An asphalt paving parameter optimization method, device and medium based on digital twinning

Abstract

The application discloses an asphalt paving parameter optimization method and device based on digital twinning, and a medium, relates to the technical field of construction optimization, and comprises the following steps: collecting real-time working environment and construction parameters of a paver, fusing and processing the real-time working environment and the construction parameters through an Internet of Things method, and generating asphalt paving construction data; analyzing paving parameters of each paver in the paving process according to construction quality optimization parameters; performing whole life cycle analysis on the paving parameters, combining historical operation data, predicting future construction parameter change trends, adjusting paver operation parameters, optimizing construction efficiency in the construction process, and generating an asphalt paving optimization scheme. The efficiency and stability of the construction process are ensured, and the quality and construction efficiency of asphalt paving are optimized.

Description

Technical Field

[0001] This invention relates to the field of construction optimization technology, and in particular to a method, equipment and medium for optimizing asphalt paving parameters based on digital twins. Background Technology

[0002] Asphalt paving plays a crucial role in highway construction, involving paver operation, paving layer quality control, and precise adjustment of various parameters during construction. The application of the Internet of Things (IoT) and digital twin methods is increasingly becoming a research focus. Through real-time data collection and feedback, it promotes the intelligent development of asphalt paving operations, improving construction accuracy and quality control. Digital twin technology creates virtual models of the construction process, synchronizing construction data in real time to achieve precise monitoring and optimization, driving the dynamic adjustment and optimization of construction parameters. By monitoring construction data in real time, existing methods have optimized and adjusted construction parameters within a certain range.

[0003] Traditional methods still have some limitations in terms of real-time performance and accuracy. They rely on human experience and on-site operation adjustments, and cannot make accurate responses to changes in the paver's working environment in real time, which may lead to significant deviations in construction results. Although IoT methods have been applied, they still lack comprehensive data-driven optimization and are difficult to provide real-time feedback and adjustment of construction parameters during construction, especially in terms of paving quality and construction parameter optimization under the influence of multiple environmental variables. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a digital twin-based method for optimizing asphalt paving parameters, which solves the problem of not being able to accurately adjust construction parameters in real time according to environmental changes.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, this invention provides a method for optimizing asphalt paving parameters based on digital twins. The method includes: collecting real-time working environment and construction parameters of the paver; fusing and processing the real-time working environment and construction parameters using an Internet of Things (IoT) approach to generate asphalt paving construction data; simulating the asphalt paving construction data to predict the construction effect during the paving process in real time, analyzing the impact of different construction parameters on the construction effect, and generating construction adjustment suggestions; real-time monitoring of the smoothness and quality of the paved layer to obtain actual paving conditions, comparing the construction adjustment suggestions with the actual paving conditions to obtain deviation information, adjusting construction parameters using the deviation information, and generating construction quality optimization parameters; analyzing the paving parameters of various pavers during the paving process based on the construction quality optimization parameters; performing a full lifecycle analysis of the paving parameters and combining historical operation data to predict future trends in construction parameter changes, and optimizing construction efficiency during the construction process by adjusting the paver's operating parameters, thereby generating an optimized asphalt paving scheme.

[0007] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the following steps are taken: The real-time working environment and construction parameters of the paver are collected, and the real-time working environment and construction parameters are fused and processed using IoT methods to generate asphalt paving construction data. Collect the real-time working environment and construction parameters of the paver to obtain the paver environment dataset, and preprocess the paver environment dataset using IoT methods to generate processed paving data; The processed paving data is synchronized and integrated using a data matching algorithm to generate fused paving information. The integrated paving information is archived and stored in a time series to generate asphalt paving construction data.

[0008] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the specific steps for simulating asphalt paving construction data and predicting the construction effect during the paving process in real time are as follows. Feature extraction and data fusion were performed on asphalt paving construction data, and paver operation features that have a significant impact on construction results were selected. A digital model of the paver's working status and construction process is established based on the paver's operating characteristics to simulate the paver's behavior and reaction in the actual construction environment and generate construction effects.

[0009] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the specific steps for analyzing the impact of different construction parameters on the construction effect and generating construction adjustment suggestions are as follows: The paver speed and hopper capacity are extracted from the construction parameters, and the impact of changes in paver speed on the construction effect is evaluated by sensitivity analysis to obtain the speed response. Based on velocity response analysis, the impact of changes in silo capacity on construction results is analyzed, identifying the continuous and uniform effects of silo capacity on the paving process, and generating a report on the impact of construction parameters. Based on the construction parameter impact analysis report, deviations and deficiencies in the construction process are identified, and construction adjustment suggestions are generated.

