Steel pile jumbo flexible cylinder pre-charging method based on force model analysis

Abstract

The present application relates to steel pile trolley equipment technical field, disclose a steel pile trolley flexible cylinder pre-charge algorithm based on stress model analysis, the algorithm first collects steel pile trolley historical operation stress data, extracts stress characteristic parameter and constructs initial pre-charge parameter set;Then analyze the historical stress content and the attribute matching of initial parameter, filter out the second pre-charge parameter set;Then analyze the stress behavior mode of steel pile trolley, obtain stress mode score value;Then analyze the pre-charge adaptability of steel pile trolley and second parameter set, filter out the third pre-charge parameter set;Finally, according to the real-time operation feedback record optimization adjustment pre-charge parameter.This algorithm realizes the scientific setting and dynamic optimization of flexible cylinder pre-charge parameter through the analysis of historical stress data and real-time operation feedback, improves the stability, reliability and construction efficiency of steel pile trolley operation, and is suitable for steel pile trolley equipment technical field.

Description

Technical Field

[0001] This invention relates to the field of steel pile trolley equipment technology, specifically to a method for pre-charging flexible cylinders of steel pile trolleys based on force model analysis. Background Technology

[0002] In modern engineering construction, steel pile trolleys are important construction equipment widely used in steel pile construction for foundation engineering projects such as bridges and buildings. During operation, the pre-charge parameter settings of the flexible cylinder have a crucial impact on the stability, reliability, and construction efficiency of the equipment.

[0003] Traditional methods for setting pre-charge parameters for the flexible cylinders of steel pile trolleys are often based on experience, lacking a systematic analysis and scientific consideration of the actual stress conditions experienced by the trolley. This approach has several shortcomings. Firstly, because it doesn't fully consider the stress characteristics of the steel pile trolley under different operating conditions, such as stress type and pressure range, the pre-charge parameters may not match the actual operational requirements, thus affecting the equipment's performance. For example, when the steel pile trolley faces impact stress, if the pre-charge parameters are set as if under continuous stress, the flexible cylinder may not be able to effectively buffer the impact, causing damage to equipment components.

[0004] Traditional methods fail to dynamically adjust to the stress behavior patterns of the steel pile trolley. During actual operation, the stress frequency of the steel pile trolley may change, such as shifting from high-frequency to medium or low-frequency stress. Traditional pre-charging methods cannot respond to these changes in a timely manner, keeping the pre-charging parameters fixed and unable to adapt to different stress conditions, thus reducing the equipment's construction efficiency and stability.

[0005] Traditional pre-charging methods lack comprehensive utilization of historical stress data and real-time operational feedback during parameter selection and optimization. They cannot select more suitable pre-charging parameters through historical data analysis, nor can they dynamically optimize and adjust pre-charging parameters based on real-time operational feedback, resulting in a lack of flexibility and adaptability in setting pre-charging parameters.

[0006] With the increasing demands for construction quality and efficiency in engineering projects, the traditional experience-based method of setting pre-charge parameters for the flexible cylinders of steel pile trolleys is no longer sufficient to meet practical needs. Therefore, there is an urgent need for a method that can scientifically set and dynamically optimize the pre-charge parameters of the flexible cylinders based on stress model analysis, comprehensively considering historical stress data and real-time operational feedback, in order to improve the operational performance and construction efficiency of steel pile trolleys. Summary of the Invention

[0007] The purpose of this invention is to provide a pre-charging method for flexible cylinders on steel pile trolleys based on stress model analysis, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a pre-charging method for a flexible cylinder on a steel pile trolley based on force model analysis, the method comprising: S1. Collect historical stress data of the steel pile trolley, extract stress characteristic parameters, and construct the initial pre-charge parameter set of the flexible cylinder; S2. Based on the historical stress data of the steel pile trolley, analyze the matching properties of the historical stress content with the parameters in the initial pre-charge parameter set, and select the second pre-charge parameter set for the flexible cylinder. S3. Analyze the stress behavior pattern of the steel pile trolley based on the historical operational stress data of the steel pile trolley. S4. Analyze the pre-charge adaptability of the steel pile trolley and the parameters in the second pre-charge parameter set based on the force behavior mode of the steel pile trolley, and select the third pre-charge parameter set of the flexible cylinder. S5. Obtain the corresponding parameter information in the third pre-charging parameter set based on the real-time operation feedback record of the steel pile trolley, and optimize and adjust the pre-charging parameters during the pre-charging process.

[0009] Preferably, step S1 includes the following specific steps: S101. Input historical stress data of the steel pile trolley into the stress data acquisition module, and extract stress characteristic parameters from the stress data. The stress characteristic parameters include stress type characteristics and pressure range characteristics. S102. Based on the force type characteristics, filter out the parameters containing the force type characteristics in the cylinder parameter management module and output them in the form of an initial pre-charge parameter set.

[0010] Preferably, step S2 includes the following specific steps: S201. Obtain historical operational stress data of the steel pile trolley, analyze the characteristics of historical stress content based on the historical operational stress data, the characteristics of the historical stress content include load identification characteristics and attribute characteristics, filter out historical stress content containing pressure range characteristics based on the load identification characteristics and the pressure range characteristics, and at the same time obtain the attribute characteristics of the parameters in the initial pre-charge parameter set. S202. Analyze the attribute matching degree between the filtered historical stress content and the parameters in the initial pre-filling parameter set based on the attribute characteristics of the filtered historical stress content and the attribute characteristics of the parameters in the initial pre-filling parameter set. S203. Sort the attribute matching degree in descending order, filter out the parameters corresponding to the set number, and output them in the form of the second pre-charge parameter set.

[0011] Preferably, step S3 includes the following specific steps: S301. Extract the stress time and pressure change depth from the filtered historical operation stress data, obtain the stress time interval between two adjacent historical operation stress data, and then take the average of the obtained stress time intervals to obtain the average stress time interval. Take the average of the obtained pressure change depths to obtain the average pressure change depth. S302. Compare the average force-bearing time interval with the preset first time interval threshold and second time interval threshold and determine the force-bearing behavior pattern of the steel pile trolley. If the average force-bearing time interval is less than or equal to the first time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be high. If the average force-bearing time interval is greater than the first time interval threshold and the force-bearing time interval is less than the second time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be medium. If the average force-bearing time interval is greater than or equal to the second time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be low. S303. Obtain the stress mode score of the steel pile trolley based on its stress behavior pattern.

[0012] Preferably, step S4 includes the following specific steps: S401. Obtain the pre-charging characteristics and pressure depth of the parameters in the second pre-charging parameter set, and analyze the pre-charging compatibility between the steel pile trolley and the parameters in the second pre-charging parameter set based on the steel pile trolley's force mode score, average pressure change depth, pre-charging characteristics and pressure depth of the parameters in the second pre-charging parameter set. S402. Sort the precharge compatibility in descending order, filter out the parameters corresponding to the set number, and output them in the form of a third precharge parameter set.

