Intelligent road construction control system and method

Abstract

The invention relates to the technical field of construction process control, in particular to an intelligent road construction control system and method. The BIM construction module is used for constructing a BIM model and providing a construction reference theoretical value; the data acquisition module is used for acquiring an actual value of the construction data; the comparative analysis module is used for inputting the actual value in the data acquisition module into the BIM model and comparing the absolute deviation ratio of the actual value with the dynamic threshold value of the theoretical value; the construction control module is used for adjusting a construction scheme based on an analysis result of the comparative analysis module; the deviation calculation unit receives the data transmitted by the data transmission unit, calculates the elevation deviation, calculates the absolute deviation ratio of the elevation deviation, predicts the final settlement amount of the road, and then deduces a dynamic threshold value in a reverse mode; then, the risk assessment unit compares the absolute deviation rate of the elevation deviation with the dynamic threshold value. And if the absolute deviation rate of the elevation deviation is greater than the dynamic threshold value, the control unit executes the adjusted construction scheme, otherwise, the original construction scheme is executed.

Description

Technical Field

[0001] The present invention relates to the technical field of construction process control, and in particular to an intelligent road construction control system and method. Background Art

[0002] When a road section becomes damaged after prolonged use, it needs to be repaired. Repairs involve more than simply patching up the damaged section; they also require identifying the cause of the damage and implementing targeted countermeasures to extend the service life of the newly constructed section. Traditionally, design, construction, and management processes operate independently, leading to untimely and inaccurate information transfer. This can lead to frequent design changes, difficulty controlling construction progress, and difficulty identifying quality risks.

[0003] To ensure that actual road construction data (actual values) more closely matches theoretical data (theoretical values) on design drawings, a BIM model is currently designed before construction begins, and theoretical values are input into the model as the data foundation for road construction. During the actual construction process, actual values are continuously input into the model, compared with the theoretical values, and the deviation between the two is compared with a preset threshold, which serves as the basis for adjusting construction strategies.

[0004] The road conditions during actual construction are more complex. For example, when encountering a soft soil foundation that may settle, the pre-set threshold value is difficult to adapt to the changed road conditions. In order to improve the accuracy of the data, a dynamic threshold value is used instead of a fixed threshold value to reduce the impact of road settlement on the accuracy of the actual construction data obtained (such as elevation deviation and flatness error). However, in the process of obtaining the dynamic threshold value, sudden extreme weather (such as heavy rain, earthquake, etc.) or construction activities (such as blasting) can cause sudden settlement of the soft soil foundation. In addition, abnormally high and low temperatures can also affect the expansion coefficient of the foundation material, causing the foundation settlement to deviate from the algorithm's prediction range, thereby causing the dynamic threshold value to be inaccurate. The inaccurate threshold value will cause the BIM model to misjudge the adjustment of the construction strategy.

[0005] In order to address the above problems, an intelligent road construction control system and method are urgently needed to improve the deficiencies in the existing technology. Summary of the Invention

[0006] One of the objectives of the present invention is to provide an intelligent road construction control system that, while acquiring dynamic thresholds, can detect sudden extreme weather events or construction activities that can trigger sudden settlement in soft soil foundations. Furthermore, abnormally high or low temperatures can affect the expansion coefficient of the foundation material, causing the foundation settlement to deviate from the algorithm's predicted range, leading to inaccurate dynamic thresholds. Such inaccurate thresholds can lead to misjudgments in the BIM model's adjustments to construction strategies, namely:

[0007] Environmental factors may cause the final construction data (foundation settlement) to be inaccurate, thereby affecting the construction control system's ability to make accurate construction adjustment decisions.

[0008] To achieve the above objectives, an intelligent road construction control system and method are provided, including a BIM construction module for constructing a BIM model and providing theoretical construction benchmark values; a data acquisition module for obtaining actual values of construction data; a comparative analysis module for entering the actual values from the data acquisition module into the BIM model and comparing the absolute deviation rate of the actual values with a dynamic threshold of the theoretical values; and a construction control module for adjusting the construction plan based on the analysis results of the comparative analysis module.

