A Smart Speed ​​Ratio Control Method for Submarine Probe Penetration

Abstract

This invention belongs to the field of intelligent speed ratio control technology, specifically relating to an intelligent speed ratio control method for seabed probe penetration, comprising the following steps: real-time acquisition of multi-dimensional detection data during the probe's descent and penetration; calculation of the real-time speed ratio based on the multi-dimensional detection data, and real-time identification of four types of strata—soft clay, hard clay, loose sand, and dense sand—in conjunction with formation response parameters; dynamic adjustment of PID parameters through a fuzzy PID controller based on the deviation between the real-time speed ratio and the target speed ratio and its rate of change, outputting the penetration rate adjustment amount, and real-time correction of the target speed ratio according to pore water pressure; closed-loop execution of rate adjustment, cyclically implementing intelligent decision-making until penetration is completed. This invention effectively improves the continuity and reliability of exploration operations under complex deep-sea geological conditions, can adaptively adjust penetration parameters, and significantly shortens the single-hole penetration operation time while ensuring data quality and equipment safety, thereby improving the overall efficiency of offshore exploration operations.

Description

Technical Field

[0001] This invention relates to the field of marine geotechnical engineering investigation, specifically to an intelligent speed ratio control method for seabed probe penetration. Background Technology

[0002] In traditional seabed static cone penetration tests (CBPTs), a fixed or pre-programmed penetration rate control method is used, much like driving a car with only cruise control across complex terrain. Based on limited geological data, a fixed penetration rate is preset. During penetration, regardless of whether soft mud or hard sand is encountered, the system mechanically maintains this speed. This method has significant drawbacks: in soft clay, high-speed penetration generates excessively high pore water pressure, severely interfering with the measurement accuracy of pore water pressure sensors; in dense sand layers, a fixed rate may cause the cone tip resistance data to be overestimated or underestimated due to the "rate effect," and the equipment load increases dramatically, posing a risk of probe damage or even drill bit jamming. Essentially, traditional methods cannot adaptively adjust to real-time formation feedback, and data quality and operational safety are highly dependent on the operator's experience.

[0003] Existing seabed-based hydrostatic cone penetration testing (HCP) systems suffer from a severe lack of flexibility in adjusting penetration parameters, particularly in deep-water and complex geological environments. Specifically, while existing systems utilize pulley systems to transmit power to the hydraulic cylinders, the connection between the pulleys and cylinders is fixed, lacking a switching device to dynamically adjust the transmission relationship based on real-time soil conditions. This prevents the system from adaptively adjusting the matching relationship between penetration force, penetration speed, and penetration stroke during operation. When operating in soft, silty surface soil, the system exhibits significant redundancy in penetration force, leading to energy waste and reduced efficiency. Furthermore, when encountering locally hard strata, biological debris, rock fragments, or artificial obstacles, the inability to promptly increase penetration force can easily cause operational stoppages, probe damage, or hydraulic cylinder overload, severely impacting exploration continuity and data reliability. Moreover, existing systems lack intelligent control mechanisms under extreme conditions, making it difficult to balance power and penetration efficiency, thus limiting their application in unmanned, high-precision marine exploration. Therefore, the core problem with existing technologies is that the transmission structure is fixed and lacks adaptive adjustment capabilities, which cannot meet the operational requirements of dynamic optimization of penetration parameters in complex seabed strata. Summary of the Invention

[0004] This invention overcomes the aforementioned shortcomings and provides an intelligent speed ratio control method for seabed probe penetration. It addresses the problems of existing technologies, such as fixed transmission structures, lack of adaptive adjustment capabilities, and inability to meet the operational requirements of dynamic optimization of penetration parameters in complex seabed environments.

[0005] To achieve the above objectives, the present invention provides an intelligent speed ratio control method for seabed probe penetration, comprising the following steps:

[0006] S1. Real-time acquisition of multi-dimensional detection data at the penetration end during the probe's downward penetration process, wherein the multi-dimensional detection data includes at least cone tip resistance, sidewall friction resistance, pore water pressure, and tilt angle attitude data;

[0007] S2. Calculate the real-time speed ratio at the current moment based on the multi-dimensional detection data, compare the real-time speed ratio with the preset real-time speed ratio benchmark value to obtain the speed ratio comparison deviation, and at the same time combine the sidewall friction change gradient and pore water pressure offset to identify the geological characteristics of the current penetration point in real time. The geological characteristics include at least four typical types: soft clay, hard clay, loose sand, and dense sand.

