Digital dynamic control method for asphalt pavement construction of highway reconstruction and extension project

Abstract

The invention discloses a digital dynamic control method for asphalt pavement construction of a highway reconstruction and extension project. The method mainly comprises the following steps: S1, collecting key parameter data of an asphalt pavement; s2, preprocessing the collected data; s3, establishing a parameter database; s4, analyzing the correlation between the compaction degree data and the informatization supervision key parameters, and screening out the key parameters having significant influence on the compaction degree according to a correlation analysis result; s5, constructing a multiple regression model and a BP artificial neural network model; s6, inputting key parameters acquired on site into the multiple regression model and the BP artificial neural network model, and predicting the compactness of the asphalt pavement in real time; s7, dynamically adjusting construction parameters according to the compaction degree data predicted in real time; s8, establishing a completed construction quality tracking system; and S9, integrating all data and models into an informatization management platform. The quality condition in the construction process can be mastered more accurately, and it is ensured that the construction quality meets the standard requirement.

Description

Technical Field

[0001] The invention relates to the technical field of asphalt pavement construction, and in particular to a digital dynamic control method for asphalt pavement construction in a highway reconstruction and expansion project. Background Art

[0002] In highway reconstruction and expansion projects, the construction quality of asphalt pavement is directly related to the durability, safety and service life of the highway. Traditional asphalt pavement construction methods often rely on the experience and on-site supervision of construction personnel, making it difficult to accurately grasp the quality status of the construction process in real time, resulting in greater volatility and uncertainty in construction quality. At the same time, due to the complex and changeable construction environment, factors such as weather changes, material performance differences, and equipment performance fluctuations may have an adverse impact on construction quality.

[0003] With the rapid development of information technology and intelligent control technology, digital and intelligent construction control methods have gradually become an important means to improve construction quality and efficiency. However, in the asphalt pavement construction of highway reconstruction and expansion projects, how to achieve real-time, accurate monitoring and dynamic adjustment of construction quality is still an urgent problem to be solved.

[0004] At present, although there are some construction monitoring systems based on sensors and data analysis on the market, most of these systems focus on monitoring a single parameter or simple data analysis, which cannot fully and deeply reflect the quality status of the construction process, let alone dynamically adjust and optimize the construction parameters. In addition, these systems often lack a complete construction quality tracking system and alarm mechanism, making it difficult to timely discover and deal with problems in construction, resulting in insufficient guarantee of construction quality and safety. Therefore, a digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion projects is proposed. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital dynamic control method for asphalt pavement construction in a highway reconstruction and expansion project, mainly comprising the following steps:

[0007] S1: compaction sensors and other related sensors are arranged at the construction site to collect key parameter data of the asphalt pavement in real time, and the data collected on site are transmitted to the central control system in real time through wireless transmission technology;

[0008] S2: preprocessing the collected raw data;

[0009] S3: Extract key parameters of information-based supervision based on the actual conditions and historical data of the construction site, conduct statistical analysis on these key parameters, and establish a parameter database;

[0010] S4: Use statistical methods to analyze the correlation between compaction data and key parameters of information-based supervision, and screen out key parameters that have a significant impact on compaction based on the results of the correlation analysis;

[0011] S5: Construct multiple regression model and BP artificial neural network model;

[0012] S6: Input the key parameters collected in real time on site into the multivariate regression model and BP artificial neural network model to predict the compaction of the asphalt pavement in real time, and then generate a compaction report based on the prediction results for reference by construction personnel and supervisors;

[0013] S7: Dynamically adjust construction parameters based on real-time predicted compaction data;

[0014] S8: Record and store all data during the construction process and establish a complete construction quality tracking system;

[0015] S9: Integrate all data and models into an information management platform to achieve comprehensive monitoring and management of the entire construction process.

[0016] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the key parameter data includes humidity, temperature, vibration frequency and construction speed.

[0017] As a preferred solution of the digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion projects described in the present invention, the preprocessing includes removing noise and outliers and performing data standardization and normalization processing on the data.

