Unmanned construction machine group cooperative regulation and control method and system based on volume-mechanics detection

Abstract

The invention relates to an unmanned construction machine group cooperative regulation and control method and system based on volume-mechanical detection. The method comprises the following steps: establishing a compactness-torsional shear strength qualified interval database; correcting the qualified interval database through the compactness correction model to obtain a corrected qualified interval database; before construction, control parameters of the unmanned road roller and the unmanned paver in each compaction stage are calibrated based on the corrected qualified interval database, and standard construction parameters are obtained; in the construction process, multi-source sensing data of a construction layer is collected in real time, the multi-source sensing data and the corrected qualified interval database are matched and compared, and a compaction quality judgment result is obtained; and adjusting standard construction parameters according to the compaction quality judgment result so as to carry out self-adaptive control on the unmanned road roller and the unmanned paver. The uniformity of construction quality and the stability of a pavement structure are improved, and meanwhile collaborative operation and closed-loop regulation and control of the unmanned construction machine group are achieved.

Description

Technical Field

[0001] This invention belongs to the field of asphalt pavement construction control technology, and particularly relates to a collaborative control method and system for unmanned construction machine fleets based on volumetric mechanical testing. Background Technology

[0002] Traditional asphalt pavement compaction typically relies on manually operated rollers to compact freshly laid, hot asphalt mixtures at temperatures generally between 150 and 180°C. Under these conditions, operators not only endure prolonged exposure to high temperatures but are also exposed to harmful substances such as asphalt volatiles and dust, posing significant occupational health risks. Furthermore, the quality of manually operated rollers is affected by subjective factors such as operating habits, mental state, and fatigue, easily leading to uneven compaction and insufficient control precision. In addition, manual construction is highly dependent on external conditions such as weather and lighting, making it difficult to achieve efficient, all-weather operation. In contrast, unmanned rollers and pavers can operate continuously in extreme environments such as high temperature, high humidity, and high altitude, effectively reducing manpower and safety risks, and enabling 24-hour continuous construction, significantly improving efficiency. Therefore, developing unmanned aerial vehicle (UAV) swarm collaborative operation and intelligent control technology for asphalt pavement construction has significant engineering value and widespread application implications.

[0003] However, existing dynamic control strategies for unmanned road rollers largely rely on construction experience or manually set initial process parameters, such as pre-fixed compaction speed, number of compaction passes, vibration frequency, and vibration amplitude, and are adjusted during operation through simple threshold comparisons. This control mode ignores the real-time changes in material properties and structural conditions during construction, resulting in delayed control response and insufficient precision. In complex and variable construction site environments, this simplified control logic can easily lead to problems such as insufficient compaction, over-compaction, or uneven compaction, thereby affecting the service performance and lifespan of the pavement structure.

[0004] Regarding the control of compaction quality, some studies have attempted to introduce compaction degree as a volumetric index into unmanned road roller (UAV) construction. By comparing the measured compaction degree with the standard compaction degree, rolling process parameters can be adjusted. However, due to the uneven spatial distribution of aggregates, asphalt, and mineral powder in asphalt mixtures during compaction, the mechanical properties vary significantly under the same compaction degree. A single volumetric index cannot comprehensively reflect the skeleton stability, shear resistance, and durability of the mixture. Therefore, traditional compaction degree-based control methods still suffer from insufficient scientific rigor and limited control effects in UAV construction.

[0005] Currently, the control methods for unmanned road rollers are mostly based on construction experience or preset thresholds for parameter setting. They lack comprehensive analysis of the dynamic changes in the volumetric state and mechanical properties of asphalt mixtures during construction, making it difficult to achieve scientific optimization and adaptive adjustment of operating parameters. This results in deficiencies in accuracy and on-site adaptability. Compaction quality evaluation systems generally focus on single volumetric indicators such as compaction degree, failing to comprehensively reflect the stability and shear resistance of the mixture's skeleton structure. This easily leads to quality defects such as insufficient compaction, over-compaction, or uneven compaction. Furthermore, existing unmanned construction systems still have limitations in real-time coordination and linkage capabilities for swarm operations, making it difficult to achieve accurate judgment and closed-loop control of compaction quality throughout the entire construction process. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for collaborative control of unmanned construction machine fleets based on volumetric mechanical detection.

