Digital-driven asphalt mixture marshall unmanned automatic compaction test system

Abstract

The present application relates to the technical field of road engineering material test, and more particularly to a digital and intelligent driving Marshall asphalt mixture unmanned automatic compaction test system, which comprises a proportioning design module for generating and issuing an initial mix proportioning design task of asphalt mixture; a physical execution module comprising an automatic equipment cluster; a central control module receiving the initial mix proportioning design task and generating a corresponding operation instruction sequence; a scheduling control module controlling each automatic equipment to execute test operation according to the operation instruction sequence and collecting multi-source working condition data and test result data in real time; the multi-source working condition data and the corresponding test result data are returned to the proportioning design module; and the proportioning design module generates a mix proportioning optimization design task in response to the test result data not meeting a preset design target and re-executes the compaction test. Through the present application, the uniformity of the compactness of the test piece can be effectively improved, the generation of invalid tests can be reduced, and the overall cycle of mix proportioning optimization can be shortened.

Description

Technical Field

[0001] This invention relates to the technical field of road engineering material testing, and in particular to a digitally driven Marshall unmanned automated compaction test system for asphalt mixtures. Background Technology

[0002] The Marshall compaction test for asphalt mixtures is a crucial step in the mix design, quality inspection, and control of road engineering materials. Its purpose is to prepare standard specimens to determine the density, porosity, and other volumetric properties and mechanical properties of the mixture. Currently, to improve testing efficiency and consistency, the industry has developed various automated equipment, such as automatic compactors and robotic arm-assisted feeding devices, to mechanize specific steps like compaction and handling, replacing manual labor and forming a single-point automation solution.

[0003] In existing testing systems, the testing process and mix design process are disconnected, making it impossible to achieve automated design, testing, and feedback optimization. Specifically, existing systems typically only execute preset compaction operations and record the final results, but the generated test data needs to be manually exported from the equipment, calculated, and then manually input into separate mix design software for analysis and judgment. If the results do not meet the design requirements, designers need to manually recalculate and adjust the mix parameters and manually start a new round of testing. This optimization process heavily relies on manual intervention and coordination, resulting in long mix optimization cycles, low efficiency, and the manual operation is prone to introducing errors or delays, making it difficult to achieve automated iterative design. Summary of the Invention

[0004] This invention provides a digitally driven, unmanned, automated Marshall compaction test system for asphalt mixtures, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The digitally-driven Marshall unmanned automated compaction test system for asphalt mixtures includes:

[0007] The mix design module is used to generate and issue initial mix design tasks for asphalt mixtures;

[0008] The physical execution module includes a cluster of automated equipment used to sequentially perform batching and weighing, mixing, molding, tamping, double-sided compaction, demolding, and specimen labeling and storage.

[0009] The central control module is communicatively connected to both the proportioning design module and the physical execution module, and is configured as follows:

[0010] Receive the initial mix design task and generate the corresponding work instruction sequence;

[0011] The scheduling and control module executes the test operations according to the sequence of work instructions, and collects multi-source working condition data and corresponding test result data in real time during the execution of the test operations.

[0012] The multi-source operating condition data and its corresponding test results data are sent back to the proportioning design module.

[0013] In response to the test results data not meeting the preset design target, the mix design module optimizes the initial mix design task based on the multi-source operating condition data, test results data, and preset design target, generates an optimized mix design task, and re-executes the compaction test accordingly.

[0014] Furthermore, the physical execution module includes:

[0015] The batching and transfer unit is used to collect, weigh, mix, mold, and heat-insulate transport materials according to the initial mix design task or the mix optimization design task.

[0016] The unmanned compaction preparation unit is used to receive the mixed material and sequentially perform the molding, tamping and double-sided compaction operations.

[0017] The post-processing and labeling unit is used for demolding, labeling, and storing the compacted and cooled specimens.

[0018] Furthermore, the unmanned compaction preparation unit includes:

[0019] An automatic tamping device includes a rotary drive component and a tamping execution component, used to automatically tamp the mixture after it has been molded; the rotary drive component is used to drive the platform carrying the Marshall test mold to rotate around a vertical axis, and the tamping execution component is located above the platform and includes a heated tamping rod capable of vertical reciprocating motion;

[0020] The fully automatic Marshall compactor cluster includes a first compactor and a second compactor arranged side by side and operating in a cross manner under the scheduling of the central control module. It is used to compact the test mold on both sides after tamping and to collect the specimen height data in real time during the compaction process.

[0021] Furthermore, when the central control module executes a continuous trial molding and compaction process, the central control module is also configured to:

[0022] Extract the initial target charge mass from the initial mix design task;

[0023] After each compaction test, the final height of the corresponding specimen is obtained. Based on the deviation between the final height and the target height, the pre-calibrated mass-height relationship model is used to calculate the charge mass compensation amount for the next specimen.

[0024] The compaction height disturbance feature is extracted from the multi-source working condition data to obtain the compaction height disturbance feature parameters; the compaction height disturbance feature parameters include the mixture consistency and the mixture temperature;

[0025] The charge mass compensation is adjusted based on the compaction height disturbance characteristic parameters, and the charge mass of the next specimen is obtained by combining the charge mass of the corresponding specimen.