[0008] S3. The deviation between the real-time speed ratio and the target speed ratio corresponding to the currently identified formation characteristics, and the rate of change of the deviation, are dynamically adjusted by a fuzzy PID controller to adjust the PID parameters and calculate the penetration rate adjustment amount. At the same time, the target speed ratio is safely corrected based on the real-time pore water pressure offset.

[0009] S4. Execute the penetration rate adjustment amount to adjust the penetration rate in a closed loop, and cyclically make intelligent speed ratio control decisions until the penetration task is completed.

[0010] Compared with the prior art, the advantages of the present invention are as follows:

[0011] 1. Traditional CPT (Continuous Penetration Testing) uses a fixed penetration rate, which cannot adapt to the mechanical response characteristics of different formations. This leads to excessive excess pore water pressure in soft clay due to high-speed penetration, contaminating pore water pressure measurement data. In sandy soils, the "rate effect" causes distortion of cone tip resistance data. This invention calculates the "cone tip resistance / penetration rate" ratio in real time and integrates it with multiple parameters such as sidewall friction gradient and pore water pressure offset to construct a refined real-time formation characteristic identification logic. This method can accurately distinguish between various typical formations such as soft clay, hard clay, loose sand, and dense sand online, and dynamically matches the optimal target rate ratio for different formations. This fundamentally suppresses measurement errors caused by inappropriate penetration rates, ensuring the reliability of the original data and the accuracy of formation profile interpretation.

[0012] 2. Traditional open-loop control methods are prone to equipment overload, probe damage, or even drill jamming due to sudden load changes when dealing with complex and variable seabed strata. This invention constructs a fuzzy PID closed-loop control system with the real-time speed ratio as the core controlled variable. The system can dynamically adjust control parameters and output the optimal penetration rate adjustment based on real-time changes in formation resistance. In particular, this method introduces a safety correction mechanism based on pore water pressure offset: when the pore pressure rises abnormally, the system can automatically reduce the target speed ratio or trigger emergency deceleration to proactively avoid the risk of hydraulic fracturing of the soil around the probe or the probe being "sucked" in. This intelligent early warning and intervention capability significantly improves operational safety and equipment durability in unknown and complex seabed environments.

[0013] 3. Traditional methods, to ensure data quality or equipment safety, often employ a conservative, single, low-speed penetration mode, or require frequent manual intervention and shutdowns, resulting in low operational efficiency. This invention, through intelligent speed ratio control, achieves an adaptive penetration rhythm that is "fast when it should be fast, slow when it should be slow." In homogeneous formations, the system can maintain a high and stable penetration rate to improve efficiency; in formation interfaces or sensitive formations, it automatically and smoothly decelerates to obtain high-quality data and ensure safety. The entire closed-loop cycle of "perception-decision-execution" operates automatically, reducing manual intervention and significantly shortening single-hole penetration time while ensuring data quality and equipment safety, thus improving the overall efficiency of offshore exploration operations. Attached Figure Description

[0014] Figure 1 This is a flowchart of the control method of the present invention;

[0015] Figure 2 This is a flowchart of the real-time speed ratio calculation and formation characteristic identification of the present invention;

[0016] Figure 3 This is a flowchart of the fuzzy PID control and target speed ratio safety correction of the present invention;

[0017] Figure 4 This is a flowchart of the penetration rate closed-loop regulation execution process of the present invention. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, this invention proposes an intelligent speed ratio control method for seabed probe penetration, specifically including:

[0020] Step S1. Collect multi-dimensional detection data of the penetration end in real time during the probe's downward penetration process. The multi-dimensional detection data of the penetration end includes at least the cone tip resistance, sidewall friction resistance, pore water pressure, and tilt angle attitude data.

