Automatic control system and method of punching pile driver based on big data

Abstract

The present invention discloses an automatic control system and method for a punching pile driver based on big data, which relates to the technical field of automatic control of punching pile drivers. The specific steps include: step one, determining the operating area of ​​the punching pile driver, and setting the dynamic control coefficient of the punching pile driver by collecting the working data of the punching pile driver in the operating area; step two, determining the stratum environment change factors of different pile holes, and constructing a prediction model for predicting the dynamic control coefficient history of future time periods based on the historical sequence of the dynamic control coefficient; step three, judging whether the construction status of the current time period is normal through the prediction model of the dynamic control coefficient, and adjusting the abnormal construction status through multi-parameter collaborative adjustment. The present invention proposes an automatic control system and method for a punching pile driver based on big data, which constructs a dynamic control coefficient prediction model by fusing the three-dimensional coordinate data of the operating area and multi-dimensional construction parameters, and realizes precise regulation based on the difference analysis of historical and real-time data.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control of punching pile drivers, and in particular relates to an automatic control system and method for punching pile drivers based on big data. Background Art

[0002] With the rapid development of modern construction projects, punching pile drivers, as an important basic construction equipment, have been widely used in the construction of large-scale infrastructure such as bridges, high-rise buildings, and subways. Traditional punching pile drivers mainly rely on manual operation or simple mechanized control. Their work efficiency and construction quality are largely affected by the operator's experience and technical level. However, with the expansion of the scale and complexity of engineering projects, traditional control methods have gradually exposed many problems.

[0003] Existing control systems for punching pile drivers mostly rely on a single sensor for data collection, lacking the ability to comprehensively analyze multi-source data. Due to the complex and ever-changing construction environment, a single data source often fails to fully reflect the actual conditions on the construction site, resulting in a weak system response to emergencies. For example, in areas with complex geological conditions, relying solely on a single sensor may not be able to promptly identify underground obstacles or geological changes, thereby increasing construction risks.

[0004] In summary, traditional punching pile driver control methods are no longer able to meet the demands of modern construction projects for efficient, precise, and safe construction. Therefore, there is an urgent need for an automated control system and method for punching pile drivers that integrates big data technology to achieve intelligent management, precise control, and efficient operation of the construction process. Such a system would not only significantly improve construction efficiency but also effectively reduce the uncertainty caused by human factors, providing more reliable technical support for engineering construction. Summary of the Invention

[0005] The purpose of the present invention is to provide an automated control system and method for a punching pile driver based on big data, so as to solve the technical problem that the existing technology cannot fully reflect the actual situation of the construction site.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The automatic control method of punching pile driver based on big data includes:

[0008] Step 1: Determine the operating area of ​​the punching pile driver, collect the working data of the punching pile driver in the operating area, and set the dynamic control coefficient of the punching pile driver;

[0009] Step 2: Determine the stratum environment change factors of different pile holes, and build a prediction model for the dynamic control coefficient history in the future time period based on the dynamic control coefficient history sequence;

[0010] Step 3: Use the prediction model of the dynamic control coefficient to determine whether the construction status in the current time period is normal, and adjust the abnormal construction status through multi-parameter coordination.

[0011] Furthermore, the working data of the punching pile driver in the working area is collected. The specific method is as follows:

[0012] The working time period of the punching pile driver is divided into t time periods, the scope and boundaries of the punching pile driver's operating area are clarified, a three-dimensional coordinate system including the entire operating area is established, the coordinate data of each pile hole of the punching pile driver is marked, and the working data of the punching pile driver is collected in real time using the relevant sensors pre-installed on the punching pile driver. The working data collected in a time period is recorded as , where A(i) represents the average vibration amplitude of the punching pile driver in the i-th time period, F(i) represents the average impact force of the punching pile driver in the i-th time period, and f(i, k) represents the hammering frequency of the k-th hammering of the punching pile driver in the i-th time period, that is, the time interval between the k-th hammering and the k+1-th hammering, and k is greater than 1.

[0013] Furthermore, the dynamic control coefficient of the punching pile driver is set as follows:

[0014] Using the formula represents the dynamic control coefficient of the punching pile driver, where represents the mean vibration amplitude of the punching pile driver during the time period i of the a-th pile hole, represents the average impact force of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punching pile driver in the i-th time period of the a-th pile hole, k is greater than or equal to 1, n(i) represents the total number of hammering times of the punching pile driver in the i-th time period of the a-th pile hole, It represents the maximum hammer frequency of the pile driver in the a-th pile hole during the i-th time period. e represents the base of the natural logarithm. The dynamic balance of the construction state is quantified by the dynamic control coefficient. The dynamic control coefficient historical sequence determined by the dynamic control coefficients of multiple historical time periods is determined.

