for the initial judgment. The state is divided into two categories: idle and moving. The peak value is less than A m / s and is not counted. The peak value is in the interval [B m / s, C m / s] and is judged as static rolling. The potential peak value is greater than D m / s and is judged as vibratory rolling. The next judgment is then performed. The sliding window size is M cycles, where M is an even number.

[0055] (2) Calculate the time difference between the potential peak and the previous peak, and make a secondary judgment using different time thresholds. If the time difference meets the time threshold [T1 s, T2 s], it is a normal state; otherwise, it is an abnormal state and no counting is performed.

[0056] (3) Change the size of the neighborhood window according to the motion state, compare the potential peak with the neighborhood period three times to remove the false peak. If the potential peak is the maximum value in the neighborhood, then count one step; otherwise, do not count steps. Among them, N, A, B, C, D, T1 and T2 are all determined according to the actual data.

[0057] 2) If the vibration sensor (1) fixed on the fixed support is an acceleration sensor, the real-time acceleration signal is first filtered, then the velocity signal value is obtained by integral transformation, and then the velocity peak value of each cycle is captured based on the above adaptive peak detection algorithm.

[0058] The GPS / BDS receiver 4, together with the RTK-GPS / BDS base station, the field rover, and the satellite, provides spatial information related to the location of the loading equipment. The RTK-GPS / BDS receiver, satellite, and GPS / BDS base station work together through a differential algorithm to achieve precise positioning of the compactor. The differential algorithm can be executed in the industrial control computer 3.

[0059] Data acquisition unit 2 receives the spatial position signal of the compactor provided by GPS / BDS receiver 4. Based on the position information and momentum compaction value (MCV), it generates a distribution map of compaction quality indicators in the compaction area. Further, it combines the compaction trajectory, vehicle speed, and number of compaction passes to generate 2D and 3D cloud maps. The specific method is as follows:

[0060] 1) The width of the vibrating roller of the roller and the arc curve or straight line traveling along the direction of travel within a set time period Δt are respectively used as the horizontal and vertical axes of the momentum compaction value (MCV) distribution map, with meters as the unit. The area formed by the horizontal and vertical axes is the compaction area formed by the vibrating roller of the roller within the set time period Δt.

[0061] 2) Based on the current position information of the roller, as well as the speed and number of rolling passes, the corresponding momentum compaction value (MCV) is filled into the 2D and 3D distribution maps in real time. The filled area is the rolling area formed by the roller vibrating wheel within a set time period Δt. The set time period Δt can be determined by combining the roller speed, the on-site rolling conditions, and the detection accuracy.

[0062] 3) After the roller completes one pass of the current rolling area, the 2D and 3D distribution maps of the momentum compaction value (MCV) of the rolling area can be formed and displayed in the form of a spatial plane or curved surface.

[0063] The specific display methods for 2D and 3D cloud maps are as follows:

[0064] 1) The momentum compaction value (MCV) is graded at certain intervals. The specific interval is selected according to the actual fill material type and the detection accuracy. The momentum compaction value (MCV) graded from large to small is mapped to the color from light to dark. The mapped color is then filled into the compaction area formed by the vibrating wheel of the roller within a set time period Δt to form a 2D cloud map.

[0065] 2) Based on the 2D cloud map, the momentum compaction value (MCV) is mapped to the Z-axis of the three-dimensional coordinate system according to its numerical value, and together with the horizontal and vertical axes, they form a 3D cloud map.

[0066] This invention can further achieve feedback control of on-site compaction operations through the feedback control module integrated in the industrial control computer 3. The feedback control module provides feedback information on the compaction operation to the compaction machine operator and on-site supervisor, enabling relevant personnel to take effective measures to improve the compaction quality of the filling project. The onboard display 5 can also use the built-in feedback control module to provide feedback information on the compaction operation to the compaction machine operator and on-site supervisor, enabling relevant personnel to take effective measures to improve the compaction quality of the filling layer. The built-in feedback control module can be integrated with the intelligent decision-making model, either alone or simultaneously, to form an intelligent compaction system; furthermore, it can be integrated with multi-machine collaborative operation control methods to achieve intelligent compaction machine group collaborative and efficient compaction operations through the built-in feedback control module.