[0021] Specifically, the CPT probe multi-parameter sensor module integrates a cone tip resistance sensor, a friction cylinder sidewall friction sensor, a cone shoulder or friction cylinder pore water pressure sensor, and a built-in inertial measurement unit. The inertial unit is used to monitor the probe's tilt and attitude data in real time, and its output pitch and roll angles are used to correct measurement errors caused by probe tilt in subsequent data processing and to evaluate the straightness of the penetration trajectory.

[0022] The multi-parameter sensor module of the CPT probe can synchronously acquire raw physical signals with strict time-domain requirements at high frequency. In this process, the inertial measurement unit plays a key role. Its output pitch and roll angle data provide a basis for real-time geometric correction of the tilt effect for the cone tip drag and sidewall friction data. This avoids the inherent error of measurement value distortion caused by probe tilt in traditional methods, thereby significantly improving the spatial geometric accuracy of the raw detection data.

[0023] More importantly, by monitoring dip changes in real time, the system can simultaneously assess the straightness of the penetration trajectory. This not only provides input data with a higher signal-to-noise ratio for subsequent stratigraphic identification, but also lays the foundation for the stable operation of the closed-loop control system. This enables intelligent control decisions to be based on real and reliable stratigraphic mechanical responses, ensuring the quality of data and the effectiveness of decisions throughout the entire process from the source.

[0024] S2. Calculate the real-time speed ratio at the current moment based on multi-dimensional detection data, compare the real-time speed ratio with the preset real-time speed ratio benchmark value to obtain the speed ratio comparison deviation, and combine the sidewall friction change gradient and pore water pressure offset to identify the geological characteristics of the current penetration point in real time. The geological characteristics include at least four typical types: soft clay, hard clay, loose sand, and dense sand.

[0025] Specifically, such as Figure 2 As shown in this embodiment, the multi-parameter detection data is first preprocessed with anti-aliasing filtering and analog-to-digital conversion to filter out high-frequency electrical noise and aliasing interference, ensuring the purity and fidelity of the subsequent digital signal. Subsequently, based on the tilt attitude data provided by the inertial measurement unit, the gradual drag and sidewall friction are geometrically corrected in real time for the tilt effect, fundamentally eliminating the measurement component error caused by the non-vertical penetration of the probe, restoring the sensor measurement value to the real formation reaction force, and providing a standardized input with clear physical meaning for formation characteristic identification. Then, combined with the real-time penetration depth fed back by the high-precision encoder, all preprocessed time-domain data sequences are uniformly resampled to the penetration depth domain to complete the standardization processing of the basic data.

[0026] This invention constructs a multi-parameter profile data sequence that is strictly aligned with depth as the reference, achieving precise synchronization and comparability of different physical quantities in the spatial dimension. This is a key prerequisite for accurate stratigraphic interface judgment and continuous parameter analysis.

[0027] The corrected real-time cone tip resistance and the real-time penetration rate obtained by the encoder differentiation at the current depth point are extracted in this depth domain. The real-time velocity ratio is calculated by the ratio of cone tip resistance to penetration rate. The real-time velocity ratio is the core control parameter that characterizes the formation response intensity at the current depth point.

[0028] The real-time speed ratio calculated in this invention creatively couples dynamic penetration behavior (rate) with static formation resistance (cone tip resistance) into a dimensionless comprehensive index. Its physical meaning is clear, and it is sensitive to formation changes, laying a direct and reliable quantitative foundation for the subsequent realization of intelligent closed-loop feedback control with speed ratio as the core.