[0015] Furthermore, the stratum environment change factors of different pile holes are determined by the following method:

[0016] Using the formula represents the stratum environment change factor of different pile holes, where a represents the a-th pile hole, Indicates the ground environment change factor of the a-th pile hole as it changes in each time period. b is the basic factor, which is preset according to the project type. For example, b=0.25 is preset for bridge pile foundation type, and b=0.18 is preset for building pile foundation type. Indicates the depth of the pile body sunk into the a-th pile hole, It represents the difference between the sinking depth of the pile body in the i-th time period and the i-1-th time period of the a-th pile hole. represents the total number of time periods in which the punching pile driver works in the a-th pile hole, i represents the i-th time period, represents the average impact force of the pile driver in the a-th pile hole during the i-th time period, Represents all the time periods during which the punching pile driver works in the a-th pile hole The average value of .

[0017] Furthermore, a prediction model for predicting the dynamic control coefficient of future time periods is constructed. The specific method is as follows:

[0018] Using the formula represents the prediction model of the dynamic control coefficient, where a represents the a-th pile hole, represents the stratum environment change factor of the a-th pile hole as it changes in each time period, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the predicted value of the dynamic control coefficient of the punching pile driver in the i+1th time period of the a-th pile hole, j represents the jth time period of the punching pile driver working in the a-th pile hole, and j is not equal to i. It represents the control weight coefficient of the punching pile driver in the jth time period of the ath pile hole.

[0019] Furthermore, the prediction model of the dynamic control coefficient is used to determine whether the construction status in the current time period is normal. The specific method is as follows:

[0020] Using a prediction model with verified accuracy, the dynamic control coefficient of the current time period is predicted based on the dynamic control coefficient of the previous time period. In order to quantify the deviation between the current construction status and the predicted status, the difference is defined as the deviation between the true value and the predicted value of the current dynamic control coefficient. When the difference in the i-th time period of the a-th pile hole is greater than or equal to the difference threshold, it is determined that the construction status deviates from the expectation and is in an abnormal state, and parameter adjustment is required.

[0021] Furthermore, the difference is defined as the deviation between the actual value and the predicted value of the current dynamic control coefficient. The specific method is as follows:

[0022] Using the formula represents the difference, where represents the difference in the i-th time period of the a-th pile hole, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, It represents the predicted value of the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the total number of hammer blows of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punch pile driver in the i-th time period of the a-th pile hole, It represents the average hammer frequency of the pile driver in the a-th pile hole during the i-th time period.

[0023] Furthermore, the abnormal construction status is adjusted through multi-parameter coordination. The specific method is as follows:

[0024] Using the formula According to the difference, the vibration amplitude, impact force and hammer frequency are calculated to adjust the reference value, where sgn represents the sign function used to control the direction. represents the adjustment reference value of the vibration amplitude in the i-th time period of the a-th pile hole, It represents the adjustment reference value of the impact force in the i-th time period of the a-th pile hole. represents the adjustment reference value of the hammer frequency in the i-th time period of the a-th pile hole, xA represents the weight coefficient of the vibration amplitude, xF represents the weight coefficient of the impact force, xf represents the weight coefficient of the hammer frequency, s, u and v are constant factors, and the vibration amplitude, impact force and hammer frequency of the punch pile driver are adjusted in the next time period according to the calculated adjustment amount.

[0025] The present invention also provides a punching pile driver automation control system based on big data, which is applied to the punching pile driver automation control method based on big data, comprising:

[0026] The dynamic control coefficient setting module is used to determine the operating area of ​​the punching pile driver and set the dynamic control coefficient of the punching pile driver by collecting the working data of the punching pile driver in the operating area;

[0027] The dynamic control coefficient prediction module is used to determine the geological environment change factors of different pile holes and build a prediction model for the dynamic control coefficient history in the future time period based on the dynamic control coefficient history sequence;

[0028] The multi-parameter collaborative adjustment module is used to determine whether the construction status in the current time period is normal through the prediction model of the dynamic control coefficient, and to adjust the abnormal construction status through multi-parameter collaborative adjustment.