[0026] Furthermore, the proportioning design module is configured to perform the following optimization steps:

[0027] Based on the deviation between the received test results data and the preset design target, and in conjunction with the corresponding multi-source operating condition data, a deviation attribution analysis is performed to determine the types of key factors leading to performance deviations.

[0028] Based on the type of key factor, a pre-stored parameter adjustment lookup table is invoked to determine the direction and magnitude of adjustment for material parameters and / or process parameters;

[0029] The initial mix design task is adjusted based on the adjustment direction and magnitude to generate the mix optimization design task.

[0030] Furthermore, the parameter adjustment reference table is obtained through pre-experiment calibration using the controlled variable method, and its construction method includes:

[0031] Select asphalt mixtures of the same type, fix other parameters, and adjust one parameter at a time: aggregate gradation, asphalt content, mixing temperature, and number of compaction times. Measure the effect of the parameter change on the porosity and height performance of the specimens, and establish the correspondence between performance deviation values ​​and parameter adjustment ranges.

[0032] Furthermore, the central control module has a built-in data association submodule, which assigns a unique identification code to each specimen and binds the collected multi-source working condition data and test result data with the identification code according to the timestamp and spatial work position information to generate a single specimen full-process data chain.

[0033] Furthermore, the post-processing and labeling unit uses a laser marking machine or a QR code labeling machine to assign unique identification information to the specimens. The unique identification information includes the mix proportion number, test time, and specimen identification code. The post-processing and labeling unit also includes an intelligent storage rack for automatically binding the specimen storage location with the unique identification information.

[0034] Furthermore, the proportioning design module is deployed on a cloud server, and the central control module is deployed on a local industrial computer;

[0035] The central control module transmits the test result data and the associated multi-source operating condition data back to the proportioning design module according to a preset cycle that is the same as the single specimen preparation cycle.

[0036] Furthermore, the deviation attribution analysis includes:

[0037] Based on the compaction curve characteristic parameters and mixture temperature in the multi-source working condition data, determine whether the performance deviation is caused by abnormal mixture consistency.

[0038] Based on the actual weighing ratio of aggregates in the multi-source working condition data, determine whether the performance deviation is caused by unreasonable aggregate gradation.

[0039] The technical solution of this invention can achieve the following technical effects:

[0040] In this embodiment, the combination of the automatic optimization algorithm of the proportion design module, the full-process data acquisition and task scheduling function of the central control module, and the automated equipment cluster of the physical execution module effectively solves the technical problems in the prior art where the experimental process and the proportion design process are separated, and the optimization process requires manual data export, parameter adjustment, and test initiation. It realizes the fully automatic closed-loop operation of proportion design, test execution, data feedback, and proportion optimization, without any manual intervention in any connecting link, eliminating the errors introduced in the manual data transcription and calculation process, improving the accuracy of proportion design, and shortening the overall cycle of proportion optimization.

[0041] By leveraging the synergistic optimization of multi-source data driven by the physical execution module, central control module, and mix design module, this method addresses the technical problem of existing testing systems that can only record the final test results and lack process data support, making it difficult to accurately predict the compaction characteristics of mixtures and dynamically optimize the testing process. This method achieves deep linkage optimization of process parameters, compaction process, and mix performance. The central control module extracts key characteristic parameters based on the collected compaction process data and feeds them back to the mix design module. While optimizing the aggregate gradation and asphalt content, the mix design module simultaneously adjusts process indicators such as compaction frequency and tamping parameters to improve the uniformity of specimen density and reduce the generation of invalid tests.

[0042] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating the digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to the present invention.

[0045] Figure 2 This is a front view of the physical execution module in an embodiment of the present invention;

[0046] Figure 3 This is an isometric view of the physical execution module in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the unmanned compaction preparation unit in an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0050] like Figure 1 As shown, the digitally driven Marshall unmanned automated compaction test system for asphalt mixtures of the present invention specifically includes the following modules:

[0051] The mix design module is used to generate and issue initial mix design tasks for asphalt mixtures;

[0052] The physical execution module includes a cluster of automated equipment used to sequentially perform batching and weighing, mixing, molding, tamping, double-sided compaction, demolding, and specimen labeling and storage.

[0053] The central control module is communicatively connected to both the proportioning design module and the physical execution module, and is configured as follows:

[0054] Receive the initial mix design task and generate the corresponding work instruction sequence;

[0055] The scheduling and control module executes the test operations according to the sequence of work instructions, and collects multi-source working condition data and corresponding test result data in real time during the execution of the test operations.

[0056] The multi-source operating condition data and its corresponding test results data are sent back to the proportioning design module.

[0057] In response to the test results data not meeting the preset design target, the mix design module optimizes the initial mix design task based on the multi-source operating condition data, test results data, and preset design target, generates an optimized mix design task, and re-executes the compaction test accordingly.

[0058] In this embodiment, the combination of the automatic optimization algorithm of the proportion design module, the full-process data acquisition and task scheduling function of the central control module, and the automated equipment cluster of the physical execution module effectively solves the technical problems in the prior art where the experimental process and the proportion design process are separated, and the optimization process requires manual data export, parameter adjustment, and test initiation. It realizes the fully automatic closed-loop operation of proportion design, test execution, data feedback, and proportion optimization, without any manual intervention in any connecting link, eliminating the errors introduced in the manual data transcription and calculation process, improving the accuracy of proportion design, and shortening the overall cycle of proportion optimization.