[0013] Preferably, step S5 includes the following specific steps: The system retrieves real-time operation feedback records of the steel pile trolley and analyzes whether the steel pile trolley performs multi-parameter comparison operations. If the steel pile trolley does not perform multi-parameter comparison operations, it retrieves the parameter information with the highest pre-charging matching in the third pre-charging parameter set and optimizes and adjusts the pre-charging parameters during the pre-charging process. If the steel pile trolley performs multi-parameter comparison operations, it retrieves the parameter information corresponding to the set sequence number in the third pre-charging parameter set and optimizes and adjusts the pre-charging parameters during the pre-charging process.

[0014] Preferably, the force type characteristics include specific force characteristics such as impact, continuous, and pulsating, and the pressure range characteristics include range characteristics of high pressure, medium pressure, and low pressure.

[0015] Preferably, the pre-charging compatibility analysis between the steel pile trolley and the parameters in the second pre-charging parameter set includes the degree of matching between the force mode score of the steel pile trolley and the parameter pre-charging characteristics, and the degree of fit between the average pressure change depth and the parameter pressure depth.

[0016] Preferably, the method for parsing the attribute matching degree is to perform a correspondence analysis between the attribute features of the filtered historical stress content and the attribute features of the parameters in the initial pre-charge parameter set, and to count the proportion of the number of matching attribute items to the total number of attribute items.

[0017] Preferably, the method for determining whether the steel pile trolley has a multi-parameter comparison operation is to count the proportion of the number of times multiple parameters are monitored simultaneously in the real-time operation feedback record of the steel pile trolley to the total number of operations. If the proportion exceeds the preset comparison operation threshold, it is determined that the steel pile trolley has a multi-parameter comparison operation.

[0018] Compared with the prior art, the beneficial effects of the present invention are: By collecting historical stress data from steel pile trolley operations and extracting stress characteristic parameters, an initial pre-charge parameter set is constructed. This allows the setting of pre-charge parameters to be based on a systematic analysis of historical stress conditions, changing the blindness of traditional experience-based parameter setting and improving the initial matching degree between pre-charge parameters and actual stress conditions.

[0019] In the process of analyzing the historical stress data and comparing the attribute matching of parameters in the initial pre-charge parameter set to select the second pre-charge parameter set, various factors such as load identification characteristics, attribute characteristics, and pressure range characteristics were fully considered. Through the analysis and sorting of attribute matching degree, the parameter range was further narrowed down to ensure that the selected parameters are more in line with the actual operation requirements, thus laying a more solid foundation for the subsequent pre-charge adaptability analysis.

[0020] The analysis of the stress behavior patterns of the steel pile trolley, including extracting stress time, pressure change depth, and calculating the average stress time interval, can accurately determine the stress frequency of the steel pile trolley and thus obtain a stress pattern score. This enables the pre-charging method to perform targeted parameter adaptation analysis based on different stress behavior patterns of the steel pile trolley, improving the adaptability of pre-charging parameters to different stress conditions.

[0021] When analyzing the pre-charging adaptability of the steel pile trolley with the parameters in the second pre-charging parameter set and selecting the third pre-charging parameter set, factors such as the steel pile trolley's stress mode score, average pressure change depth, parameter pre-charging characteristics, and pressure depth were comprehensively considered. Through sorting and screening of pre-charging adaptability, it was ensured that the parameters in the third pre-charging parameter set could better adapt to the actual stress conditions of the steel pile trolley, further improving the accuracy and effectiveness of the pre-charging parameters.

[0022] The pre-charging parameters are optimized and adjusted based on the real-time operation feedback records of the steel pile trolley. When there is no multi-parameter comparison operation, the parameter with the highest pre-charging matching is selected for optimization; when there is a multi-parameter comparison operation, the parameter within the set sequence number is selected for optimization. This dynamic optimization and adjustment mechanism enables the pre-charging parameters to respond in real time to changes in the operating status of the steel pile trolley, ensuring that the flexible cylinder is always in the optimal pre-charging state. This improves the stability, reliability, and construction efficiency of the steel pile trolley operation, reduces the probability of equipment failure, and extends the service life of the equipment. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the working principle of the flexible cylinder pre-charge method for steel pile trolley based on force model analysis as described in this invention. Figure 2 Design diagrams constructed for the initial precharge parameter set; Figure 3 Design drawings for screening the second pre-charge parameter set; Figure 4 This is a sub-process for analyzing force-induced behavior patterns. Figure 5 Design drawings for optimizing and adjusting pre-charge parameters. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-5 The present invention relates to a pre-charging method for a flexible cylinder on a steel pile trolley based on force model analysis, the specific implementation steps of which are as follows: S1. Collect historical operational stress data of the steel pile trolley, extract stress characteristic parameters, and construct an initial pre-charge parameter set for the flexible cylinder. Input the historical operational stress data of the steel pile trolley into the stress data acquisition module, extract the stress characteristic parameters from the stress data, which include stress type characteristics and pressure range characteristics, and then filter the parameters containing stress type characteristics in the cylinder parameter management module according to the stress type characteristics, and output them in the form of an initial pre-charge parameter set.

[0026] S2. Based on the historical stress data of the steel pile trolley, analyze the attribute matching between the historical stress content and the parameters in the initial pre-charge parameter set, and filter out the second pre-charge parameter set for the flexible cylinder. Obtain the historical stress data of the steel pile trolley, and analyze the characteristics of the historical stress content based on this data. These characteristics include load identification characteristics and attribute characteristics. Filter out historical stress content containing pressure range characteristics according to load identification characteristics and pressure range characteristics. At the same time, obtain the attribute characteristics of the parameters in the initial pre-charge parameter set. Analyze the attribute matching degree between the filtered historical stress content and the attribute characteristics of the parameters in the initial pre-charge parameter set, sort the attribute matching degree in descending order, and filter out the corresponding parameters within the set sequence number, outputting them in the form of the second pre-charge parameter set.

[0027] S3. Analyze the force behavior pattern of the steel pile trolley based on historical operational force data. Extract the force time and pressure change depth from the filtered historical operational force data, obtain the force time interval between two adjacent historical operational force data, average the obtained force time intervals to obtain the average force time interval, and average the obtained pressure change depths to obtain the average pressure change depth. Compare the average force time interval with a preset first time interval threshold and a second time interval threshold to determine the force behavior pattern of the steel pile trolley. If the average force time interval is less than or equal to the first time interval threshold, the force frequency of the steel pile trolley is determined to be high; if the average force time interval is greater than the first time interval threshold but less than the second time interval threshold, the force frequency of the steel pile trolley is determined to be medium; if the average force time interval is greater than or equal to the second time interval threshold, the force frequency of the steel pile trolley is determined to be low. Obtain the force pattern score of the steel pile trolley based on its force behavior pattern.