[0007] The technical solution adopted in this invention is:

[0008] Firstly, a collaborative control method for unmanned construction vehicle fleets based on volumetric mechanical detection is provided, including: A database of acceptable ranges for compaction degree and torsional shear strength was established by testing asphalt mixture specimens. The compaction degree of asphalt mixture specimens was tested, a compaction degree correction model was established, and the qualified interval database was obtained by correcting the qualified interval database through the compaction degree correction model. Before construction, the control parameters of unmanned road rollers and unmanned pavers at each compaction stage were calibrated based on the corrected qualified interval database to obtain standard construction parameters; During construction, multi-source sensing data of the construction layer is collected in real time, and the multi-source sensing data is matched and compared with the corrected qualified interval database to obtain the compaction quality judgment result. The standard construction parameters are adjusted based on the compaction quality assessment results, thereby enabling adaptive control of unmanned road rollers and unmanned pavers.

[0009] Furthermore, by testing asphalt mixture specimens, a database of acceptable ranges for compaction degree and torsional shear strength was established, including: Under laboratory conditions, asphalt mixture specimens with different compaction states were prepared according to the design mix proportions. Multiple compaction gradients were set, and torsional shear strength tests were conducted under corresponding temperature conditions to establish the evolution relationship between compaction degree and torsional shear strength. For each experimental compaction degree K i and its corresponding experimental temperature T i Determine the corresponding experimental torsional shear strength τ i The qualified range, the expression for the qualified range is: ; in, For τ i The average value, σ i For τ i The standard deviation, and τ i coefficient of variation C v Satisfy C v The requirement is ≤0.1; Establish a database of acceptable intervals for compaction degree and torsional shear strength based on the acceptable interval expression.

[0010] Furthermore, compaction tests were conducted on asphalt mixture specimens, and a compaction correction model was established. The corrected acceptable interval database was obtained by correcting the database using the compaction correction model, including: The compaction degree of asphalt mixture specimens under different compaction conditions was tested using a nucleus-free density meter to obtain the original compaction degree K. w ; According to K w With K i The measurement error correction amount ΔK is calculated, and the formula for ΔK is: ; in, For K w The average value; Based on ΔK and K w A compaction degree correction model is constructed, and its expression is as follows: ; Among them, K j For K w Corrected compaction degree; The corrected qualified interval database is obtained by correcting the qualified interval database using the compaction correction model.

[0011] Furthermore, prior to construction, the control parameters of the unmanned roller and unmanned paver at each compaction stage were calibrated based on the corrected qualified interval database to obtain standard construction parameters, including: Before construction, based on the corrected qualified section database, the test section was simulated under ideal construction conditions. The unmanned paver and unmanned roller were controlled to complete the operation of each compaction stage in sequence according to the preset standard operating parameters. The compaction stage includes the initial compaction stage, the intermediate compaction stage and the final compaction stage. At each compaction stage, the ideal compaction degree and ideal torsional shear strength of the construction layer are simultaneously collected using a shear characteristic detection device with a coreless density meter and an integrated temperature sensing module. In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is calibrated by combining the real-time evolution characteristics of ideal compaction degree and ideal torsional shear strength.s1 Standard vibration frequency f s1 Standard amplitude A s1 and standard number of compaction passes N s1 ; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is determined based on the changing trends of ideal compaction degree and ideal torsional shear strength. s2 Compared with the standard number of compaction passes N s2 ; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is determined based on the range where the ideal torsional shear strength tends to stabilize. s3 and standard number of compaction passes N s3 ; Based on the standard compaction speed and standard number of passes of unmanned steel wheel rollers and unmanned rubber-tired rollers at each compaction stage, the standard paving speed V of the unmanned paver is determined by reverse calculation. p Standard construction parameters are obtained by combining the parameters of unmanned steel wheel rollers, unmanned rubber-tired rollers, and unmanned pavers.

[0012] Furthermore, during construction, multi-source sensing data of the construction layer is collected in real time. This multi-source sensing data is then matched and compared with a corrected qualified interval database to obtain the compaction quality judgment result, including: During the construction process, after the unmanned steel wheel roller and the unmanned rubber-tired roller complete the compaction operation, the measured compaction degree is collected at the same detection position of the construction layer by a nucleusless density meter, the measured temperature and measured torsional shear strength are collected by a torsional shear strength testing device, and the positioning information and time information are collected by a positioning and timing device. Based on the spatiotemporal registration algorithm, the measured compaction degree, measured temperature and measured torsional shear strength are time-series aligned and spatially matched according to the location information and time information, and fused into a unified compaction state data packet; The compaction quality judgment result is obtained by matching and comparing the compaction status data packet with the corrected qualified interval database.

[0013] Furthermore, by matching and comparing the compaction status data packet with the corrected acceptable range database, the compaction quality judgment result is obtained, including: Location information R is obtained by parsing the compaction status data packet. S Time information t S Measured compaction degree K at time S and measured torsional shear strength τ S ; K S and τ S Match and compare with the corrected qualified interval database; If K S The corresponding τ S∈ Then determine R S t S The compaction quality assessment result at that time was qualified; If K S The corresponding τ S > Then determine R S t S The compaction quality assessment result at that time was over-compaction; If K S The corresponding τ S < Then determine R S t S The compaction quality assessment result at that time was under-compaction.