[0009] The data acquisition module collects actual values of data including temperature, elevation, and foundation settlement. The comparative analysis module extracts normal values of foundation settlement using an isolation forest algorithm to obtain a dynamic threshold. The comparative analysis module calculates elevation deviation and obtains its absolute deviation rate. The comparative analysis module compares the absolute deviation rate with the dynamic threshold. If the absolute deviation rate is greater than the dynamic threshold, the construction control module adjusts the construction plan; otherwise, the construction plan is not adjusted.

[0010] The normal value of the foundation settlement excludes abnormal temperature values and abnormal numerical values.

[0011] In the above technical solution, the comparative analysis module includes a deviation calculation unit, a risk assessment unit and a visualization unit;

[0012] The deviation calculation unit receives the data transmitted by the data transmission unit to calculate the elevation deviation and calculates the absolute deviation rate of the elevation deviation. The deviation calculation unit uses a hyperbola fitting method to fit the settlement-time curve to predict the final road settlement, and then deduces the dynamic threshold based on the correction formula, supporting multi-dimensional comparison of time, space, and different construction stages;

[0013] The risk assessment unit compares the absolute deviation rate of the elevation deviation with the size of the dynamic threshold, thereby warning of potential risks including road structure stability and durability;

[0014] The visualization unit displays the distribution of absolute deviation rate and dynamic threshold value through a heat map.

[0015] In another technical solution, the deviation calculation unit uses the isolation forest algorithm to eliminate abnormal temperature and abnormal data size in the foundation settlement as follows:

[0016] Preprocessing: Simultaneously collect foundation settlement (S) and temperature (T), use linear interpolation to delete obvious outliers, and then eliminate the dimensional differences of foundation settlement and temperature data. The formula is: (μ is the mean, σ is the standard deviation);

[0017] Construct an isolation forest model: randomly extract a subset of samples from the data of S and T and randomly select a split value, recursively partition the data until the stopping condition is met (a node contains only one sample or reaches the maximum depth), and then generate multiple isolated trees to form an isolation forest;

[0018] Calculate the path length: For each data point x, calculate its path length h(x) in each tree (that is, the number of splits from the root node to the leaf node). The formula is: (n represents the number of trees, which is a positive integer between 100 and 1000);

[0019] Get the anomaly score: ( is the expected path length, H(n) = ln(n) + γ, is the harmonic number, and γ ≈ 0.5772 is the Euler constant);

[0020] Abnormality judgment: s(x, n) tends to 1 for abnormality, and s(x, n) tends to 0 for normality;

[0021] Screening: Set the screening threshold of foundation settlement to eliminate foundation settlement with abnormal temperature and abnormal value.

[0022] Furthermore, the deviation calculation unit predicts the final road settlement by fitting the settlement-time curve using a hyperbola fitting method based on the foundation settlement excluding temperature anomalies and abnormal numerical values, and then infers the dynamic threshold value based on the correction formula in the following steps:

[0023] Obtain the cumulative settlement at different time points (t i , S i ), determine S0 (take the foundation settlement at t = 0, if not measured, it can be assumed to be 0);

[0024] Transform the hyperbolic formula into a linear formula:

[0025] make: Slope intercept Then use linear regression to fit the relationship between y and x to obtain the slope m and intercept b;

[0026] Solve for the final settlement S f And parameter α: From the slope m, we can get, By intercept b and S f It can be obtained that α=b·(S f -S0); calculate the correlation coefficient R 2 , substitute t back into the hyperbola formula to predict the final settlement at any time in the future; then modify the formula based on the threshold:

[0027] Furthermore, the formula for calculating the absolute deviation rate of the elevation deviation by the deviation calculation unit is: The result is expressed as a percentage, reflecting the numerical difference between the actual value of the elevation deviation and the theoretical value.

[0028] Finally, the absolute deviation rate of the elevation deviation calculated by the deviation calculation unit is compared with the dynamic threshold value. If the absolute deviation rate of the elevation deviation is greater than the dynamic threshold value, the optimization unit adjusts the construction plan; otherwise, the construction plan is not adjusted.