[0067] Furthermore, based on the real-time measured MCV value within the compaction area, and combined with interpolation algorithms (such as Kriging interpolation), a compaction quality assessment model for the entire working face of the compaction area based on MCV and Kriging interpolation algorithms can be constructed, thereby achieving efficient and comprehensive assessment of the compaction quality of the entire compaction area during compaction construction.

[0068] Furthermore, the MCV value and the operating parameters of the compactor can be used as inputs to the intelligent decision-making model. The optimization of the compaction process of the fill material is treated as a multi-stage decision problem. From an overall optimal perspective, the compaction process is dynamically optimized globally. The optimization objective is to minimize the time required for a unit length layer to reach the target compaction quality from the current compaction quality. The output is the optimal compaction scheme (including the number of additional compaction passes and the vibration compaction parameters for each pass). The optimal compaction scheme is then fed back to the unmanned compaction system in real time for execution. Based on this, a closed-loop intelligent compaction system is formed through perception, decision-making, and control.

[0069] Furthermore, the multi-machine collaborative operation control method can be integrated to achieve intelligent and efficient collaborative compaction operations of a group of compactors through a built-in feedback control module.

[0070] This invention relates to the detection and control of compaction quality in filling projects based on the momentum method. It utilizes a newly designed vibration signal acquisition and processing system to achieve its function, and the compaction quality of the filling project is detected according to the following steps:

[0071] According to specifications, standards, and on-site construction requirements, sampling points were taken and tested within the compaction area using conventional testing methods. The actual compaction quality of the compaction area was measured and compared with the momentum compaction value (MCV) calculated by the system of this invention, showing a close correlation. Specifically, a comparative analysis of the detection accuracy and characterization effect of the testing method proposed in patent CN115162309A and this invention is conducted with examples:

[0072] The original data comes from a highway construction project underway in Hengyang City, Hunan Province. A section of the roadbed under construction was selected as the test site. The filling material was low-liquid-limit silty soil. A 16-meter-long strip was demarcated from this site as a comparative verification strip, with an effective compaction length of 10 meters. The working conditions were high speed, high frequency, low amplitude, and the number of compaction passes were 2 static compactions and 8 vibratory compactions. The momentum compaction value I within the effective compaction length was calculated according to the calculation formulas of patent CN115162309A and this invention. p The momentum compaction value (MCV) was calculated, and correlation analysis was performed with the number of compaction passes and the degree of compaction, respectively. The results are as follows: Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, it can be seen that the MCV index of this invention is significantly higher than that of patent CN115162309A. p The indicators offer better detection accuracy and characterization results.

[0073] Therefore, the contact-type roller compaction momentum detection technology proposed in this invention has the characteristics of contact, continuous, real-time, accurate, low dispersion, and low cost. It is very suitable for continuous compaction quality detection and control in filling projects. By controlling the vibration frequency and amplitude of the roller's vibrating wheel through feedback, intelligent continuous compaction function can be easily achieved. This facilitates timely problem detection by filling compaction operators and on-site supervisors, and is also easy to integrate with a remote monitoring center for remote management personnel or owners to monitor and manage on-site filling construction operations. It is applicable to the compaction quality detection and control of various types of filling materials simultaneously, and features low data dispersion, high detection accuracy, and high economy. This invention can be used for continuous real-time detection and control of compaction quality in filling projects without damaging the filling body. This improves the quality and efficiency of roller compaction operations, ultimately ensuring the filling quality and construction efficiency of projects such as highways, railways, airports, and dams.