[0014] Furthermore, when the compaction quality assessment result is over-compaction, Adjust the standard construction parameters based on the compaction quality assessment results, including: Based on the compaction quality judgment result, a Boolean judgment value Q is determined for over-compression. The expression for Q is: ; In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is set. s1 Adjusted to Standard vibration frequency f s1 Adjusted to Standard amplitude A s1 Adjusted to Standard number of compaction passes N s1 Adjusted to k V1 k f1 k A1 and k N1 The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to Standard number of compaction passes N s2 Adjusted to k V2 and k N2 The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is increased. s3 Adjusted to Standard number of compaction passes N s3 Adjusted to k V3 and k N3 The preset control coefficient; The standard paving speed V of the unmanned paver p Adjusted to kp1 This is the preset control coefficient.

[0015] Furthermore, when the compaction quality assessment result is under-compaction, Adjust the standard construction parameters based on the compaction quality assessment results, including: Based on the compaction quality judgment result, a Boolean judgment value Q is determined for over-compression. The expression for Q is: ; In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is set. s1 Adjusted to Standard vibration frequency f s1 Adjusted to Standard amplitude A s1 Adjusted to Standard number of compaction passes N s1 Adjusted to , , , and The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to Standard number of compaction passes N s2 Adjusted to , and The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is increased. s3 Adjusted to Standard number of compaction passes N s3 Adjusted to , and The preset control coefficient; The standard paving speed V of the unmanned paver p Adjusted to , This is the preset control coefficient.

[0016] Secondly, a collaborative control system for unmanned construction machinery fleets based on volumetric mechanical detection is provided, including: The qualified interval database establishment module is used to establish a qualified interval database of compaction degree-torsional shear strength by testing asphalt mixture specimens; The compaction correction module is used to test the compaction degree of asphalt mixture specimens, establish a compaction correction model, and obtain a corrected qualified interval database by correcting the qualified interval database through the compaction correction model. The control parameter calibration module is used to calibrate the control parameters of unmanned road rollers and unmanned pavers at each compaction stage based on the corrected qualified interval database before construction, so as to obtain standard construction parameters. The compaction quality judgment module is used to collect multi-source sensing data of the construction layer in real time during the construction process, match and compare the multi-source sensing data with the corrected qualified interval database, and obtain the compaction quality judgment result. The unmanned construction machine group collaborative control module is used to adjust standard construction parameters based on the compaction quality judgment results, thereby enabling adaptive control of unmanned road rollers and unmanned pavers.

[0017] The beneficial effects achieved by this invention are as follows: A database of acceptable intervals for compaction degree and torsional shear strength was established by testing asphalt mixture specimens. Compaction degree was measured on the asphalt mixture specimens, and a compaction degree correction model was established. This model was then used to correct the acceptable interval database, resulting in a corrected acceptable interval database. Before construction, the control parameters of the unmanned roller and unmanned paver at each compaction stage were calibrated based on the corrected acceptable interval database to obtain standard construction parameters. During construction, multi-source sensing data of the construction layer was collected in real time and compared with the corrected acceptable interval database to obtain compaction quality judgment results. The standard construction parameters were adjusted based on the compaction quality judgment results to achieve adaptive control of the unmanned roller and unmanned paver. A dual-index synergistic acceptable interval system integrating compaction degree and torsional shear strength was constructed. By combining a database of unmanned compaction, a corrected model without a nuclear density meter, and standardized parameter calibration of test sections, this invention enables precise operational control of unmanned rollers and pavers throughout the entire construction process. The invention can dynamically acquire the volumetric-mechanical comprehensive judgment results of compacted material at the construction site and automatically generate and issue optimized control commands for compaction speed, vibration frequency, amplitude, number of compaction passes, paving speed, and travel path based on the judgment logic of the corrected qualified interval database. This ensures adaptive matching of equipment operating parameters with site conditions at each construction stage. It not only effectively avoids quality problems such as insufficient compaction, over-compaction, and unevenness, significantly improving the uniformity of construction quality and the stability of the pavement structure, but also realizes collaborative operation and closed-loop control of unmanned construction machinery fleets, greatly improving construction efficiency and the long-term service performance of the pavement. Attached Figure Description

[0018] Figure 1 This is a flowchart of the unmanned construction machine swarm collaborative control method based on volume-mechanical detection according to the present invention; Figure 2 This is a structural diagram of the unmanned construction machine swarm collaborative control system based on volume-mechanical detection of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0020] The present invention applies a construction collaborative control platform based on a volume-mechanical detection-based collaborative control method for unmanned construction machine groups, and establishes a high-speed wireless data transmission path with the unmanned construction machine group, a nuclear density meter, and a torsional shear strength detection device with an integrated temperature sensing module, thereby realizing data interaction; The unmanned construction fleet includes unmanned steel wheel rollers, unmanned rubber-tired rollers, and unmanned pavers.