[0029] A second object of the present invention is to provide a method for an intelligent road construction control system, comprising the following steps:

[0030] S1, the BIM construction module constructs a BIM model based on the design drawings as the theoretical value of the road repair construction data;

[0031] S2, the data acquisition module obtains the actual values of temperature, elevation and foundation settlement data during the construction process;

[0032] S3, the comparative analysis module calculates the absolute deviation rate and the dynamic threshold of the elevation deviation based on the actual value of the construction data collected by the data collection module, and compares the absolute deviation rate and the dynamic threshold of the elevation deviation;

[0033] S4. The construction control module gives correction suggestions for road construction based on the comparison results in S3, that is: if the absolute deviation rate of the elevation deviation is greater than the dynamic threshold, the optimization unit adjusts the construction plan; otherwise, the construction plan is not adjusted. After the comparative analysis module eliminates environmental influencing factors or construction activity factors, the accuracy of the actual value of the construction data is improved, thereby improving the accuracy of the construction adjustment plan given by the BIM model.

[0034] Based on the above description, it can be seen that compared with the prior art, the beneficial effects of the present invention are:

[0035] The deviation calculation unit receives data from the data transmission unit and calculates the elevation deviation and the absolute deviation rate of the elevation deviation. The deviation calculation unit then predicts the final road settlement and infers the dynamic threshold. The risk assessment unit then compares the absolute deviation rate of the elevation deviation with the dynamic threshold. If the absolute deviation rate of the elevation deviation exceeds the dynamic threshold, the control unit implements the adjusted construction plan; otherwise, the original construction plan is implemented.

[0036] By removing data from foundation settlement that is affected by the environment and construction activities, the accuracy of the dynamic threshold is improved, making the established BIM model more accurate in judging construction strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 It is the overall structural diagram of the present invention.

[0039] The meaning of each number in the figure is:

[0040] 100, BIM construction module; 110, modeling unit; 120, storage and interface unit; 130, version control unit;

[0041] 200, data acquisition module; 210, sensor network; 220, image acquisition unit; 230, data transmission unit;

[0042] 300, comparative analysis module; 310, deviation calculation unit; 320, risk assessment unit; 330, visualization unit;

[0043] 400, construction control module; 410, optimization unit; 420, control unit. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Example 1:

[0046] See also Figure 1 As shown, the purpose of this embodiment is to provide an intelligent road construction control system, including a BIM construction module 100 for constructing a BIM model and providing a construction benchmark theoretical value; a data acquisition module 200 for obtaining actual values of construction data; a comparison and analysis module 300 for entering the actual values in the data acquisition module 200 into the BIM model and comparing the absolute deviation rate of the actual values with a dynamic threshold of the theoretical value; and a construction control module 400 for adjusting the construction plan based on the analysis results of the comparison and analysis module 300.

[0047] The data acquisition module 200 collects actual values of data including temperature, elevation, and foundation settlement. The comparative analysis module 300 uses the isolation forest algorithm to extract normal values of foundation settlement to obtain a dynamic threshold. The comparative analysis module 300 calculates elevation deviation and obtains its absolute deviation rate. The comparative analysis module 300 compares the absolute deviation rate with the dynamic threshold. If the absolute deviation rate is greater than the dynamic threshold, the construction control module 400 adjusts the construction plan; otherwise, the construction plan is not adjusted.

[0048] The normal value of foundation settlement excludes abnormal temperature values and abnormal numerical values.

[0049] First, the BIM construction module 100 includes a modeling unit 110, a storage and interface unit 120, and a version control unit 130;

[0050] The modeling unit 110 generates a BIM model based on the design drawings, supports parametric adjustment, and the storage and interface unit 120 manages BIM model data and provides an interface for docking with external systems (such as the sensor network 210 and ERP). The version control unit 130 is used to record the BIM model change history to ensure the traceability of construction data.

[0051] Next, the data acquisition module 200 includes a sensor network 210, an image acquisition unit 220, and a data transmission unit 230;

[0052] The sensor network 210 is used to collect actual values of road construction process data, including temperature at the construction site, road elevation, and foundation settlement;

[0053] The image acquisition unit 220 is used to capture images and videos of the construction process. It consists of a camera installed at a fixed position on the construction equipment or at the construction site to visually record the construction situation and facilitate visual monitoring and analysis of the construction quality.

[0054] The data transmission unit 230 is used to transmit data acquired by the sensor network 210 and the image acquisition unit 220. It usually adopts a wireless communication module, such as a 4G / 5G module or a ZigBee module, to ensure that the data can be transmitted in a timely and accurate manner for real-time processing and analysis.