[0029] It should be noted that, in this embodiment, the gradient of sidewall frictional resistance change, the pore water pressure offset relative to hydrostatic pressure, and the speed ratio comparison deviation are further calculated within a preset short-term window. The gradient of sidewall frictional resistance change represents the result of the sidewall frictional resistance ratio analysis within the preset short-term window; the pore water pressure offset represents the degree of deviation between pore water pressure and hydrostatic pressure; and the speed ratio comparison deviation represents the degree of deviation between the real-time speed ratio and the preset real-time speed ratio benchmark value. This step incorporates the dynamic changes (gradient) of formation response and key state quantities (pore pressure) into the analysis framework, breaking through the limitations of traditional CPT relying solely on isolated parameter thresholds for discrimination, and significantly enhancing the system's ability to identify complex geological conditions such as thin interbedded layers and transition zones. Subsequently, the real-time speed ratio, speed ratio comparison deviation, gradient of sidewall frictional resistance change, and pore water pressure offset are jointly constructed into a multi-dimensional real-time decision vector. This operation achieves... The method integrates and characterizes multi-source heterogeneous data, transforming the original physical quantities into a set of high-dimensional features that comprehensively reflect the mechanical behavior and permeability of the strata, providing a complete and dimensionally unified input for intelligent decision-making. Finally, a predefined strata characteristic identification logic matrix is ​​input into this multi-dimensional real-time decision vector for pattern matching. The strata characteristic identification logic matrix defines the correspondence between different parameter combinations and four typical strata types: soft clay, hard clay, loose sand, and dense sand. This logic matrix is ​​essentially a set of decision rules encapsulating expert experience and historical data patterns. It can efficiently and reliably map complex multi-parameter combinations to typical strata types such as soft clay, hard clay, loose sand, and dense sand. This not only realizes real-time, online, and automated identification of strata characteristics, but also ensures the transparency of the identification process and the interpretability of the results through logical and structured rules, providing accurate working condition judgment basis for subsequent precise control.

[0030] It should be noted that, in this embodiment, the pre-set formation characteristic identification logic matrix includes rule one, rule two, rule three, and rule four, specifically as follows:

[0031] Rule 1 states that if the real-time speed ratio is lower than the first speed ratio threshold and the pore water pressure offset is positive, it is initially identified as a soft clay soil layer.

[0032] Rule 2 states that if the real-time speed ratio is higher than the second threshold of the speed ratio, and the gradient of the sidewall frictional resistance is greater than or equal to the gradient threshold, then it is initially identified as a hard clay layer.

[0033] Rule 3 states that if the real-time speed ratio is within the speed ratio threshold range, the speed ratio comparison deviation is greater than or equal to the speed ratio comparison deviation threshold, and the pore water pressure offset is close to zero, then it is initially identified as a loose sandy stratum. The speed ratio threshold range represents the closed interval formed by the first speed ratio threshold and the second speed ratio threshold.

[0034] Rule 4 states that if the real-time speed ratio is higher than the second threshold of the speed ratio, and the absolute value of the sidewall frictional resistance increases synchronously, then it is initially identified as a dense sandy stratum.

[0035] Specifically, four predefined core rules constitute an efficient decision tree. Rule 1: When the real-time velocity ratio is significantly low and accompanied by a positive pore water pressure shift, the system identifies it as a soft clay stratum. This rule effectively captures the typical characteristics of soft clay—low resistance and pore pressure accumulation due to its low strength and poor permeability—avoiding misclassification as other low-resistance strata. Rule 2: When the real-time velocity ratio is significantly high and the sidewall frictional resistance rises rapidly, the system identifies it as a hard clay stratum. This rule, by combining high resistance with a significant frictional growth trend, can accurately distinguish hard clay from dense sand layers that may also have high resistance, improving the identification accuracy of highly plastic soils. Rule 3: When the real-time velocity ratio is in the medium range and deviates significantly from the benchmark value but pore pressure dissipates well, the system identifies it as a loose sand stratum. This rule utilizes the characteristics of sandy soil—good permeability, fast pore pressure response, and its resistance being sensitive to penetration rate—to achieve reliable identification of loose, non-cohesive strata. Rule 4: When the real-time speed ratio is extremely high and the absolute value of the sidewall frictional resistance increases significantly, the system identifies it as a dense sandy stratum. This rule accurately identifies the characteristics of high-density, high-internal-friction angle sand by recognizing the strong correlation between "high cone tip resistance" and "high sidewall frictional resistance," providing a key criterion for adopting a protective penetration strategy in the hardest strata. Overall, this rule set based on multi-parameter combination logic transforms complex geotechnical engineering experience into clear, automatically executable criteria, enabling the system to achieve rapid, consistent, and engineering-logical automated identification when facing typical seafloor strata. This lays a solid and reliable foundation for subsequent intelligent rate control based on operational condition cognition.