Claims

1. A method for continuously detecting compaction quality of a momentum-based filling work, characterized by, The method comprises the following steps: Step 1: Collect the vertical vibration signal generated when the vibration wheel of the roller compactor interacts with the soil compaction layer, and collect the spatial position information related to the compaction position; wherein the vertical vibration signal is a vertical acceleration signal or a vertical velocity signal, and the vertical vibration signal is collected by a vibration sensor (1) fixed on a fixed support, and the fixed support is directly connected with the vibration wheel of the roller compactor; Step 2: Capture the speed peak value in each cycle of the vertical vibration signal by the following method: 1) If the vibration sensor (1) fixed on the fixed support is a velocity sensor, then based on the adaptive wave peak detection algorithm, the motion state is determined by using the corresponding threshold interval according to the difference of the velocity amplitude corresponding to the three states of vibration wheel static, static rolling and vibration rolling, and then the corresponding neighborhood window size is determined according to the motion state, the neighborhood comparison is carried out, and the adaptive speed peak capture under different motion states is realized; 2) If the vibration sensor (1) fixed on the fixed support is an acceleration sensor, then the real-time collected acceleration signal is filtered first, and then the velocity signal value is obtained by integral transformation, and then the adaptive wave peak detection algorithm is used to capture the speed peak value in each cycle. The steps of the adaptive wave peak detection algorithm are as follows: (1) Real-time acquisition of vertical velocity signal, then filtering processing, then use sliding window to find potential peak value of velocity, limit velocity threshold to make initial judgment, divide state into two categories: idle and movement, peak value less than A m / s is idle state, do not count; peak value in interval[ B m / s, C m / s] is judged as static rolling; potential peak value greater than D m / s is judged as vibrating rolling, proceed to next step; wherein the size of sliding window is M cycles, M is even; (2) Calculate the potential peak value and the time difference of the previous peak value, and make a secondary judgment using different time thresholds. If the time difference meets the time threshold T 1 s, T 2 s] is normal, otherwise it is abnormal and is not counted. (3) According to the change of motion state, the size of neighborhood window is changed, and the potential peak is compared with the neighborhood period for three times to remove the false peak, and if the potential peak is the maximum value in the neighborhood, one step is recorded, otherwise, no step is recorded. N 、 A 、 B 、 C 、 D 、 T 1 and T 2 are determined according to actual data; Calculate the limit compaction momentum of the vibration wheel in each cycle, and then calculate the momentum compaction quality continuous detection index, i.e. momentum compaction value MCV; the calculation formula of MCV is as follows: wherein, represents a calibration coefficient of the filling material, determined according to a particle size distribution range of the filling material, and the smaller the particle size distribution range is, the smaller the value is, and the larger the particle size distribution range is, the larger the value is; represents an average value of the limit compaction momentum of the vertical vibration signal in a cycle. represents the limit compaction momentum of the nth cycle, and the vertical vibration momentum of the vibrating wheel is obtained as , , , , , , , , the maximum vertical speed of the vibrating wheel during the process of contacting the soil compaction layer, i.e. the peak value in a single cycle. Step 3: Generate the space-time compaction quality distribution map of the compaction area in combination with the spatial position information.

2. The method according to claim 1, wherein When the MCV value is higher than the measurement result of the conventional point sampling method and the corresponding value of MCV, it is determined that the compaction quality of the current filling area meets the standard, otherwise it is determined as unqualified, and the unqualified area is fed back to the driver in time for supplementary compaction operation or auxiliary improvement of compaction quality operation.

3. The method according to claim 1, wherein Taking the momentum compaction value MCV and the working parameters of the roller compactor as the input of the intelligent decision model, taking the compaction process optimization of the filling material as a multi-stage decision problem, and from the overall optimal angle, the global dynamic optimization of the compaction process of the filling material is carried out, the optimization target is to make the unit length paving layer from the current compaction quality compaction to the target compaction quality in the shortest time, and the output is the optimal compaction scheme, including the number of compaction passes still needed and the vibration compaction parameters of each pass, and the optimal compaction scheme is fed back to the unmanned compaction system in real time to perform the compaction operation, thereby forming a closed-loop intelligent compaction system through sensing, decision-making and control.

4. The method according to claim 1, wherein The multi-machine collaborative operation control method realizes intelligent compaction machine group collaborative efficient compaction operation through the built-in feedback control module.

5. The method according to claim 1, wherein According to the space-time compaction quality distribution map of the compaction area, the 2D and 3D cloud maps are formed in combination with the compaction trajectory, driving speed and compaction passes.

6. The method according to claim 5, wherein The specific method for forming 2D and 3D cloud maps is as follows: 1) the width of the roller compactor vibration wheel and the arc curve or straight line along the driving direction within the set time period traveled by the vibration wheel are taken as the horizontal and vertical axes, respectively, and the area formed by the horizontal and vertical axes is the set time period in which the vibration wheel travels; 2) According to the current position information of the roller compactor and the driving speed and compaction passes, the corresponding momentum compaction value MCV is filled into the 2D and 3D distribution maps in real time, and the filling area is the compaction area; 3) When the roller compactor completes one pass of compaction in the whole compaction area, the 2D and 3D distribution maps of the momentum compaction value MCV of the compaction area can be formed to be displayed in the form of spatial plane or curved surface.

Images