Open caisson sinking construction control method and system based on accurate sinking resistance detection

Abstract

The invention discloses an open caisson sinking construction control method and system based on accurate sinking resistance detection. The open caisson sinking construction control method comprises the steps that an end resistance monitoring device and a side friction resistance monitoring device are pre-buried in an open caisson structure; in the sinking process of the open caisson, end resistance and side friction resistance data are collected; the data processing center dynamically calculates the real-time proportion of the end resistance and the total side friction resistance in the sinking total resistance, and analyzes the spatial distribution characteristics of the real-time proportion; based on the real-time proportion and the spatial distribution characteristics, the sinking state of the open caisson is intelligently judged, and the sinking state at least comprises normal sinking, end resistance type sinking difficulty, side resistance type sinking difficulty, inclination risk and sudden sinking risk; and according to the intelligently judged sinking state, a targeted control instruction is generated and output so as to control a sinking assisting system deployed on the open caisson to execute partitioned and graded resistance reduction or stable sinking operation. Real-time sensing, intelligent diagnosis and accurate active control of the sinking process of the open caisson are achieved, and the construction safety, efficiency and engineering quality are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of caisson construction technology. More specifically, this invention relates to a caisson sinking construction control method and control system based on accurate detection of sinking resistance. Background Technology

[0002] Caisson foundations, as an important form of deep foundation, are widely used in bridges, water conservancy, and underground engineering. The core of their construction is to remove soil from within the caisson and rely on the structure's own weight to overcome the side friction resistance of the caisson wall and the end resistance of the cutting edge, allowing the caisson to gradually sink. However, as projects become deeper and larger, and the construction environment becomes increasingly complex, traditional construction methods have revealed significant limitations. Currently, monitoring of the sinking process mainly focuses on geometric postures such as elevation and tilt, or uses scattered earth pressure cells for local static pressure observation. These methods cannot obtain a real-time, panoramic view of the overall distribution and dynamic changes of the resistance experienced by the caisson structure. Therefore, the judgment of difficulty in sinking during construction is often delayed, and it is difficult to distinguish whether the excessive end resistance is due to the cutting edge encountering a hard layer or due to the caisson wall encountering huge side friction resistance in a certain soil layer. Correction of tilt is mostly done retrospectively, and adjustment measures, such as off-center excavation and weighting, rely on experience and lack quantitative basis. Even more challenging is the lack of effective prediction and early warning capabilities for the risk of sudden sinking. The activation of sinking-aiding measures such as air curtains and high-pressure water jetting is usually decided based on experience, which can lead to problems such as inappropriate control intensity, excessively wide or insufficient range of action. This not only results in low efficiency but may also trigger new risks of attitude loss of control or sudden sinking. In short, existing technologies lack a closed-loop control system that integrates real-time perception of the entire cross-section, intelligent fusion of multi-source data, precise diagnosis of construction status, and coordinated execution of control measures. This makes it difficult to meet the extremely high requirements for safety, accuracy, and efficiency in modern large-scale and complex caisson engineering. Summary of the Invention

[0003] The purpose of this invention is to provide a method and control system for caisson sinking construction based on precise detection of sinking resistance. Through three-dimensional and real-time resistance and attitude perception, combined with intelligent data analysis and status diagnosis, the type, location and cause of sinking obstacles can be accurately identified. On this basis, predictive, progressive and zoned precise construction control can be achieved, thereby ensuring that the caisson sinks smoothly, safely and efficiently to the design elevation.