[0021] like Figure 1 As shown, this embodiment of the invention provides a collaborative control method for unmanned construction machine fleets based on volumetric mechanical detection, including: 101. By testing asphalt mixture specimens, a database of reasonable ranges for compaction degree and torsional shear strength was established. In this embodiment, under laboratory conditions, asphalt mixture specimens with different compaction states were prepared according to the designed mix proportions, and multiple compaction degree gradients were set; for example, 90%, 92%, 94%, 96%, and 98%; and torsional shear strength tests were carried out under the corresponding temperature conditions to establish the evolution relationship between compaction degree and torsional shear strength. For each experimental compaction degree K i and its corresponding experimental temperature T i Determine the corresponding experimental torsional shear strength τ i The qualified range, the expression for the qualified range is: ; in, For τ i The average value, σ i For τ i The standard deviation, and τ i coefficient of variation C v Satisfy C v The requirement is ≤0.1; Establish a database of acceptable intervals for compaction degree and torsional shear strength based on the acceptable interval expression.

[0022] 102. The compaction degree of asphalt mixture specimens was tested, a compaction degree correction model was established, and the qualified interval database was corrected by the compaction degree correction model to obtain the corrected qualified interval database. In this embodiment, a nucleus-free density meter was used to test the compaction degree of asphalt mixture specimens under different compaction states under the above laboratory conditions, and the original compaction degree K was obtained. w ; According to K w With K iThe measurement error correction amount ΔK is calculated, and the formula for ΔK is: ; in, For K w The average value; Based on ΔK and K w A compaction degree correction model is constructed, and its expression is as follows: ; Among them, K j For K w Corrected compaction degree; The corrected qualified interval database is obtained by correcting the qualified interval database using a compaction correction model; The construction of a corrected acceptable interval database can correct the compaction degree-torsional shear strength acceptable interval database established under laboratory conditions and the compaction degree error under actual scenarios.

[0023] 103. Before construction, the control parameters of unmanned road rollers and unmanned pavers at each compaction stage are calibrated based on the corrected qualified interval database to obtain standard construction parameters. In this embodiment, before formal construction, the unmanned paver and unmanned roller are controlled to complete the operation of each compaction stage in sequence according to the preset standard operating parameters under the ideal construction conditions of the test section based on the corrected qualified section database. The compaction stage includes the initial compaction stage, the intermediate compaction stage and the final compaction stage. At each compaction stage, the ideal compaction degree and ideal torsional shear strength of the construction layer are simultaneously collected using a shear characteristic detection device with a coreless density meter and an integrated temperature sensing module. In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is calibrated by combining the real-time evolution characteristics of ideal compaction degree and ideal torsional shear strength. s1 Standard vibration frequency f s1 Standard amplitude A s1 and standard number of compaction passes N s1 ; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is determined based on the changing trends of ideal compaction degree and ideal torsional shear strength. s2 Compared with the standard number of compaction passes N s2 ; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is determined based on the range where the ideal torsional shear strength tends to stabilize. s3 and standard number of compaction passes N s3 ; Based on the standard compaction speed and standard number of passes of unmanned steel wheel rollers and unmanned rubber-tired rollers at each compaction stage, the standard paving speed of unmanned pavers was determined. Vp The process of reverse reasoning is as follows: First, constrained by the operational requirements of unmanned steel-wheel rollers and unmanned rubber-tired rollers, the standard parameters for the initial compaction, intermediate compaction, and final compaction stages of unmanned steel-wheel rollers and unmanned rubber-tired rollers are as follows: V s1 , N s1 ), ( V s2 , N s2 ), ( V s3 , N s3 The theoretical processing time for calculating a unit length is: ; in, This is a reduction factor for parallel / overlapping operations, applied when multiple similar devices are running in parallel or using strip overlap. Reflects efficiency gain, default value Given a value of 1, the maximum production capacity speed of the drone swarm can be obtained. The expression for the maximum production capacity speed is: ; This upper limit speed The maximum permissible forward speed of the unmanned paver without causing downstream compaction bottlenecks is given; secondly, to meet the thermal-temporal window and operational safety constraints, a minimum safe longitudinal distance between the unmanned paver and the first roller is introduced. D min And the earliest allowed waiting time before the mill begins. t min To ensure that after paving, it can be t min Initial compaction should be initiated internally to avoid rear-end collisions with unmanned rollers. The unmanned paving speed should not be lower than the safe speed, which is calculated using the following formula: ; In addition, the follow-up constraint of the first roller must be considered. To ensure coordination between the unmanned paver and the initial compaction operation, the paving speed should not exceed the operating capacity of the roller during the initial compaction stage. V L = V s1 ; In summary, the standard paving speed for unmanned pavers is determined as follows: V p =max( V safe ,min(V H , V L )); Ultimately, will { V p , V s1 , N s1 , V s2 , N s2 , V s3 , N s3} Compile these parameters with the calibrated vibration frequency, amplitude, and other parameters to form a standard construction parameter set for the fleet, providing a standard basis for the full-process coordinated control of the unmanned construction fleet; Standard construction parameters are obtained by combining the parameters of unmanned steel wheel rollers, unmanned rubber-tired rollers, and unmanned pavers.

