Method and device for reinforcing pile tip in karst area and storage medium

Abstract

The invention discloses a karst area pile end reinforcing method and device and a storage medium, and relates to the technical field of pile end reinforcing. According to the method, micro sensor sets are pre-buried at a pile end and around the pile end, air pressure response data in a load induction state are collected in real time, a central control server is constructed for data processing, and the data processing result is obtained; by means of the method, real-time monitoring of the microenvironment of the pile end contact face is achieved. The MEMS micro air pressure sensor and the micro-seismic receiver array act together, and accurate sensing of the pile end and the surrounding stratum is ensured. After a standard response data set is obtained through preprocessing of air pressure response data, a pile end cavity risk score Rvoid is calculated and output based on a cavity risk analysis algorithm model. According to the method, the pile tip risk score Rvoid is obtained, preliminary comparison evaluation is conducted based on the output result of the pile tip cavity risk score Rvoid, the karst cavity condition is judged, the limitation of a traditional statics method can be overcome, higher-precision dynamic monitoring is achieved, and the reinforcement precision of the pile tip in the karst area is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of pile end reinforcement, and in particular to a method, a device and a storage medium for reinforcing a pile end in a karst area. Background Art

[0002] The field of geological engineering reinforcement, as an important part of civil engineering, covers a variety of technical means including foundation treatment, pile foundation reinforcement, soil layer improvement, etc., aiming to improve the bearing capacity of the foundation and prevent foundation settlement and structural damage. With the acceleration of urbanization, the development and construction of karst areas are increasing. The geological structures in these areas usually have unstable factors such as cavities and cracks, which bring huge challenges to engineering construction. In such an environment, pile foundations are an indispensable part of infrastructure construction, and their stability and reinforcement effect directly determine the safety of buildings. The reinforcement method of pile ends in karst areas, that is, a new reinforcement method designed for karst geological characteristics, can accurately identify whether there are gaps, cracks and other weaknesses on the contact surface of the pile end, and reinforce them through intelligent grouting to ensure the stability and safety of the pile foundation structure.

[0003] At present, in karst areas, traditional pile foundation reinforcement methods usually rely on static testing, acoustic wave detection and other methods to evaluate the stability of the foundation and the contact condition of the pile end. However, these traditional methods have problems such as limited detection range, difficulty in real-time feedback of detection results, and delayed adjustment of reinforcement measures. Static detection can only provide limited information on the bearing capacity of the soil layer, and cannot monitor the changes below the pile end in real time, especially for the detection of deep and hidden gaps, which is easy to miss potential problems. Acoustic wave detection faces strong environmental interference and can only provide the stress distribution of the pile body in a specific direction, and cannot fully and accurately reveal the complexity of the entire pile end contact surface. The above shortcomings have led to the phenomenon of unstable reinforcement measures or excessive reinforcement during construction in karst areas, wasting resources, affecting construction efficiency and building safety. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method, device and storage medium for reinforcing pile ends in karst areas, which solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps: S1. Pre-buried micro sensor groups at the pile end and around the pile end, real-time collection of air pressure response data of the microenvironment air pressure of the pile end under load induced state, and construction of a central control server, and transmission of the acquired air pressure response data to the central control server, and pre-processing of the air pressure response data in the central control server to obtain a standard response data set; S2. Construct a cavity risk analysis algorithm model in the central control server, extract the standard response data set and input it into the cavity risk analysis algorithm model, calculate and output the pile end cavity risk score Rvoid, and perform a preliminary comparative evaluation based on the pile end cavity risk score Rvoid, and trigger the microseismic strategy mechanism based on the preliminary comparative evaluation; S3. After the initial comparison and evaluation of the triggering microseismic strategy mechanism, the microseismic receiver array in the microsensor group is activated, pre-grouting is started, microseismic wave data is collected, and the microseismic response vector Ewave is calculated and output based on the microseismic wave data; S4, based on the obtained pile end cavity risk score Rvoid and microseismic response vector Ewave, combined with the grouting period function Finj, a grouting triplet Ginj is generated to perform linkage control of the grouting strategy; S5. Based on the pile end cavity risk score Rvoid and the microseismic response vector Ewave, the reinforcement effectiveness value Seff is calculated and output, and the preset effectiveness threshold Sth is compared with the reinforcement effectiveness value Seff for secondary evaluation to determine the reinforcement situation.

[0006] Preferably, said S1 includes S11 and S12; S11, pre-embed a MEMS micro air pressure sensor in the micro sensor at the bottom center area of ​​the pile end and several symmetrical points on the pile side wall to record in real time the pressure change of the micro environment of the pile end contact surface during the sinking process; The micro sensor includes a MEMS micro air pressure sensor and a microseismic receiver array; By setting up three load platforms, the pile body is actively loaded, the load induces the response behavior of the contact surface between the pile end and the stratum, and the air pressure response data is collected in real time through micro sensors; The active loading includes a light loading stage and a heavy loading stage; In the light loading stage, the pile driving force is set to 100KN by using a rotary pile and a pile driver controller, and each level of loading lasts for 10 seconds to simulate detection penetration; In the heavy loading stage, the pile driving force is increased to 700KN by using the rotary pile and pile driver controller, and each level is applied for 30 seconds to observe the continuity and sudden change of air pressure; The air pressure response data includes micro air pressure Wp, air pressure response delay time ΔT and air pressure signal frequency density Up; The micro air pressure Wp is acquired by collecting through a MEMS micro air pressure sensor; The air pressure response delay time ΔT is obtained by setting a detection window and searching for the point where the air pressure starts to change; The air pressure signal frequency density Up is obtained by performing Fourier FFT transformation on the diagnostic signal and extracting the energy density of the main frequency band; S12. Through the MEMS module of the MEMS micro air pressure sensor, using LoRa wireless transmission technology, remotely connect the MEMS micro air pressure sensor to the central control server, transmit the real-time obtained air pressure response data to the central control server, and preprocess the air pressure response data in the central control server to obtain a standard response data set; The preprocessing includes synchronization processing and normalization processing; The synchronization processing is to establish a time synchronization module with the pile foundation equipment, and pair the air pressure response with the time stamp of the active load through the time synchronization module to generate air pressure response data with consistent time stamps; The normalization processing is to eliminate the influence of dimension on the air pressure response data with consistent time stamps by using the standard deviation normalization method Z-Score; The standard response data set includes the micro air pressure Wp(t) at time t, the air pressure response delay time ΔT(t) at time t, and the air pressure signal frequency density Up(t) at time t.