[0004] The technical solution adopted by this invention to solve this technical problem is: a method for controlling the sinking construction of caissons based on accurate detection of sinking resistance, comprising the following steps: S1. Pre-embed end resistance monitoring devices in the critical stress area of ​​the cutting edge of the caisson structure, and pre-embed side friction monitoring devices in layers along the depth direction on the outside of the caisson wall to form a three-dimensional monitoring network for sinking resistance. S2. During the sinking process of the caisson, the end resistance and side friction resistance data are collected in real time and transmitted to the data processing center. S3. The data processing center dynamically calculates the real-time proportion of end resistance and total side friction resistance in the total sinking resistance based on real-time data, and analyzes its spatial distribution characteristics. S4. Based on the real-time proportion and spatial distribution characteristics, intelligently determine the sinking state of the caisson. The sinking state includes at least: normal sinking, end-resistance type difficult sinking, side-resistance type difficult sinking, tilting risk, and sudden sinking risk. S5. Based on the intelligent judgment of the sinking status, generate and output targeted control commands to control the sinking aid system deployed on the caisson to perform zoned and graded drag reduction or sinking stabilization operations.

[0005] As a further aspect of the present invention, step S3, the analysis of its spatial distribution characteristics includes: Construct a two-dimensional distribution cloud map of end resistance on the cutting foot tread plane; Construct a one-dimensional distribution curve of side friction along the depth direction of the wellbore.

[0006] As a further aspect of the present invention, step S4, the intelligent judgment specifically includes: If the proportion of end resistance exceeds the first preset threshold and is evenly distributed, it is judged to be an end resistance type that is difficult to sink. If the proportion of total side friction exceeds the second preset threshold, it is judged as a side-resistance type that is difficult to sink, and the specific depth range of abnormally increased resistance is identified based on the side friction distribution curve. If the difference between the end resistance or the layered side friction resistance in the circumferential direction of the caisson exceeds the third preset threshold, it is determined that there is a risk of tilting. If the total resistance to sinking is detected to decrease unexpectedly and sharply within a short period of time, it is judged that there is a risk of sudden sinking.

[0007] As a further aspect of the present invention, in step S5, generating targeted control commands includes: When it is determined that the material is difficult to sink due to side resistance, an instruction is generated to activate the air curtain unit or thixotropic mud injection unit corresponding to the depth range where the resistance increases abnormally. When it is determined that the well is difficult to sink due to end resistance, an instruction is generated to start the high-pressure water jet system of the cutting edge in the corresponding high-pressure resistance area, and / or adjust the position and sequence of soil sampling in the well. When a risk of tilting is detected, instructions are generated to take measures to reduce drag or increase counterweight on the side with greater resistance.

[0008] As a further aspect of the present invention, the intelligent judgment in step S4 further includes: Real-time acquisition of the caisson's geometric attitude data; A comprehensive diagnostic vector for sinking state is established, which integrates drag distribution characteristic values ​​and attitude characteristic values; The comprehensive diagnostic vector is matched with a pre-set diagnostic rule library based on mechanical principles and construction experience to distinguish between real resistance anomalies caused by abrupt changes in the strata and pseudo-anomalies caused by stress concentration caused by caisson attitude deviation.

[0009] As a further aspect of the present invention, the control execution in step S5 employs a predictive and progressive control strategy, specifically including: Trend prediction is based on historical data sequences of resistance and sinking speed to predict the trend of change. If it is predicted that sinking will be difficult or sudden, predictive control commands will be generated. Intensity levels: The control commands define multiple intensity levels for each sinking aid measure; Gradual execution and feedback: The actuator starts the sinking aid measures from a low gear and dynamically adjusts the control gear based on the subsequent rate of resistance reduction, until the sinking state returns to normal.

[0010] The present invention also provides an intelligent control system for caisson construction to implement the method, comprising: The intelligent sensing module includes the end resistance monitoring device embedded in the cutting edge and the side friction resistance monitoring device embedded in the well wall; The data acquisition and transmission module is used to collect and transmit sensor data; The intelligent analysis and decision-making module, deployed in the data processing center, is used to perform intelligent judgments from S3 to S4 and generate control commands; The execution control module is used to receive the control commands and control the sinking system in a zoned and hierarchical manner.

[0011] As a further aspect of the present invention, the sinking aid system includes: an air curtain system that is independently controlled in layers along the depth direction of the well wall, a zoned and controllable high-pressure water jetting system deployed on the cutting edge plane, and a thixotropic mud system for injecting lubricating medium into the outside of the well wall; the execution control module is capable of independently controlling any designated zone or level of unit in the above systems.