[0059] By leveraging the synergistic optimization of multi-source data driven by the physical execution module, central control module, and mix design module, this method addresses the technical problem that existing testing systems can only record the final test results and lack process data support, making it difficult to accurately predict the compaction characteristics of mixtures and dynamically optimize the testing process. This method achieves deep linkage optimization of process parameters, compaction process, and mix performance. The central control module extracts key characteristic parameters based on the collected compaction process data and feeds them back to the mix design module. While optimizing the aggregate gradation and asphalt content, the mix design module simultaneously adjusts process indicators such as compaction frequency and tamping parameters to improve the uniformity of specimen density and reduce the generation of invalid tests.

[0060] The synergistic interaction of the above modules is not a simple functional superposition, but rather the formation of a data-driven integrated system for testing and design, producing technical effects that existing single-point automation technologies cannot achieve. Specifically, it enables the spatiotemporal correlation and traceability of data throughout the entire testing process, ensuring that all working condition data and mix proportion parameters are precisely matched, whereas traditional systems can only provide discrete final result data. It achieves the goal of safe operation by completely isolating people and materials, avoiding occupational health risks such as high-temperature asphalt fumes and mechanical injuries, while further improving the standardization of testing due to the lack of human intervention.

[0061] In some embodiments of the present invention, such as Figures 2 to 4 As shown, to achieve integrated assembly line operation of the physical execution module, this embodiment integrates discrete test procedures into a unified assembly line layout in space. Through centralized scheduling by a central control module, the mold and materials serve as the sole transfer carriers, enabling automatic transfer, positioning, and process execution between various specialized equipment. Simultaneously, process data is fed back in real time and used to adjust subsequent process parameters, forming an unmanned preparation process that is continuous in both space and time and possesses online adaptive capabilities. The physical execution module includes a batching and transfer unit for collecting, weighing, mixing, and heat-insulating conveying materials according to the initial mix design task or the mix optimization design task; an unmanned compaction preparation unit for receiving the mixed material and sequentially performing tamping and double-sided compaction operations; and a post-processing and labeling unit for demolding, marking, and storing the compacted and cooled specimens. Logically, according to the flow sequence of materials and molds, it includes the following stages:

[0062] The first stage is material preparation and conveying. The batching and transfer unit automatically weighs various aggregates and mineral powders from the integrated silo according to the mix proportion instructions issued by the central control module, and injects asphalt at a preset temperature through a high-precision asphalt metering device. The weighed materials are then fed into a mixing pot with heating and stirring functions, and mixing is completed according to the set temperature and time. The high-temperature mixture after mixing is temporarily stored in a storage silo with continuous heat preservation function. When the molding station is ready, the insulated screw conveyor at the bottom of the storage silo starts, directly conveying the predetermined mass of mixture through insulated pipes to the Marshall mold located at the molding station. This process ensures the initial accuracy of the loading quality through closed-loop control of the conveyor's start-stop time and speed, and reduces temperature segregation through continuous heat preservation.

[0063] The second stage is specimen preparation, namely the operation process of the unmanned compaction preparation unit. Specifically, the unmanned compaction preparation unit includes an automatic tamping device for automatically tamping the mixture after it has been molded. The automatic tamping device includes a rotary drive component and a tamping execution component. The rotary drive component drives the platform carrying the Marshall test mold to rotate around a vertical axis. The tamping execution component is located above the platform and includes a heated tamping rod capable of vertical reciprocating motion. The unmanned compaction preparation unit also includes a cluster of fully automatic Marshall compactors. The cluster of fully automatic Marshall compactors includes a first compactor and a second compactor arranged side by side and operating in a cross manner under the scheduling of the central control module. These are used to compact the mold on both sides after it has been tamped and to collect specimen height data in real time during the compaction process.

[0064] The completed mold is picked up by a robotic arm and transferred to an automatic tamping device. The automatic tamping device is activated, and its rotary drive component drives the platform carrying the mold to rotate slowly along a preset mosquito coil-shaped trajectory. At the same time, the tamping execution component above drives the heated tamping rod to perform vertical reciprocating motion at a constant frequency. The insertion depth of the tamping rod is precisely controlled by a servo mechanism to ensure that each tamping reaches a fixed position. The combination of the rotary motion and the tamping motion makes the tamping rod form uniformly distributed tamping points on the cross-section of the mold, replacing the randomness of manual tamping and improving the uniformity of the material compaction.

[0065] After tamping is completed, the first robotic arm transfers the test mold to the compaction station; the first and second compactors in the fully automatic Marshall compactor cluster work in tandem under the scheduling of the central control module; when the first compactor compacts the first side of test mold A, the second compactor can compact the second side of test mold B, which has already been compacted on the first side and flipped, or it can be in standby mode; after receiving the test mold, any compactor automatically completes centering and clamping, and then hammers according to the procedure; the high-precision displacement sensor integrated on the compaction hammer guide device measures the height of the upper surface of the specimen in real time after each hammering, forming a continuous compaction height sequence; after the number of single-side compactions reaches the set value, the compactor automatically releases the clamping;

[0066] Next, the test mold is transferred by the robotic arm to a dedicated flipping mechanism. The magnetic chuck of the flipping mechanism fixes the test mold base plate, the robotic arm holds the test mold sleeve and lifts it, and another robotic arm holds the mold cylinder in the middle of the test mold containing the test piece and lifts it, separating the three. Subsequently, the other robotic arm holds the mold cylinder and rotates it precisely 180 degrees to complete the flipping of the test piece. Finally, the other robotic arm descends to reset the mold cylinder to the base plate, and the robotic arm descends to reset the sleeve, completing the mold closing. After the mold is closed, the test mold is transferred by the robotic arm to another idle compaction machine for a second compaction. After both sides are compacted, the test mold is transferred to the cooling area.