[0007] Preferably, the S2 includes S21 and S22; S21. Build a cavity risk analysis algorithm model in the central control server, extract the standard response data set, input it into the cavity risk analysis algorithm model for calculation, and output the pile tip cavity risk score Rvoid to measure the karst cavity value; The pile tip cavity risk score Rvoid is calculated and output through the following cavity risk analysis algorithm model; ; In the formula, exp represents the exponential function, d represents the micro variable, dt represents the time micro variable, a1 represents the instantaneous air pressure response weight coefficient, which is used to determine the influence of the pile tip air pressure change speed on the risk score, a2 represents the response delay sensitivity coefficient, which is used to amplify or reduce the role of the response delay in the score, and a3 represents the spectrum oscillation penalty factor, which is used to emphasize or weaken the negative weight of the frequency noise on the score; The meaning of the formula. The core of the formula is a sigmoid-type mapping function, which converts the standard response data set into a standardized pile tip cavity risk score Rvoid ∈ (0,1). If the pile tip encounters low-stiffness media such as cavities, soft layers or holes, then when the load acts: the air pressure response is slow or delayed; the air pressure fluctuates violently or irregularly; the proportion of low-frequency dominant waves is large. These phenomena can be extracted from the three groups of signal characteristics, and this formula is used to comprehensively evaluate the severity of these abnormal signals and reflect whether there are high-risk positions with "structural non-closure"; It represents the rate of change of micro air pressure at time t, which indicates the "transient response speed" of the air pressure around the pile tip during the load application process. If this value is large, it means that the air pressure changes violently, indicating that the pile tip suddenly enters a cavity or loose layer from a dense soil layer, with strong compressibility and intense pressure fluctuations; similar to the effect of the pile head falling into a cavity, it will induce rapid gas expansion or extrusion. It represents the reciprocal of the air pressure response delay time, that is, the sensitivity of the response. The denser the formation and the closer the combination of the pile tip and the rock mass, the faster the pressure responds after the load is applied and the smaller the delay; if the pile tip is in a soft or cavity area, the propagation of the formation buffer pressure will be delayed greatly, and the reciprocal of the air pressure response delay time approaches 0. Therefore, this item reflects the quality of the rigid contact at the pile tip. The frequency density Up(t) of the air pressure signal at time t comes from the spectral analysis of the air pressure response signal, which represents the proportion of the energy of low-frequency or oscillating waves in the signal. High-frequency dominance indicates strong rigidity or short-term fluctuations, and normal rigid connection; if low-frequency dominance has small fluctuations but long duration, it means that the structure is loose, with oscillations or ripple-type diffusion occurring, indicating obvious cavity characteristics; so in most cases, the higher the spectral density, the "softer" the environment, the easier it is to resonate and the more unstable it is.

[0008] Preferably, S22: Based on the output result of the pile tip cavity risk score Rvoid, conduct a preliminary comparative evaluation to judge the karst cavity situation, and trigger the microseismic strategy mechanism based on the preliminary comparative evaluation result. The specific evaluation content is as follows; When the pile tip cavity risk score Rvoid ≤ 0.54, it represents a normal response, and at this time, enter the normal grouting reinforcement process; When the pile tip cavity risk score Rvoid > 0.54, it represents the existence of cavity risk, and at this time, start the microseismic strategy mechanism.

[0009] Preferably, the said S3 includes S31 and S32; S31: After triggering the microseismic strategy mechanism, conduct pre-grouting, and start the microseismic receiver array in the micro sensors to collect the microseismic wave signals during the pre-grouting process. The microseismic wave signals include envelope amplitude, main frequency response, and propagation time difference, and perform feature extraction on the microseismic wave signals to obtain microseismic wave data; The said microseismic wave data includes the microseismic envelope intensity Au(t) at time t, the dominant frequency Wf(t) at time t, and the microseismic wave propagation distance tensor Dr(t) at time t; The microseismic envelope intensity Au is obtained by averaging the envelope amplitude of the microseismic wave signal; The dominant frequency Wf is obtained by using high-frequency filtering Fourier FFT transformation on the main frequency response; The microseismic wave propagation distance tensor Dr is obtained by analyzing the propagation path lengths of microseismic wave signals from the seismic source, i.e., the grouting position, to multiple receiving points through a three-dimensional acoustic wave array; S32. After normalizing the obtained microseismic wave data, perform correlation calculations to output the microseismic response vector Ewave, and analyze the slurry entry path, fracture state, and closing trend; The microseismic response vector Ewave is calculated and output through the following algorithm formula; ; In the formula, Dr(t) -1 represents the reciprocal of the microseismic wave propagation distance tensor. The smaller the distance, the faster the propagation, and at the same time, it is easier to form the main diffusion direction; The microseismic envelope intensity Au(t) at time t indicates strong grouting energy release, easy loosening of the structure, and strong fracture connectivity; The dominant frequency Wf(t) at time t indicates obvious structural resonance, indicating the existence of fractures.

[0010] Preferably, S4 includes S41 and S42; S41. Based on the pile tip cavity risk score Rvoid and the microseismic response vector Ewave, output the grouting pressure and grouting slurry density respectively; Perform karst ground response feedback adaptive calculation based on the pile tip cavity risk score Rvoid and the microseismic response vector Ewave to output the grouting rhythm function Finj(t) at time t; the grouting rhythm function Finj(t) at time t is calculated and output through the following algorithm formula: , where Q0 represents the basic grouting flow rate, r1 represents the cavity response driving factor, and r2 represents the fracture diffusion penalty factor; S42. Based on the obtained grouting pressure and grouting slurry density combined with the grouting rhythm function Finj(t) at time t, generate a grouting triple Ginj, and send it to the PLC module of the on-site grouting equipment through the central control server to control the grouting pressure, grouting slurry density, and grouting rhythm for triple linkage to generate a linkage control grouting strategy; The grouting triple Ginj is generated through the following combination method; ; In the formula, P0 represents the basic pressure, p0 represents the basic density, b1 represents the pressure adjustment coefficient, indicating the amplification effect of the cavity risk on the pressure, b2 represents the density adjustment coefficient, controlling the adjustment effect of the diffusion state on the slurry consistency, and all parameters in the formula are dimensionless parameter values after normalization processing; where represents the grouting pressure, the actual pressure of the injected slurry, which is the driving force for penetrating fractures; It represents the density of the grouting slurry, which is used to control the permeability, adhesiveness and setting property of the slurry; The grouting rhythm function Finj(t) at time t controls the dynamic characteristics such as the injection speed, intermittent period, total duration, etc.

[0011] Preferably, the S5 includes S51 and S52; S51. After the execution of the linkage control grouting strategy is completed, secondary pre-grouting is carried out at this time, and the micro sensors are started again to collect the secondary micro air pressure Wp' and secondary microseismic wave signals after reinforcement, and the secondary microseismic response vector Ewave' is calculated and output based on the secondary microseismic wave signals. The reinforcement effectiveness value Seff is obtained through comprehensive calculation based on the secondary micro air pressure Wp' and secondary microseismic response vector Ewave' after reinforcement; The reinforcement effectiveness value Seff is calculated and output through the following algorithm formula; ; In the formula, log represents the logarithmic function, and Wp(t)' represents the micro air pressure after reinforcement at time t; Among them, It represents the air pressure response ratio term, indicating the change of the pressure reaction after reinforcement. Taking the logarithm here is to compress the numerical gradient and avoid the out-of-control of extreme values; It represents the diffusion path contraction factor, indicating whether the crack is closed or the degree of diffusion is weakened after reinforcement.