[0010] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the following steps are taken: Real-time detection of the smoothness and quality of the paving layer to obtain the actual paving conditions, and comparison of the construction adjustment suggestions with the actual paving conditions to obtain deviation information. Collect surface images and height data of the paving layer to obtain a paving layer dataset. Then, use machine vision methods to denoise, enhance contrast, and remove background interference from the paving layer dataset to obtain the actual paving conditions. The construction adjustment suggestions are compared with the actual paving conditions point by point to generate deviation information.

[0011] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the specific steps for adjusting construction parameters using deviation information to generate construction quality optimization parameters are as follows: Based on the deviation information analysis, the influence relationship between the paver working mode and hopper capacity on the construction effect is analyzed, and the adjusted construction parameters are generated by adjusting the paver speed, hopper capacity and hopper control strategy. Based on the adjusted construction parameters, the paving process of the paver under different operating parameters is simulated by digital twin simulation, and the changes in the smoothness and compaction quality of the paved layer are predicted to generate construction quality optimization parameters.

[0012] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the specific steps of analyzing the paving parameters of various pavers during the paving process based on construction quality optimization parameters are as follows: Collect paver operating parameters, and based on construction quality optimization parameters, analyze the impact of paver vibration frequency, paving layer thickness, and paver pressure on construction quality, and generate a construction quality impact analysis report; Based on the construction quality impact analysis report, assess the need to adjust the paver speed and hopper capacity, and generate paving parameters.

[0013] As a preferred embodiment of the digital twin-based asphalt paving parameter optimization method of the present invention, the following steps are taken: performing a full lifecycle analysis of paving parameters and combining historical operation data to predict future trends in construction parameters, and optimizing construction efficiency during the construction process by adjusting paver operating parameters to generate an optimized asphalt paving scheme. By analyzing the changes in the operating parameters of the paver at different construction stages, the long-term impact of the paving parameters at each stage on the construction effect is analyzed, and a life cycle analysis report is generated. By combining life cycle analysis reports with historical operation data, statistical analysis methods are used to predict future trends in construction parameters. Based on the future construction parameter change trends, analyze the change trends of paver speed and hopper capacity, adjust paver operation settings, and generate adjusted paver operation parameters. Based on the adjusted paver operating parameters, the paving quality and efficiency during the construction process are evaluated through digital twin simulation, and an optimized asphalt paving scheme is generated.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the digital twin-based asphalt paving parameter optimization method as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the digital twin-based asphalt paving parameter optimization method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By collecting real-time working environment and construction parameters of the paver, and integrating these parameters using digital twin methods and IoT technology, asphalt paving construction data is generated, thus providing basic data support for subsequent construction effect prediction and parameter optimization; by analyzing various paver operating parameters during the paving process through construction quality optimization parameter analysis, paving parameters are generated, and the optimization analysis of these parameters improves the paving quality and efficiency during construction, ensuring continuous improvement of construction results; the implementation of the digital twin technology solution ensures the high efficiency and stability of the construction process and optimizes the quality and construction efficiency of asphalt paving. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for optimizing asphalt paving parameters based on digital twins.

[0019] Figure 2 This is a flowchart of data acquisition and fusion processing.

[0020] Figure 3 This is a flowchart for detecting and comparing deviations in actual paving conditions.

[0021] Figure 4 Generate flowcharts for full lifecycle analysis and optimization solutions. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for optimizing asphalt paving parameters based on digital twins, comprising the following steps: S1. Collect the real-time working environment and construction parameters of the paver, and use the Internet of Things (IoT) method to fuse and process the real-time working environment and construction parameters to generate asphalt paving construction data.

[0026] S1.1 Collect the real-time working environment and construction parameters of the paver, obtain the paver environment dataset, and preprocess the paver environment dataset using IoT methods to generate processed paving data.

[0027] It should be noted that the real-time operating environment of the paver is collected, including multiple data points such as temperature, humidity, pressure, position, speed, and operating height. These data are transmitted in real-time to the data acquisition platform, forming a paver environment dataset. After acquiring the paver environment dataset, IoT methods are used to preprocess it. During data cleaning, invalid and duplicate data are removed. The preprocessed paver environment dataset is the processed paving data.