[0012] The present invention has at least the following beneficial effects: First, by constructing a three-dimensional monitoring network covering the cutting edge plane and the longitudinal direction of the well wall, it has for the first time realized the accurate perception and visualization of the end resistance and side friction resistance in the entire cross section in real time and dynamically during the sinking process, providing data support for construction decision-making.

[0013] Secondly, by dynamically calculating the proportion of resistance and analyzing its spatial distribution, the system can intelligently and quickly distinguish the type and specific location of settlement obstacles. For example, it can accurately locate the depth of specific soil layers that cause difficult settlement or local hard points at the cutting edge, changing the previous judgment mode that relied on fuzzy experience. Furthermore, by integrating attitude data with a pre-set diagnostic rule base, the system has the ability to distinguish between real stratum resistance and stress concentration caused by its own tilt, greatly reducing the risk of misjudgment and making control decisions more scientific and reliable.

[0014] Furthermore, the predictive and progressive control strategy introduced in this invention can provide early warnings and gentle intervention based on data trends, effectively avoiding control lag and abrupt operations. This not only prevents the risk of sudden sinking but also improves sinking efficiency and energy utilization efficiency. The entire system achieves an intelligent closed loop from perception, analysis, decision-making to execution, enabling precise targeted control of sinking aids such as air curtains and high-pressure water jets at different levels and in different areas. This significantly improves the control accuracy of sinking attitude, the safety of the construction process, and the overall project efficiency, making it particularly suitable for large-scale caisson projects with complex geological conditions and high construction requirements.

[0015] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall structure and layout of the system of the present invention; Among them, 1-end resistance monitoring device, 2-cable, 3-side friction resistance monitoring device, 4-data processing center, 5-data acquisition station. Detailed Implementation

[0017] The present invention will now be described in detail and completely with reference to the accompanying drawings. Those skilled in the art will be able to implement the present invention based on these descriptions. Before describing the present invention with reference to the accompanying drawings, it should be particularly noted that the technical solutions and features provided in various parts of the present invention, including the following description, can be combined with each other without conflict.

[0018] Furthermore, the embodiments of the present invention described below are generally only some, not all, of the embodiments of the present invention. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific implementation process is as follows: like Figure 1As shown, this invention provides a method for controlling the sinking construction of caissons based on accurate detection of sinking resistance, comprising the following steps: S1. An end resistance monitoring device 1 is pre-embedded in the critical stress area of ​​the cutting edge of the caisson structure, and a side friction resistance monitoring device 3 is pre-embedded in layers along the depth direction on the outside of the caisson wall to form a three-dimensional monitoring network for sinking resistance; In step S1, the end resistance monitoring device 1 is arranged in the corner area, the midpoint of the long side and / or the midpoint of the short side of the cutting edge of the caisson to form an end resistance monitoring grid; the side friction resistance monitoring device 3 is arranged vertically in layers along the outside of the caisson wall, with multiple monitoring points set in each layer along the circumference of the caisson wall to form a three-dimensional monitoring network for side friction resistance; S2. During the sinking process of the caisson, the end resistance and side friction resistance data are collected in real time and transmitted to the data processing center 4. S3. The data processing center 4 dynamically calculates the real-time proportion of end resistance and total side friction resistance in the total sinking resistance based on real-time data, and analyzes its spatial distribution characteristics. S4. Based on the real-time proportion and spatial distribution characteristics, intelligently determine the sinking state of the caisson. The sinking state includes at least: normal sinking, end-resistance type difficult sinking, side-resistance type difficult sinking, tilting risk, and sudden sinking risk. S5. Based on the intelligent judgment of the sinking status, generate and output targeted control commands to control the sinking aid system deployed on the caisson to perform zoned and graded drag reduction or sinking stabilization operations.