[0055] Secondly, the comparative analysis module 300 includes a deviation calculation unit 310, a risk assessment unit 320 and a visualization unit 330;

[0056] The deviation calculation unit 310 receives the data transmitted by the data transmission unit 230 to calculate the elevation deviation and the absolute deviation rate of the elevation deviation. The deviation calculation unit 310 uses a hyperbola fitting method to fit the settlement-time curve to predict the final road settlement. Then, based on the correction formula, the dynamic threshold is deduced to measure the magnitude of the numerical difference between the actual value and the theoretical value, regardless of the direction of the difference (positive or negative). The result is expressed as a percentage.

[0057] The risk assessment unit 320 compares the absolute deviation rate of the elevation deviation with the size of the dynamic threshold, and the visualization unit 330 displays the distribution of the absolute deviation rate and the dynamic threshold through a heat map.

[0058] Finally, the construction control module 400 includes an optimization unit 410 and a control unit 420 . The optimization unit 410 generates correction suggestions based on the comparison results of the risk assessment unit 320 .

[0059] It should be noted that the steps of the deviation calculation unit 310 to eliminate abnormal temperature and abnormal data size in the foundation settlement using the isolation forest algorithm are as follows:

[0060] Preprocessing: Simultaneously collect foundation settlement (S) and temperature (T), use linear interpolation to delete obvious outliers, and then eliminate the dimensional differences of foundation settlement and temperature data. The formula is: (μ is the mean, σ is the standard deviation);

[0061] Construct an isolation forest model: randomly extract a subset of samples from the data of S and T and randomly select a split value, recursively partition the data until the stopping condition is met (a node contains only one sample or reaches the maximum depth), and then generate multiple isolated trees to form an isolation forest;

[0062] Calculate the path length: For each data point x, calculate its path length h(x) in each tree (that is, the number of splits from the root node to the leaf node). The formula is: (n represents the number of trees, a positive integer between 100 and 1000);

[0063] Get the anomaly score: ( is the expected path length, H(n) = ln(n) + γ, is the harmonic number, and γ ≈ 0.5772 is the Euler constant);

[0064] Abnormality judgment: s(x, n) tends to 1 for abnormality, and s(x, n) tends to 0 for normality;

[0065] Screening: Set the screening threshold of foundation settlement to eliminate foundation settlement with abnormal temperature and abnormal value.

[0066] Preferably, the deviation calculation unit 310 predicts the final road settlement by fitting the settlement-time curve using a hyperbola fitting method based on the foundation settlement with the exception of temperature anomalies and abnormal numerical values, and then deduces the dynamic threshold value based on the correction formula in the following steps:

[0067] Obtain the cumulative settlement at different time points (t i , S i ), determine S0 (take the foundation settlement at t = 0, if not measured, it can be assumed to be 0);

[0068] Transform the hyperbolic formula into a linear formula:

[0069] Where S(t) represents the cumulative settlement at time t;

[0070] S f represents the final settlement (to be determined);

[0071] S0 represents the initial settlement (usually the settlement at time t). When t approaches ∞, S(t) approaches S f , that is, the foundation settlement tends to be stable;

[0072] α represents the fitting parameter, which reflects the change of sedimentation rate over time. The larger α is, the faster the sedimentation rate decays.

[0073] make: Slope intercept Then use linear regression to fit the relationship between y and x to obtain the slope m and intercept b;

[0074] Solve for the final settlement S f And parameter α: α=b·(S f -S0);

[0075] Calculate the correlation coefficient R 2 , substitute t back into the hyperbola formula to predict the final settlement at any time in the future;

[0076] It should be disclosed that R 2 Indicates how well the linear regression formula fits the data, that is, the proportion of the variation explained by the regression formula to the total variation. Its value range is [0,1].

[0077] R 2 =1: Indicates that the regression formula fits the data perfectly, that is, all data points fall on the regression line and the regression formula explains all the variation.

[0078] R 2 =0: Indicates that the regression formula does not explain any variation, that is, the model is independent of the data.

[0079] 0 <R 2 <1: Indicates that the regression formula explains some, but not all, of the variation. 2 The closer it is to 1, the better the regression formula fits; 2 The closer it is to 0, the worse the fitting effect.

[0080] The calculation formula is as follows:

[0081]

[0082] Among them, y i is the actual observed value of foundation settlement; is the predicted value predicted by the linear regression formula; is the mean of the actual observation values of foundation settlement; n is the number of actual observation values of foundation settlement.