[0029] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0030] 1. The present invention establishes a three-dimensional coordinate system encompassing the entire operating area and marks the coordinate data of the pile holes, facilitating subsequent data analysis and construction optimization. The present invention also sets the dynamic control coefficient of the punching pile driver by integrating pre-processed working data within a time period. The dynamic control coefficient is used to quantify the dynamic balance of the construction state, which helps to evaluate the stability and consistency of the punching pile driver during the construction process. Determining the historical sequence of the dynamic control coefficient allows for further analysis of the changing trend of the construction state, providing a basis for construction optimization and decision-making.

[0031] 2. By determining the formation environment change factor, the present invention helps to accurately capture the changing trend of the formation environment. Based on the historical sequence of the dynamic control coefficient and the formation environment change factor, the established prediction model can accurately predict the dynamic control coefficient in the future time period. The application of the prediction model also helps to reduce the uncertainty and risk in the construction process, thereby improving the safety and controllability of the construction.

[0032] 3. By combining variance analysis with multi-parameter coordinated control, this invention enables real-time monitoring of construction status and dynamic adjustment of the punching pile driver's operating parameters, ensuring efficient and safe construction. Combining the technical advantages of spatial-parameter fusion, precise prediction, high sensitivity, and stable control, this significantly enhances the intelligent level of the punching pile driver, providing reliable technical support for construction under complex geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 paying any creative work.

[0034] Figure 1 A step diagram of an automated control method for a punching pile driver based on big data is shown;

[0035] Figure 2 A diagram showing the steps of a method for coordinating multiple parameters to adjust abnormal construction conditions;

[0036] Figure 3 The module diagram of the punching pile driver automation control system based on big data is shown. DETAILED DESCRIPTION

[0037] 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.

[0038] Example 1: Figure 1 、 Figure 2 The automated control method for a punching pile driver based on big data shown in the figure specifically includes the following steps:

[0039] Step 1: Determine the operating area of ​​the punching pile driver, and set the dynamic control coefficient of the punching pile driver by collecting the working data of the punching pile driver in the operating area.

[0040] The working time period of the punching pile driver is divided into t time periods. The scope and boundaries of the punching pile driver's operating area are clearly defined in each working time period. The operating area is determined based on the engineering design drawings and on-site survey data. The boundaries of the operating area are accurately delineated through the GPS positioning system and the geographic information system, and the specific location of each pile hole driven by the punching pile driver is marked. The determination of the operating area includes not only the plane position but also the depth range. The pile body sinking depth data of each pile is determined, and a three-dimensional coordinate system containing the entire operating area is established. The coordinate data of each pile hole driven by the punching pile driver is marked. The specific coordinate data of the pile hole includes the plane coordinates (x, y). Based on the establishment of the three-dimensional coordinate system, the relevant sensors pre-installed on the punching pile driver are used to collect the working data of the punching pile driver in real time, including collecting the vibration amplitude of the punching pile driver during operation through a vibration sensor, measuring the impact force of the punching pile driver during the hammering process through a force sensor, and determining the hammering frequency by recording the time interval between adjacent hammerings of the punching pile driver within a time period. The working data collected within a time period is recorded as , where A(i) represents the average vibration amplitude of the punching pile driver in the i-th time period, F(i) represents the average impact force of the punching pile driver in the i-th time period, and f(i, k) represents the hammering frequency of the k-th hammering of the punching pile driver in the i-th time period, that is, the time interval between the k-th hammering and the k+1-th hammering, where k is greater than 1;

[0041] The collected working data is filtered and denoised, and outliers in the working data are removed to obtain pre-processed working data. By integrating the pre-processed working data of the punching pile driver in a time period, the dynamic control coefficient of the punching pile driver is set as a comprehensive representation indicator of the construction status of the punching pile driver in the time period. The specific formula of the dynamic control coefficient is as follows:

[0042] ;

[0043] in, represents the mean vibration amplitude of the punching pile driver during the time period i of the a-th pile hole, represents the average impact force of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punching pile driver in the i-th time period of the a-th pile hole, k is greater than or equal to 1, n(i) represents the total number of hammering times of the punching pile driver in the i-th time period of the a-th pile hole, It represents the maximum hammer frequency of the pile driver in the a-th pile hole during the i-th time period. e represents the base of the natural logarithm. The dynamic balance of the construction state is quantified by the dynamic control coefficient. The dynamic control coefficient historical sequence determined by the dynamic control coefficients of multiple historical time periods is determined.