[0067] The third stage is post-processing. The molded specimen, cooled to room temperature, is transported to an automatic demolding machine by a conveyor. The ejection mechanism of the demolding machine smoothly ejects the molded specimen from the mold. Subsequently, the specimen passes through a labeling or marking machine, and according to the task information issued by the central control module, a unique QR code or laser-engraved number is automatically affixed to the side of the specimen. Finally, the marked specimen is placed in the designated slot of the intelligent storage rack by a robotic arm or conveyor belt. The slot information is bound to the specimen identification and uploaded to the management system.

[0068] In this embodiment, by connecting the spatial assembly line layout with the robotic arm / transfer system, the unavoidable waiting time and manual operation time between processes in the existing process are integrated into continuous and overlapping mechanical operation time, thereby compressing the single specimen preparation cycle; the scheduling strategy of double-sided compaction instrument cross-operation further halves the time occupied by the longest process, compaction; for the flipping action necessary for double-sided compaction, the decomposition, flipping, and resetting process in this embodiment, through the coordination of multiple actuators, reliably completes the fine operation of the combined tooling without human intervention, solves the key non-standard action problem in unmanned assembly lines, ensures the continuity of the entire process, and makes the physical execution module no longer a simple collection of multiple automated devices, but an organic whole that can respond to upper-level intelligent decisions and output high-quality standardized specimens.

[0069] In some embodiments of the present invention, the central control module is deployed on a local industrial computer and has a built-in task scheduling submodule, data acquisition submodule, data association submodule and communication submodule. The communication submodule uses industrial Ethernet to establish bidirectional communication connections with the allocation design module and the physical execution module respectively. The communication protocol adopts TCP / IP protocol to ensure stable and low latency data transmission.

[0070] Specifically, the central control module receives the initial mix design task issued by the mix design module through the communication submodule. The task includes the mix proportion parameters of aggregates, mineral powder, and asphalt, process parameters such as mixing temperature, mixing time, number of tamping passes, and number of compaction passes, as well as quality parameters such as target height and target density of the specimen. After reading the above parameters, the task scheduling submodule generates the corresponding operation instruction sequence according to the process sequence of the physical execution module. The instruction sequence includes the start time, action parameters, and process connection sequence of each unit. For the fully automatic Marshall compactor cluster, the instruction sequence specifies the cross-operation triggering condition of the first compactor and the second compactor. The triggering condition is that any compactor completes single-sided compaction and releases the test mold compression state.

[0071] The task scheduling submodule sends a sequence of work instructions to the physical execution module through the communication submodule, and simultaneously activates the data acquisition submodule. The data acquisition submodule pre-sets a list of multi-source working condition data, which includes aggregate weighing values, asphalt injection volume, mixing temperature, mixing time, and mixture conveying temperature of the batching and transfer unit; tamping frequency, tamping depth, mold rotation trajectory parameters, number of tamping passes, specimen height after each tamping pass, and compactor motor current of the unmanned compaction preparation unit; and specimen demolding pressure, identification information, and storage location information of the post-processing and labeling unit. The data acquisition submodule receives real-time data uploaded by sensors from each unit through the communication submodule, and the acquisition frequency is matched with the work cycle of each unit. For example, the acquisition frequency is 10Hz in the batching and weighing stage and 1Hz in the compaction stage, ensuring that the acquired data covers the entire test process.

[0072] The data association submodule assigns a unique identification code to each specimen. It establishes a one-to-one correspondence between the collected multi-source data and the specimen identification code, based on timestamps and spatial workstation information, forming a complete data chain for each specimen. This data chain includes all operational data from material preparation to warehousing, as well as test result data, such as specimen density, porosity, and specimen height. Simultaneously, the data association submodule performs real-time verification of the collected data. The verification rule is whether the parameters of each process are within preset threshold ranges. When situations arise, such as aggregate weighing deviations exceeding thresholds or abnormal specimen height change rates during compaction, the data association submodule triggers the task scheduling submodule. The task scheduling submodule adjusts subsequent operation instructions according to preset rules; for example, when the specimen height is lower than the target value, the loading quality of subsequent specimens is adjusted.

[0073] The communication submodule of the central control module transmits the full-process data chain bound to the identity code back to the proportioning design module according to a preset cycle. The transmission cycle is consistent with the single specimen preparation cycle, ensuring that the proportioning design module can obtain test data in real time for proportion optimization.

[0074] In this embodiment, the central control module achieves orderly connection of each unit of the physical execution module through task decomposition and collaborative scheduling, avoiding process waiting and improving equipment utilization; it forms a traceable full-process data chain through real-time acquisition and correlation of multi-source data; it achieves adaptive control of the test process through data verification and dynamic adjustment of instructions, improving the consistency of specimen quality; and the cooperation of various technical features makes the central control module the hub connecting design and execution, realizing intelligent control of the test process.