[0028] S1.2. The processed paving data is synchronized in time and integrated with information through a data matching algorithm to generate fused paving information.

[0029] It should be noted that time-stamp matching is performed on data collected from different sensors to ensure that each data record is consistent with the paver's working time. Interpolation algorithms are used to fill gaps between time points, ensuring the continuity and integrity of the time-series data. Environmental data collected from different sensors are integrated using matching algorithms to form unified fused paving information. This integration process needs to consider the differences in data sources; for example, GPS location data and temperature sensor data may have different sampling frequencies. Through data interpolation and fusion methods, all data can be compared and analyzed on a unified timeline to generate fused paving information.

[0030] It should also be noted that data matching algorithms are methods used to align and synchronize data from different data sources or sensors. The core objective is to pair data collected at different times based on timestamps or other matching identifiers, thereby forming a consistent and comparable dataset.

[0031] S1.3. The integrated paving information is archived and stored in a time series to generate asphalt paving construction data.

[0032] It should be noted that the integrated paving information is sorted by time to ensure that all data is arranged according to the collection time, forming a complete time series. The construction of the time series needs to ensure that the environmental data and construction parameters at each time point accurately correspond, so as to trace the paving process at each moment in subsequent analysis. A time series storage method is used to archive the environmental data, integrating different types of environmental data (such as temperature, humidity, pressure, paver position, etc.) with construction parameters (such as paver speed, silo capacity, etc.) in chronological order and storing them in a format that facilitates rapid retrieval. During data storage, it is essential to ensure that data at each moment is accurately recorded and to prevent loss or duplication, resulting in asphalt paving construction data.

[0033] S2. Simulate the asphalt paving construction data, predict the construction effect in real time during the paving process, analyze the impact of different construction parameters on the construction effect, and generate construction adjustment suggestions.

[0034] S2.1. Perform feature extraction and data fusion on asphalt paving construction data, and screen out the paver operation features that have a significant impact on the construction effect.

[0035] It should be noted that construction parameter characteristics, such as paver operating speed, hopper capacity, paving layer thickness, vibration frequency, and paver pressure, are extracted from asphalt paving construction data. These construction parameter characteristics reflect the paver's operating status under different construction environments. Data fusion is performed, unifying data from different sensors (such as temperature, humidity, pressure, and paver speed) to eliminate redundancy and inconsistencies, ensuring that each data point accurately represents changes during the paving process. Multiple operational features, such as paver speed, hopper capacity, and paving layer thickness, are extracted from the asphalt paving construction data. The values ​​of each operational feature are changed one by one, and the impact of these changes on construction quality (such as paving layer smoothness and density) is observed. By comparing the sensitivity of different features to construction results, the operational features with the greatest impact are identified, thereby determining the parameters that need to be optimized.

[0036] It should also be noted that sensitivity analysis is a method used to assess the degree to which changes in input variables affect output results. By changing the values ​​of one or more input parameters and observing the impact of these changes on the model, sensitivity analysis can help identify which input variables affect the system's behavior. Sensitivity analysis is commonly used in optimization design, risk assessment, and decision support to ensure that key factors are adequately considered and to make appropriate adjustments in model design or practical operation.

[0037] S2.2 Establish a digital model of the paver's working status and construction process based on the paver's operating characteristics, simulate the paver's behavior and reaction in the actual construction environment, and generate construction effects.

[0038] It should be noted that paver operating characteristics (such as speed, hopper capacity, and vibration frequency) are used as input variables. Combined with the physical laws governing the construction process, a mathematical model reflecting the relationship between the paver's working state and the construction effect is constructed. The mathematical model simulates the paver's behavior under different operating conditions using numerical calculation methods, predicting construction effects (such as smoothness, density, and thickness). The establishment of the mathematical model relies on a detailed analysis of the paver's mechanical characteristics and the construction site environment to ensure accurate reflection of the dynamic changes during the paving process. The simulation process employs numerical calculation methods, using numerical iteration and simulation analysis to predict the paver's construction effects under different operating conditions, such as changes in the smoothness, density, and thickness of the paved layer. Through digital modeling of the paver's working state and the construction process, the paver's behavior and reactions can be simulated in real time, thereby generating the construction effect.

[0039] S2.3 Extract paver speed and hopper capacity from construction parameters, and evaluate the impact of paver speed changes on construction effect through sensitivity analysis to obtain speed response.