[0083] Then according to the threshold correction formula:

[0084] Assume that the observation data of a project is as follows:

[0085] Time t (days) Final settlement S(t)(mm) 10 15 20 25 30 32 40 37 50 40

[0086] Step 1: Perform linearization and calculate Assume S0 = 0, then

[0087]

[0088] Step 2: Linear regression, fitting y and t, and obtaining a slope m = 0.025.

[0089] Step 3: Solve for S f ,

[0090] Step 4: Verify, compare the fitted curve with the actual data, if R 2 If it is close to 1, the fit is good.

[0091] Furthermore, the deviation calculation unit 310 calculates the absolute deviation rate of the elevation deviation using the formula: Assuming that the theoretical value of the roadbed elevation deviation predicted based on historical data is 10.2 mm, and the actual elevation deviation measured by the elevation displacement sensor is 10 mm, the formula is substituted to calculate:

[0092] On the basis of the above, the absolute deviation rate of the elevation deviation calculated by the deviation calculation unit 310 is compared with the dynamic threshold value. If the absolute deviation rate of the elevation deviation is greater than the dynamic threshold value, the optimization unit 410 adjusts the construction plan, otherwise the construction plan is not adjusted.

[0093] Example 2:

[0094] This embodiment, based on the content provided in Example 1, aims to provide a method for an intelligent road construction control system. The specific steps are as follows:

[0095] S1. The modeling unit 110 constructs a BIM model based on the design drawings as a theoretical value of the road repair construction data.

[0096] S2. The sensor network 210 obtains actual values of temperature, elevation, and foundation settlement data during the construction process.

[0097] S3. The deviation calculation unit 310 receives the data transmitted by the data transmission unit 230 to calculate the elevation deviation and the absolute deviation rate of the elevation deviation. The deviation calculation unit 310 predicts the final settlement of the road and then infers the dynamic threshold. Then, the risk assessment unit 320 compares the absolute deviation rate of the elevation deviation with the size of the dynamic threshold.

[0098] S4. The optimization unit 410 gives correction suggestions for road construction based on the comparison results in S3: if the absolute deviation rate of the elevation deviation is greater than the dynamic threshold, the optimization unit 410 adjusts the construction plan, otherwise the construction plan is not adjusted; finally, the control unit 420 executes the construction adjustment strategy given by the optimization unit 410, and optimizes the actual value of the construction data through the above steps, reducing the impact of environmental factors or construction activity factors on the actual value of the construction data, thereby improving the accuracy of the BIM model in giving construction adjustment suggestions.

[0099] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent road construction control system, comprising a BIM construction module (100) for constructing a BIM model and providing a construction benchmark theoretical value; a data acquisition module (200) for acquiring actual values of construction data; a comparison and analysis module (300) for entering the actual values in the data acquisition module (200) into the BIM model and comparing the absolute deviation rate of the actual values with a dynamic threshold of the theoretical values; and a construction control module (400) for adjusting a construction plan based on the analysis results of the comparison and analysis module (300). Its characteristics are: The data acquisition module (200) acquires actual values of data including temperature, elevation, and foundation settlement; the comparative analysis module (300) extracts normal values of foundation settlement using an isolation forest algorithm to obtain a dynamic threshold; the comparative analysis module (300) calculates elevation deviation and obtains its absolute deviation rate; and the comparative analysis module (300) compares the absolute deviation rate with the dynamic threshold; if the absolute deviation rate is greater than the dynamic threshold, the construction control module (400) adjusts the construction plan; otherwise, the construction plan is not adjusted; The normal value of the foundation settlement excludes abnormal temperature values and abnormal numerical values.

2. An intelligent road construction control system according to claim 1, characterized in that: The BIM construction module (100) includes a modeling unit (110), a storage and interface unit (120), and a version control unit (130); The modeling unit (110) generates a BIM model based on a design drawing, the storage and interface unit (120) manages BIM model data and provides an interface for docking with an external system, and the version control unit (130) is used to record a history of changes to the BIM model.