[0028] S4. Based on the force behavior pattern of the steel pile trolley, analyze the pre-charge adaptability between the steel pile trolley and the parameters in the second pre-charge parameter set, and select the third pre-charge parameter set for the flexible cylinder. Obtain the pre-charge characteristics and pressure depth of the parameters in the second pre-charge parameter set. Based on the force pattern score of the steel pile trolley, the average pressure change depth, the pre-charge characteristics and pressure depth of the parameters in the second pre-charge parameter set, analyze the pre-charge adaptability between the steel pile trolley and the parameters in the second pre-charge parameter set, sort the pre-charge adaptability in descending order, select the corresponding parameters within the set sequence number, and output them in the form of the third pre-charge parameter set.

[0029] S5. Obtain the corresponding parameter information from the third pre-charging parameter set based on the real-time operation feedback record of the steel pile trolley, and optimize and adjust the pre-charging parameters during the pre-charging process. Obtain the real-time operation feedback record of the steel pile trolley, analyze whether the steel pile trolley performs a multi-parameter comparison operation. If no multi-parameter comparison operation exists, obtain the parameter information with the highest pre-charging matching in the third pre-charging parameter set, and optimize and adjust the pre-charging parameters during the pre-charging process. If a multi-parameter comparison operation exists, obtain the corresponding parameter information within the set sequence number in the third pre-charging parameter set, and optimize and adjust the pre-charging parameters during the pre-charging process.

[0030] Example 1: The specific implementation of step S1 is as follows: Historical operational stress data of the steel pile trolley needs to be input into the stress data acquisition module. This historical operational stress data is a collection of all stress-related data recorded by various sensors or data acquisition devices during the steel pile trolley's previous actual operations. This data covers the stress conditions of the steel pile trolley under different operating scenarios and working conditions, such as stress information during pile driving, position adjustment, and under different loads.

[0031] The force characteristic parameters need to be extracted from these input force data. These parameters mainly fall into two categories: force type characteristics and pressure range characteristics. Force type characteristics specifically include different types such as impact, continuous, and pulsating forces. Impact force characteristics typically occur at the moment the pile driving operation is performed, during which the pile driving trolley experiences a brief but intense impact force. Continuous force characteristics are common when the pile driving trolley is stably supporting the pile or in a fixed operating state, continuously subjected to a relatively stable force. Pulsating force characteristics may appear in certain special operating environments or equipment operating conditions, manifesting as periodic changes in force magnitude.

[0032] The pressure range characteristics include high, medium, and low pressure ranges. These high, medium, and low pressure ranges need to be defined based on the specific model, design parameters, and actual operational requirements of the steel pile trolley. Different steel pile trolleys may have different pressure range classification standards. For example, for a specific model of steel pile trolley, pressure values ​​of 0-30 MPa might be classified as low pressure, 30-80 MPa as medium pressure, and above 80 MPa as high pressure. When extracting pressure range characteristics, it is necessary to analyze and classify the pressure values ​​in historical operational stress data to determine which pressure range they belong to.

[0033] After extracting the force type and pressure range characteristics, parameter filtering is required in the cylinder parameter management module based on these characteristics. The cylinder parameter management module stores various parameters related to the pre-charge of the flexible cylinder, categorized and organized according to different force type characteristics. For example, for impact force types, the module stores a series of pre-charge parameters suitable for use under impact conditions, including the initial value of the pre-charge pressure, pre-charge speed, and pressure holding time; corresponding parameter sets are also available for continuous force types and pulsating force types.

[0034] The filtering process involves extracting force type characteristics from historical operational force data and then searching for and selecting all parameters containing those characteristics within the cylinder parameter management module. For example, if the extracted force type characteristics are primarily impact, then all parameters marked as suitable for impact force types will be filtered from the module.

[0035] The selected parameters are output as an initial pre-charge parameter set. This initial pre-charge parameter set forms the basis for subsequent parameter selection and optimization, preliminarily determining the parameter range that may be suitable for pre-charging the flexible cylinder of the steel pile trolley, considering the characteristics of the force type. It is important to note that the output format of the initial pre-charge parameter set must conform to data storage and transmission specifications to ensure that subsequent steps can accurately obtain and use this parameter information.

[0036] Throughout the implementation process, it is crucial to ensure the accuracy and stability of the stress data acquisition module, guaranteeing the complete and accurate acquisition of historical stress data from the steel pile trolley. Simultaneously, the parameters in the cylinder parameter management module require regular updates and maintenance based on actual conditions to ensure they match the actual performance and operational requirements of the steel pile trolley. Furthermore, when extracting stress characteristic parameters and screening parameters, scientific and reasonable methods must be employed to avoid human interference and ensure the reliability and effectiveness of the initial pre-charge parameter set.

[0037] It is also necessary to consider the diversity and complexity of historical operational stress data, which may contain some data anomalies or noise. Therefore, before inputting data into the stress data acquisition module, some data preprocessing work, such as data cleaning and noise reduction, may be required to improve data quality and usability. When extracting stress characteristic parameters, it is also necessary to fully consider various possible influencing factors to ensure that the extracted characteristic parameters can accurately reflect the actual stress situation of the steel pile trolley.

[0038] Furthermore, the construction of the initial pre-charge parameter set is not a one-time process; it may require continuous adjustments and optimizations based on feedback from subsequent steps. For example, during the subsequent parameter screening process, if it is found that some parameters in the initial pre-charge parameter set do not match the actual operating conditions, the data collection and feature extraction process needs to be re-examined. If necessary, data collection and parameter screening can be repeated to ensure the accuracy and applicability of the initial pre-charge parameter set.

[0039] Example 2: The specific implementation of step S2 is as follows: Historical operational stress data of the steel pile trolley needs to be obtained. This data originates from information collected and stored in real-time by the steel pile trolley during past operations using sensors, data loggers, and other equipment. It covers the stress conditions under different operating periods and conditions, including the magnitude, direction, duration of the force, and corresponding environmental parameters. For example, the load borne by the steel pile trolley varies under different geological conditions, and its historical operational stress data will record these differences in detail.