[0012] Preferably, S52. The effectiveness threshold Sth is set based on the standard value of the pile tip grouting reinforcement in the karst area, and the effectiveness threshold Sth is compared with the obtained reinforcement effectiveness value Seff for the second time to evaluate the reinforcement situation of the pile tip after the execution of the linkage control grouting strategy is completed. The specific evaluation content is as follows; When the reinforcement effectiveness value Seff ≥ the effectiveness threshold Sth, it indicates that the grouting closure meets the standard, that is, the air pressure increases significantly and the crack diffusion weakens. At this time, it is determined that the reinforcement is successful, and a prompt is given to enter the next process; When the reinforcement effectiveness value Seff < the effectiveness threshold Sth, it indicates that the grouting is not thorough, that is, the air pressure change is not significant or the crack is still open. At this time, it is determined that the reinforcement is abnormal. At this time, based on the current risk score Rvoid of the pile tip cavity and the microseismic response vector Ewave after reinforcement, S4 is iteratively executed to adjust the grouting pressure, grouting slurry density and grouting rhythm until the reinforcement is successful and the iteration stops.

[0013] A reinforcement device for the pile tip in the karst area, including a data acquisition device, a data processing device and a reinforcement control device; The data acquisition device is used to obtain air pressure response data and microseismic wave data, and transmit the data to the data processing device through the MEMS module; The data processing device is used to receive the air pressure response data and microseismic wave data, and after pre-processing, perform data calculation and analysis, output the grouting pressure and grouting slurry density and summarize them with the grouting rhythm into a linkage control grouting strategy; The reinforcement control device controls the pressure, density and grouting rhythm of the grouting pump and executes the grouting strategy by connecting the data processing device with the PLC module of the grouting pump.

[0014] A storage medium for reinforcing pile ends in karst areas, wherein the storage medium stores a computer program, and when the computer program is executed, any one of the above-mentioned methods for reinforcing pile ends in karst areas is implemented.

[0015] The present invention provides a method, device and storage medium for reinforcing pile ends in karst areas, which have the following beneficial effects: (1) This method embeds a micro sensor group at the pile end and around the pile end to collect the air pressure response data under the load-induced state in real time, and builds a central control server for data processing. This method realizes real-time monitoring of the microenvironment of the pile end contact surface. The MEMS micro air pressure sensor and the microseismic receiver array work together to ensure accurate perception of the pile end and the surrounding strata. After preprocessing the air pressure response data and obtaining the standard response data set, the cavity risk score Rvoid of the pile end is calculated and output based on the cavity risk analysis algorithm model. A preliminary comparative evaluation is performed based on the output results of the pile end cavity risk score Rvoid to determine the karst cavity situation. This scheme can overcome the limitations of traditional statics or acoustic wave detection methods, achieve more accurate dynamic monitoring, and effectively improve the reinforcement accuracy of pile ends in karst areas.

[0016] (2) This method combines the pile end cavity risk score Rvoid and the microseismic response vector Ewave with the grouting period function Finj to generate the grouting triplet Ginj, and then controls the grouting strategy in a linked manner. This dynamic adjustment mechanism based on real-time feedback enables the grouting pressure, slurry density and grouting rhythm to be flexibly adjusted according to the real-time monitoring data on site, ensuring the adaptability of the grouting process to different geological conditions. Through this strategy, the excessive or insufficient grouting that may exist in traditional reinforcement methods is avoided, the economy and accuracy of grouting are effectively improved, the resource consumption in the grouting process is optimized, and the reinforcement effect is improved.

[0017] (3) After the linkage control grouting strategy is executed, this method compares the pile end cavity risk score Rvoid and the microseismic response vector Ewave before and after the two stages, and the calculated reinforcement effectiveness value Seff can evaluate the reinforcement effect in real time. If the reinforcement effectiveness value Seff is greater than the preset effectiveness threshold Sth, it means that the reinforcement effect of the pile end meets the standard and the grouting closure is good, and it will automatically enter the next process; otherwise, it will automatically return to adjust the grouting parameters and perform secondary reinforcement. Through the automated feedback and optimization mechanism, this method ensures the reliability and stability of the reinforcement effect, reduces human errors and unnecessary reinforcement times, and greatly improves the efficiency and safety of the reinforcement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the steps of a method for reinforcing pile ends in karst areas according to the present invention; Figure 2 The figure is a schematic diagram of the connection flow of a reinforcement device for pile ends in karst areas according to the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0020] Example 1 See also Figure 1 The present invention provides a method for reinforcing pile ends in karst areas. To achieve the above purpose, the present invention is implemented through the following technical scheme: comprising the following steps: S1. Pre-buried micro sensor groups at the pile end and around the pile end, real-time collection of air pressure response data of the microenvironment air pressure of the pile end under load induced state, and construction of a central control server, and transmission of the acquired air pressure response data to the central control server, and pre-processing of the air pressure response data in the central control server to obtain a standard response data set; S2. Construct a cavity risk analysis algorithm model in the central control server, extract the standard response data set and input it into the cavity risk analysis algorithm model, calculate and output the pile end cavity risk score Rvoid, and perform a preliminary comparative evaluation based on the pile end cavity risk score Rvoid, and trigger the microseismic strategy mechanism based on the preliminary comparative evaluation; S3. After the initial comparison and evaluation of the triggering microseismic strategy mechanism, the microseismic receiver array in the microsensor group is activated, pre-grouting is started, microseismic wave data is collected, and the microseismic response vector Ewave is calculated and output based on the microseismic wave data; S4. Based on the obtained pile - tip cavity risk score \(R_{void}\) and the micro - seismic response vector \(E_{wave}\), combine with the grouting cycle function \(F_{inj}\) to generate a grouting triple \(G_{inj}\) for combined generation, and perform linkage control of the grouting strategy; S5. Calculate and output the reinforcement effectiveness value \(S_{eff}\) based on the pile - tip cavity risk score \(R_{void}\) and the micro - seismic response vector \(E_{wave}\), and preset an effectiveness threshold \(S_{th}\) for a secondary comparison and evaluation with the reinforcement effectiveness value \(S_{eff}\) to judge the reinforcement situation.