[0018] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the construction of the multivariate regression model includes the following steps:

[0019] S511: Define model variables

[0020] Setting the compaction level is the dependent variable;

[0021] The selected key parameters x1, x2, ..., x p is the independent variable;

[0022] S512: Building a regression model

[0023] Multiple linear regression model formula:

[0024]

[0025] in:

[0026] β0, β1, ..., β p There are P+1 unknown parameters;

[0027] ∈ is an unmeasured random error, and it is usually assumed that ∈~N(0,σ 2 );

[0028] S513: Training regression model

[0029] Estimate the regression coefficients using the method of least squares:

[0030]

[0031] in:

[0032] X is the independent variable matrix;

[0033] is the dependent variable vector;

[0034] is the regression coefficient vector;

[0035] S513: Use cross-validation method to evaluate model performance and calculate the error between predicted value and actual value;

[0036] Mean square error formula:

[0037]

[0038] Where P is the sample size, is the actual value, is the predicted value;

[0039] S514: If the model performs poorly, you can optimize the model.

[0040] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the construction of BP artificial neural network model includes the following steps:

[0041] S521: Define model structure

[0042] Input layer: set key parameters x1, x2, ..., x p is the input node;

[0043] Hidden layer: set several hidden layers, each layer contains several neurons;

[0044] Output layer: Setting compaction is the output node;

[0045] S522: Initialize weight w ij and bias b j is a random value;

[0046] S523: Forward Propagation

[0047] Calculate the output value from the input layer to the output layer;

[0048] S524: Calculate the error between the output value and the actual value;

[0049] S525: Back Propagation

[0050] Calculate the gradient of the error with respect to the weights and update the weights and biases;

[0051] S526: Iteratively train the BP neural network model using the training set data;

[0052] S527: Use the validation set data to evaluate the model performance. If the performance is poor, optimize the model.

[0053] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project of the present invention, the forward propagation formula is:

[0054]

[0055] in, is the output of the jth node, w ij is the weight, b j is the bias, f is the activation function;

[0056] The weight update formula is:

[0057]

[0058] Among them, η is the learning rate.

[0059] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the specific steps of dynamically adjusting the construction parameters are as follows:

[0060] S71: Setting the upper and lower thresholds of compaction degree;

[0061] S72: dynamically adjusting construction parameters according to the predicted compaction data;

[0062] S73: Develop an alarm mechanism;

[0063] S74: Alarm processing

[0064] The alarm information is notified to the construction personnel and supervisors in real time through the control system interface and mobile devices, and the construction personnel take emergency measures according to the alarm information.

[0065] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the rules for adjusting the construction parameters are as follows:

[0066] If the predicted compaction degree is lower than the lower threshold, the vibration frequency is increased; if the predicted compaction degree is higher than the upper threshold, the vibration frequency is decreased;

[0067] If the predicted compaction degree is lower than the lower threshold, increase the number of compactions; if the predicted compaction degree is higher than the upper threshold, reduce the number of compactions;

[0068] If the predicted compaction degree is lower than the lower threshold, the construction speed is reduced; if the predicted compaction degree is higher than the upper threshold, the construction speed is increased.

[0069] As a preferred solution of the digital dynamic control method for asphalt pavement construction of highway reconstruction and expansion project described in the present invention, the rules of the alarm mechanism are as follows:

[0070] When the predicted compaction data exceeds the set upper or lower threshold, the alarm mechanism is triggered;

[0071] When the adjusted vibration frequency exceeds the safety range of the equipment, the alarm mechanism is triggered;

[0072] When the adjusted compaction times exceed a reasonable range, the alarm mechanism is triggered;

[0073] When the adjusted construction speed exceeds a reasonable range, the alarm mechanism is triggered.

[0074] As a preferred solution of the digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion projects described in the present invention, the central control system has an automatic control function, which can automatically adjust the parameters of the construction equipment according to the predicted compaction data and trigger an alarm when necessary.

[0075] Beneficial effects of the present invention:

[0076] 1. The present invention collects key parameters of asphalt pavement in real time (such as humidity, temperature, vibration frequency, construction speed, etc.), and combines multiple regression model and BP artificial neural network model to predict compaction in real time, so as to more accurately grasp the quality status during the construction process. This method reduces the uncertainty of human judgment and improves the prediction accuracy of compaction, which helps construction personnel to adjust construction parameters in time and ensure that the construction quality meets the standard requirements. At the same time, the automated control function can quickly respond to the prediction results, optimize the construction process, and significantly improve the construction efficiency.

[0077] 2. The present invention establishes a complete construction quality tracking system to record and store all data in the construction process. These data can not only be used for real-time analysis and monitoring of construction quality, but also provide valuable data support for subsequent construction summary, quality assessment and improvement. In addition, all data and models are integrated into an information management platform, which realizes comprehensive monitoring and management of the entire construction process and improves the overall management level of the project.