[0040] Based on historical operational stress data, the characteristics of historical stress content are analyzed. These characteristics include load identification features and attribute features. Load identification features primarily characterize the specific type and attributes of the load trolley bears during operation, such as whether the load comes from the weight of the steel pile, resistance during pile driving, or other external environmental factors. It also identifies the magnitude and mode of application of the load. For example, when driving steel piles of different diameters or materials, the load identification features will clearly record the specifications of the steel piles to distinguish different load conditions. Attribute features describe the nature of the stress content itself, including the type of stress (e.g., impact, continuous, pulsating), the pressure range (high pressure, medium pressure, low pressure), and the frequency of stress.

[0041] After parsing the load identification and attribute characteristics, it is necessary to filter historical stress content containing the pressure range characteristic based on the load identification characteristics and the pressure range characteristic mentioned in the previous steps. Specifically, first, determine the load scenario to be analyzed based on the load identification characteristics, and then, combined with the pressure range characteristic, filter out the stress content from historical operation stress data that has a pressure value within a specific range (such as high pressure, medium pressure, or low pressure) under that load scenario. For example, when the load identification characteristic is "driving a steel pile with a diameter of 500mm", combine the high pressure range in the pressure range characteristic to filter out all historical stress records where the pressure value is within the high pressure range under that load condition.

[0042] It is necessary to obtain the attribute characteristics of the parameters in the initial pre-charge parameter set. Each parameter in the initial pre-charge parameter set has specific attribute characteristics, which describe the applicable scenarios, working range, and correlation with other parameters. For example, the attribute characteristics of a certain pre-charge pressure parameter may include its applicable force type as impact, its applicable pressure range as high pressure, and the adjustment method of the parameter during the pre-charge process.

[0043] Based on the attribute characteristics of the screened historical stress content and the attribute characteristics of the parameters in the initial pre-charge parameter set, the attribute matching degree between the two is analyzed. The method for analyzing the attribute matching degree is to perform a one-by-one correspondence analysis between the attribute characteristics of the screened historical stress content and the attribute characteristics of the parameters in the initial pre-charge parameter set. Specifically, for each screened historical stress content, its included attribute characteristics (such as stress type, pressure range, stress frequency, etc.) are compared one by one with the attribute characteristics of each parameter in the initial pre-charge parameter set, and the number of matching attribute items is counted. Then, the proportion of the number of matching attribute items to the total number of attribute items is calculated; this proportion is the attribute matching degree. For example, if the attribute characteristics of a certain historical stress content include three attribute items: stress type (impact), pressure range (high pressure), and stress frequency (high frequency), and the attribute characteristics of a certain parameter also contain these three attribute items and match them completely, then the number of matching attribute items is 3. If the total number of attribute items is 3, the attribute matching degree is 100%; if the parameter's attribute characteristics only match 2 attribute items, the attribute matching degree is 66.67%.

[0044] After calculating the attribute matching degree between all filtered historical stress data and the parameters in the initial pre-filled parameter set, the attribute matching degrees are sorted in descending order. This sorting prioritizes parameters with high matching degrees for easier subsequent filtering. Once sorted, the corresponding parameters are selected based on a set sequence number range and output as a second pre-filled parameter set. For example, if the goal is to filter the top 20% of parameters, then the parameters with the highest attribute matching degrees will be selected to form the second pre-filled parameter set.

[0045] When implementing this step, it is crucial to ensure the completeness and accuracy of historical operational stress data. Missing or incorrect historical data can lead to inaccurate parsed load identification and attribute characteristics, consequently affecting the calculation of attribute matching and the selection results of the second pre-filled parameter set. Therefore, after acquiring historical operational stress data, it may be necessary to perform data validation and preprocessing to remove outlier data and supplement missing data to ensure data quality.

[0046] The attribute characteristics of the parameters in the initial pre-charge parameter set need to accurately describe their actual properties. If the attribute characteristics are unclear or incorrectly defined, it will lead to deviations when matching them with the attribute characteristics of historical stress content, thus affecting the effectiveness of the second pre-charge parameter set. Therefore, when constructing the initial pre-charge parameter set, the attribute characteristics of each parameter need to be strictly defined and reviewed to ensure they match the parameter's actual function and applicable scenarios.

[0047] The set range of serial numbers needs to be reasonable. The range should be determined based on actual needs and experience, ensuring that the selected parameters have a high degree of attribute matching while also ensuring that the size of the second pre-filled parameter set is appropriate for subsequent processing. If the range of serial numbers is set too small, the number of selected parameters may be too few to meet the actual pre-filling requirements; if the range of serial numbers is set too large, it may include too many parameters with low matching degrees, affecting the effectiveness of subsequent parameter selection.

[0048] In the process of resolving attribute matching scores, it is also necessary to consider the potential differences in importance among different attribute items. For example, force type and pressure range characteristics may be more important than force frequency characteristics, and different weights can be assigned when calculating attribute matching scores. However, in this implementation, all attribute items are assigned the same weight by default, and the matching score is calculated only by statistically analyzing the proportion of matching attribute items. If it is necessary to consider the differences in the weights of attribute items in practical applications, further improvements can be made in subsequent optimizations.

[0049] Step S2 involves acquiring historical operational stress data, analyzing its load identification and attribute characteristics, and filtering relevant historical stress content based on pressure range characteristics. It then obtains the attribute characteristics of parameters in the initial pre-charge parameter set, calculates the attribute matching degree between the two sets, and sorts and filters them to finally obtain the second pre-charge parameter set. This step plays a crucial role in the entire method, both further optimizing the initial pre-charge parameter set and providing a more accurate parameter basis for subsequently filtering the third pre-charge parameter set based on stress behavior patterns.

[0050] Example 3: The specific implementation of step S3 is as follows: It is necessary to extract the stress time and pressure change depth from the filtered historical operation stress data. The filtered historical operation stress data here refers to the portion of historical stress data that has a high degree of matching with the initial pre-charge parameter set attributes after processing in step S2. Stress time specifically includes the start and end times of each stress occurrence, as well as the distribution of stress points throughout the entire operation. For example, if a steel pile trolley experiences continuous stress from 9:00 AM to 9:30 AM in a certain operation, the stress time during this period needs to be accurately recorded. Pressure change depth refers to the magnitude of pressure change relative to the initial or reference pressure during the stress process. For example, if the pressure increases from the initial 40 MPa to 60 MPa, the change depth is 20 MPa, or the maximum fluctuation range of the pressure within a certain range.

[0051] Obtain the time interval between two adjacent historical stress data points. The time interval between adjacent stress data points is calculated as follows: the start time of the later stress data point minus the end time of the previous stress data point, or, depending on the specific data recording method, the time difference between two adjacent stress events. For example, if the previous stress event ended at 9:30 and the next stress event started at 9:45, the time interval is 15 minutes. After obtaining multiple adjacent stress time intervals, calculate the arithmetic mean of these time intervals to obtain the average stress time interval. Similarly, for the pressure change depth, collect the pressure change depth values ​​for each stress event from the filtered historical stress data, and then calculate the arithmetic mean of these values ​​to obtain the average pressure change depth. The calculation of these two averages requires a sufficient amount of historical data to ensure the reliability of the results; for example, at least 100 sets of adjacent stress time intervals and pressure change depth data need to be collected.