[0021] In this embodiment, a micro - pressure response data induced by load is collected in real - time at the pile tip and its surrounding area through a micro - sensor group, and is transmitted to a central control server for pre - processing and constructing a standard response data set. This data collection and processing mechanism ensures a comprehensive monitoring of the pile - tip micro - environment and provides accurate basic data for subsequent analysis. Next, by constructing a cavity risk analysis algorithm model, input the standard response data set, calculate and output the pile - tip cavity risk score \(R_{void}\), and based on this score, conduct a preliminary evaluation to judge the pile - tip cavity situation and decide whether to trigger the micro - seismic strategy mechanism. On this basis, if the cavity risk score \(R_{void}\) is relatively high, start the micro - seismic sensor array to collect micro - seismic wave data, further calculate the micro - seismic response vector \(E_{wave}\), and analyze the diffusion path, crack state, and closing trend of the grout in the cracks through micro - seismic data. Then, combining the cavity risk score \(R_{void}\) and the micro - seismic response vector \(E_{wave}\), this method uses the grouting cycle function \(F_{inj}\) to generate a grouting triple \(G_{inj}\) and optimize the grouting strategy under linkage control. During this process, the grouting pressure, slurry density, and grouting rhythm can be adjusted according to real - time feedback to ensure the effectiveness and accuracy during the reinforcement process. Finally, by comparing the data before and after reinforcement, calculate and output the reinforcement effectiveness value \(S_{eff}\), and conduct a secondary comparison and evaluation with the set effectiveness threshold \(S_{th}\) to judge the reinforcement effect. If the standard is not met, the grouting parameters will be automatically adjusted and re - reinforced. Through this implementation method, a full - process closed - loop control from real - time monitoring, dynamic adjustment to effect evaluation is achieved, effectively improving the accuracy and efficiency of pile foundation reinforcement in karst areas. Compared with traditional methods, this method can identify pile - tip cavities and cracks in real - time, automatically adjust the grouting plan, and avoid over - grouting or under - grouting phenomena during the traditional grouting process. An effective feedback mechanism and effect evaluation ensure that the effect of each round of grouting can be accurately evaluated, greatly reducing the risk of resource waste and reinforcement failure, and enhancing the stability and safety of the reinforced pile foundation. In addition, this intelligent reinforcement method has strong adaptability and can be flexibly adjusted under different geological conditions, making the pile - tip reinforcement in karst areas more efficient and reliable.

[0022] Embodiment 2 Please refer to Figure 1 , specifically: S1 includes S11 and S12; S11. Embedded MEMS micro-pressure sensors, which are one type of micro-sensors, at the center of the bottom of the pile tip and several symmetric points on the pile side wall to record in real time the pressure changes in the micro-environment of the pile tip contact surface during the sinking process; The micro-sensors include MEMS micro-pressure sensors and a micro-seismic receiver array; By setting up 3 load platforms to actively load the pile body, inducing the response behavior of the pile tip and the formation contact surface by the load, and collecting the air pressure response data in real time through the micro-sensors; The active loading includes a light loading stage and a heavy loading stage; In the light loading stage, by using the rotary drilling rig and the pile press controller, the pile pressing force is set to 100 KN, and the duration of each stage of loading is 10 seconds to simulate the detection of penetration; In the heavy loading stage, by using the rotary drilling rig and the pile press controller, the pile pressing force is increased to 700 KN, and after each application, it stays for 30 seconds to observe the continuity and mutation of the air pressure change; The air pressure response data includes the micro air pressure Wp, the air pressure response delay time △T, and the air pressure signal frequency density Up; The micro air pressure Wp is collected by the MEMS micro-pressure sensor; The air pressure response delay time △T is obtained by setting a detection window and searching for the point where the air pressure starts to change; The air pressure signal frequency density Up is obtained by performing a Fourier FFT transform on the diagnostic signal and extracting the energy density of the main frequency band; S12. Through the MEMS module of the MEMS micro-pressure sensor, using LoRa wireless transmission technology, remotely connect the MEMS micro-pressure sensor to the central control server, transmit the real-time obtained air pressure response data to the central control server, and preprocess the air pressure response data in the central control server to obtain a standard response data set; The preprocessing includes synchronization processing and normalization processing; The synchronization processing is carried out by establishing a time synchronization module with the pile foundation equipment, and pairing the air pressure response with the time stamp of the active loading through the time synchronization module to generate air pressure response data with consistent time stamps; The normalization processing eliminates the influence of dimension on the air pressure response data with consistent time stamps by using the standard deviation normalization method Z-Score; The standard response data set includes the micro air pressure Wp(t) at time t, the air pressure response delay time △T(t) at time t, and the air pressure signal frequency density Up(t) at time t.

[0023] In this embodiment, the method simulates the actual response behavior of the contact surface between the pile end and the stratum by arranging micro air pressure sensors at the central area of ​​the bottom of the pile end and the symmetrical points of the pile side wall, and using the load application process of two stages of light loading and heavy loading. A 100KN pile pressure force is applied in the light loading stage to simulate the initial sinking of the pile body, and a 700KN pile pressure force is applied in the heavy loading stage to observe the changes in the air pressure response under a larger load. The air pressure response data induced by these loads include micro air pressure Wp, air pressure response delay time ΔT, and air pressure signal frequency density Up, which are collected by MEMS sensors and the frequency band energy density is extracted by Fourier FFT transform. These data provide rich basic information for subsequent analysis, which can reveal the state of contact between the pile end and the rock formation and determine whether there are potential cavities, soft layers or cracks. Through LoRa wireless transmission technology, the collected air pressure response data is transmitted to the central control server in real time, and the data is preprocessed, including synchronization and normalization. The synchronization processing matches the air pressure response data with the active loading timestamp through the time synchronization module established with the pile foundation equipment to ensure accurate data pairing; the normalization processing uses the standard deviation normalization method Z-Score to eliminate the dimension effect and ensure the comparability of each data in subsequent analysis. The final standard response data set includes the micro-air pressure Wp in the time series, the air pressure response delay time ΔT, and the air pressure signal frequency density Up. The core purpose of this implementation method is to obtain the dynamic air pressure response information of the pile end and the surrounding strata under load through precise sensor layout and real-time data acquisition, accurately reflect the microenvironmental changes of the pile end contact surface, and then provide reliable data support for subsequent reinforcement analysis and decision-making. Through high-precision air pressure response data preprocessing, the dimension effect and time error are eliminated, and standardized data input is provided for cavity risk analysis and microseismic response analysis, ensuring the reliability and accuracy of data analysis. The beneficial effects of this method are: real-time monitoring of the pile end status can timely discover potential cavities or soft layers; through precise data preprocessing and analysis, a more reliable risk assessment basis is provided. Traditional static or acoustic detection methods often have limitations and are difficult to accurately evaluate the microenvironmental changes of the pile end contact surface. This method, by introducing a micro-air pressure response mechanism, not only achieves remote sensing of abnormal gaps, but also provides real-time feedback on reinforcement effects, improving the accuracy and effectiveness of reinforcement. Through dynamic monitoring and adaptive feedback mechanisms, it reduces the error of manual judgment, optimizes the use of resources during the reinforcement process, and greatly improves the safety and economy of the project.