[0075] More specifically, the task scheduling submodule improves the matching degree between the final height of the specimen and the target height by dynamically adjusting the loading quality during continuous trial compaction. Its technical solution is further optimized based on the above embodiment. Specifically, the storage unit of the central control module pre-stores a mass-height relationship model, which is constructed based on the volume-mass physical correlation of the Marshall specimen. The model expression is as follows:

[0076]

[0077] Where m is the mass of the specimen loaded in g; k is the compaction correction coefficient for the mixture, dimensionless; and ρ is the bulk relative density of the compacted asphalt mixture specimen in g / cm³. 3The parameter is taken from the preset value in the mix design task; D is the inner diameter of the Marshall mold in cm, which is an inherent parameter of the mold; h is the target height of the specimen in cm. The compaction correction coefficient k is obtained through pre-test calibration. The calibration method is as follows: select asphalt mixture of the same type as the specimen to be tested, conduct compaction tests with at least 3 groups of different charge masses, measure the final height of each group of specimens, and calculate the value of compaction correction coefficient k through linear fitting. After calibration, the k value is stored in the storage unit.

[0078] During the execution of the job instruction sequence, the task scheduling submodule simultaneously performs a dynamic optimization process for loading quality. The specific steps are as follows:

[0079] Step T1: The task scheduling submodule extracts the initial target loading mass m0 from the received initial mix design task. This parameter is calculated by combining the target height h0 and target density ρ0 with the above-mentioned mass-height relationship model. The task scheduling submodule uses m0 as the loading mass instruction for the first specimen and sends it to the weighing system of the batching and transfer unit through the communication submodule.

[0080] Step T2: After the first specimen completes double-sided compaction and is transferred to the cooling zone, the data acquisition submodule collects the final measured height h1 of the specimen. The data association submodule binds this final height data with the specimen identification code and transmits it to the task scheduling submodule. The task scheduling submodule calls the quality-height relationship model to calculate the deviation value Δh = h1 - h0 between the final measured height h1 and the target height h0. Based on the deviation value Δh, the basic compensation amount Δm1 of the loading quality is calculated. The calculation formula is:

[0081]

[0082] When the deviation value Δh is positive, the basic compensation amount Δm1 is negative; when the deviation value Δh is negative, the basic compensation amount Δm1 is positive.

[0083] Step T3: The task scheduling submodule calls the multi-source working condition data collected by the data acquisition submodule and extracts the compaction height disturbance characteristic parameters from it. The compaction height disturbance characteristic parameters include at least the mixture consistency and the mixture temperature. The mixture consistency is detected online using an asphalt mixture consistency tester. A sampling and testing station is set at the outlet of the insulated pipe of the batching and transfer unit. Before the mixture is transported to the molding station, the sampling mechanism automatically collects a preset mass of hot mixture sample and transfers it to the consistency tester. The tester applies a constant shear rate to the hot mixture through a built-in rotating probe and collects the resistance torque experienced by the probe during rotation in real time. The resistance torque is positively correlated with the mixture consistency. The tester has a built-in calibration curve, which is obtained by fitting the pre-test data of the same type of mixture. The resistance torque value can be directly converted into the mixture consistency value. The temperature of the mixture is collected in real time by a temperature sensor at the insulated pipe of the batching and transfer unit; the storage unit pre-stores a table of disturbance parameter compensation coefficients, which is based on a pre-test. The pre-test method is as follows: control the loading mass to remain constant, adjust the temperature and consistency of the mixture to conduct a compaction test, measure the change in specimen height under different parameter combinations, and calculate the corresponding disturbance parameter compensation coefficient α; the task scheduling submodule retrieves the corresponding compensation coefficient α based on the real-time collected mixture consistency and temperature.

[0084] Step T4: The task scheduling submodule multiplies the basic loading mass compensation amount Δm1 by the compensation coefficient α to obtain the adjusted loading mass compensation amount Δm = Δm1 × α; the task scheduling submodule then calculates the current specimen loading mass m. n Add the adjusted charge mass compensation Δm to obtain the charge mass m of the next specimen. n+1 =m n +Δm; The task scheduling submodule writes the loading quality into a new operation instruction sequence and sends it to the batching and transfer unit through the communication submodule to guide the loading operation of the next specimen; After each specimen is compacted, the task scheduling submodule repeats the above steps T2 to T4 to achieve dynamic iterative optimization of the loading quality during continuous compaction.

[0085] In this embodiment, the mass-height relationship model is constructed based on a well-defined physical formula, and the model parameters can be calibrated through conventional pre-experiments in the field. The loading quality optimization process is executed independently by the task scheduling submodule without the intervention of the proportioning design module, thus shortening the response time for parameter adjustment. By introducing disturbance characteristic parameters such as mixture consistency and temperature, the basic compensation amount is corrected to avoid inaccurate loading quality adjustment caused by single height deviation compensation. This function works in conjunction with the original process scheduling function of the task scheduling submodule to improve the consistency of specimen height without adding test procedures.