[0052] After obtaining the average stress interval, it needs to be compared with a preset first and second time interval threshold to determine the stress behavior pattern of the piling trolley. The setting of these two thresholds needs to be determined based on the specific model of the piling trolley, the type of operation, and industry standards. For example, for a certain model of piling trolley, the preset first time interval threshold is 10 minutes, and the second time interval threshold is 30 minutes. If the average stress interval is less than or equal to 10 minutes, it indicates that the piling trolley is frequently subjected to force within a short period, indicating a high stress frequency. This is common in continuous piling or high-frequency vibration operations. If the average stress interval is greater than 10 minutes but less than 30 minutes, it indicates a moderate stress frequency, possibly corresponding to intermittent piling or periodic adjustment operations. If the average stress interval is greater than or equal to 30 minutes, it indicates a low stress frequency, possibly occurring during occasional stress during long preparation or standby periods.

[0053] After determining the high, medium, or low stress frequency type, a stress pattern score needs to be obtained based on the stress behavior pattern of the steel pile trolley. The stress pattern score can be determined using preset scoring rules. For example, a score of 80-100 points might correspond to high stress frequency, 50-79 points to medium, and 0-49 points to low. The specific scoring range and score need to be set according to actual operational needs and equipment characteristics. For instance, when the stress frequency is determined to be high, a specific score is assigned based on the corresponding scoring range and the position of the average stress time interval within that range. For example, if the average stress time interval is 5 minutes, which may be considered a relatively high frequency within the high-frequency range, the score could be set to 90 points; if the average stress time interval is 9 minutes, which is close to the first time interval threshold, the score could be set to 85 points.

[0054] When implementing this step, it is crucial to ensure the validity of the filtered historical stress data. Insufficient data or the presence of outliers can lead to significant deviations in the calculations of the average stress time interval and average pressure change depth, thus affecting the accuracy of stress behavior pattern judgment and stress pattern scoring. Therefore, before extracting data, the validity of the filtered historical stress data must be verified, removing obviously abnormal time records and pressure change depth values, such as data with negative stress time intervals or pressure change depths exceeding the normal operating range of the equipment.

[0055] The preset first and second time interval thresholds need to be adjusted periodically based on the actual operation of the steel pile trolley and changes in equipment performance. For example, when the steel pile trolley is replaced with a more efficient power system or the operating scenario changes, the distribution of force time intervals may change. In this case, it is necessary to reassess the rationality of the thresholds to avoid errors in force behavior pattern judgment due to outdated thresholds.

[0056] The calculation rules for the stress pattern score also need to be optimized based on feedback from actual applications. For example, in actual operations, it has been found that the requirements for pre-charge parameters are closer to those for stress with medium frequencies than for stress with high frequencies. In this case, the scoring rules can be adjusted to appropriately expand the range of high-frequency scoring intervals, or the correspondence between the score value and the stress frequency can be adjusted so that the score value better reflects the actual stress conditions and the requirements for pre-charge parameters.

[0057] When calculating the average stress time interval and average pressure change depth, it is also necessary to consider the time span of the data and the diversity of operating scenarios. If historical data only comes from a specific time period or a single operating scenario, the calculated average value may not represent the stress characteristics of the steel pile trolley under different working conditions. Therefore, in the data screening process, stress data from various scenarios such as different seasons, different geological conditions, and different operating tasks should be included as much as possible to improve the representativeness of the average value.

[0058] When determining the force-induced behavior pattern, there may be a situation where the average force-induced time interval is exactly equal to the threshold. In this case, it is necessary to clarify the determination rules, such as stipulating that when it is equal to the threshold, it should be classified into a lower-level frequency type, or the determination should be made according to the specific time interval distribution trend to ensure the consistency and operability of the determination results.

[0059] Step S3 involves statistically analyzing the time intervals and pressure change depths of the filtered historical stress data, combining this with preset thresholds to determine the stress behavior patterns, and assigning corresponding stress pattern scores. This provides key stress characteristic parameters for the subsequent step S4, which analyzes pre-charge adaptability. This step acts as a bridge connecting historical stress data and pre-charge parameter screening in the entire method, and its accuracy directly affects the effectiveness of subsequent parameter screening.

[0060] Example 4: The specific implementation of step S4 is as follows: It is necessary to obtain the pre-charge characteristics and pressure depth of the parameters in the second pre-charge parameter set. The second pre-charge parameter set is the parameter set obtained after screening in step S2, where each parameter has specific pre-charge characteristics and pressure depth. Pre-charge characteristics mainly include the parameter's role in the pre-charge process, the pre-charge method, the pre-charge time parameter, and its adaptation tendency to the force-induced behavior pattern. For example, the pre-charge characteristics of a certain pre-charge pressure parameter may indicate that it is suitable for scenarios with high force frequencies, requiring rapid attainment of the set pressure value during pre-charge; while the pre-charge characteristics of another pre-charge flow parameter may indicate that it is more suitable for slowly filling the gas to maintain pressure stability under low force frequencies. Pressure depth refers to the pressure action depth range corresponding to the parameter. For example, a parameter with a pressure depth of 20-50 MPa means that it can function well within this pressure depth range.

[0061] Based on the stress mode score of the steel pile trolley, the average pressure change depth, and the pre-charging characteristics and pressure depth of the parameters in the second pre-charging parameter set, the pre-charging compatibility between the steel pile trolley and the parameters in the second pre-charging parameter set is analyzed. The pre-charging compatibility analysis mainly includes two core dimensions: first, the degree of matching between the stress mode score of the steel pile trolley and the parameter pre-charging characteristics; and second, the degree of fit between the average pressure change depth and the parameter pressure depth.

[0062] Taking the analysis of the matching degree between the stress mode score of the steel pile trolley and the parameter pre-charge characteristics as an example, assuming that the stress mode score of the steel pile trolley is determined to be 90 points through step S3, corresponding to a high-frequency stress behavior mode. At this time, it is necessary to examine whether the pre-charge characteristics of each parameter in the second pre-charge parameter set are suitable for high-frequency stress scenarios. For example, the pre-charge characteristics of parameter A are clearly marked as "suitable for high-frequency stress, pre-charge response time ≤ 1 second", while the pre-charge characteristics of parameter B are "suitable for medium-frequency stress, pre-charge response time ≤ 5 seconds". In the matching process, the pre-charge characteristics of parameter A are more consistent with the high-frequency stress mode score of 90 points of the steel pile trolley, because its pre-charge response time can better meet the needs of rapid pressure adjustment under high-frequency stress; while the pre-charge characteristics of parameter B have a relatively low degree of matching with the current stress mode.