[0078] 3. The present invention introduces an alarm mechanism, which can automatically trigger an alarm when the predicted compaction data exceeds the set threshold or the construction parameter adjustment exceeds the reasonable range. This mechanism not only helps to discover problems in construction in a timely manner, but also notifies construction personnel and supervisors in real time through the control system interface and mobile devices, enabling them to quickly take emergency measures to avoid potential safety hazards and quality problems. This ability of instant feedback and rapid response significantly enhances safety management and emergency response capabilities during construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0080] Figure 1 The present invention is a flowchart of a digital dynamic control method for asphalt pavement construction in a highway reconstruction and expansion project. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0082] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0083] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0084] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0085] Example 1

[0086] Reference Figure 1 , which is the first embodiment of the present invention, provides a digital dynamic control method for asphalt pavement construction of a highway reconstruction and expansion project, which mainly includes the following steps:

[0087] S1: Compaction sensors and other related sensors are arranged at the construction site to collect key parameter data of asphalt pavement in real time, such as compaction, temperature, humidity, vibration frequency, construction speed, etc. The data collected on site are transmitted to the central control system in real time through wireless transmission technology;

[0088] S2: Preprocess the collected raw data, such as removing noise and outliers and standardizing and normalizing the data to facilitate subsequent model training and analysis;

[0089] S3: Based on the actual conditions and historical data of the construction site, extract the key parameters of information-based supervision, such as compaction, temperature, humidity, vibration frequency, construction speed, etc., and conduct statistical analysis on these key parameters to establish a parameter database;

[0090] S4: Use statistical methods (such as Pearson correlation coefficient, Spearman correlation coefficient, etc.) to analyze the correlation between compaction data and key parameters of information-based supervision, and screen out key parameters that have a significant impact on compaction based on the results of the correlation analysis;

[0091] S5: Construct multiple regression model and BP artificial neural network model;

[0092] S6: Input the key parameters collected in real time on site into the multivariate regression model and BP artificial neural network model to predict the compaction of the asphalt pavement in real time, and then generate a compaction report based on the prediction results for reference by construction personnel and supervisors;

[0093] S7: Dynamically adjust construction parameters such as vibration frequency of compaction equipment, number of compactions, construction speed, etc. according to real-time predicted compaction data;

[0094] S8: Record and store all data during the construction process and establish a complete construction quality tracking system;

[0095] S9: Integrate all data and models into an information management platform to achieve comprehensive monitoring and management of the entire construction process.

[0096] The above steps combine on-site test data, key parameters of information-based supervision, correlation analysis, multiple regression model and BP artificial neural network model to achieve real-time prediction and dynamic control of compaction during asphalt pavement construction, ensuring the stability and improvement of construction quality. This method not only improves construction efficiency, but also effectively reduces construction costs.

[0097] Specifically, the construction of the multivariate regression model includes the following steps:

[0098] S511: Define model variables

[0099] Setting the compaction level is the dependent variable;

[0100] The selected key parameters x1, x2, ..., x p is the independent variable;

[0101] S512: Building a regression model

[0102] Multiple linear regression model formula:

[0103]

[0104] in:

[0105] β0, β1, ..., β p There are P+1 unknown parameters;

[0106] ∈ is an unmeasured random error, and it is usually assumed that ∈~N(0,σ 2 );

[0107] S513: Training regression model

[0108] Estimate the regression coefficients using the method of least squares:

[0109]

[0110] in:

[0111] X is the independent variable matrix;

[0112] is the dependent variable vector;

[0113] is the regression coefficient vector;

[0114] S513: Use cross-validation method to evaluate model performance and calculate the error between predicted value and actual value;

[0115] Mean square error formula:

[0116]

[0117] Where P is the sample size, is the actual value, is the predicted value;

[0118] S514: If the model performs poorly, you can optimize the model.

[0119] Through the above specific steps and formulas, a multivariate regression model can be constructed and used to predict the compaction degree of asphalt pavement in real time and assist in the dynamic adjustment of the construction process.