3. The intelligent road construction control system according to claim 1, characterized in that: The data acquisition module (200) includes a sensor network (210), an image acquisition unit (220) and a data transmission unit (230); The sensor network (210) is used to collect actual values of road construction process data, the image acquisition unit (220) is used to capture images and videos of the construction process, and the data transmission unit (230) is used to transmit data acquired by the sensor network (210) and the image acquisition unit (220).

4. An intelligent road construction control system according to claim 3, characterized in that: The comparative analysis module (300) includes a deviation calculation unit (310), a risk assessment unit (320) and a visualization unit (330); The deviation calculation unit (310) receives data transmitted by the data transmission unit (230) to calculate the elevation deviation and calculates the absolute deviation rate of the elevation deviation. The deviation calculation unit (310) predicts the final road settlement by fitting the settlement-time curve using a hyperbola fitting method, and then deduces the dynamic threshold based on the correction formula. The risk assessment unit (320) compares the absolute deviation rate of the elevation deviation with the size of the dynamic threshold. The visualization unit (330) displays the distribution of the absolute deviation rate and the dynamic threshold through a heat map.

5. An intelligent road construction control system according to claim 4, characterized in that: The construction control module (400) includes an optimization unit (410) and a control unit (420), wherein the optimization unit (410) generates correction suggestions based on the comparison results of the risk assessment unit (320).

6. The intelligent road construction control system according to claim 4, characterized in that: The deviation calculation unit (310) eliminates abnormal temperature and abnormal data size in the foundation settlement using the isolation forest algorithm as follows: Preprocessing: Simultaneously collect foundation settlement (S) and temperature (T), use linear interpolation to delete obvious outliers, and then eliminate the dimensional differences of foundation settlement and temperature data; Construct an isolation forest model: randomly extract a subset of samples from the data of S and T and randomly select a split value, recursively partition the data until the stopping condition is met (a node contains only one sample or reaches the maximum depth), and then generate multiple isolated trees to form an isolation forest; Calculate the path length: For each data point x, calculate its path length h(x) in each tree (that is, the number of splits from the root node to the leaf node) Get the anomaly score: Abnormality judgment: If s(x, n) tends to 1, it is abnormal; if s(x, n) tends to 0, it is normal; Screening: Set the screening threshold of foundation settlement to eliminate foundation settlement with abnormal temperature and abnormal value.

7. An intelligent road construction control system according to claim 6, characterized in that: The deviation calculation unit (310) predicts the final road settlement by fitting the settlement-time curve using a hyperbola fitting method based on the foundation settlement with the exception of temperature anomalies and abnormal numerical values, and then deduces the dynamic threshold value based on the correction formula in the following steps: Obtain the cumulative settlement at different time points (t i , S i ), determine S0 (take the foundation settlement at t = 0, which can be assumed to be 0 if not measured); transform the hyperbola formula into a linear formula, and then use linear regression to fit the relationship between y and x to obtain the slope m and intercept b; solve the final settlement S f and parameter α; calculate the correlation coefficient R 2 , substitute t back into the hyperbola formula to predict the final settlement at any time in the future; then calculate the dynamic threshold value according to the threshold correction formula.

8. The intelligent road construction control system according to claim 4, characterized in that: The formula for calculating the absolute deviation rate of the elevation deviation by the deviation calculation unit (310) is:

9. The intelligent road construction control system according to claim 5, characterized in that: The absolute deviation rate of the elevation deviation calculated by the deviation calculation unit (310) is compared with the dynamic threshold value. If the absolute deviation rate of the elevation deviation is greater than the dynamic threshold value, the optimization unit (410) adjusts the construction plan; otherwise, the construction plan is not adjusted.

10. A method for an intelligent road construction control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, a BIM construction module (100) constructs a BIM model based on the design drawings as a theoretical value of the road repair construction data; S2, a data acquisition module (200) obtains actual values of temperature, elevation and foundation settlement data during the construction process; S3, the comparison and analysis module (300) calculates the absolute deviation rate and the dynamic threshold of the elevation deviation based on the actual value of the construction data collected by the data collection module (200), and compares the absolute deviation rate and the dynamic threshold of the elevation deviation; S4, the construction control module (400) provides correction suggestions for road construction based on the comparison results in S3. After the comparative analysis module (200) eliminates environmental factors or construction activity factors, the accuracy of the actual value of the construction data is improved, thereby improving the accuracy of the construction adjustment plan given by the BIM model.

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