[0020] In this embodiment, during the caisson structure construction stage, i.e., before concrete pouring, multiple high-precision earth pressure sensors are securely installed at key points below the caisson's cutting edge base plate, such as the four corners and the center of each side, forming a direct monitoring array for end resistance. Simultaneously, on the inner surface of the outer formwork of the caisson wall, another set of earth pressure sensors is evenly arranged circumferentially along the vertical direction at intervals, for example, every five meters, to sense the lateral frictional resistance generated by the friction between the caisson wall and the soil layer. All sensor cables 2 are pre-embedded and led to the top of the caisson, forming a three-dimensional monitoring network covering the key areas of sinking stress from the cutting edge to the caisson wall. When the caisson begins to sink after soil removal, these sensors continue to operate, transmitting the collected raw electrical signals of end resistance and lateral frictional resistance in real time to a centralized data processing center 4 on the ground via wired or wireless transmission. The computing equipment in this center processes the data in real time, the core of which is to dynamically calculate the percentage of the sum of end resistance and the sum of lateral frictional resistance relative to the total sum of the two at the current moment, i.e., their real-time proportion. Furthermore, the system performs a preliminary analysis of the spatial relationships of all sensor data points, extracting distribution characteristics. Based on these calculated proportions and spatial characteristics, the system's built-in intelligent analysis algorithm can make qualitative judgments about the real-time status of the caisson. For example, it can identify whether the difficulty in penetrating is due to encountering a hard layer at the bottom, excessive friction in a section of soil on the side causing blockage, or uneven resistance distribution leading to a tilting trend. Once a specific abnormal state is identified, the system automatically generates a clear operating instruction. This instruction does not involve taking general sinking aid measures, but rather precisely targets the specific zone and action level of the corresponding sinking aid equipment deployed on the caisson. For example, it can activate the air curtain in the 15-20 meter deep section on the north side of the caisson, setting the output power to level two. Thus, construction control transforms from a passive process relying on experience feedback into an active and precise closed-loop process based on real-time mechanical perception.

[0021] In another embodiment, step S3, analyzing its spatial distribution characteristics includes: A two-dimensional distribution cloud map of end resistance on the cutting edge tread surface is constructed. This cloud map is formed as follows: An array of pressure sensors is pre-embedded in the key stress areas of the caisson's cutting edge tread surface, forming a monitoring grid covering the entire tread surface. Each sensor measures the soil reaction force at its location in real time and assigns it two-dimensional coordinates on the tread surface. After data collection, the system uses a spatial interpolation algorithm to calculate the continuous and complete pressure distribution at all locations on the tread surface based on the coordinates and pressure values ​​of these discrete measuring points. This data is then rendered into an intuitive two-dimensional cloud map using contour lines of different color temperatures or colors. This cloud map instantly reveals the spatial uniformity of the soil support force under the cutting edge. If localized high-temperature spots appear on the cloud map, it directly indicates the presence of a hard layer or obstacle beneath the corresponding area. This is not only the fundamental basis for diagnosing tilt risk or difficulty in sinking due to end resistance, but also crucial for subsequently directing the high-pressure water jetting system at the cutting edge. A one-dimensional distribution curve of side friction resistance along the depth direction of the well wall was constructed. Along the outer depth direction of the well wall, the system arranged multiple monitoring sections at preset elevations. Each section acquired a comprehensive characteristic value of the circumferential side friction resistance at that depth. Connecting the data points of each layer with depth as the vertical axis and the corresponding side friction resistance value as the horizontal axis, a continuous curve reflecting the variation of side friction resistance with depth was formed. Each peak on the curve identifies a high-friction soil layer, and the steepness of the peak reveals abrupt changes in the geological properties. When the caisson experiences side resistance-induced sinking difficulty, the curve immediately shows an abnormally high peak. The system can automatically lock the depth range corresponding to this peak, thereby accurately locating the specific friction layer causing the sinking difficulty. This diagnostic result directly drives the air curtain or thixotropic mud system, enabling it to work precisely, performing layered, point-to-point drag reduction only in specific depth ranges with abnormally high resistance, greatly improving the efficiency and economy of sinking assistance measures.