[0086] In some embodiments of the present invention, the proportioning design module is deployed on a cloud server and has built-in data parsing submodule, deviation attribution factor module, parameter optimization submodule and task generation submodule, and establishes a two-way communication connection with the central control module through a communication network. The method for generating the initial mix design task by the mix design module is implemented based on the technical solution described in the invention patent application number CN202410816703.5, "An Intelligent Design Method and System for Asphalt Mixture Proportion". The specific process is as follows: determine the asphalt construction quality target, select the base asphalt category and asphalt mixture components according to the quality target; input the quality target, base asphalt category and asphalt mixture components into a hybrid neural network mix design model trained based on an asphalt mixture mix design database to generate an initial asphalt mixture mix design scheme; acquire engineering environmental data information of the current project construction, extract key features affecting the mix design scheme, and correct the initial mix design scheme according to the key features; construct a digital twin quality prediction model based on the engineering environmental data information, input the quality target and the corrected mix design scheme into the model for implementation simulation, optimize again according to the simulation results, form the initial mix design task, and send it to the central control module.

[0087] Furthermore, the optimization process for the mix design module when the test results do not meet the preset design objectives is as follows:

[0088] Step S1: The data parsing submodule receives the full-process data chain with the bound specimen identification code uploaded by the central control module. The data chain includes aggregate weighing deviation values, asphalt injection volume, mixture mixing temperature and heat preservation time of the batching and transfer unit, tamping frequency, tamping depth, compaction curve characteristic parameters, and final specimen height of the unmanned compaction preparation unit, and specimen density, porosity, and other test result data of the post-processing and identification unit. The data parsing submodule verifies the data integrity according to preset rules, removes invalid data, extracts key performance indicators from the test result data, compares them with preset design targets, and determines the performance indicator deviation value. When the deviation value of any key performance indicator exceeds the allowable range, the deviation factorization module is triggered.

[0089] Step S2: The deviation attribution module retrieves the full-process operating condition data from the data chain and performs deviation attribution analysis based on the volume composition mechanism of asphalt mixture and the influence law of process parameters. If the specimen void ratio is higher than the preset target, the characteristic parameters of the compaction curve and the mixing temperature of the mixture are checked simultaneously. When the slope of the compaction curve tends to flatten in the later stage and the mixing temperature of the mixture is lower than the preset value, based on the well-known mechanism that the consistency of the mixture increases with the decrease of temperature and the fluidity deteriorates, resulting in insufficient compaction, it is determined that the void ratio exceeds the standard due to excessive mixture consistency and insufficient compaction. When the slope of the compaction curve is normal and the aggregate weighing data shows that the proportion of fine aggregate is lower than the design value, based on the volume composition mechanism that insufficient fine aggregate cannot fill the gaps between coarse aggregate, it is determined that the void ratio exceeds the standard due to unreasonable aggregate gradation. If the specimen height deviation exceeds the allowable range, combined with the charging quality compensation record and the mixture temperature data, based on the law that temperature fluctuation affects the compaction of the mixture and thus changes the specimen height, it is determined that the deviation is caused by inaccurate charging quality adjustment or mixture temperature fluctuation.

[0090] Step S3: The parameter optimization submodule adjusts material and process parameters based on the deviation attribution results. For performance deviations caused by unreasonable aggregate gradation, it adjusts the proportion of each aggregate grade; if the fine aggregate proportion is insufficient, it appropriately increases the fine aggregate proportion; if the coarse aggregate skeleton is unstable, it increases the coarse aggregate proportion. For performance deviations caused by abnormal mixture consistency, it adjusts the asphalt dosage or mixing temperature; if the consistency is too high, it reduces the asphalt dosage or increases the mixing temperature, and vice versa for too low consistency. For performance deviations caused by insufficient compaction, it adjusts the number of compaction passes or the tamping frequency to improve the mixture density. (Parameter optimization submodule) The parameter adjustment range is determined based on a parameter adjustment reference table established through pre-tests. The parameter adjustment reference table is calibrated through pre-tests using the controlled variable method. The specific calibration method is as follows: select asphalt mixtures of the same type, fix other parameters, and adjust parameters such as aggregate gradation, asphalt content, and mixing temperature individually. Measure the impact of parameter changes on performance indicators such as porosity and height of the specimens, establish a one-to-one correspondence table between performance deviation values ​​and parameter adjustment ranges, and store it in the mix design module. The parameter optimization submodule directly retrieves the adjustment range from the reference table based on the performance deviation values ​​and adjusts the material and process parameters accordingly.

[0091] Step S4: The task generation submodule integrates the adjusted material and process parameters to generate a mix design optimization task. The task includes optimized aggregate gradation, asphalt content, mixing temperature, number of tamping passes, and number of compaction passes. Simultaneously, the preset performance targets corresponding to the optimization task are updated. The task generation submodule sends the mix design optimization task to the central control module via the communication network. The central control module generates a new sequence of operation instructions based on the optimization task, driving the physical execution module to perform a new round of compaction tests. After the new round of tests is completed, the central control module uploads the new full-process data chain to the mix design module. The mix design module repeats the above steps until the test results meet the preset design targets.

[0092] In this embodiment, the proportioning design module performs deviation attribution analysis by integrating full-process operating data, making parameter adjustment more targeted and avoiding the blindness of traditional optimization methods; it simultaneously adjusts material parameters and process parameters to achieve the adaptation of material properties and process conditions, improving the consistency of specimen performance; the entire optimization process requires no manual intervention and forms a complete self-optimization process with the central control module and physical execution module, making proportioning optimization work more efficient and accurate.