[0024] 104. During the construction process, multi-source sensing data of the construction layer is collected in real time, and the multi-source sensing data is matched and compared with the corrected qualified interval database to obtain the compaction quality judgment result. In this embodiment, after the unmanned steel wheel roller and the unmanned rubber-tired roller complete the compaction operation during the construction process, the measured compaction degree is collected at the same detection position of the construction layer by a non-nuclear density meter, the measured temperature and measured torsional shear strength are collected by a torsional shear strength detection device, and the positioning information and time information are collected by a positioning and timing device. Based on the spatiotemporal registration algorithm, the measured compaction degree, measured temperature, and measured torsional shear strength are time-series aligned and spatially matched according to the location and time information, and fused to form a unified compaction status data package. The unified compaction status data package provides basic data support for subsequent compaction quality evaluation and intelligent control of construction parameters. Location information R is obtained by parsing the compaction status data packet. S Time information t S Measured compaction degree K at time S and measured torsional shear strength τ S ; K S and τ S Match and compare with the corrected qualified interval database; If K S The corresponding τ S ∈ Then determine R S t S The compaction quality assessment result at that time was qualified; If KS The corresponding τ S > Then determine R S t S The compaction quality assessment result at that time was over-compaction; If K S The corresponding τ S < Then determine R S t S The compaction quality assessment result at that time was under-compaction.

[0025] 105. Adjust standard construction parameters based on the compaction quality assessment results to enable adaptive control of unmanned road rollers and unmanned pavers.

[0026] In this embodiment, when the compaction quality judgment result is qualified, no adjustment is required to the standard construction parameters; When the compaction quality assessment result is over-compaction, Based on the compaction quality judgment result, a Boolean judgment value Q is determined for over-compression. The expression for Q is: ; In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is set. s1 Adjusted to Standard vibration frequency f s1 Adjusted to Standard amplitude A s1 Adjusted to Standard number of compaction passes N s1 Adjusted to k V1 k f1 k A1 and k N1 The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to Standard number of compaction passes N s2 Adjusted to k V2 and k N2 The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is increased. s3 Adjusted to Standard number of compaction passes N s3 Adjusted to k V3 and k N3 The preset control coefficient; Because the increased operating speed of unmanned steel-wheel rollers and unmanned rubber-tired rollers after adjustment has led to an improvement in the overall compaction efficiency, in order to maintain the continuity and matching of construction procedures, it is necessary to adjust the standard paving speed V of the unmanned paver. p Adjusted to k p1 A preset control coefficient is used to ensure the rhythm of paving and compaction is coordinated. When the compaction quality assessment result is under-compaction. In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is set. s1 Adjusted to Standard vibration frequency f s1 Adjusted to Standard amplitude A s1 Adjusted to Standard number of compaction passes N s1 Adjusted to , , , and The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to Standard number of compaction passes N s2 Adjusted to , and The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is increased. s3 Adjusted to Standard number of compaction passes N s3 Adjusted to , and The preset control coefficient; When the compaction quality is determined to be insufficient and parameter adjustments are triggered, the operating speeds of the unmanned steel wheel roller and the unmanned rubber-tired roller will be reduced accordingly, resulting in a decrease in the overall compaction efficiency. To maintain the continuity and matching of construction procedures and avoid rhythm imbalance between paving and compaction, the standard paving speed V of the unmanned paver will be adjusted. p Adjusted to , This is the preset control coefficient.