[0022] In another embodiment, step S4 specifically includes the following intelligent judgment: if the proportion of end resistance exceeds a first preset threshold and is evenly distributed, it is judged as end resistance type difficult to sink; if the proportion of total side friction resistance exceeds a second preset threshold, it is judged as side resistance type difficult to sink, and the specific depth range of abnormally increased resistance is identified according to the side friction resistance distribution curve; if the difference between end resistance or layered side friction resistance in the circumferential direction of the caisson exceeds a third preset threshold, it is judged that there is a risk of tilting; if the total sinking resistance is detected to drop sharply and unexpectedly in a short period of time, it is judged that there is a risk of sudden sinking.

[0023] In this embodiment, the system's intelligent judgment logic is based on the real-time percentage of end resistance calculated through continuous monitoring. Once this value exceeds a first threshold preset based on the caisson's design weight and geological estimates, and combined with the analysis of the generated end resistance cloud map, it is found that the high-pressure zone is widely and evenly distributed rather than a few isolated peaks. In this case, the system will clearly diagnose it as end-resistance type difficult to sink, meaning that the resistance mainly comes from a large area of ​​hard strata below the cutting edge. Conversely, if the system finds that the end resistance percentage is not high, but the total side friction resistance percentage exceeds another preset second threshold, it will trigger the side-resistance type difficult to sink judgment process. At this time, the system will automatically retrieve the generated side friction resistance-depth distribution curve and use algorithms to identify abnormally high-valued sections on the curve, thereby accurately pinpointing the specific depth range of abnormally increased resistance, such as from 10 to 15 meters below the ground. In addition, the system will also calculate the difference between the readings of the sensors at symmetrical positions around the caisson in real time. If the difference between the upper resistance in a certain direction or the side friction resistance at a certain depth exceeds a third threshold, it will immediately determine that there is a risk of tilting. The assessment of sudden subsidence risk is based on monitoring the instantaneous rate of change of historical data on total subsidence resistance. Once an unexpected and sharp drop is detected, an early warning is issued.

[0024] In another implementation, step S5, generating targeted control commands includes: When it is determined that the material is difficult to sink due to side resistance, an instruction is generated to activate the air curtain unit or thixotropic mud injection unit corresponding to the depth range where the resistance increases abnormally. When it is determined that the well is difficult to sink due to end resistance, an instruction is generated to start the high-pressure water jet system of the cutting edge in the corresponding high-pressure resistance area, and / or adjust the position and sequence of soil sampling in the well. When a risk of tilting is detected, instructions are generated to take measures to reduce drag or increase counterweight on the side with greater resistance.

[0025] In this embodiment, the generation of control commands is closely linked to the state diagnosis results. When the system diagnoses a side-resistance type of difficult sinking and locates a specific depth range, the generated command will not require the activation of all air curtains, but will precisely specify the opening of one or more air curtain pipeline valves corresponding to the abnormal depth range, or control the thixotropic mud to be injected only into the well wall outside that range. If the diagnosis is end-resistance type of difficult sinking, the command will drive the high-pressure water jetting device to perform targeted water jetting disturbance only on the high-pressure red area shown on the end resistance cloud map, and may also suggest that the excavation equipment in the well prioritize soil removal below the corresponding area. When a tilting risk is judged, the command will pay more attention to balance, for example, requiring the temporary addition of counterweights or drag reduction measures on the side with higher detected resistance to suppress sinking. This spatially targeted control based on precise diagnosis greatly improves the effectiveness and economy of sinking assistance measures and reduces the uncertainty caused by blind intervention.