[0044] Step 2: Determine the stratum environment change factors of different pile holes, and build a prediction model for the dynamic control coefficient history in the future time period based on the historical sequence of the dynamic control coefficient.

[0045] Based on the impact force data and pile sinking depth data of the punching pile driver in each historical time period, the ground environment change factor of different pile holes is determined. The specific formula is as follows:

[0046] ;

[0047] Among them, a represents the a-th pile hole, Indicates the ground environment change factor of the a-th pile hole as it changes in each time period. b is the basic factor, which is preset according to the project type. For example, b=0.25 is preset for bridge pile foundation type, and b=0.18 is preset for building pile foundation type. Indicates the depth of the pile body sunk into the a-th pile hole, It represents the difference between the sinking depth of the pile body in the i-th time period and the i-1-th time period of the a-th pile hole. represents the total number of time periods in which the punching pile driver works in the a-th pile hole, i represents the i-th time period, represents the average impact force of the pile driver in the a-th pile hole during the i-th time period, Represents all the time periods during which the punching pile driver works in the a-th pile hole The average value of .

[0048] By integrating the historical series of dynamic control coefficients with the stratum environment change factors of different pile holes in each time period, a prediction model for the dynamic control coefficient is established. The specific formula is as follows:

[0049] ;

[0050] Among them, a represents the a-th pile hole, represents the stratum environment change factor of the a-th pile hole as it changes in each time period, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the predicted value of the dynamic control coefficient of the punching pile driver in the i+1th time period of the a-th pile hole, j represents the jth time period of the punching pile driver working in the a-th pile hole, and j is not equal to i. It represents the control weight coefficient of the punching pile driver in the jth time period of the ath pile hole.

[0051] A prediction error threshold is set. When the error between the predicted value and the actual value obtained by the prediction model is greater than or equal to the prediction error threshold, the parameters and weight coefficients of the prediction model need to be adjusted until the error between the predicted value and the actual value is less than the error threshold. In order to further improve the accuracy of the prediction model, the real-time working data of the punching pile driver is used to dynamically correct the prediction model. Whenever the punching pile driver completes a time period, its working data within the time period is input into the prediction model, and the prediction deviation is adjusted according to real-time feedback, thereby optimizing the performance of the prediction model.

[0052] Step 3: Use the prediction model of the dynamic control coefficient to determine whether the construction status in the current time period is normal, and adjust the abnormal construction status through multi-parameter coordination.

[0053] Using a prediction model with verified accuracy, the dynamic control coefficient of the current time period is predicted based on the dynamic control coefficient of the previous time period. To quantify the deviation between the current construction status and the predicted status, the difference degree is defined as the deviation between the actual value of the current dynamic control coefficient and the predicted value. The calculation formula for the difference degree is as follows:

[0054] ;

[0055] in, represents the difference in the i-th time period of the a-th pile hole, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, It represents the predicted value of the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the total number of hammer blows of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punch pile driver in the i-th time period of the a-th pile hole, It represents the average hammer frequency of the punch pile driver during the i-th time period of the a-th pile hole. The difference combines the deviation of the dynamic control coefficient and the fluctuation of the vibration amplitude, which helps to fully reflect the abnormality of the construction status.

[0056] A difference threshold is preset. When the difference in the i-th time period of the a-th pile hole is greater than or equal to the difference threshold, it is determined that the construction status deviates from the expected state and is in an abnormal state, and parameter adjustment is required;

[0057] According to the difference, the vibration amplitude, impact force and hammer frequency adjustment reference values ​​are calculated. The specific formula is as follows:

[0058] ;

[0059] Among them, sgn represents the sign function used to control the direction, represents the adjustment reference value of the vibration amplitude in the i-th time period of the a-th pile hole, It represents the adjustment reference value of the impact force in the i-th time period of the a-th pile hole. represents the adjustment reference value of the hammer frequency in the i-th time period of the a-th piling hole, xA represents the weight coefficient of the vibration amplitude, xF represents the weight coefficient of the impact force, xf represents the weight coefficient of the hammer frequency, s, u and v are constant factors, s is used to quickly suppress vibration abnormalities when the difference is high, u is used to smooth the impact force adjustment curve to avoid sudden changes, and v is used to gradually adjust the hammer frequency. In this embodiment, it is set that s is equal to 1, u is equal to -1, and v is equal to 2. According to the calculated adjustment amount, the vibration amplitude, impact force and hammer frequency of the punching pile driver are adjusted in the next time period.