[0024] Example 3 See also Figure 1 , specifically: S2 includes S21 and S22; S21. Construct a cavity risk analysis algorithm model in the central control server, extract the standard response data set, and input it into the cavity risk analysis algorithm model for calculation to output the pile tip cavity risk score Rvoid, which measures the karst cavity value; The pile tip cavity risk score Rvoid is calculated and output through the following cavity risk analysis algorithm model; ; In the formula, exp represents the exponential function, d represents the micro variable, dt represents the time micro variable, a1 represents the instantaneous air pressure response weight coefficient, which is used to determine the influence of the pile tip air pressure change speed on the risk score, a2 represents the response delay sensitivity coefficient, which is used to amplify or reduce the role of the response delay in the score, and a3 represents the spectral oscillation penalty factor, which is used to emphasize or weaken the negative weight of the frequency noise on the score; The meaning of the formula. The core of the formula is a sigmoid-type mapping function, which converts the standard response data set into a standardized pile tip cavity risk score Rvoid ∈ (0,1). If the pile tip encounters low-stiffness media such as cavities, soft layers or holes, then when the load acts: the air pressure response is slow or delayed; the air pressure fluctuates violently or irregularly; the low-frequency dominant wave accounts for a large proportion. These phenomena can be extracted from the three groups of signal characteristics, and this formula is used to comprehensively evaluate the severity of these abnormal signals and reflect whether there is a high-risk position of "structural non-closure"; represents the micro air pressure change rate at time t, which represents the "transient response speed" of the air pressure around the pile tip during the load application process. If this value is very large and the atmospheric pressure changes violently, it means that the pile tip suddenly enters a cavity or loose layer from a dense soil layer, with strong compressibility and violent pressure fluctuations; similar to the effect of the pile head falling into a hole, it will induce rapid gas expansion or extrusion; represents the reciprocal of the air pressure response delay time, that is, the sensitivity of the reaction. The denser the formation and the closer the pile tip is combined with the rock mass, the faster the pressure responds after the load is applied and the smaller the delay; if the pile tip is in a soft or cavity, the propagation of the formation buffer pressure will be delayed greatly, and the reciprocal of the air pressure response delay time approaches 0. Therefore, this item reflects the rigid contact quality of the pile tip; The air pressure signal frequency density Up(t) at time t comes from the spectral analysis of the air pressure response signal, which represents the energy proportion of low-frequency or oscillating waves in the signal. High-frequency dominance indicates strong rigidity or short-term fluctuations, and normal rigid connection; if the low-frequency dominant fluctuation is small but lasts for a long time, it means that the structure is loose, with oscillations or ripple-type diffusion, indicating obvious cavity characteristics; therefore, in most cases, the higher the spectral density, the "softer" the environment, the easier it is to resonate and the more unstable.

[0025] S22. Based on the output result of the pile tip cavity risk score Rvoid, conduct a preliminary comparative evaluation to judge the karst cavity situation, and trigger the microseismic strategy mechanism based on the preliminary comparative evaluation result. The specific evaluation content is as follows; When the pile tip cavity risk score Rvoid ≤ 0.54, it indicates a normal response, and at this time, enter the normal grouting reinforcement process; When the pile tip cavity risk score Rvoid > 0.54, it indicates the existence of cavity risk, and at this time, start the microseismic strategy mechanism.

[0026] In this embodiment, the method constructs a cavity risk analysis algorithm model in the central control server. The model extracts standard response data sets, such as the micro - air pressure change rate, air pressure response delay time, and air pressure signal frequency density, for calculation, and outputs the pile tip cavity risk score Rvoid. Through the comprehensive analysis of these data characteristics, the model uses the sigmoid - type mapping function to convert the standard response data into a standardized cavity risk score, ranging from 0 to 1. If the formation contacted by the pile tip is relatively soft or there is a cavity, the air pressure response under the action of the load will show characteristics such as delay, violent fluctuation, or low - frequency dominant waves. The algorithm model can accurately reflect the cavity risk at the pile tip through the weight evaluation of these abnormal signals. The instantaneous air pressure response weight coefficient, response delay sensitivity coefficient, and spectrum oscillation penalty factor respectively control the speed of air pressure change, the influence of response delay, and the influence of low - frequency noise on the score. The Rvoid obtained by comprehensively evaluating these factors can accurately judge whether there is a risk of "structural non - closure" at the pile tip. Then, based on the calculated cavity risk score, a preliminary comparative evaluation will be carried out to judge the specific situation of the karst cavity. This method effectively identifies potential cavities or soft layers at the pile tip through real - time data collection and precise calculation, thus realizing accurate risk assessment. Compared with traditional single static or acoustic detection methods, this method can monitor and accurately evaluate the cavity risk in real - time in a dynamic environment, making the grouting reinforcement plan more targeted and efficient. Through the accurate calculation of the cavity risk score, the triggering of the microseismic strategy mechanism can ensure the selection of appropriate reinforcement strategies for different geological conditions, avoiding the phenomena of over - grouting or insufficient reinforcement, thereby improving the reliability and stability of the reinforcement effect. In addition, through the standardized processing of the cavity risk score and microseismic feedback control, this method provides a scientific basis for the subsequent grouting process, improves the overall efficiency of the reinforcement project, avoids unnecessary resource waste and safety hazards, and finally realizes more efficient and economical pile foundation reinforcement in karst areas.

[0027] Embodiment 4 Please refer to Figure 1 , specifically: S3 includes S31 and S32; S31. After triggering the microseismic strategy mechanism, pre-grouting is carried out, and the microseismic receiver array in the micro-sensor is activated to collect the microseismic wave signals during the pre-grouting process. The microseismic wave signals include envelope amplitude, main frequency response, and propagation time difference, and feature extraction is performed on the microseismic wave signals to obtain microseismic wave data; The microseismic wave data includes the microseismic envelope intensity Au(t) at time t, the dominant frequency Wf(t) at time t, and the microseismic wave propagation distance tensor Dr(t) at time t; The microseismic envelope intensity Au is obtained by averaging the envelope amplitude of the microseismic wave signal; The dominant frequency Wf is obtained by using high-frequency filtering Fourier FFT transform on the main frequency response; The microseismic wave propagation distance tensor Dr is obtained by analyzing the propagation path lengths of the microseismic wave signals from the seismic source, i.e., the grouting position, to multiple receiving points through a three-dimensional acoustic wave array; S32. After normalizing the obtained microseismic wave data, correlation calculations are performed to output the microseismic response vector Ewave, and the slurry entry path, fracture state, and closing trend are analyzed; The microseismic response vector Ewave is calculated and output through the following formula; ; In the formula, Dr(t) -1 represents the reciprocal of the microseismic wave propagation distance tensor. The smaller the distance, the faster the propagation, and at the same time, it is easier to form the main diffusion direction; The microseismic envelope intensity Au(t) at time t indicates strong release of grouting energy, easy loosening of the structure, and strong fracture connectivity; The dominant frequency Wf(t) at time t indicates obvious structural resonance, indicating the existence of fractures; This formula realizes the quantification and directional modeling of the response of the underground fracture structure during the grouting process through the triple coupling of: seismic wave intensity, i.e., the energy dimension; dominant frequency, i.e., the structural rigidity dimension; distance attenuation, i.e., the spatial dimension, to obtain a spatio-temporal response vector.