[0036] S3. The deviation and rate of change of the real-time speed ratio obtained in step S2 and the target speed ratio corresponding to the currently identified formation characteristics are dynamically adjusted by a fuzzy PID controller, and the penetration rate adjustment amount is calculated. At the same time, the target speed ratio is safely corrected according to the real-time pore water pressure offset.

[0037] Specifically, such as Figure 3 As shown in this embodiment, the speed ratio comparison deviation and its rate of change are first calculated in real time, serving as the core input for dynamically adjusting PID parameters through a fuzzy PID controller. If the absolute value of the speed ratio comparison deviation is greater than or equal to the speed ratio comparison deviation reference value, the proportional coefficient adjustment is increased to improve the response speed, while the integral coefficient adjustment is limited to avoid integral saturation. Increasing the proportional coefficient adjustment directly improves the response speed and rigidity of the control system, enabling the penetration drive mechanism to react quickly to large changes in formation resistance and rapidly adjust the speed to track the target speed ratio. This effectively prevents data distortion or equipment overload caused by slow response during rapid alternations between soft and hard formations. At the same time, limiting the integral coefficient adjustment avoids the limitations of traditional PID controllers in response to sudden changes in formation resistance. To address the "integral saturation" problem that easily arises with large deviations, this system prevents the control system output from falling into deep saturation and losing its regulating capability, ensuring the robustness and rapid recovery capability of the controller under extreme conditions. If the absolute value of the speed ratio comparison deviation is less than the speed ratio comparison deviation reference value and the deviation change rate is greater than or equal to the deviation change rate reference value, the differential coefficient adjustment is increased to suppress overshoot. This strategy gives the system a strong "predictive" damping effect, which can sensitively suppress rate oscillations and overshoot caused by local formation inhomogeneity or sensor noise, thereby ensuring the stability and smoothness of the penetration rate regulation process and avoiding the negative impact of frequent and drastic rate fluctuations on probe stability, data continuity, and the lifespan of mechanical components.

[0038] Based on the dual rules of deviation magnitude and trend, this invention realizes dynamic optimization and adaptive matching of PID parameters in different control stages, enabling the fuzzy PID controller to have excellent characteristics of fast response, anti-saturation and strong damping, fundamentally improving the dynamic quality and overall stability of the intelligent speed ratio closed-loop control system.

[0039] Next, the penetration rate adjustment is calculated based on the adjusted PID parameters. The calculation formula is as follows:

[0040] ,

[0041] Among them, among them, , as well as This indicates the real-time adjustment amount of the proportional coefficient, integral coefficient, and derivative coefficient after dynamic adjustment; This indicates the speed ratio deviation in the current control cycle; This indicates the speed ratio deviation in the previous control cycle; This indicates the speed ratio deviation between the previous two control cycles; This indicates the amount of adjustment to the penetration rate of the output.

[0042] In this embodiment, the final decision is made using the classic incremental PID algorithm based on the real-time proportional, integral, and derivative coefficients dynamically optimized by the fuzzy PID controller. Its expression clearly reflects how historical deviation information is weighted and fused: the proportional term acts on the latest deviation change, the integral term focuses on the accumulation of the current deviation, and the derivative term responds to the acceleration of the deviation change. This calculation process organically combines the intelligent tuning of fuzzy logic with the precise calculation of PID, enabling the generation of control quantities to inherit the flexibility of fuzzy rules in adapting to nonlinearity while maintaining the rigor and determinism of the PID algorithm itself. The formula explicitly includes the current, previous, and previous two speed ratio deviations, allowing the controller to not only focus on the current error state but also perceive the trend and acceleration of error changes. This enables a kind of "predictive" compensation for future changes in formation resistance, significantly improving the tracking accuracy and feedforward adjustment capability of the control system for continuously changing formations. The final output penetration rate adjustment is the result of comprehensive optimization based on multi-cycle deviation information. This method effectively smooths out accidental fluctuations caused by instantaneous sensor noise or microscopic inhomogeneities in the formation, ensuring that the rate adjustment command is robust and tends to eliminate systematic deviations. This guarantees the continuity and smoothness of the penetration rate adjustment, and ultimately achieves stable and precise intelligent speed ratio tracking control in the highly dynamic seabed penetration environment.