[0040] It should be noted that two parameters, paver speed and hopper capacity, are extracted from asphalt paving construction data. Data analysis is then performed on paver speed and hopper capacity to determine their range of variation. Numerical simulation is used to adjust the values ​​of paver speed and hopper capacity separately, observing the impact of different parameter combinations on the construction effect, and further identifying their sensitivity to key construction indicators such as paved layer smoothness and density. Through these operations, the optimal adjustment range for paver speed and hopper capacity can be obtained. By calculating the rate of change in construction effect under different paver speeds, the sensitivity of paver speed changes to the construction effect is evaluated. The sensitivity analysis results will show the degree of response of paver speed to the construction effect, that is, the change in construction effect caused by each adjustment of paver speed, thus obtaining the speed response.

[0041] It should also be noted that sensitivity analysis is a method for assessing the impact of changes in input variables on results. In this method, each operational characteristic (such as paver speed, hopper capacity, etc.) is adjusted one by one, and the effects of these changes on construction quality (such as smoothness and density) are observed. By comparing the impact of changes in different parameters on construction results, sensitivity analysis can identify characteristics that have a significant impact on construction quality, thereby providing a basis for optimizing paver operation, ensuring that key parameters in the construction process are optimized, and improving construction results.

[0042] The expression for calculating the rate of change of construction effect under different paver speeds is: ; in, The rate of change in construction effect under different paver speeds; The speed of the paver is The impact of paver speed on construction results is quantified using construction quality indicators such as smoothness and density. Smoothness reflects the surface smoothness of the paved layer, while density measures the compaction degree of the paved layer. These values ​​are used to assess the specific impact of paver speed changes on construction quality. The speed of the paver is The construction effect refers to the smoothness and density of the paved layer. As a key indicator of construction quality, the smoothness and density values ​​are used in the calculation to reflect the specific impact of the paver speed on the paving quality. The higher the value, the better the construction quality. This is the initial value for the paver speed; This represents the change in the paver speed.

[0043] S2.4. Analyze the impact of the change in silo capacity on the construction effect based on the speed response, identify the continuity and uniformity effects of the silo capacity on the paving process, and generate an analysis report on the impact of construction parameters.

[0044] It should be noted that by adjusting the paver speed at multiple set values and recording the change in the quality of the paving layer after each adjustment. Sensitivity analysis quantifies the difference in construction effects brought by each speed change by comparing the impact amplitude of the paver speed change on the construction effect. For example, by comparing the changes in the flatness and density of the paving layer when the paver speed increases from low to high, calculate the change rate of the construction effect to identify the impact degree of the paver speed on the construction quality. Set the optimization range of the paver speed to ensure that the paver speed within the optimization range can maximize the construction effect (such as flatness, density, etc.). The setting of the optimization range is based on the impact degree of the paver speed change on the construction quality, and select a speed range that can both ensure the construction quality and improve the construction efficiency. Adjust different values of the silo capacity and the construction effect during the paving process, especially the impact on the continuity and uniformity of the paving layer. In the analysis process, use numerical simulation and simulation calculation methods to gradually change the silo capacity and observe the changes in the uniformity (such as thickness change) and continuity (such as gapless paving) of the paving layer. By comparing the construction effects under different silo capacities, evaluate the role degree of the silo capacity on the construction effect. Further, by analyzing the impact of the change in silo capacity on the paving process, identify its continuity and uniformity effects during the paving process. For example, a small silo capacity may lead to uneven paving layers, while a large silo capacity helps with the continuity and uniformity of paving, and generate an analysis report on the impact of construction parameters.

[0045] S2.5. According to the analysis report on the impact of construction parameters, identify the deviations and deficiencies in the construction process and generate construction adjustment suggestions.

[0046] It should be noted that by quantitatively analyzing the construction effect, calculate the deviation degree between the actual paving layer quality index and the target paving layer quality index parameters. Use the error analysis method to compare each parameter affecting the paving layer quality index during the construction process one by one, and focus on analyzing the impact degree of the paver speed and silo capacity on the paving layer quality index. By calculating the impact amplitude of different construction parameters on the construction effect, identify the link that has the greatest impact on the construction quality, especially the adjustment requirements of the paver speed and silo capacity, and determine the construction link that needs to be improved to ensure that all parameters in the construction process are within the optimal range and generate construction adjustment suggestions.