[0028] In this embodiment, after triggering the microseismic strategy mechanism, the method realizes the accurate judgment of the slurry diffusion path, fracture state, and closing trend through the acquisition and analysis of microseismic wave data during the pre-grouting process. First, by starting the microseismic receiver array in the micro-sensor, the microseismic wave signals during the pre-grouting process are collected in real time. These signals include the microseismic envelope intensity Au, the dominant frequency Wf, and the microseismic wave propagation distance tensor Dr. The microseismic envelope intensity is obtained by averaging the envelope amplitude of the microseismic wave signal, which reflects the intensity of energy release during the grouting process, indicating the loosening degree of the structure and the fracture connectivity; the dominant frequency analyzes the frequency response of the signal through Fourier transform, indicating whether there is a resonance phenomenon in the structure and revealing the existence and activity of fractures; the microseismic wave propagation distance tensor analyzes the propagation path length of the microseismic signal from the seismic source to multiple receiving points through a three-dimensional acoustic wave array, reflecting the diffusion characteristics of the slurry in the fractures. After normalizing the obtained microseismic wave data, correlation calculation is used to output the microseismic response vector Ewave, and then the slurry diffusion path, fracture state, and closing trend are analyzed. The microseismic response vector comprehensively considers the speed of the propagation path, the intensity of energy release, and the frequency response, and can provide reliable data basis for the subsequent adjustment of the grouting strategy. For example, the reciprocal of the propagation distance Dr(t) −1 represents the fracture propagation rate. A smaller distance means faster propagation and may form the main diffusion path; while the microseismic envelope intensity Au and the dominant frequency Wf reveal the stability of the structure and the existence of fractures. Through this information, it can be judged whether the slurry smoothly enters the fractures, whether the fractures are in an expanding state, or whether they have started to close. The purpose of implementing this method is to accurately identify the slurry diffusion path, fracture state, and closing trend through the real-time monitoring and analysis of microseismic wave signals, so as to optimize the grouting strategy. In traditional grouting reinforcement methods, the reinforcement effect can only be judged by estimation and experience, while this method can provide real-time feedback on the dynamic changes of fractures through accurate microseismic response data analysis, making the reinforcement process more dynamically controllable. Through this technology, the situations of over-grouting or under-grouting can be effectively avoided, the resource utilization efficiency during the grouting process can be improved, and the accuracy and efficiency of the reinforcement effect can be ensured. Finally, this method significantly improves the safety and reliability of pile foundation reinforcement in karst areas, while reducing the uncertainty and risk during the construction process.

[0029] Embodiment 5 Please refer to Figure 1 , specifically: S4 includes S41 and S42; S41. Output the grouting pressure and grouting slurry density respectively based on the pile tip cavity risk score Rvoid and the microseismic response vector Ewave; Based on the pile - end cavity risk score Rvoid and the micro - seismic response vector Ewave, an adaptive calculation of the karst ground response feedback outputs the grouting rhythm function Finj(t) at time t; the grouting rhythm function Finj(t) at time t is calculated and output through the following algorithm formula: , where Q0 represents the basic grouting flow rate, r1 represents the cavity response driving factor, and r2 represents the fracture diffusion penalty factor; S42. Based on the obtained grouting pressure and grouting slurry density, combined with the grouting rhythm function Finj(t) at time t, a grouting triple Ginj is generated and sent to the PLC module of the on - site grouting equipment through the central control server to perform triple linkage control of the grouting pressure, grouting slurry density, and grouting rhythm, generating a linkage control grouting strategy; The grouting triple Ginj is generated through the following combination method; ; In the formula, P0 represents the basic pressure, p0 represents the basic density, b1 represents the pressure adjustment coefficient, representing the amplification effect of the cavity risk on the pressure, b2 represents the density adjustment coefficient, controlling the adjustment effect of the diffusion state on the slurry consistency, and all parameters in the formula are dimensionless parameter values after normalization processing; Among them represents the grouting pressure, the actual pressure of the injected slurry, which is the driving force for penetrating the fracture; represents the grouting slurry density, which is used to control the permeability, adhesiveness, and setting property of the slurry; The grouting rhythm function Finj(t) at time t controls dynamic characteristics such as the injection speed, intermittent period, and total duration.

[0030] In this embodiment, the method realizes adaptive control during the grouting process by combining the pile tip cavity risk score Rvoid and the microseismic response vector Ewave, and generates a grouting triple through precise calculation, thereby optimizing the grouting effect. Based on the pile tip cavity risk score Rvoid and the microseismic response vector Ewave, the grouting pressure and slurry density are respectively output, and these parameters are key variables during the grouting process. Then, through the karst ground response feedback adaptive calculation, the grouting rhythm function Finj is obtained, which includes the basic grouting flow rate Q0, the cavity response driving factor r1, and the fracture diffusion penalty factor r2. These factors are used to adjust the dynamic characteristics such as the grouting flow rate, the intermittent period, and the total injection time. This grouting rhythm function enables the grouting process to precisely adjust its rate and timing according to the real-time feedback data of the pile tip. By combining the aforementioned grouting pressure, slurry density, and grouting rhythm function Finj, a grouting triple Ginj is generated through an adaptive algorithm, including the dynamic linkage control of the grouting pressure, slurry density, and grouting rhythm. These control parameters are then sent to the PLC module of the on-site grouting equipment through the central control server to achieve precise control, ensuring that the grouting process at each stage meets the requirements of the actual formation conditions and avoiding the limitations of the static grouting strategy in the traditional method. The core purpose of this implementation method is to dynamically optimize the grouting process through real-time feedback and adaptive calculation, automatically adjust the grouting pressure, slurry density, and grouting rhythm according to different pile tip cavity risks and fracture states, and maximize the reinforcement effect. Compared with the traditional grouting method, this solution can respond to changes in the geological environment in real time by introducing intelligent adaptive control, effectively avoiding the risks of over-grouting or under-grouting, improving the utilization efficiency of resources, and ensuring the reliability and stability of the reinforcement effect. This method significantly improves the flexibility and precision of the grouting process. By dynamically adjusting the various parameters of grouting, it ensures that the slurry can accurately penetrate and fill the fractures, achieving an ideal reinforcement effect.