[0060] By adjusting operating parameters in real time, we ensure that the construction status is always within the expected range, avoiding construction quality problems caused by sudden changes in the formation or equipment abnormalities.

[0061] Example 2, as Figure 3 The big data-based punching pile driver automation control system shown in the figure specifically includes the following contents:

[0062] The dynamic control coefficient setting module divides the working time period of the punching pile driver into t time periods, and defines the scope and boundaries of the punching pile driver's operating area in each working time period. The operating area is determined based on the engineering design drawings and on-site survey data. The boundaries of the operating area are accurately delineated through the GPS positioning system and geographic information system, and the specific location of the pile hole driven by the punching pile driver each time is marked. The determination of the operating area includes not only the plane position but also the depth range. The pile body sinking depth data of each pile is determined, and a three-dimensional coordinate system containing the entire operating area is established. The coordinate data of each pile hole driven by the punching pile driver is marked. The specific coordinate data of the pile hole includes the plane coordinates (x, y). Based on the establishment of the three-dimensional coordinate system, the relevant sensors pre-installed on the punching pile driver are used to collect the working data of the punching pile driver in real time, including collecting the vibration amplitude of the punching pile driver during operation through a vibration sensor, measuring the impact force of the punching pile driver during the hammering process using a force sensor, and determining the hammering frequency by recording the time interval between adjacent hammerings of the punching pile driver within a time period. The working data collected within a time period is recorded as , where A(i) represents the average vibration amplitude of the punching pile driver in the i-th time period, F(i) represents the average impact force of the punching pile driver in the i-th time period, and f(i, k) represents the hammering frequency of the k-th hammering of the punching pile driver in the i-th time period, that is, the time interval between the k-th hammering and the k+1-th hammering, where k is greater than 1;

[0063] The collected working data is filtered and denoised, and outliers in the working data are removed to obtain pre-processed working data. By integrating the pre-processed working data of the punching pile driver in a time period, the dynamic control coefficient of the punching pile driver is set as a comprehensive representation indicator of the construction status of the punching pile driver in the time period. The specific formula of the dynamic control coefficient is as follows:

[0064] ;

[0065] in, represents the mean vibration amplitude of the punching pile driver during the time period i of the a-th pile hole, represents the average impact force of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punching pile driver in the i-th time period of the a-th pile hole, k is greater than or equal to 1, n(i) represents the total number of hammering times of the punching pile driver in the i-th time period of the a-th pile hole, It represents the maximum hammer frequency of the pile driver in the a-th pile hole during the i-th time period. e represents the base of the natural logarithm. The dynamic balance of the construction state is quantified by the dynamic control coefficient. The dynamic control coefficient historical sequence determined by the dynamic control coefficients of multiple historical time periods is determined.

[0066] The dynamic control coefficient prediction module determines the ground environment change factor of different pile holes in each time period based on the impact force data and pile sinking depth data of the punching pile driver in each historical time period. The specific formula is as follows:

[0067] ;

[0068] Among them, a represents the a-th pile hole, Indicates the ground environment change factor of the a-th pile hole as it changes in each time period. b is the basic factor, which is preset according to the project type. For example, b=0.25 is preset for bridge pile foundation type, and b=0.18 is preset for building pile foundation type. Indicates the depth of the pile body sunk into the a-th pile hole, It represents the difference between the sinking depth of the pile body in the i-th time period and the i-1-th time period of the a-th pile hole. represents the total number of time periods in which the punching pile driver works in the a-th pile hole, i represents the i-th time period, represents the average impact force of the pile driver in the a-th pile hole during the i-th time period, Represents all the time periods during which the punching pile driver works in the a-th pile hole The average value of .

[0069] By integrating the historical series of dynamic control coefficients with the stratum environment change factors of different pile holes in each time period, a prediction model for the dynamic control coefficient is established. The specific formula is as follows:

[0070] ;

[0071] Among them, a represents the a-th pile hole, represents the stratum environment change factor of the a-th pile hole as it changes in each time period, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the predicted value of the dynamic control coefficient of the punching pile driver in the i+1th time period of the a-th pile hole, j represents the jth time period of the punching pile driver working in the a-th pile hole, and j is not equal to i. It represents the control weight coefficient of the punching pile driver in the jth time period of the ath pile hole.