[0063] Next, examine the degree of agreement between the average pressure change depth and the parameter pressure depth. Assume that the average pressure change depth calculated in step S3 is 35 MPa, while the pressure depth of parameter C in the second pre-charge parameter set is 30-60 MPa, and the pressure depth of parameter D is 10-30 MPa. In this case, the lower limit of parameter C's pressure depth (30 MPa) is less than the average pressure change depth of 35 MPa, and the upper limit (60 MPa) is greater than 35 MPa, indicating that the pressure depth of parameter C can cover the current average pressure change depth, showing a high degree of agreement. However, the upper limit of parameter D's pressure depth (30 MPa) is less than the average pressure change depth of 35 MPa, failing to fully cover the actual pressure change range, indicating a low degree of agreement.

[0064] Based on a comprehensive analysis of these two dimensions, the pre-charge adaptability of each parameter is evaluated holistically. During the evaluation, the matching degree between the force pattern score and the pre-charge characteristics, and the fit between the average pressure change depth and the pressure depth, are weighted (by default, both have equal weight). Then, a comprehensive calculation is performed to obtain the pre-charge adaptability score for each parameter. For example, parameter A scores 90 points in force pattern matching and 80 points in pressure depth fit, resulting in a total adaptability score of (90+80)÷2=85 points; parameter C scores 70 points in force pattern matching and 90 points in pressure depth fit, resulting in a total adaptability score of (70+90)÷2=80 points.

[0065] After calculating the pre-charge adaptability scores for all parameters, the scores are sorted in descending order. The purpose of this sorting is to clearly demonstrate the degree of adaptation of each parameter to the current stress characteristics of the steel pile trolley, placing parameters with higher adaptability at the top. For example, after sorting, parameter A's 85 points rank first, parameter C's 80 points rank third, and parameters B and D, with lower scores, rank lower.

[0066] After sorting, the corresponding parameters are filtered according to the set sequence number range to form the third pre-filled parameter set. The set sequence number range needs to be determined according to the actual needs, for example, filtering the top 30% of parameters. Assuming there are 10 parameters in the second pre-filled parameter set, filtering the top 3 parameters (i.e., sequence numbers 1-3) will result in parameters A, C, and another parameter E with a high fit score being selected into the third pre-filled parameter set and output in the form of the third pre-filled parameter set.

[0067] When implementing this step, it is crucial to ensure the accuracy of the pre-charge characteristics and pressure depth of the parameters in the second pre-charge parameter set. If the description of the pre-charge characteristics does not match the actual function, or if the pressure depth is incorrectly labeled, it will lead to deviations in the pre-charge compatibility analysis. For example, if parameter B's pre-charge characteristics are actually applicable to high-frequency stress but are incorrectly labeled as medium-frequency stress, it will cause errors in the calculation of its matching degree with the high-frequency stress mode of the steel pile trolley, thus affecting the screening results. Therefore, when obtaining the pre-charge characteristics and pressure depth of the parameters, the definition and labeling of the parameters need to be strictly reviewed to ensure consistency with their actual performance.

[0068] The accuracy of the stress mode score and the average pressure change depth of the steel pile trolley is also crucial. If the stress mode score is too high or too low due to abnormal historical data, or if the average pressure change depth is calculated incorrectly, it will directly lead to inaccurate baseline data for the pre-charging compatibility analysis. For example, in step S3, if a medium-frequency stress condition is mistakenly classified as high-frequency, the stress mode score will be too high. This will cause the pre-charging compatibility analysis to favor parameters suitable for high frequencies, while actual working conditions may require parameters suitable for medium frequencies, thus affecting the pre-charging effect.

[0069] The set range of serial numbers needs to be reasonable. A range that is too narrow will result in too few parameters being selected, which may not be able to cover the actual pre-charge requirements; a range that is too wide will include too many parameters with low adaptability, increasing the difficulty of subsequent parameter optimization. For example, if the range is set to the top 10%, only one parameter may be selected, leaving a lack of alternatives during the actual pre-charge process; if it is set to the top 50%, five parameters may be retained, some of which have low adaptability and require more optimization work.

[0070] When comprehensively evaluating pre-charge adaptability, the weighting allocation also needs to be adjusted according to the actual situation. If, in a specific operating scenario, the force pattern score has a more critical impact on the pre-charge parameters, its weight can be appropriately increased; if matching the depth of pressure change is more important, then the weight of this dimension can be increased. By default, both have the same weight, but in actual applications, they can be dynamically adjusted according to equipment characteristics and operational requirements.

[0071] In the example analysis, it is assumed that the steel pile trolley is in a high-frequency stress mode and the average pressure change depth is 35 MPa. In actual applications, different combinations of stress modes and pressure change depths may be encountered. For example, when the stress mode score of the steel pile trolley is 60 points (medium-frequency stress) and the average pressure change depth is 20 MPa, the pre-charge adaptability analysis will tend to select parameters whose pre-charge characteristics are suitable for medium-frequency stress and whose pressure depth covers 20 MPa, such as parameters B and D whose pressure depth meets the conditions. In this case, the parameter selection results will be completely different from those in the high-frequency scenario.

[0072] Step S4 analyzes the matching degree between the stress characteristics of the steel pile trolley and the parameter characteristics in the second pre-charge parameter set, and selects parameters with higher adaptability to form a third pre-charge parameter set, providing a more accurate parameter pool for subsequent real-time pre-charge parameter optimization. This step plays a crucial role in the entire method by accurately mapping historical stress characteristics to specific pre-charge parameters, and its accuracy directly affects the fit between the pre-charge parameters and the actual stress conditions.

[0073] Example 5: The specific implementation of step S5 is as follows: It is necessary to obtain real-time operation feedback records of the steel pile trolley. These records originate from information transmitted in real-time to the control system by various sensors and data acquisition modules during the current operation of the steel pile trolley. The real-time operation feedback records include various parameters of the steel pile trolley during operation, such as the current force magnitude, pressure changes, operating status of each component, and operator commands. For example, at a certain moment, if the steel pile trolley is performing pile driving operations, the real-time operation feedback record will show that the current force type is impact, the pressure value fluctuates between 80-100 MPa, and simultaneously record whether the operator has performed parameter adjustments.