[0031] Example 6 Please refer to Figure 1 , specifically: S5 includes S51 and S52; S51. After the implementation of the linkage control grouting strategy is completed, secondary pre-grouting is carried out at this time, and the micro sensors are started again to collect the secondary micro air pressure Wp' and the secondary microseismic wave signal after reinforcement, and the secondary microseismic response vector Ewave' is calculated and output based on the secondary microseismic wave signal. The reinforcement effectiveness value Seff is obtained through comprehensive calculation based on the secondary micro air pressure Wp' and the secondary microseismic response vector Ewave' after reinforcement; The reinforcement effectiveness value Seff is calculated and output through the following algorithm formula; ; In the formula, log represents the logarithmic function, and Wp(t)' represents the micro air pressure after reinforcement at time t; Among them, represents the air pressure response ratio term, indicating the change in pressure response after reinforcement. Taking the logarithm here is to compress the numerical gradient and avoid the out-of-control of extreme values; represents the diffusion path contraction factor, indicating whether the fissures are closed or the degree of diffusion is weakened after reinforcement.

[0032] S52. Set the effectiveness threshold Sth based on the standard value of pile - end grouting reinforcement in karst areas, and conduct a secondary comparative evaluation of the effectiveness threshold Sth and the obtained reinforcement effectiveness value Seff to judge the reinforcement situation at the pile end after the implementation of the linkage - controlled grouting strategy. The specific evaluation content is as follows; When the reinforcement effectiveness value Seff ≥ the effectiveness threshold Sth, it indicates that the grouting closure meets the standard, that is, the air pressure increases significantly and the fissure diffusion weakens. At this time, it is determined that the reinforcement is successful, and a prompt is given to enter the next process; When the reinforcement effectiveness value Seff < the effectiveness threshold Sth, it indicates that the grouting is not thorough, that is, the air pressure change is not significant or the fissures are still open. At this time, it is determined that the reinforcement is abnormal. Based on the current risk score Rvoid of the pile - end cavity and the micro - seismic response vector Ewave after reinforcement, iterate and execute S4 to adjust the grouting pressure, grouting slurry density, and grouting rhythm until the reinforcement is successful and the iteration stops.

[0033] In this embodiment, the method further improves the reliability and accuracy of pile foundation reinforcement in karst areas by performing secondary verification and optimization on the grouting reinforcement effect. After first executing the linkage control grouting strategy, secondary pre-grouting is performed, and the microsensor is started to collect the secondary micro-air pressure Wp' and microseismic wave signal data after reinforcement. Based on these secondary microseismic wave signals, the secondary microseismic response vector Ewave' is calculated and output, and the reinforcement effectiveness value Seff is obtained by comprehensive calculation in combination with the micro-air pressure Wp and microseismic response vector Ewave after reinforcement. The calculation of this reinforcement effectiveness value evaluates whether the reinforcement effect meets expectations by comparing the air pressure changes before and after reinforcement with the contraction of the diffusion path, thereby ensuring the effectiveness of the reinforcement measures. Whether the grouting is successful is judged by performing a secondary comparative evaluation of the calculated reinforcement effectiveness value Seff and the effectiveness threshold Sth. If the reinforcement effectiveness value Seff is greater than or equal to the effectiveness threshold Sth, it means that the grouting process is effective, the cracks have been closed, and the grouting closure meets the standard, and the next process is entered; if the reinforcement effectiveness value Seff is lower than the effectiveness threshold Sth, it means that the reinforcement effect is not up to standard, there are unclosed gaps or cracks, and the grouting parameters will be automatically adjusted according to the current reinforced cavity risk score Rvoid and the microseismic response vector Ewave, and the grouting process will be repeated until the expected reinforcement effect is achieved. The core purpose of this implementation method is to achieve accurate evaluation and dynamic optimization of the grouting effect through secondary microseismic data analysis and reinforcement effectiveness evaluation. By introducing the reinforcement effectiveness value Seff as a quantitative indicator, it is ensured that each round of reinforcement can effectively fill the gaps, close the cracks, and meet the predetermined stability standards. Through secondary reinforcement verification, it is possible to timely discover and solve the insufficiently reinforced areas, avoid the problems of "over-reinforcement" or "inadequate reinforcement" in traditional reinforcement methods, and improve the reliability and accuracy of the reinforcement process.

[0034] Example 7 See also Figure 2 , a reinforcement device for pile ends in karst areas, comprising a data acquisition device, a data processing device and a reinforcement control device; The data acquisition device is used to obtain air pressure response data and microseismic wave data, and transmit the data to the data processing device through the MEMS module; The data processing device is used to receive the air pressure response data and microseismic wave data, and after pre-processing, perform data calculation and analysis, output the grouting pressure and grouting slurry density, and summarize them with the grouting rhythm into a linkage control grouting strategy; The reinforcement control device controls the pressure, density and grouting rhythm of the grouting pump and executes the grouting strategy by connecting the data processing device with the PLC module of the grouting pump. Example 8

[0035] See also Figure 2, a reinforcement storage medium at the pile tip in a karst area. The storage medium stores a computer program, and when the computer program is executed, it realizes a reinforcement method at the pile tip in a karst area according to any one of the above.

[0036] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention.

Claims

1. A method for reinforcing pile ends in karst areas, characterized in that: The following steps are involved: S1. Pre-buried micro sensor groups at the pile end and around the pile end, real-time collection of air pressure response data of the microenvironment air pressure of the pile end under load induced state, and construction of a central control server, and transmission of the acquired air pressure response data to the central control server, and pre-processing of the air pressure response data in the central control server to obtain a standard response data set; S2. Construct a cavity risk analysis algorithm model in the central control server, extract the standard response data set and input it into the cavity risk analysis algorithm model, calculate and output the pile end cavity risk score Rvoid, and perform a preliminary comparative evaluation based on the pile end cavity risk score Rvoid, and trigger the microseismic strategy mechanism based on the preliminary comparative evaluation; S3. After the initial comparison and evaluation of the triggering microseismic strategy mechanism, the microseismic receiver array in the microsensor group is activated, pre-grouting is started, microseismic wave data is collected, and the microseismic response vector Ewave is calculated and output based on the microseismic wave data; S4, based on the obtained pile end cavity risk score Rvoid and microseismic response vector Ewave, combined with the grouting period function Finj, a grouting triplet Ginj is generated to perform linkage control of the grouting strategy; S5. Based on the pile end cavity risk score Rvoid and the microseismic response vector Ewave, the reinforcement effectiveness value Seff is calculated and output, and the preset effectiveness threshold Sth is compared with the reinforcement effectiveness value Seff for secondary evaluation to determine the reinforcement situation.