[0026] In another implementation, the intelligent judgment in step S4 further includes: Real-time acquisition of the caisson's geometric attitude data; A comprehensive diagnostic vector for the sinking state was established, integrating resistance distribution characteristic values ​​and attitude characteristic values. This comprehensive diagnostic vector is a multi-dimensional data package that simultaneously encapsulates the following key information at the same time: resistance distribution characteristic values, attitude characteristic values, and reference characteristic values. Resistance distribution characteristic values ​​include features from the end resistance distribution cloud map (such as the maximum pressure value and its coordinates, and the variance of the pressure distribution) and features from the side friction resistance distribution curve (such as the maximum friction resistance value and its corresponding depth, and the points where the curve's slope changes). Attitude characteristic values ​​include the real-time tilt angles of the caisson in the north-south and east-west directions, and its planar deviation relative to the design axis. Reference characteristic values ​​include the current sinking stage, cumulative sinking depth, and the stratum type at the current depth as predicted in the geological survey report. The comprehensive diagnostic vector is matched with a pre-set diagnostic rule base established based on mechanical principles and construction experience to distinguish between true resistance anomalies caused by abrupt changes in the geological formation and pseudo-anomalies caused by stress concentration due to caisson attitude deviation. For example, the diagnostic rule base includes rule A, rule B, and rule C.

[0027] Rule A diagnoses pseudo-anomalies of stress concentration caused by attitude deviation. If the attitude characteristics show a significant northward tilt of the caisson, and the end resistance cloud map shows a local high-pressure peak in the northward cutting edge region, and the side friction curve does not show a synchronous abnormal increase at the corresponding depth on the northward side, then the diagnosis is: attitude deviation leads to stress concentration at the northward cutting edge, which is a pseudo-anomaly. The recommended primary measure is to correct the deviation, rather than to disturb the formation at this high-pressure point.

[0028] Rule B diagnoses end resistance anomalies caused by actual strata abrupt changes. If the attitude characteristics show that the caisson attitude is basically upright, and the end resistance cloud map shows a local high pressure peak with drastic pressure gradient changes, and the location of the high pressure point roughly matches the geological prediction of rock surface undulations or isolated rock locations, then the diagnosis result is: the cutting edge has encountered a local hard layer, such as an isolated rock. The recommended measure is to start high-pressure water jetting at the corresponding point for precise breaking.

[0029] Rule C diagnoses lateral resistance anomalies caused by actual soil layer changes. If the lateral friction curve shows a significant, uniform peak in the entire circumference within a certain depth range, and this depth matches the depth of the dense sand layer marked in the geological report, and the posture does not change significantly, then the diagnosis is: entering a high friction soil layer, belonging to the true lateral resistance type that is difficult to settle. The recommended measure is to activate the air curtain system of the corresponding layer to reduce drag.

[0030] The system compares the real-time generated comprehensive diagnostic vector with the premises of each rule in the rule base, calculates the matching degree, selects the rule with the highest matching degree, executes its conclusion, and outputs qualitative diagnostic results and quantitative control suggestions. This process realizes the transformation from raw data to decision knowledge.

[0031] In another embodiment, the control execution in step S5 employs a predictive and progressive control strategy, specifically including: Trend prediction is based on historical data sequences of resistance and sinking speed to predict the trend of change. If it is predicted that sinking will be difficult or sudden, predictive control commands will be generated. Intensity levels: The control commands define multiple intensity levels for each sinking aid measure; Gradual execution and feedback: The actuator starts the sinking aid measures from a low gear and dynamically adjusts the control gear based on the subsequent rate of resistance reduction, until the sinking state returns to normal.

[0032] In this implementation, the system not only examines current data but also continuously analyzes historical sequences of resistance and sinking speed, using predictive algorithms to infer short-term trends. If the prediction indicates that, based on the current trend, the sinking speed will soon fall below a safe threshold or the resistance will exceed a critical value, the system will generate an early warning control command to prepare for activation. This command defines multiple intensity levels for each sinking-aiding operation, such as dividing the air curtain's supply pressure into low, medium, and high levels. When the command is executed, the actuator does not immediately start at full power but begins at the lowest level. After activation, the system closely monitors key indicators, such as the rate of resistance decrease, as feedback signals. If the feedback indicates that the resistance decreases slowly and does not meet expectations, the system will gradually increase the intensity level of the sinking-aiding measures according to preset logic. Once the sinking state returns to the normal range, the system will instruct the actuator to gradually decrease the intensity level until it shuts off smoothly. This early intervention based on trend prediction, and the stepless or stepped adjustment of intensity based on real-time feedback, makes the control process smoother, safer, and more efficient.