[0093] Specifically, the detailed implementation example of the AC-20 asphalt mixture mix proportion optimization cycle is as follows:

[0094] Step P1: Designers establish work order task information through the mix design module deployed in the cloud. This module combines the construction quality target of AC-20 asphalt mixture, calls the built-in hybrid neural network mix model and digital twin quality prediction model to generate the initial mix proportion A. After being integrated by the task generation sub-module, it is sent to the central control module deployed on the local industrial control computer through the communication network.

[0095] Step P2: After receiving the mix ratio A, the communication submodule of the central control module reads the material and process parameters, generates the corresponding work instruction sequence, and sends it to the batching and transfer unit of the physical execution module. According to the instructions, the batching and transfer unit automatically weighs each grade of aggregate and mineral powder from the integrated silo, including 2452g of material #1, 2299g of material #2, 843g of material #3, 1916g of material #4, and 153g of mineral powder. 336g of asphalt is injected through a high-precision asphalt metering device. The weighed materials are put into the mixing pot and mixed at the mixing temperature of 165℃ and the mixing time of 180s as set in the instructions. The mixed material is temporarily stored in a storage silo with heat preservation function. After heat preservation for 2 hours, the heat-preserved screw conveyor at the bottom of the storage silo transports the mixed material to the molding station through closed-loop control of start-stop time and speed.

[0096] Step P3: The robotic arm of the physical execution module grabs the Marshall test mold and transfers it to the mold assembly station. The loading robot injects 1200g of mixture according to the operation instructions. At this time, the temperature of the mixture is 150℃. The data acquisition submodule synchronously collects the conveying temperature data of this batch of mixture and binds it with the spatial station information of the test mold.

[0097] Step P4: The completed mold is transferred by a robotic arm to the automatic tamping device of the unmanned compaction preparation unit. The rotary drive component drives the mold platform to rotate along a preset mosquito coil-shaped trajectory. The heated tamping rod of the tamping execution component vertically reciprocates at a constant frequency, and the insertion depth is precisely controlled by a servo mechanism. After tamping, the robotic arm transfers the mold to an idle compactor in the fully automatic Marshall compactor cluster. The central control module schedules the compactor to automatically complete centering and compaction, and start double-sided compaction, compacting 75 times on each side. During the compaction process, the high-precision displacement sensor and compaction power sensor integrated into the compaction hammer guide device... Real-time data acquisition: The data acquisition submodule receives data at a frequency of 1Hz, while the task scheduling submodule simultaneously plots the compaction curve. After compaction, the data acquisition submodule records the final height of the specimen as 61.5mm, and the data association submodule assigns a unique identification code "0111260001-A-01" to the specimen, binding the height data, compaction parameters, and identification code to form a single specimen process data segment. The central control module calls the pre-calibrated mass-height relationship model and, combined with the target height requirement of 63.5±1.3mm, determines that the specimen height does not meet the preset standard and the loading quality needs to be adjusted.

[0098] Step P5: The test mold is transferred to the post-processing and marking unit by the robotic arm. The ejection mechanism of the automatic demolding machine smoothly ejects the test piece. The laser marking machine marks the test piece with the identification code "0111260001-A-01". The identification includes the mix ratio number, test time and identification code. Then the robotic arm transfers the test piece to the designated grid of the intelligent storage rack. The grid information and identification code are automatically bound and uploaded to the central control module.

[0099] Step P6: The task scheduling submodule of the central control module calculates the basic compensation amount for the loading mass based on the mass-height relationship model and the collected temperature data of the mixture. After adjusting the disturbance parameters, the loading mass of the next specimen is determined to be 1210g. The task scheduling submodule generates a new sequence of operation instructions and sends them to the loading robot and subsequent units. The loading robot injects the mixture at 1210g. After the test mold undergoes processes such as tamping and compaction, the data acquisition submodule records the final height of the specimen as 63.3mm. After the data association submodule binds the data, the central control module determines that it meets the target requirements. Following this process, the system continuously completes the preparation of 4 specimens. Each specimen is assigned a unique identification code, forming a complete end-to-end data chain.

[0100] Step P7: After all specimens have cooled to room temperature, the density weighing equipment in the post-processing and labeling unit automatically collects the density data of each specimen. The central control module summarizes the data and calculates that the average bulk relative density of the specimen group is 2.410 and the porosity is 5.1%. The data association submodule binds the test results data of this group with the full-process working condition data of mix proportion A. The central control module transmits the data back to the cloud proportion design module through the communication submodule according to the preset cycle corresponding to the single specimen preparation cycle.

[0101] Step P8: The data parsing submodule of the mix design module receives the data link, verifies its integrity, extracts the porosity index, compares it with the preset target range of 4.3%-4.5%, determines that the deviation value exceeds the allowable range, and triggers the deviation attribution module. The deviation attribution module retrieves the full-process working condition data, conducts attribution analysis based on the volume composition mechanism of asphalt mixture, and combines the characteristic parameters of the compaction curve and aggregate weighing data to determine that the excessive porosity is caused by unreasonable aggregate gradation. The parameter optimization submodule calls the pre-stored parameter adjustment comparison table to determine the adjustment direction and range of aggregate gradation, fine-tunes the proportion of each aggregate grade, and generates mix proportion B. The task generation submodule sends mix proportion B to the central control module.