[0047] The expression for calculating the impact amplitude of different construction parameters on the construction effect is: ; Where, The extent to which different construction parameters affect the construction effect; For paver speed; This refers to the capacity of the silo. The sensitivity of construction results to changes in paver speed is determined by using sensitivity analysis to assess the impact of paver speed changes on construction quality. The sensitivity of construction results to changes in silo capacity is determined by using sensitivity analysis to assess the impact of changes in silo capacity on construction quality. This represents the change in paver speed; This represents the change in the capacity of the silo.

[0048] S3. Real-time detection of the flatness and quality of the paved layer, obtaining the actual paving conditions, comparing the construction adjustment suggestions with the actual paving conditions to obtain deviation information, using the deviation information to adjust the construction parameters, and generating construction quality optimization parameters.

[0049] It should be noted that existing methods rely on manual monitoring or simple sensor data collection to monitor the smoothness and quality of the paved layer. These methods mostly involve post-construction inspections, and adjustments depend on manual experience and on-site operation. They cannot provide timely feedback and adjustments based on the paver's specific operation and actual conditions, which may lead to quality fluctuations and deviations during construction.

[0050] This invention utilizes machine vision to detect the smoothness and quality of the paved layer in real time, comparing the real-time data with construction adjustment suggestions. By adjusting construction parameters based on deviation feedback, it ensures precise control at every stage of the paving process, achieving continuous optimization of construction quality. This method can respond to changes in the paving environment in real time, improving construction accuracy and efficiency while reducing manual intervention.

[0051] S3.1 Collect surface images and height data of the paving layer to obtain the paving layer dataset, and use machine vision methods to denoise, enhance contrast and remove background interference to obtain the actual paving situation.

[0052] It should be noted that surface images and height information of the paved layer are collected. These images and height information reflect the smoothness, thickness, and other surface features of the paved layer, resulting in a paved layer dataset. Noise in the images is removed using filtering algorithms (such as Gaussian filtering or median filtering) to ensure the accuracy of the paved layer dataset. Contrast stretching is used to enhance contrast, making the surface features of the paved layer more prominent for subsequent analysis. After denoising and contrast enhancement, background removal algorithms (such as background modeling and differencing methods) are used to remove background interference from the images, ensuring that only the actual condition of the paved layer is preserved.

[0053] It should also be noted that background removal algorithms are image processing methods used to separate the foreground and background from an image. Background removal algorithms remove background interference and highlight the foreground by identifying the differences between background information (such as static parts) and foreground objects (such as moving objects) in an image. Common methods include background modeling, background subtraction, and motion detection. Background removal algorithms typically utilize changes in time-series images to identify the background and improve the detection and recognition accuracy of foreground targets by removing background information.

[0054] S3.2 Compare the construction adjustment suggestions with the actual paving conditions point by point to generate deviation information.

[0055] It should be noted that each parameter in the construction adjustment suggestions should be matched with the corresponding data in the actual paving situation to ensure that the data compared are from the same time point or location. For each comparison data point, the difference between the construction adjustment suggestions and the actual paving situation should be calculated. For example, if there is a deviation between the paver speed set in the construction adjustment suggestions and the speed recorded during the actual paving process, the deviation value should be calculated, and its impact on the paving effect should be determined. Using the deviation calculation method, the difference value between each construction adjustment suggestion and the actual paving situation should be obtained, generating deviation information for each data point.

[0056] S3.3 Analyze the relationship between the paver's working mode and hopper capacity and the construction effect based on the deviation information, and generate adjusted construction parameters by adjusting the paver speed, hopper capacity and hopper control strategy.

[0057] It should be noted that, by comparing the proposed construction adjustments with the actual construction results, the impact of changes in paver speed and hopper capacity on the construction effect should be calculated. Sensitivity analysis should be used to assess the sensitivity of adjustments to the paver's operating mode and hopper capacity to construction quality. Based on the sensitivity, the optimization direction for paver speed, hopper capacity, and hopper control strategies should be determined. Adjustments to paver speed and hopper capacity should be made to achieve optimal coordination during construction, thereby improving construction results and generating adjusted construction parameters.

[0058] It should also be noted that the paver's operating mode includes a combination of operating parameters such as paver speed, hopper capacity, vibration frequency, paving layer thickness, and paver pressure. These parameters collectively affect the paver's performance and construction quality during the construction process.