[0033] This invention also provides an intelligent control system for caisson construction to implement the method, comprising: an intelligent sensing module, including the end resistance monitoring device 1 embedded in the cutting edge and the side friction resistance monitoring device 3 embedded in the caisson wall; a data acquisition and transmission module, used to collect and transmit sensor data, including a data acquisition station 5 installed at the top of the caisson; an intelligent analysis and decision module, deployed in the data processing center 4, used to perform intelligent judgments S3 to S4 and generate control commands; and an execution control module, used to receive the control commands and control the sinking system in zones and levels. The underlying layer of this system is the intelligent sensing module, composed of the aforementioned sensors pre-embedded in the cutting edge and the caisson wall, responsible for directly acquiring mechanical signals. Connected to this is the data acquisition and transmission module, typically composed of waterproof junction boxes distributed in each section of the caisson, signal amplifiers, and dedicated cables 2 or wireless transmission nodes laid on the caisson wall, which efficiently and faithfully converge the original signals to the data acquisition station 5 at the top of the caisson. The intelligent analysis and decision module has a built-in high-performance computing unit and dedicated analysis software, running the aforementioned data processing, status diagnosis, and command generation algorithms in real time. Finally, the execution control module includes an industrial-grade programmable controller, a relay cabinet, and drive circuits leading to the air curtain valves, high-pressure water jet pumps, and mud injection valves in each zone. It is responsible for translating digital instructions into concrete actions. These four modules work closely together through standard industrial protocols, ensuring the entire control method operates reliably and automatically in real-world engineering environments.

[0034] In another embodiment, the sinking aid system includes: an air curtain system with independent layered control along the depth direction of the well wall; a zoned, controllable high-pressure water jetting system deployed on the cutting edge plane; and a thixotropic mud system for injecting lubricating medium into the well wall. The execution control module can independently control any designated zone or level unit in the above systems. The air curtain system is designed with dense layering along the depth direction of the well wall, for example, an independent annular air supply pipeline is provided every two meters or every section of the caisson height, and multiple air outlets that can be individually controlled by solenoid valves are distributed circumferentially on each pipeline. The high-pressure water jetting system not only has nozzles deployed around the cutting edge, but may also have upward or oblique nozzles deployed below the cutting edge tread surface, and all these nozzles are divided into multiple zones, each of which can be independently controlled for opening, closing, and water pressure. The thixotropic mud injection system also has a layered and zoned pipeline design. The core of the entire execution control module is a programmable controller with abundant control point outputs, each of which precisely corresponds to a specific control unit in a specific zone or level of the sinking aid system. Thus, when a command is received to open the air curtain in the section 15 to 20 meters deep on the north side or to spray water into the high-pressure zone at the southeast corner of the cutting edge, the controller can precisely drive the corresponding valve or group of pumps to achieve precise opening of the sinking aid equipment.

[0035] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for controlling the sinking construction of caissons based on precise detection of sinking resistance, characterized in that, Includes the following steps: S1. Pre-embed end resistance monitoring devices in the critical stress area of ​​the cutting edge of the caisson structure, and pre-embed side friction monitoring devices in layers along the depth direction on the outside of the caisson wall to form a three-dimensional monitoring network for sinking resistance. S2. During the sinking process of the caisson, the end resistance and side friction resistance data are collected in real time and transmitted to the data processing center. S3. The data processing center dynamically calculates the real-time proportion of end resistance and total side friction resistance in the total sinking resistance based on real-time data, and analyzes its spatial distribution characteristics. S4. Based on the real-time proportion and spatial distribution characteristics, intelligently determine the sinking state of the caisson. The sinking state includes at least: normal sinking, end-resistance type difficult sinking, side-resistance type difficult sinking, tilting risk, and sudden sinking risk. S5. Based on the intelligent judgment of the sinking status, generate and output targeted control commands to control the sinking aid system deployed on the caisson to perform zoned and graded drag reduction or sinking stabilization operations.