[0074] It is necessary to analyze whether the steel pile trolley performs multi-parameter comparison operations. The analysis method is to statistically analyze the proportion of times multiple parameters are monitored simultaneously in the real-time operation feedback records of the steel pile trolley out of the total number of operations. Simultaneous monitoring of multiple parameters here refers to the system monitoring and collecting data on two or more pre-charge-related parameters in real time within the same operation period. For example, if the system monitors the pre-charge pressure, pre-charge flow rate, and pressure maintenance time simultaneously 20 times within a 1-hour operation period, and the total number of operations is 30, then the proportion of times multiple parameters are monitored simultaneously is (20 ÷ 30) × 100% ≈ 66.67%.

[0075] A preset comparison operation threshold is established, which is determined based on the operating characteristics of the steel pile trolley and the design requirements of the control system. For example, the preset comparison operation threshold is 50%. If the statistically obtained proportion exceeds 50%, it is determined that the steel pile trolley is performing multi-parameter comparison operations; if the proportion does not exceed 50%, it is determined that there are no multi-parameter comparison operations.

[0076] When it is determined that the steel pile trolley does not perform a multi-parameter comparison operation, it is necessary to obtain the parameter information with the highest pre-charge matching performance from the third pre-charge parameter set. The third pre-charge parameter set is the parameter set obtained after filtering in step S4, where each parameter has a different pre-charge matching performance score. The parameter with the highest pre-charge matching performance is the parameter ranked first in the pre-charge adaptability score sorting. For example, if the third pre-charge parameter set contains parameters F, G, and H, with pre-charge adaptability scores of 90, 85, and 80 respectively, then parameter F is the parameter with the highest pre-charge matching performance. It is necessary to obtain specific information about parameter F, such as the set value of the pre-charge pressure, the pre-charge time, and the pressure maintenance method.

[0077] After obtaining this parameter information, the pre-charge parameters are optimized and adjusted during the pre-charge process. This optimization involves comparing the current stress conditions with the parameters that best match the pre-charge requirements, based on data from real-time operation feedback records. If discrepancies exist, the parameters are fine-tuned. For example, if the pre-charge pressure setting for parameter F is 90 MPa, but the real-time operation feedback records show that the actual pressure requirement due to the current stress is 95 MPa, the pre-charge pressure needs to be adjusted from 90 MPa to 95 MPa to meet the current operational needs.

[0078] When it is determined that the steel pile trolley involves multi-parameter comparison operations, it is necessary to obtain the parameter information corresponding to the set sequence number in the third pre-charge parameter set. The parameters within the set sequence number refer to the parameters that rank among the top few in the pre-charge adaptability score ranking, and the specific number is set according to actual needs. For example, if the set sequence number is the first 3, then it is necessary to obtain the parameter information of the top 3 parameters in the pre-charge adaptability score ranking, such as the specific parameter content of parameter F (90 points), parameter G (85 points), and parameter H (80 points).

[0079] After obtaining this parameter information, the pre-charge parameters are optimized and adjusted during the pre-charge process. This optimization requires comprehensive consideration of the interrelationships and influences between multiple parameters. For example, if parameter F is the pre-charge pressure of 90 MPa, parameter G is the pre-charge flow rate of 50 L / min, and parameter H is the pressure maintenance time of 10 seconds, under multi-parameter comparison operations, these three parameters need to be adjusted simultaneously based on changes in the force conditions recorded in real-time operation feedback. For instance, when the force frequency increases, it may be necessary to increase the pre-charge pressure to 95 MPa and simultaneously increase the pre-charge flow rate to 60 L / min to ensure that sufficient gas can be quickly charged under high-frequency force to maintain pressure stability.

[0080] When implementing this step, it is crucial to ensure the real-time nature and accuracy of the real-time operation feedback records. Delays or data errors in these records can lead to misjudgments regarding whether the steel pile trolley is performing multi-parameter comparisons, and deviations in optimizing pre-charge parameters. For example, incorrectly counting the number of times multiple parameters are monitored simultaneously in the real-time feedback records can result in discrepancies between the assessment and the actual situation, thus affecting parameter adjustment strategies. Therefore, it is necessary to ensure the stability of the data acquisition module and transmission link, and to regularly calibrate the sensors to guarantee the quality of the real-time operation feedback records.

[0081] The preset comparison operation threshold needs to be reasonable. If the threshold is set too high, it may fail to detect multi-parameter comparison operations even when they occur, leading to the continued use of a single-parameter optimization strategy, which fails to meet actual operational needs. If the threshold is set too low, multi-parameter optimization strategies may be triggered frequently, increasing the system's computational burden. For example, if the threshold is set to 30%, it may be frequently reached during normal operation, causing the system to be constantly in a multi-parameter optimization state, affecting pre-filling efficiency. Therefore, a suitable comparison operation threshold needs to be determined through extensive testing, based on the actual operating scenarios of the steel pile trolley and the processing capabilities of the control system.

[0082] When acquiring parameter information from the third pre-charge parameter set, it is crucial to ensure the completeness and accuracy of this information. The parameter information in the third pre-charge parameter set should include all key parameters related to the pre-charge process, such as pre-charge pressure, pre-charge flow rate, and pressure maintenance time, and the values ​​of these parameters should be accurate. For example, if the pre-charge pressure setting for parameter F is incorrectly recorded as 90 MPa, when it should actually be 80 MPa, it will lead to excessively high pressure during optimization adjustments, potentially damaging the equipment.

[0083] When optimizing and adjusting pre-charge parameters, it is also necessary to consider the magnitude and frequency of parameter adjustments. Excessive adjustment magnitude may lead to system instability, while excessive adjustment frequency will affect operational efficiency. For example, frequent adjustments to the pre-charge pressure can cause cylinder pressure fluctuations, affecting the operational accuracy of the steel pile trolley. Therefore, a reasonable parameter adjustment strategy needs to be developed, and parameters should be adjusted gradually based on the changing trends recorded in real-time operational feedback to avoid over-adjustment.

[0084] To illustrate with a specific example: Suppose that in a certain work scenario, the real-time operation feedback record of the steel pile trolley shows that during a 2-hour operation period, there were a total of 40 operations, of which 25 operations simultaneously monitored the three parameters of pre-charge pressure, pre-charge flow rate, and pressure maintenance time. The calculation shows that the percentage of simultaneous monitoring of multiple parameters is (25 ÷ 40) × 100% = 62.5%, exceeding the preset comparison operation threshold of 50%. Therefore, it is determined that the steel pile trolley is performing multi-parameter comparison operations. At this point, the information of the top 3 parameters with the highest pre-charge adaptability scores in the third pre-charge parameter set is obtained. Let's assume these three parameters are parameter F (pre-charge pressure 90 MPa, pre-charge flow rate 50 L / min, pressure maintenance time 10 seconds), parameter G (pre-charge pressure 85 MPa, pre-charge flow rate 45 L / min, pressure maintenance time 8 seconds), and parameter H (pre-charge pressure 80 MPa, pre-charge flow rate 40 L / min, pressure maintenance time 6 seconds). According to real-time operation feedback records, the current stress type is impact, with pressure values ​​fluctuating between 90-100 MPa and a high frequency of stress. Therefore, it is necessary to comprehensively adjust these three parameters: adjust the pre-charge pressure to 95 MPa, the pre-charge flow rate to 55 L / min, and the pressure maintenance time to 12 seconds to adapt to the requirements of high-frequency impact stress.