[0059] S3.4 Based on the adjusted construction parameters, the paving process of the paver under different operating parameters is simulated by digital twin simulation, and the changes in the smoothness and compaction quality of the paved layer are predicted to generate construction quality optimization parameters.

[0060] It should be noted that the adjusted paver speed, hopper capacity, and other operating parameters are input into the simulation model. The simulation model predicts the paver's behavior and response under different operating conditions by real-time simulation of the paver's working state and the construction site environment. The simulation calculates the impact of different operating parameters on the smoothness and compaction of the paved layer. Changes in the smoothness and compaction quality of the paved layer are dynamically tracked using a digital twin model, and combined with physical laws, the degree of influence of the paver's working state on the construction effect is analyzed. Through multiple simulations, the construction effect under different combinations of operating parameters is evaluated, and the optimal paver operating parameters are identified. The paver operating parameters provide a basis for optimizing construction quality parameters and ensure that the smoothness and compaction quality during the paving process reaches the best level, generating optimized construction quality parameters.

[0061] S4. Analyze the paving parameters of various pavers during the paving process based on the construction quality optimization parameters.

[0062] S4.1 Collect paver operating parameters, and based on construction quality optimization parameters, analyze the impact of paver vibration frequency, paving layer thickness, and paver pressure on construction quality, and generate a construction quality impact analysis report.

[0063] It should be noted that various operational parameters of the paver, such as paver vibration frequency, paving layer thickness, and paver pressure, are collected, as these parameters significantly impact construction quality. Numerical calculations are used to analyze each of these parameters individually, assessing their influence on construction quality indicators such as paving layer smoothness, thickness uniformity, and compaction. For example, paver vibration frequency may affect paving layer compaction, while paver pressure may directly affect paving layer density and uniformity. The numerical range of paver operational parameters (such as paver speed and hopper capacity) is varied, and the changes in construction quality (such as smoothness and density) are observed. Then, the impact of each parameter change on construction quality is calculated, assessing its sensitivity to construction results. By comparing the differences in construction results under different parameter settings, the parameters with significant impact on construction quality are identified, thus determining the priority of optimization adjustments and generating a construction quality impact analysis report.

[0064] S4.2 Based on the construction quality impact analysis report, assess the adjustment needs of paver speed and hopper capacity, and generate paving parameters.

[0065] It should be noted that the analysis of construction quality influencing factors detailed in the construction quality impact analysis report should be conducted, with particular attention to the effects of paver speed and hopper capacity on the smoothness, thickness uniformity, and compaction of the paved layer. By comparing the differences in construction effects under different paver speeds and hopper capacities, the range within which these two parameters need adjustment during construction should be determined. For example, excessively high paver speeds may lead to uneven paved layers, while insufficient hopper capacity may result in insufficient continuity of the paved layer. Based on the required adjustment range, the optimal range for paver speed and hopper capacity should be evaluated, and paving parameters should be generated.

[0066] S5. Conduct a full life cycle analysis of paving parameters and combine them with historical operation data to predict future trends in construction parameters. By adjusting the paver's operating parameters, optimize the construction efficiency during the construction process and generate an optimized asphalt paving scheme.

[0067] It should be noted that existing methods rely on manually collecting and analyzing historical operational data to make simple adjustments to construction parameters. These methods depend primarily on experience in setting construction parameters and cannot predict future changes in construction parameters through systematic analysis. They also lack effective ways to optimize efficiency during the construction process. Therefore, adjustments to construction parameters are often delayed and difficult to achieve long-term optimization.

[0068] This invention performs a full lifecycle analysis of paving parameters and combines this with historical operational data to accurately predict future trends in construction parameters. Based on this prediction, the paver's operating parameters are dynamically adjusted, thereby continuously optimizing construction efficiency during the construction process. This method, through forward-looking analysis and intelligent adjustment, improves construction quality and efficiency, ensuring optimal control of all parameters during construction.

[0069] S5.1 By analyzing the changes in the operating parameters of the paver at different construction stages, analyze the long-term impact of the paving parameters at each stage on the construction effect and generate a life cycle analysis report.

[0070] It should be noted that operational parameter data for the paver should be collected at each construction stage (such as paving preparation, paving execution, and post-paving). This includes key parameters such as paver speed, hopper capacity, and paver pressure. Numerical simulation and data fitting methods, combined with historical construction data, should be used to calculate the cumulative impact of parameters such as paver speed and hopper capacity on the construction effect at different construction stages. This approach helps identify the long-term impact trends of operational parameters during construction, aiding in the prediction of changes in construction results and generating a lifecycle analysis report.