[0043] It should be noted that the present invention also requires a safety correction to the target speed ratio based on the real-time pore water pressure offset, by comparing the real-time pore water pressure offset with a preset pressure offset threshold range, specifically:

[0044] If the real-time pore water pressure offset is less than the lower limit threshold of the pressure offset, it is determined to be in a safe state. The target speed ratio adopts the preset standard target speed ratio. This design ensures that within the normal range of pore pressure, the system can give full play to its intelligent control performance and carry out efficient penetration with the optimal speed ratio without sacrificing work efficiency due to conservatism.

[0045] If the real-time pore water pressure offset exceeds the upper limit threshold of the pressure offset, it is judged as a dangerous state. The target speed ratio is set to zero, and the penetration rate emergency reduction program is triggered to protect the probe and ensure the validity of the data. This mechanism constitutes the final safety defense of the control system. When abnormally high pore pressure is detected (which may indicate approaching soil failure or probe sealing failure), it can interrupt the normal optimized control cycle with the highest priority and the fastest speed, and command the drive system to reduce speed or stop urgently. This minimizes the risk of serious accidents such as the probe being "adsorbed" and stuck by excessive static pore pressure, hydraulic fracturing of the surrounding soil, or mechanical damage due to excessive load. It fundamentally ensures the safety of expensive subsea equipment and prevents the continued collection of unreliable data under invalid or dangerous working conditions.

[0046] If the real-time pore water pressure offset is within the pressure offset threshold range, it is determined to be in an early warning state. Based on the linear or nonlinear interpolation of the real-time pore water pressure offset within the pressure offset threshold range, the preset standard target speed ratio based on the currently identified formation characteristics is reduced to obtain a reduction coefficient. The preset standard target speed ratio and the reduction coefficient are then interactively processed to obtain the corrected target speed ratio.

[0047] Specifically, the pressure offset threshold range represents a closed interval between the lower and upper limits of the pressure offset threshold. When the offset falls into this warning interval, it is determined that the system is in a warning state, and a dynamic correction mechanism is immediately activated. Based on the specific location of the offset within the interval, a reduction coefficient between 0 and 1 is calculated through linear or nonlinear interpolation, and then the standard target speed ratio given by the formation characteristic identification module is reduced in real time. This process creatively transforms pore water pressure, a key safety and quality indicator, from passive monitoring to active control input, realizing an advanced and flexible response of the control target to pore pressure risks. Through this continuous reduction method linked to the offset, rather than a simple on / off switch, the system can finely and gradually reduce the target penetration intensity (speed ratio) according to the severity of the pore pressure increase, thereby proactively and smoothly slowing down the penetration in the early stages of risk, allowing time for the dissipation of excess pore water pressure, and effectively avoiding the risks of data distortion (such as in clay), increased soil disturbance, or even probe "lock-up" that may be caused by a rapid accumulation of pore pressure. This interpolation-based reduction strategy ensures the continuity of control commands, avoids the impact of sudden changes in the target speed ratio on the drive system, and maintains the stability and smoothness of the entire closed-loop control process while ensuring the validity of test data and equipment safety.

[0048] It should be noted that the thresholds and benchmark values ​​of parameters such as the first threshold for speed ratio, the second threshold for speed ratio, the threshold for changing gradient, and the threshold for speed ratio comparison deviation used for identifying the characteristics of the underlying layer in this invention are initial ranges set by those skilled in the art based on their exploration experience with different strata characteristics, and are adaptively adjusted in combination with real-time detection data. These are common knowledge to those skilled in the art.

[0049] The multi-state, hierarchical correction strategy of this invention achieves seamless connection and automatic upgrading from "optimized control" to "early warning adjustment" and then to "safety protection", making the intelligent speed ratio control system have high efficiency, adaptability and extremely high safety.