[0072] A prediction error threshold is set. When the error between the predicted value and the actual value obtained by the prediction model is greater than or equal to the prediction error threshold, the parameters and weight coefficients of the prediction model need to be adjusted until the error between the predicted value and the actual value is less than the error threshold. In order to further improve the accuracy of the prediction model, the real-time working data of the punching pile driver is used to dynamically correct the prediction model. Whenever the punching pile driver completes a time period, its working data within the time period is input into the prediction model, and the prediction deviation is adjusted according to real-time feedback, thereby optimizing the performance of the prediction model.

[0073] The multi-parameter collaborative adjustment module uses a prediction model with verified accuracy to predict the dynamic control coefficient of the current time period based on the dynamic control coefficient of the previous time period. To quantify the deviation between the current construction status and the predicted status, the difference degree is defined as the deviation between the actual value of the current dynamic control coefficient and the predicted value. The calculation formula for the difference degree is as follows:

[0074] ;

[0075] in, represents the difference in the i-th time period of the a-th pile hole, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, It represents the predicted value of the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the total number of hammer blows of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punch pile driver in the i-th time period of the a-th pile hole, It represents the average hammer frequency of the punch pile driver during the i-th time period of the a-th pile hole. The difference combines the deviation of the dynamic control coefficient and the fluctuation of the vibration amplitude, which helps to fully reflect the abnormality of the construction status.

[0076] A difference threshold is preset. When the difference in the i-th time period of the a-th pile hole is greater than or equal to the difference threshold, it is determined that the construction status deviates from the expected state and is in an abnormal state, and parameter adjustment is required;

[0077] According to the difference, the vibration amplitude, impact force and hammer frequency adjustment reference values ​​are calculated. The specific formula is as follows:

[0078] ;

[0079] Among them, sgn represents the sign function used to control the direction, represents the adjustment reference value of the vibration amplitude in the i-th time period of the a-th pile hole, It represents the adjustment reference value of the impact force in the i-th time period of the a-th pile hole. represents the adjustment reference value of the hammer frequency in the i-th time period of the a-th piling hole, xA represents the weight coefficient of the vibration amplitude, xF represents the weight coefficient of the impact force, xf represents the weight coefficient of the hammer frequency, s, u and v are constant factors, s is used to quickly suppress vibration abnormalities when the difference is high, u is used to smooth the impact force adjustment curve to avoid sudden changes, and v is used to gradually adjust the hammer frequency. In this embodiment, s is set to 1, u is set to -1, and v is set to 2.

[0080] By adjusting operating parameters in real time, we ensure that the construction status is always within the expected range, avoiding construction quality problems caused by sudden changes in the formation or equipment abnormalities.

[0081] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

[0082] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The automatic control method of punching pile driver based on big data is characterized in that: include: Step 1: Determine the operating area of ​​the punching pile driver, collect the working data of the punching pile driver in the operating area, and set the dynamic control coefficient of the punching pile driver; The working time period of the punching pile driver is divided into t time periods, and the working data of the punching pile driver is collected in real time. The working data collected in a time period is recorded as , where A(i) represents the average vibration amplitude of the punching pile driver in the i-th time period, F(i) represents the average impact force of the punching pile driver in the i-th time period, and f(i, k) represents the hammering frequency of the k-th hammering of the punching pile driver in the i-th time period, that is, the time interval between the k-th hammering and the k+1-th hammering, where k is greater than 1; Step 2: Determine the stratum environment change factors of different pile holes, and build a prediction model for the dynamic control coefficient history in the future time period based on the dynamic control coefficient history sequence; Using the formula represents the stratum environment change factor of different pile holes, where a represents the a-th pile hole, Indicates the stratum environment change factor of the a-th pile hole as it changes in each time period. b is the basic factor, which is preset according to the project type. Indicates the depth of the pile body sunk into the a-th pile hole, It represents the difference between the sinking depth of the pile body in the i-th time period and the i-1-th time period of the a-th pile hole. represents the total number of time periods in which the punching pile driver works in the a-th pile hole, i represents the i-th time period, represents the average impact force of the pile driver in the a-th pile hole during the i-th time period, Represents all the time periods during which the punching pile driver works in the a-th pile hole The average value of Step 3: Use the prediction model of the dynamic control coefficient to determine whether the construction status in the current time period is normal, and adjust the abnormal construction status through multi-parameter coordination.