[0071] S5.2 Combine the life cycle analysis report with historical operation data, and use statistical analysis methods to predict future trends in construction parameters.

[0072] It should be noted that the conclusions of the life cycle analysis report regarding the long-term impact of paving parameters at each stage should be compared and integrated with the actual construction parameters in historical operational data. Through data matching, consistency between the two in terms of time and operational parameters should be ensured. The cumulative impact of changes in operational parameters at each construction stage on the construction effect should be calculated, the possible change paths of construction parameters in future construction should be assessed, and a future construction parameter prediction report should be generated.

[0073] S5.3. Based on the future trend of construction parameters, analyze the trend of paver speed and hopper capacity, adjust the paver operation settings, and generate the adjusted paver operation parameters.

[0074] It should be noted that the report's analysis of trends in paver speed and hopper capacity helps assess anticipated parameter changes during future construction. For example, it analyzes the fluctuation trends of paver speed at different construction stages and the long-term impact of hopper capacity changes on construction effectiveness. By reviewing historical operational data and combining it with a digital twin simulation model, a reasonable range for paver speed and hopper capacity changes is determined. Based on the predicted trends in future construction parameters, paver operating settings are adjusted. For example, if it is predicted that paver speed needs to be increased to improve construction efficiency or the smoothness of the paved layer, the paver speed is adjusted to a new optimized value. If changes in hopper capacity may lead to construction discontinuities, the hopper capacity is adjusted accordingly, generating adjusted paver operating parameters.

[0075] S5.4 Based on the adjusted paver operating parameters, the paving quality and efficiency during the construction process are evaluated through digital twin simulation, and an optimized asphalt paving scheme is generated.

[0076] It should be noted that the adjusted paver operating parameters (such as paver speed and hopper capacity) are input into the digital twin simulation model to simulate the paver's construction process under different operating conditions. Based on the paver's dynamic characteristics and the environmental conditions of the construction site, the digital twin simulation model calculates the paver's operating status and paving effect at each stage in real time. During the simulation, the impact of paver speed and hopper capacity on the smoothness, density, and construction efficiency of the paved layer is analyzed. Through multiple simulations, the optimal combination of operating parameters is identified, and the paving quality and efficiency during construction are optimized. The simulation results provide a basis for further adjustment of construction parameters, ensuring the maximization of construction objectives and generating an optimized asphalt paving scheme.

[0077] This embodiment also provides a computer device applicable to the asphalt paving parameter optimization method based on digital twins, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the asphalt paving parameter optimization method based on digital twins as proposed in the above embodiment.

[0078] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0079] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the digital twin-based asphalt paving parameter optimization method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0080] In summary, this invention achieves the following: First, it collects real-time operating environment and construction parameters of the paver, and then integrates these parameters using digital twin technology and IoT technology to generate asphalt paving construction data. This provides fundamental data support for subsequent construction effect prediction and parameter optimization. Second, it analyzes various paver operating parameters during the paving process to generate paving parameters. Optimizing these parameters improves paving quality and efficiency, ensuring continuous improvement in construction results. Third, the implementation of the digital twin technology ensures the high efficiency and stability of the construction process and optimizes the quality and effectiveness of asphalt paving.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing asphalt paving parameters based on digital twins, characterized in that: include, The real-time working environment and construction parameters of the paver are collected, and the real-time working environment and construction parameters are fused and processed through the Internet of Things to generate asphalt paving construction data. Simulation of asphalt paving construction data is used to predict the construction effect during the paving process in real time, analyze the impact of different construction parameters on the construction effect, and generate construction adjustment suggestions. The smoothness and quality of the paved layer are detected in real time to obtain the actual paving conditions. The construction adjustment suggestions are compared with the actual paving conditions to obtain deviation information. The construction parameters are adjusted using the deviation information to generate construction quality optimization parameters. Based on the construction quality optimization parameters, analyze the paving parameters of various pavers during the paving process; By conducting a full lifecycle analysis of paving parameters and combining them with historical operation data, we can predict future trends in construction parameters and optimize construction efficiency by adjusting paver operating parameters, thereby generating an optimized asphalt paving scheme.