2. A method for reinforcing pile ends in karst areas according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, pre-embed a MEMS micro air pressure sensor in the micro sensor at the bottom center area of ​​the pile end and several symmetrical points on the pile side wall to record in real time the pressure change of the micro environment of the pile end contact surface during the sinking process; The micro sensor includes a MEMS micro air pressure sensor and a microseismic receiver array; By setting up three load platforms, the pile body is actively loaded, the load induces the response behavior of the contact surface between the pile end and the stratum, and the air pressure response data is collected in real time through micro sensors; The active loading includes a light loading stage and a heavy loading stage; In the light loading stage, the pile driving force is set to 100KN by using a rotary pile and a pile driver controller, and each level of loading lasts for 10 seconds to simulate detection penetration; In the heavy loading stage, the pile driving force is increased to 700KN by using the rotary pile and pile driver controller, and each level is applied for 30 seconds to observe the continuity and sudden change of air pressure; The air pressure response data includes micro air pressure Wp, air pressure response delay time ΔT and air pressure signal frequency density Up; The micro air pressure Wp is acquired by collecting through a MEMS micro air pressure sensor; The air pressure response delay time ΔT is obtained by setting a detection window and searching for the point where the air pressure starts to change; The air pressure signal frequency density Up is obtained by performing Fourier FFT transformation on the diagnostic signal and extracting the energy density of the main frequency band; S12, through the MEMS module of the MEMS micro air pressure sensor, using LoRa wireless transmission technology, remotely connecting the MEMS micro air pressure sensor to the central control server, transmitting the real-time acquired air pressure response data to the central control server, pre-processing the air pressure response data in the central control server, and obtaining a standard response data set; The preprocessing includes synchronization processing and normalization processing; The synchronization process is performed by establishing a time synchronization module with the pile foundation equipment, and the air pressure response is paired with the timestamp of the active loading through the time synchronization module to generate air pressure response data with consistent timestamps; The normalization process eliminates the dimension effect of the air pressure response data with consistent timestamps by using the standard deviation normalization method Z-Score; The standard response data set includes the micro-air pressure Wp(t) at time t, the air pressure response delay time ΔT(t) at time t, and the air pressure signal frequency density Up(t) at time t.

3. A method for reinforcing pile ends in karst areas according to claim 2, characterized in that: The S2 includes S21 and S22; S21. Construct a cavity risk analysis algorithm model in the central control server, extract the standard response data set, input it into the cavity risk analysis algorithm model to calculate and output the pile end cavity risk score Rvoid to measure the karst cavity value.

4. A method for reinforcing pile ends in karst areas according to claim 3, characterized in that: S22. Based on the output results of the pile end cavity risk score Rvoid, a preliminary comparative assessment is conducted to determine the karst cavity situation, and the microseismic strategy mechanism is triggered based on the preliminary comparative assessment results. The specific assessment contents are as follows; When the pile end cavity risk score Rvoid≤0.54, it indicates a normal response, and the normal grouting reinforcement process is entered; When the pile end cavity risk score Rvoid>0.54, it indicates that there is a cavity risk, and the microseismic strategy mechanism is activated.

5. A method for reinforcing pile ends in karst areas according to claim 4, characterized in that: The S3 includes S31 and S32; S31, after the microseismic strategy mechanism is triggered, pre-grouting is performed, and the microseismic receiver array in the microsensor is started to collect microseismic wave signals during the pre-grouting process, wherein the microseismic wave signals include envelope amplitude, main frequency response and propagation time difference, and feature extraction is performed on the microseismic wave signals to obtain microseismic wave data; The microseismic wave data includes the microseismic envelope intensity Au(t) at time t, the dominant frequency Wf(t) at time t, and the microseismic wave propagation distance tensor Dr(t) at time t; S32, after normalization processing is performed on the acquired microseismic wave data, correlation calculation is performed to output the microseismic response vector Ewave, and the slurry entry path, fracture state and closure trend are analyzed.

6. The method for reinforcing pile ends in karst areas according to claim 1, characterized in that: The S4 includes S41 and S42; S41, output grouting pressure and grouting slurry density based on the pile end cavity risk score Rvoid and the microseismic response vector Ewave respectively; The grouting rhythm function Finj(t) at time t is output by adaptive calculation of karst ground response feedback based on the pile end cavity risk score Rvoid and microseismic response vector Ewave; S42. Based on the obtained grouting pressure and grouting slurry density combined with the grouting rhythm function Finj(t) at time t, a grouting triplet Ginj is generated, and sent to the PLC module of the on-site grouting equipment through the central control server to control the grouting pressure, grouting slurry density and grouting rhythm in a triple linkage to generate a linkage control grouting strategy.

7. A method for reinforcing pile ends in karst areas according to claim 6, characterized in that: The S5 includes S51 and S52; S51. After the linkage control grouting strategy is executed, the secondary pre-grouting is carried out and the micro-sensor is started for the second time to collect the secondary micro-air pressure Wp' and the secondary micro-seismic wave signal after reinforcement, and the secondary micro-seismic response vector Ewave' is calculated and output based on the secondary micro-seismic wave signal. The reinforcement effectiveness value Seff is obtained by comprehensive calculation based on the secondary micro-air pressure Wp' and the secondary micro-seismic response vector Ewave' after reinforcement.

8. The method for reinforcing pile ends in karst areas according to claim 6, characterized in that: S52, setting an effectiveness threshold Sth based on the standard value of pile end grouting reinforcement in karst areas, and performing a secondary comparative evaluation on the effectiveness threshold Sth and the obtained reinforcement effectiveness value Seff to determine the reinforcement condition of the pile end after the linkage control grouting strategy is executed. The specific evaluation contents are as follows; When the reinforcement effectiveness value Seff ≥ effectiveness threshold Sth, it means that the grouting closure meets the standard. At this time, it is judged that the reinforcement is successful and the next process is prompted; When the reinforcement effectiveness value Seff is less than the effectiveness threshold Sth, it means that the grouting is not thorough. At this time, it is judged as reinforcement abnormality. At this time, based on the current reinforced pile end cavity risk score Rvoid and the microseismic response vector Ewave, iterative execution S4 is performed to adjust the grouting pressure, grouting slurry density and grouting rhythm until the reinforcement is successful and the iteration is stopped.

9. A reinforcement device for pile ends in karst areas, used to implement a reinforcement method for pile ends in karst areas as claimed in any one of claims 1 to 8, characterized in that: It includes a data acquisition device, a data processing device and a reinforcement control device; The data acquisition device is used to obtain air pressure response data and microseismic wave data, and transmit the data to the data processing device through the MEMS module; The data processing device is used to receive the air pressure response data and microseismic wave data, and after pre-processing, perform data calculation and analysis, output the grouting pressure and grouting slurry density and summarize them with the grouting rhythm into a linkage control grouting strategy; The reinforcement control device controls the pressure, density and grouting rhythm of the grouting pump and executes the grouting strategy by connecting the data processing device with the PLC module of the grouting pump.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed, the method for reinforcing the pile end in a karst area according to any one of claims 1 to 8 is implemented.

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