[0027] The beneficial effects achieved by the embodiments of the present invention are as follows: This invention constructs a database of synergistic acceptable intervals for compaction degree and torsional shear strength under laboratory conditions, avoiding the limitations of relying solely on volumetric indicators such as compaction degree. It enables simultaneous evaluation of the compactness and mechanical properties of asphalt mixtures, effectively identifying problems such as insufficient compaction, excessive compaction, and uneven compaction. This improves the accuracy and scientific nature of compaction quality judgment from the source, providing a reliable basis for precise control during construction. By introducing a two-level correction mechanism—indoor and on-site—and using a nucleus-free density meter to test specimens with different compaction degrees in the laboratory, a correction model was established, followed by a secondary correction based on test section data. This significantly improved the measurement accuracy and adaptability of the nucleus-free density meter in actual construction environments. This correction method effectively overcomes measurement deviations caused by the equipment under different construction conditions, ensuring the comparability and consistency of compaction degree data under different working conditions. Under ideal working conditions in the test section, this invention calibrated the parameters of unmanned road rollers and unmanned pavers at each compaction stage, and established a standard construction parameter system covering rolling speed, vibration frequency, amplitude, number of rolling passes and paving speed. This system can be directly transferred to large-scale construction, enabling rapid deployment and precise adaptation of construction equipment parameter configuration, and improving the organization efficiency and quality stability of on-site construction. When underpressure or overpressure is detected, the operating parameters of the unmanned roller and the speed of the unmanned paver can be optimized simultaneously to ensure that the rhythm of each process is matched in space and time, and realize the collaborative optimization of the unmanned construction machine group and the closed-loop control of the entire process of construction quality. Through dynamic control, the compaction process can automatically optimize the construction rhythm according to changes in material properties and site conditions. This avoids energy consumption and construction time waste caused by repeated rolling, and also prevents skeleton damage and shear performance degradation caused by over-compaction. While ensuring the uniformity of construction quality, this method can effectively extend the service life of the pavement and significantly improve construction economy and durability.

[0028] Based on the above embodiments, the collaborative control method for unmanned construction machine swarms based on volume-mechanical detection is described below through embodiments.

[0029] like Figure 2 As shown, this embodiment of the invention provides a collaborative control system for unmanned construction machinery fleets based on volumetric-mechanical detection, comprising: The qualified interval database establishment module 201 is used to establish a qualified interval database of compaction degree-torsional shear strength by testing asphalt mixture specimens. The compaction correction module 202 is used to test the compaction of asphalt mixture specimens, establish a compaction correction model, and obtain a corrected qualified interval database by correcting the qualified interval database through the compaction correction model. The control parameter calibration module 203 is used to calibrate the control parameters of the unmanned road roller and unmanned paver at each compaction stage based on the corrected qualified interval database before construction, so as to obtain standard construction parameters. The compaction quality judgment module 204 is used to collect multi-source sensing data of the construction layer in real time during the construction process, match and compare the multi-source sensing data with the corrected qualified interval database, and obtain the compaction quality judgment result. The unmanned construction machine group collaborative control module 205 is used to adjust the standard construction parameters based on the compaction quality judgment results, thereby enabling adaptive control of the unmanned road roller and unmanned paver.

[0030] The beneficial effects achieved by this invention are as follows: The qualified interval database establishment module 201 establishes a qualified interval database of compaction degree and torsional shear strength by testing asphalt mixture specimens; the compaction degree correction module 202 conducts compaction degree tests on asphalt mixture specimens, establishes a compaction degree correction model, and corrects the qualified interval database through the compaction degree correction model to obtain a corrected qualified interval database; the control parameter calibration module 203 calibrates the control parameters of unmanned rollers and unmanned pavers at each compaction stage based on the corrected qualified interval database before construction to obtain standard construction parameters; the compaction quality judgment module 204 collects multi-source sensing data of the construction layer in real time during construction, matches and compares the multi-source sensing data with the corrected qualified interval database to obtain the compaction quality judgment result; the unmanned construction machine group collaborative control module 205 adjusts the standard construction parameters according to the compaction quality judgment result, thereby performing adaptive control of unmanned rollers and unmanned pavers. By constructing a dual-index collaborative qualified interval database integrating compaction degree and torsional shear strength, and combining a corrected model without a core density meter with standardized parameter calibration of test sections, this invention achieves precise operational control of unmanned rollers and pavers throughout the entire construction process. The invention can dynamically acquire the volumetric-mechanical comprehensive evaluation results of compacted material at the construction site, and automatically generate and issue optimized control commands for rolling speed, vibration frequency, amplitude, number of rolling passes, paving speed, and travel path based on the judgment logic of the corrected qualified interval database. This ensures adaptive matching of equipment operating parameters with site conditions at each construction stage. It not only effectively avoids quality problems such as insufficient compaction, over-compaction, and unevenness, significantly improving the uniformity of construction quality and the stability of the pavement structure, but also realizes collaborative operation and closed-loop control of unmanned construction machine fleets, greatly improving construction efficiency and the long-term service performance of the pavement.