2. The automatic control method for punching pile driver based on big data according to claim 1 is characterized in that: Set the dynamic control coefficient of the punching pile driver. The specific method is: Using the formula represents the dynamic control coefficient of the punching pile driver, where represents the mean vibration amplitude of the punching pile driver during the time period i of the a-th pile hole, represents the average impact force of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punching pile driver in the i-th time period of the a-th pile hole, k is greater than or equal to 1, n(i) represents the total number of hammering times of the punching pile driver in the i-th time period of the a-th pile hole, It represents the maximum hammer frequency of the pile driver in the a-th pile hole during the i-th time period. e represents the base of the natural logarithm. The dynamic balance of the construction state is quantified by the dynamic control coefficient. The dynamic control coefficient historical sequence determined by the dynamic control coefficients of multiple historical time periods is determined.

3. The automatic control method of punching pile driver based on big data according to claim 1 is characterized in that: Construct a prediction model for the dynamic control coefficient history of future time periods. The specific method is as follows: Using the formula represents the prediction model of the dynamic control coefficient, where a represents the a-th pile hole, represents the stratum environment change factor of the a-th pile hole as it changes in each time period, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the predicted value of the dynamic control coefficient of the punching pile driver in the i+1th time period of the a-th pile hole, j represents the jth time period of the punching pile driver working in the a-th pile hole, and j is not equal to i. It represents the control weight coefficient of the punching pile driver in the jth time period of the ath pile hole.

4. The automatic control method for punching pile driver based on big data according to claim 1 is characterized in that: The prediction model of the dynamic control coefficient is used to determine whether the construction status in the current time period is normal. The specific method is as follows: Using a prediction model with verified accuracy, the dynamic control coefficient of the current time period is predicted based on the dynamic control coefficient of the previous time period. In order to quantify the deviation between the current construction status and the predicted status, the difference is defined as the deviation between the true value and the predicted value of the current dynamic control coefficient. When the difference in the i-th time period of the a-th pile hole is greater than or equal to the difference threshold, it is determined that the construction status deviates from the expectation and is in an abnormal state, and parameter adjustment is required.

5. The automatic control method for punching pile driver based on big data according to claim 4 is characterized in that: The difference is defined as the deviation between the actual value and the predicted value of the current dynamic control coefficient. The specific method is as follows: Using the formula represents the difference, where represents the difference in the i-th time period of the a-th pile hole, represents the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, It represents the predicted value of the dynamic control coefficient of the punching pile driver in the i-th time period of the a-th pile hole, represents the total number of hammer blows of the pile driver during the time period i of the a-th pile hole, represents the hammering frequency of the kth hammering of the punch pile driver in the i-th time period of the a-th pile hole, It represents the average hammer frequency of the pile driver in the a-th pile hole during the i-th time period.

6. The automatic control method for punching pile driver based on big data according to claim 5 is characterized in that: Abnormal construction status is adjusted through multi-parameter coordination. The specific method is as follows: Using the formula According to the difference, the vibration amplitude, impact force and hammer frequency are calculated to adjust the reference value, where sgn represents the sign function used to control the direction. represents the adjustment reference value of the vibration amplitude in the i-th time period of the a-th pile hole, It represents the adjustment reference value of the impact force in the i-th time period of the a-th pile hole. represents the adjustment reference value of the hammer frequency in the i-th time period of the a-th pile hole, xA represents the weight coefficient of the vibration amplitude, xF represents the weight coefficient of the impact force, xf represents the weight coefficient of the hammer frequency, s, u and v are constant factors, and the vibration amplitude, impact force and hammer frequency of the punch pile driver are adjusted in the next time period according to the calculated adjustment amount.

7. A punching pile driver automation control system based on big data, applied to the punching pile driver automation control method based on big data according to any one of claims 1 to 6, characterized in that: include: The dynamic control coefficient setting module is used to determine the operating area of ​​the punching pile driver and set the dynamic control coefficient of the punching pile driver by collecting the working data of the punching pile driver in the operating area; The dynamic control coefficient prediction module is used to determine the geological environment change factors of different pile holes and build a prediction model for the dynamic control coefficient history in the future time period based on the dynamic control coefficient history sequence; The multi-parameter collaborative adjustment module is used to determine whether the construction status in the current time period is normal through the prediction model of the dynamic control coefficient, and to adjust the abnormal construction status through multi-parameter collaborative adjustment.

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