[0085] Step S5 involves acquiring real-time operation feedback records from the steel pile trolley to determine if a multi-parameter comparison operation exists. Based on the determination result, corresponding parameter information is obtained from the third pre-charging parameter set, and optimization adjustments are made during the pre-charging process. This step plays a crucial role in the entire method by responding to operational needs in real time and dynamically optimizing pre-charging parameters, ensuring that the pre-charging parameters always match the real-time stress conditions of the steel pile trolley. During implementation, strict control is required across multiple stages, from data acquisition and judgment rule setting to parameter acquisition and optimization adjustments, to ensure that the optimization and adjustment of pre-charging parameters accurately and promptly respond to the operational needs of the steel pile trolley, improving the pre-charging effect of the flexible cylinder and the operational performance of the steel pile trolley.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pre-charging method for a flexible cylinder on a steel pile trolley based on force model analysis, characterized in that, The specific steps include the following: S1. Collect historical stress data of the steel pile trolley, extract stress characteristic parameters, and construct the initial pre-charge parameter set of the flexible cylinder; S2. Based on the historical stress data of the steel pile trolley, analyze the matching properties of the historical stress content with the parameters in the initial pre-charge parameter set, and select the second pre-charge parameter set for the flexible cylinder. S3. Analyze the stress behavior pattern of the steel pile trolley based on its historical operational stress data, including the following specific steps: S301. Extract the stress time and pressure change depth from the filtered historical operation stress data, obtain the stress time interval between two adjacent historical operation stress data, and then take the average of the obtained stress time intervals to obtain the average stress time interval. Take the average of the obtained pressure change depths to obtain the average pressure change depth. S302. Compare the average force-bearing time interval with the preset first time interval threshold and second time interval threshold and determine the force-bearing behavior pattern of the steel pile trolley. If the average force-bearing time interval is less than or equal to the first time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be high. If the average force-bearing time interval is greater than the first time interval threshold and the force-bearing time interval is less than the second time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be medium. If the average force-bearing time interval is greater than or equal to the second time interval threshold, the force-bearing frequency of the steel pile trolley is determined to be low. S303. Obtain the stress mode score of the steel pile trolley based on its stress behavior mode. S4. Analyze the pre-charge adaptability of the steel pile trolley and the parameters in the second pre-charge parameter set based on the force behavior mode of the steel pile trolley, and select the third pre-charge parameter set of the flexible cylinder. S5. Obtain the corresponding parameter information in the third pre-charging parameter set based on the real-time operation feedback record of the steel pile trolley, and optimize and adjust the pre-charging parameters during the pre-charging process.

2. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 1, characterized in that, S1 includes the following specific steps: S101. Input historical stress data of the steel pile trolley into the stress data acquisition module, and extract stress characteristic parameters from the stress data. The stress characteristic parameters include stress type characteristics and pressure range characteristics. S102. Based on the force type characteristics, filter out the parameters containing the force type characteristics in the cylinder parameter management module and output them in the form of an initial pre-charge parameter set.

3. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 2, characterized in that, S2 includes the following specific steps: S201. Obtain historical operational stress data of the steel pile trolley, analyze the characteristics of historical stress content based on the historical operational stress data, the characteristics of the historical stress content include load identification characteristics and attribute characteristics, filter out historical stress content containing pressure range characteristics based on the load identification characteristics and the pressure range characteristics, and at the same time obtain the attribute characteristics of the parameters in the initial pre-charge parameter set. S202. Analyze the attribute matching degree between the filtered historical stress content and the parameters in the initial pre-filling parameter set based on the attribute characteristics of the filtered historical stress content and the attribute characteristics of the parameters in the initial pre-filling parameter set. S203. Sort the attribute matching degree in descending order, filter out the parameters corresponding to the set number, and output them in the form of the second pre-charge parameter set.

4. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 1, characterized in that, S4 includes the following specific steps: S401. Obtain the pre-charging characteristics and pressure depth of the parameters in the second pre-charging parameter set, and analyze the pre-charging compatibility between the steel pile trolley and the parameters in the second pre-charging parameter set based on the steel pile trolley's force mode score, average pressure change depth, pre-charging characteristics and pressure depth of the parameters in the second pre-charging parameter set. S402. Sort the precharge compatibility in descending order, filter out the parameters corresponding to the set number, and output them in the form of a third precharge parameter set.

5. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 4, characterized in that, S5 includes the following specific steps: The system retrieves real-time operation feedback records of the steel pile trolley and analyzes whether the steel pile trolley performs multi-parameter comparison operations. If the steel pile trolley does not perform multi-parameter comparison operations, it retrieves the parameter information with the highest pre-charging matching in the third pre-charging parameter set and optimizes and adjusts the pre-charging parameters during the pre-charging process. If the steel pile trolley performs multi-parameter comparison operations, it retrieves the parameter information corresponding to the set sequence number in the third pre-charging parameter set and optimizes and adjusts the pre-charging parameters during the pre-charging process.

6. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 2, characterized in that, The force type characteristics include specific force characteristics of impact, continuous, and pulsating, and the pressure range characteristics include range characteristics of high pressure, medium pressure, and low pressure.

7. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 4, characterized in that, The pre-charging compatibility analysis between the steel pile trolley and the parameters in the second pre-charging parameter set includes the degree of matching between the force mode score of the steel pile trolley and the parameter pre-charging characteristics, and the degree of fit between the average pressure change depth and the parameter pressure depth.

8. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 3, characterized in that, The method for parsing the attribute matching degree is to perform a correspondence analysis between the attribute characteristics of the filtered historical stress content and the attribute characteristics of the parameters in the initial pre-charge parameter set, and to count the proportion of the number of matching attribute items to the total number of attribute items.

9. The pre-charging method for the flexible cylinder of the steel pile trolley based on force model analysis as described in claim 5, characterized in that, The method for analyzing whether the steel pile trolley has a multi-parameter comparison operation is to count the proportion of the number of times multiple parameters are monitored simultaneously in the real-time operation feedback record of the steel pile trolley to the total number of operations. If the proportion exceeds the preset comparison operation threshold, it is determined that the steel pile trolley has a multi-parameter comparison operation.

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