[0031] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0032] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0033] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0034] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0035] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A collaborative control method for unmanned construction machine fleets based on volumetric-mechanical detection, characterized in that, include: A database of acceptable ranges for compaction degree and torsional shear strength was established by testing asphalt mixture specimens. The compaction degree of the asphalt mixture specimens was tested, a compaction degree correction model was established, and the qualified interval database was corrected by the compaction degree correction model to obtain a corrected qualified interval database. Before construction, the control parameters of unmanned road rollers and unmanned pavers at each compaction stage are calibrated based on the corrected qualified interval database to obtain standard construction parameters. During construction, multi-source sensing data of the construction layer is collected in real time, and the multi-source sensing data is matched and compared with the corrected qualified interval database to obtain the compaction quality judgment result. The standard construction parameters are adjusted based on the compaction quality judgment results, thereby enabling adaptive control of the unmanned road roller and the unmanned paver. Before construction, the control parameters of the unmanned road roller and unmanned paver at each compaction stage are calibrated based on the corrected qualified interval database to obtain standard construction parameters, including: Before construction, based on the corrected qualified section database, the test section was simulated under ideal construction conditions. The unmanned paver and unmanned roller were controlled to complete the operation of each compaction stage in sequence according to the preset standard operating parameters. At each compaction stage, the ideal compaction degree and ideal torsional shear strength of the construction layer are simultaneously collected using a shear characteristic detection device with a coreless density meter and an integrated temperature sensing module. Based on the ideal compaction degree and the ideal torsional shear strength, the standard rolling speed and standard number of rolling passes of the unmanned road roller are obtained; Based on the standard compaction speed and the standard number of compaction passes, the standard paving speed of the unmanned paver is determined by reverse calculation, and the standard construction parameters are obtained by combining the parameters of the unmanned roller and the unmanned paver.

2. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical detection according to claim 1, characterized in that, The unmanned road rollers include unmanned steel-drum road rollers and unmanned rubber-tired road rollers, and the compaction stage includes an initial compaction stage, a secondary compaction stage, and a final compaction stage. The process of obtaining the standard compaction speed and standard number of compaction passes of the unmanned road roller based on the ideal compaction degree and the ideal torsional shear strength includes: In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is calibrated by combining the real-time evolution characteristics of the ideal compaction degree and the ideal torsional shear strength. s1 Standard vibration frequency f s1 Standard amplitude A s1 and standard number of compaction passes N s1 ; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is determined based on the changing trends of the ideal compaction degree and the ideal torsional shear strength. s2 Compared with the standard number of compaction passes N s2 ; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is determined based on the range where the ideal torsional shear strength tends to stabilize. s3 and standard number of compaction passes N s3 .

3. The method for coordinated control of unmanned construction machine fleets based on volumetric-mechanical detection according to claim 2, characterized in that, The method involves testing asphalt mixture specimens to establish a database of acceptable ranges for compaction degree and torsional shear strength, including: Under laboratory conditions, asphalt mixture specimens with different compaction states were prepared according to the design mix proportions. Multiple compaction gradients were set, and torsional shear strength tests were conducted under corresponding temperature conditions to establish the evolution relationship between compaction degree and torsional shear strength. For each experimental compaction degree K i and its corresponding experimental temperature T i Determine the corresponding experimental torsional shear strength τ i The qualified range, the expression of which is: ； Among them, the For the τ i The average value of σ i For the τ i The standard deviation of τ, and the τ i coefficient of variation C v Satisfy C v The requirement is ≤0.1; A database of acceptable intervals for compaction degree and torsional shear strength is established based on the acceptable interval expression.

4. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical detection according to claim 3, characterized in that, The process of conducting compaction tests on the asphalt mixture specimens, establishing a compaction correction model, and correcting the qualified interval database using the compaction correction model to obtain a corrected qualified interval database includes: The compaction degree of the asphalt mixture specimens under different compaction conditions was tested using the aforementioned nucleus-free density meter to obtain the original compaction degree K. w ; According to the K w With the K i The measurement error correction amount ΔK is calculated, and the formula for ΔK is: ； Among them, the For the K w The average value; According to ΔK and K w A compaction degree correction model is constructed, and the expression of the compaction degree correction model is as follows: ； Wherein, K j For the K w Corrected compaction degree; The qualified interval database is obtained by correcting the qualified interval database using the compaction correction model.

5. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical testing according to claim 4, characterized in that, During construction, multi-source sensing data of the construction layer is collected in real time. This multi-source sensing data is then matched and compared with the corrected qualified interval database to obtain the compaction quality judgment result, including: During the construction process, after the unmanned steel wheel roller and the unmanned rubber-tired roller complete the compaction operation, the measured compaction degree is collected at the same detection position of the construction layer by the nucleusless density meter, the measured temperature and measured torsional shear strength are collected by the torsional shear strength detection device, and the positioning information and time information are collected by the positioning and timing device. Based on the spatiotemporal registration algorithm, the measured compaction degree, measured temperature and measured torsional shear strength are time-series aligned and spatially matched according to the positioning information and the time information, and fused to form a compaction state data packet with a unified format; The compaction quality judgment result is obtained by matching and comparing the compaction status data packet with the corrected qualified interval database.

6. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical testing according to claim 5, characterized in that, The step of matching and comparing the compaction status data packet with the corrected qualified interval database to obtain the compaction quality judgment result includes: The location information R is obtained by parsing the compaction status data packet. S The time information t mentioned above S The measured compaction degree K at time [time] S and the measured torsional shear strength τ S ; K S and the τ S Match and compare with the corrected qualified interval database; If the K S The corresponding τ S ∈ Then determine the R S The t mentioned above S The compaction quality assessment result at that time was qualified; If the K S The corresponding τ S > Then determine the R S The t mentioned above S The compaction quality assessment result at that time was over-compaction; If the K S The corresponding τ S < Then determine the R S The t mentioned above S The compaction quality assessment result at that time was under-compaction.

7. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical testing according to claim 6, characterized in that, When the compaction quality judgment result is over-compaction... The adjustment of the standard construction parameters based on the compaction quality assessment result includes: Based on the compaction quality determination result being an over-compression result, a Boolean determination value Q is determined, and the expression for Q is: ； In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is... s1 Adjusted to The standard vibration frequency f s1 Adjusted to The standard amplitude A s1 Adjusted to The standard number of compaction passes N s1 Adjusted to The k V1 The k f1 The k A1 and the k N1 The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to The standard number of compaction passes N s2 Adjusted to The k V2 and the k N2 The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is... s3 Adjusted to The standard number of compaction passes N s3 Adjusted to The k V3 and the k N3 The preset control coefficient; The standard paving speed V of the unmanned paver. p Adjusted to The k p1 This is the preset control coefficient.

8. The method for coordinated control of unmanned construction machine fleets based on volumetric mechanical testing according to claim 6, characterized in that, When the compaction quality assessment result is under-compaction... The adjustment of the standard construction parameters based on the compaction quality assessment result includes: Based on the compaction quality determination result being an over-compression result, a Boolean determination value Q is determined, and the expression for Q is: ； In the initial compaction stage, the standard compaction speed V of the unmanned steel wheel roller is... s1 Adjusted to The standard vibration frequency f s1 Adjusted to The standard amplitude A s1 Adjusted to The standard number of compaction passes N s1 Adjusted to The The above The above and stated The preset control coefficient; During the secondary compaction stage, the standard compaction speed V of the unmanned rubber-tired roller is increased. s2 Adjusted to The standard number of compaction passes N s2 Adjusted to The and stated The preset control coefficient; In the final compaction stage, the standard compaction speed V of the unmanned steel wheel roller is... s3 Adjusted to The standard number of compaction passes N s3 Adjusted to The and stated The preset control coefficient; The standard paving speed V of the unmanned paver. p Adjusted to The This is the preset control coefficient.

9. A collaborative control system for unmanned construction machinery fleets based on volumetric-mechanical detection, characterized in that, include: The qualified interval database establishment module is used to establish a qualified interval database of compaction degree-torsional shear strength by testing asphalt mixture specimens; The compaction correction module is used to test the compaction degree of the asphalt mixture specimens, establish a compaction correction model, and correct the qualified interval database through the compaction correction model to obtain a corrected qualified interval database. The control parameter calibration module is used to control the unmanned paver and unmanned roller to complete each compaction stage sequentially according to preset standard operating parameters before construction, based on the ideal construction conditions of the test section simulated by the corrected qualified interval database. During each compaction stage, the ideal compaction degree and ideal torsional shear strength of the construction layer are simultaneously collected by a shear characteristic detection device with a coreless density meter and an integrated temperature sensing module. Based on the ideal compaction degree and ideal torsional shear strength, the standard rolling speed and standard number of rolling passes of the unmanned roller are obtained. Based on the standard rolling speed and standard number of rolling passes, the standard paving speed of the unmanned paver is determined by reverse calculation. The standard construction parameters are obtained by combining the parameters of the unmanned roller and the unmanned paver. The compaction quality judgment module is used to collect multi-source sensing data of the construction layer in real time during the construction process, match and compare the multi-source sensing data with the corrected qualified interval database, and obtain the compaction quality judgment result. The unmanned construction machine group collaborative control module is used to adjust the standard construction parameters according to the compaction quality judgment results, thereby performing adaptive control of the unmanned road roller and the unmanned paver.

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