2. The method for optimizing asphalt paving parameters based on digital twins as described in claim 1, characterized in that: The process involves collecting the real-time operating environment and construction parameters of the paver, and then fusing these parameters using IoT methods to generate asphalt paving construction data. The specific steps are as follows: Collect the real-time working environment and construction parameters of the paver to obtain the paver environment dataset, and preprocess the paver environment dataset using IoT methods to generate processed paving data; The processed paving data is synchronized and integrated using a data matching algorithm to generate fused paving information. The integrated paving information is archived and stored in a time series to generate asphalt paving construction data.

3. The method for optimizing asphalt paving parameters based on digital twins as described in claim 2, characterized in that: The specific steps for simulating asphalt paving construction data and predicting the construction effect in real time during the paving process are as follows. Feature extraction and data fusion were performed on asphalt paving construction data, and paver operation features that have a significant impact on construction results were selected. A digital model of the paver's working status and construction process is established based on the paver's operating characteristics to simulate the paver's behavior and reaction in the actual construction environment and generate construction effects.

4. The method for optimizing asphalt paving parameters based on digital twins as described in claim 3, characterized in that: The analysis of the impact of different construction parameters on the construction effect and the generation of construction adjustment suggestions follow these steps: The paver speed and hopper capacity are extracted from the construction parameters, and the impact of changes in paver speed on the construction effect is evaluated by sensitivity analysis to obtain the speed response. Based on velocity response analysis, the impact of changes in silo capacity on construction results is analyzed, identifying the continuous and uniform effects of silo capacity on the paving process, and generating a report on the impact of construction parameters. Based on the construction parameter impact analysis report, deviations and deficiencies in the construction process are identified, and construction adjustment suggestions are generated.

5. The method for optimizing asphalt paving parameters based on digital twins as described in claim 4, characterized in that: The process involves real-time detection of the smoothness and quality of the paved layer, obtaining actual paving conditions, and comparing the proposed adjustments with the actual paving conditions to obtain deviation information. The specific steps are as follows: Collect surface images and height data of the paving layer to obtain a paving layer dataset. Then, use machine vision methods to denoise, enhance contrast, and remove background interference from the paving layer dataset to obtain the actual paving conditions. The construction adjustment suggestions are compared with the actual paving conditions point by point to generate deviation information.

6. The method for optimizing asphalt paving parameters based on digital twins as described in claim 5, characterized in that: The specific steps for adjusting construction parameters using deviation information to generate construction quality optimization parameters are as follows. Based on the deviation information analysis, the influence relationship between the paver working mode and hopper capacity on the construction effect is analyzed, and the adjusted construction parameters are generated by adjusting the paver speed, hopper capacity and hopper control strategy. Based on the adjusted construction parameters, the paving process of the paver under different operating parameters is simulated by digital twin simulation, and the changes in the smoothness and compaction quality of the paved layer are predicted to generate construction quality optimization parameters.

7. The method for optimizing asphalt paving parameters based on digital twins as described in claim 6, characterized in that: The specific steps for analyzing various paving machine parameters during the paving process based on construction quality optimization parameters are as follows: Collect paver operating parameters and, based on construction quality optimization parameters, analyze the impact of paver vibration frequency, paving layer thickness, and paver pressure on construction quality, and generate a construction quality impact analysis report. Based on the construction quality impact analysis report, assess the need to adjust the paver speed and hopper capacity, and generate paving parameters.

8. The method for optimizing asphalt paving parameters based on digital twins as described in claim 7, characterized in that: The process involves performing a full lifecycle analysis of paving parameters and combining this with historical operational data to predict future trends in construction parameters. By adjusting paver operating parameters, construction efficiency is optimized, and an optimized asphalt paving plan is generated. The specific steps are as follows: By analyzing the changes in the operating parameters of the paver at different construction stages, the long-term impact of the paving parameters at each stage on the construction effect is analyzed, and a life cycle analysis report is generated. By combining life cycle analysis reports with historical operation data, statistical analysis methods are used to predict future trends in construction parameters. Based on the future construction parameter change trends, analyze the change trends of paver speed and hopper capacity, adjust paver operation settings, and generate adjusted paver operation parameters. Based on the adjusted paver operating parameters, the paving quality and efficiency during the construction process are evaluated through digital twin simulation, and an optimized asphalt paving scheme is generated.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the asphalt paving parameter optimization method based on digital twins as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the asphalt paving parameter optimization method based on digital twins as described in any one of claims 1 to 8.

Images