2. The method for controlling caisson sinking construction based on accurate detection of sinking resistance as described in claim 1, characterized in that, In step S3, the analysis of its spatial distribution characteristics includes: Construct a two-dimensional distribution cloud map of end resistance on the cutting foot tread plane; Construct a one-dimensional distribution curve of side friction along the depth direction of the wellbore.

3. The method for controlling caisson sinking construction based on accurate detection of sinking resistance as described in claim 1, characterized in that, In step S4, the intelligent judgment specifically includes: If the proportion of end resistance exceeds the first preset threshold and is evenly distributed, it is judged to be an end resistance type that is difficult to sink. If the proportion of total side friction exceeds the second preset threshold, it is judged as a side-resistance type that is difficult to sink, and the specific depth range of abnormally increased resistance is identified based on the side friction distribution curve. If the difference between the end resistance or the layered side friction resistance in the circumferential direction of the caisson exceeds the third preset threshold, it is determined that there is a risk of tilting. If the total resistance to sinking is detected to decrease unexpectedly and sharply within a short period of time, it is judged that there is a risk of sudden sinking.

4. The method for controlling caisson sinking construction based on precise detection of sinking resistance as described in claim 3, characterized in that, In step S5, generating targeted control commands includes: When it is determined that the material is difficult to sink due to side resistance, an instruction is generated to activate the air curtain unit or thixotropic mud injection unit corresponding to the depth range where the resistance increases abnormally. When it is determined that the well is difficult to sink due to end resistance, an instruction is generated to start the high-pressure water jet system of the cutting edge in the corresponding high-pressure resistance area, and / or adjust the position and sequence of soil sampling in the well. When a risk of tilting is detected, instructions are generated to take measures to reduce drag or increase counterweight on the side with greater resistance.

5. The method for controlling caisson sinking construction based on accurate detection of sinking resistance as described in claim 1, characterized in that, The intelligent judgment in step S4 also includes: Real-time acquisition of the caisson's geometric attitude data; A comprehensive diagnostic vector for sinking state is established, which integrates drag distribution characteristic values ​​and attitude characteristic values; The comprehensive diagnostic vector is matched with a pre-set diagnostic rule library based on mechanical principles and construction experience to distinguish between real resistance anomalies caused by abrupt changes in the strata and pseudo-anomalies caused by stress concentration caused by caisson attitude deviation.

6. The method for controlling caisson sinking construction based on precise detection of sinking resistance as described in claim 1 or 5, characterized in that, The control execution in step S5 employs a predictive and incremental control strategy, specifically including: Trend prediction is based on historical data sequences of resistance and sinking speed to predict the trend of change. If it is predicted that sinking will be difficult or sudden, predictive control commands will be generated. Intensity levels: The control commands define multiple intensity levels for each sinking aid measure; Gradual execution and feedback: The actuator starts the sinking aid measures from a low gear and dynamically adjusts the control gear based on the subsequent rate of resistance reduction, until the sinking state returns to normal.

7. A smart control system for caisson construction to implement the method according to any one of claims 1-6, characterized in that, include: The intelligent sensing module includes the end resistance monitoring device embedded in the cutting edge and the side friction resistance monitoring device embedded in the well wall; The data acquisition and transmission module is used to collect and transmit sensor data; The intelligent analysis and decision-making module, deployed in the data processing center, is used to perform intelligent judgments from S3 to S4 and generate control commands; The execution control module is used to receive the control commands and control the sinking system in a zoned and hierarchical manner.

8. The system as described in claim 7, characterized in that, The sinking aid system includes: an air curtain system that is independently controlled in layers along the depth direction of the well wall, a zoned and controllable high-pressure water jetting system deployed on the cutting edge plane, and a thixotropic mud system for injecting lubricating medium into the outside of the well wall; the execution control module can independently control any designated zone or level of unit in the above systems.

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