[0120] Specifically, the construction of the BP artificial neural network model includes the following steps:

[0121] S521: Define model structure

[0122] Input layer: set key parameters x1, x2, ..., x p is the input node;

[0123] Hidden layer: set several hidden layers, each layer contains several neurons;

[0124] Output layer: Setting compaction is the output node;

[0125] S522: Initialize weight w ij and bias b j is a random value;

[0126] S523: Forward Propagation

[0127] Calculate the output value from the input layer to the output layer;

[0128] S524: Calculate the error between the output value and the actual value;

[0129] S525: Back Propagation

[0130] Calculate the gradient of the error with respect to the weights and update the weights and biases;

[0131] S526: Iteratively train the BP neural network model using the training set data;

[0132] S527: Use the validation set data to evaluate the model performance. If the performance is poor, optimize the model.

[0133] The forward propagation formula is:

[0134]

[0135] in, is the output of the jth node, w ij is the weight, b j is the bias, f is the activation function;

[0136] The weight update formula is:

[0137]

[0138] Among them, η is the learning rate.

[0139] Through the above specific steps and formulas, a BP artificial neural network model can be constructed and used to predict the compaction degree of asphalt pavement in real time and assist in the dynamic adjustment of the construction process.

[0140] Specifically, the specific steps of dynamically adjusting the construction parameters are:

[0141] S71: Setting the upper and lower thresholds of the compaction degree, for example, the upper threshold is 95% and the lower threshold is 85%.

[0142] S72: Dynamically adjust construction parameters based on predicted compaction data.

[0143] The rules for adjusting construction parameters are as follows:

[0144] If the predicted compaction degree is lower than the lower threshold, the vibration frequency is increased; if the predicted compaction degree is higher than the upper threshold, the vibration frequency is decreased;

[0145] If the predicted compaction degree is lower than the lower threshold, increase the number of compactions; if the predicted compaction degree is higher than the upper threshold, reduce the number of compactions;

[0146] If the predicted compaction degree is lower than the lower threshold, the construction speed is reduced; if the predicted compaction degree is higher than the upper threshold, the construction speed is increased.

[0147] S73: Develop an alarm mechanism.

[0148] The rules of the alarm mechanism are as follows:

[0149] When the predicted compaction data exceeds the set upper or lower threshold, the alarm mechanism is triggered;

[0150] When the adjusted vibration frequency exceeds the safety range of the equipment, the alarm mechanism is triggered;

[0151] When the adjusted compaction times exceed a reasonable range, the alarm mechanism is triggered;

[0152] When the adjusted construction speed exceeds a reasonable range, the alarm mechanism is triggered.

[0153] S74: Alarm processing

[0154] The alarm information is notified to the construction personnel and supervisors in real time through the control system interface and mobile devices. The construction personnel take emergency measures according to the alarm information, such as suspending construction, checking equipment, adjusting construction plans, etc. The alarm information is recorded in the construction quality tracking system for subsequent analysis and tracking.

[0155] Specifically, the central control system has an automated control function, which can automatically adjust the parameters of the construction equipment according to the predicted compaction data, and trigger an alarm when necessary to ensure construction quality and safety.

[0156] In summary, the present invention can more accurately grasp the quality status during the construction process by real-time collection of key parameters of asphalt pavement (such as humidity, temperature, vibration frequency, construction speed, etc.), and combining multiple regression models and BP artificial neural network models to predict compaction in real time. This method reduces the uncertainty of human judgment and improves the prediction accuracy of compaction, which helps construction personnel to adjust construction parameters in a timely manner to ensure that the construction quality meets the standard requirements. At the same time, the automated control function can quickly respond to prediction results, optimize the construction process, and significantly improve construction efficiency.

[0157] The present invention establishes a complete construction quality tracking system to record and store all data during the construction process. These data can not only be used to analyze and monitor the construction quality in real time, but also provide valuable data support for subsequent construction summary, quality assessment and improvement. In addition, all data and models are integrated into an information management platform, which realizes comprehensive monitoring and management of the entire construction process and improves the overall management level of the project.