[0102] Step P9: After the central control module receives the mix ratio B, the task scheduling submodule generates a new sequence of operation instructions, and the physical execution module starts a new round of unmanned testing. Throughout the entire optimization cycle, the smart screen displays the operating status of each module, specimen identification code, key parameter data, and test progress in real time. All operating condition data and test result data are traceable through the full-process data chain.

[0103] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A digitally driven, unmanned, automated Marshall compaction test system for asphalt mixtures, characterized in that, include: The mix design module is used to generate and issue initial mix design tasks for asphalt mixtures; The physical execution module includes a cluster of automated equipment used to sequentially perform batching and weighing, mixing, molding, tamping, double-sided compaction, demolding, and specimen labeling and storage. The central control module is communicatively connected to both the proportioning design module and the physical execution module, and is configured as follows: Receive the initial mix design task and generate the corresponding work instruction sequence; The scheduling and control module executes the test operations according to the sequence of work instructions, and collects multi-source working condition data and corresponding test result data in real time during the execution of the test operations. The multi-source operating condition data and its corresponding test results data are sent back to the proportioning design module. In response to the test results data not meeting the preset design target, the mix design module optimizes the initial mix design task based on the multi-source working condition data, test results data, and preset design target, generates an optimized mix design task, and re-executes the compaction test accordingly. When the central control module performs a continuous trial molding and compaction process, the central control module is also configured to: Extract the initial target charge mass from the initial mix design task; After each compaction test, the final height of the corresponding specimen is obtained. Based on the deviation between the final height and the target height, the pre-calibrated mass-height relationship model is used to calculate the charge mass compensation amount for the next specimen. The compaction height disturbance feature is extracted from the multi-source working condition data to obtain the compaction height disturbance feature parameters; the compaction height disturbance feature parameters include the mixture consistency and the mixture temperature; The charge mass compensation is adjusted based on the compaction height disturbance characteristic parameters, and the charge mass of the next specimen is obtained by combining the charge mass of the corresponding specimen.

2. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 1, characterized in that, The physical execution module includes: The batching and transfer unit is used to collect, weigh, mix, mold, and heat-insulate transport materials according to the initial mix design task or the mix optimization design task. The unmanned compaction preparation unit is used to receive the mixed material and perform tamping and double-sided compaction operations in sequence. The post-processing and labeling unit is used for demolding, labeling, and storing the compacted and cooled specimens.

3. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 2, characterized in that, The unmanned compaction preparation unit includes: An automatic tamping device includes a rotary drive component and a tamping execution component, used to automatically tamp the mixture after it has been molded; the rotary drive component is used to drive the platform carrying the Marshall test mold to rotate around a vertical axis, and the tamping execution component is located above the platform and includes a heated tamping rod capable of vertical reciprocating motion; The fully automatic Marshall compactor cluster includes a first compactor and a second compactor arranged side by side and operating in a cross manner under the scheduling of the central control module. It is used to compact the test mold on both sides after tamping and to collect the specimen height data in real time during the compaction process.

4. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to any one of claims 1-3, characterized in that, The proportioning design module is configured to perform the following optimization steps: Based on the deviation between the received test results data and the preset design target, and in conjunction with the corresponding multi-source operating condition data, a deviation attribution analysis is performed to determine the types of key factors leading to performance deviations. Based on the type of key factor, call the pre-stored parameter adjustment lookup table to determine the direction and magnitude of adjustment for material parameters and / or process parameters; The initial mix design task is adjusted based on the adjustment direction and magnitude to generate the mix optimization design task.

5. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 4, characterized in that, The parameter adjustment reference table was obtained through pre-experiment calibration using the controlled variable method, and its construction method includes: Select asphalt mixtures of the same type, fix other parameters, and adjust one parameter at a time: aggregate gradation, asphalt content, mixing temperature, and number of compaction times. Measure the effect of the parameter change on the porosity and height performance of the specimens, and establish the correspondence between performance deviation values ​​and parameter adjustment ranges.

6. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 1, characterized in that, The central control module has a built-in data association submodule. The data association submodule assigns a unique identification code to each specimen and binds the collected multi-source working condition data and test result data with the identification code according to the timestamp and spatial work position information to generate a single specimen full-process data chain.

7. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 2, characterized in that, The post-processing and labeling unit uses a laser marking machine or a QR code labeling machine to assign unique identification information to the specimens. The unique identification information includes the mix proportion number, test time, and specimen identification code. The post-processing and labeling unit also includes an intelligent storage rack, which is used to automatically bind the specimen storage location with the unique identification information.

8. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 1, characterized in that, The proportioning design module is deployed on a cloud server, and the central control module is deployed on a local industrial computer; The central control module transmits the test result data and the associated multi-source operating condition data back to the proportioning design module according to a preset cycle that is the same as the single specimen preparation cycle.

9. The digitally driven Marshall unmanned automated compaction test system for asphalt mixtures according to claim 4, characterized in that, The deviation attribution analysis includes: Based on the compaction curve characteristic parameters and mixture temperature in the multi-source working condition data, determine whether the performance deviation is caused by abnormal mixture consistency. Based on the actual weighing ratio of aggregates in the multi-source working condition data, determine whether the performance deviation is caused by unreasonable aggregate gradation.

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