[0158] The present invention introduces an alarm mechanism that can automatically trigger an alarm when the predicted compaction data exceeds the set threshold or the construction parameter adjustment exceeds a reasonable range. This mechanism not only helps to promptly discover problems in construction, but also notifies construction personnel and supervisors in real time through the control system interface and mobile devices, allowing them to quickly take emergency measures to avoid potential safety hazards and quality problems. This ability to provide instant feedback and rapid response significantly enhances safety management and emergency response capabilities during construction.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion projects, characterized in that: The following steps are involved: S1: compaction sensors and other related sensors are arranged at the construction site to collect key parameter data of the asphalt pavement in real time, and the data collected on site are transmitted to the central control system in real time through wireless transmission technology; S2: preprocessing the collected raw data; S3: Extract key parameters of information-based supervision based on the actual conditions and historical data of the construction site, conduct statistical analysis on these key parameters, and establish a parameter database; S4: Use statistical methods to analyze the correlation between compaction data and key parameters of information-based supervision, and screen out key parameters that have a significant impact on compaction based on the results of the correlation analysis; S5: Construct multiple regression model and BP artificial neural network model; S6: Input the key parameters collected in real time on site into the multivariate regression model and BP artificial neural network model to predict the compaction of the asphalt pavement in real time, and then generate a compaction report based on the prediction results for reference by construction personnel and supervisors; S7: Dynamically adjust construction parameters based on real-time predicted compaction data; S8: Record and store all data during the construction process and establish a complete construction quality tracking system; S9: Integrate all data and models into an information management platform to achieve comprehensive monitoring and management of the entire construction process.

2. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The key parameter data include humidity, temperature, vibration frequency and construction speed.

3. The digital dynamic control method for asphalt pavement construction of a highway reconstruction and expansion project as claimed in claim 1, characterized in that: The preprocessing includes removing noise and outliers and performing data standardization and normalization on the data.

4. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The construction of the multivariate regression model comprises the following steps: S511: Define model variables Set compaction degree y as the dependent variable; The selected key parameters x1, x2, ..., x p is the independent variable; S512: Building a regression model Multiple linear regression model formula: y=β0+β1x1+β2x2+…+β p x p +∈ in: β0, β1, ..., β p There are P+1 unknown parameters; ∈ is an unmeasured random error, and it is usually assumed that ∈~N(0,σ 2 ); S513: Training regression model Estimate the regression coefficients using the method of least squares: in: X is the independent variable matrix; y is the dependent variable vector; is the regression coefficient vector; S513: Use cross-validation method to evaluate model performance and calculate the error between predicted value and actual value; Mean square error formula: Where P is the number of samples, y i is the actual value, is the predicted value; S514: If the model performs poorly, you can optimize the model.

5. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: Described constructing BP artificial neural network model comprises the following steps: S521: Define model structure Input layer: set key parameters x1, x2, ..., x p is the input node; Hidden layer: set several hidden layers, each layer contains several neurons; Output layer: Set compaction degree y as output node; S522: Initialize weight w ij and bias b j is a random value; S523: Forward Propagation Calculate the output value from the input layer to the output layer; S524: Calculate the error between the output value and the actual value; S525: Back Propagation Calculate the gradient of the error with respect to the weights and update the weights and biases; S526: Iteratively train the BP neural network model using the training set data; S527: Use the validation set data to evaluate the model performance. If the performance is poor, optimize the model.

6. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 5, characterized in that: The forward propagation formula is: Among them, y j is the output of the jth node, w ij is the weight, b j is the bias, f is the activation function; The weight update formula is: Among them, η is the learning rate.

7. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The specific steps of dynamically adjusting the construction parameters are as follows: S71: Setting the upper and lower thresholds of compaction degree; S72: dynamically adjusting construction parameters according to the predicted compaction data; S73: Develop an alarm mechanism; S74: Alarm processing The alarm information is notified to the construction personnel and supervisors in real time through the control system interface and mobile devices, and the construction personnel take emergency measures according to the alarm information.

8. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The rules for adjusting the construction parameters are as follows: If the predicted compaction degree is lower than the lower threshold, the vibration frequency is increased; if the predicted compaction degree is higher than the upper threshold, the vibration frequency is decreased; If the predicted compaction degree is lower than the lower threshold, increase the number of compactions; if the predicted compaction degree is higher than the upper threshold, reduce the number of compactions; If the predicted compaction degree is lower than the lower threshold, the construction speed is reduced; if the predicted compaction degree is higher than the upper threshold, the construction speed is increased.

9. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The rules of the alarm mechanism are as follows: When the predicted compaction data exceeds the set upper or lower threshold, the alarm mechanism is triggered; When the adjusted vibration frequency exceeds the safety range of the equipment, the alarm mechanism is triggered; When the adjusted compaction times exceed a reasonable range, the alarm mechanism is triggered; When the adjusted construction speed exceeds a reasonable range, the alarm mechanism is triggered.

10. The digital dynamic control method for asphalt pavement construction in highway reconstruction and expansion engineering as claimed in claim 1, characterized in that: The central control system has an automatic control function, which can automatically adjust the parameters of the construction equipment according to the predicted compaction data and trigger an alarm when necessary.

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