[0050] S4. Execute the penetration rate adjustment amount to adjust the penetration rate in a closed loop, and cyclically make intelligent speed ratio control decisions until the penetration task is completed.

[0051] Specifically, such as Figure 4 As shown, the penetration rate adjustment is implemented in a closed-loop manner. The intelligent control unit first superimposes the calculated rate adjustment amount with the actual rate of the previous cycle to generate a precise target penetration rate command, which is then sent to the servo controller in real time via a high-speed fieldbus. This process ensures millisecond-level seamless connection between control decisions and execution commands, providing a link foundation for achieving high dynamic response. After receiving the command, the servo controller does not perform a simple step speed change, but drives the motor and uses an S-curve acceleration / deceleration algorithm to plan the execution trajectory. This key design makes the rate of change of penetration rate (acceleration) continuous and controlled, fundamentally eliminating the mechanical shock and stress fluctuations caused by sudden rate changes to the CPT probe, probe rod, and precision sensors, ensuring the stability of the measurement process and the continuity of data acquisition, while significantly improving the service life and reliability of the drive mechanism. At the same time, the servo controller also monitors the actual load pressure of the drive system in parallel and in real time. This dual monitoring mechanism constitutes embedded safety redundancy: when the drive pressure exceeds the safety threshold, the controller will temporarily override the rate command from the upper layer and prioritize the execution of local safety speed limit protection. This effectively prevents mechanical overload caused by extreme geological conditions (such as boulders) that the intelligent algorithm could not foresee, ensuring that hardware safety has the highest priority under all circumstances. After completing this smooth rate adjustment, the system immediately returns to the data acquisition and decision-making loop, thus forming a complete, high-speed, and adaptive closed loop from "sensing the geological conditions - intelligent decision-making - smooth execution - re-sensing".

[0052] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0053] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent speed ratio control in seabed probe penetration, characterized in that, Includes the following steps: S1. Real-time acquisition of multi-dimensional detection data at the penetration end during the probe's downward penetration process, wherein the multi-dimensional detection data includes at least cone tip resistance, sidewall friction resistance, pore water pressure, and tilt angle attitude data; S2. Calculate the real-time speed ratio at the current moment based on the multi-dimensional detection data, compare the real-time speed ratio with the preset real-time speed ratio benchmark value to obtain the speed ratio comparison deviation, and combine the sidewall friction change gradient and pore water pressure offset to identify the formation characteristics of the current penetration point in real time. S3. The deviation between the real-time speed ratio and the target speed ratio corresponding to the currently identified formation characteristics, and the rate of change of the deviation, are dynamically adjusted by a fuzzy PID controller to adjust the PID parameters and calculate the penetration rate adjustment amount. At the same time, the target speed ratio is safely corrected based on the real-time pore water pressure offset. S4. Execute the penetration rate adjustment amount to adjust the penetration rate in a closed loop, and cyclically make intelligent speed ratio control decisions until the penetration task is completed.

2. The intelligent speed ratio control method for seabed probe penetration according to claim 1, characterized in that, In step S1, the multi-dimensional detection data is synchronously acquired through a multi-parameter sensor module integrated into the CPT probe; The multi-parameter sensor module includes at least: a cone tip resistance sensor, a friction cylinder sidewall friction resistance sensor, a cone shoulder or friction cylinder pore water pressure sensor, and a built-in inertial measurement unit. The inertial measurement unit monitors the probe's tilt attitude data in real time, corrects measurement errors caused by probe tilt based on the tilt attitude data, and evaluates the straightness of the penetration trajectory.

3. The intelligent speed ratio control method for seabed probe penetration according to claim 1, characterized in that, In step S2, the specific steps for calculating the real-time speed ratio are as follows: S21. Perform anti-aliasing filtering and analog-to-digital conversion on the original analog signal, and perform real-time geometric correction of the tip drag and sidewall friction based on the tilt angle attitude data; S22. The real-time penetration depth is obtained by a high-precision encoder fed back by the penetration drive mechanism. Based on the real-time penetration depth, the preprocessed time domain data sequence is uniformly converted to the penetration depth domain to form a multi-parameter profile data sequence with depth as the reference. S23. In the penetration depth domain, the real-time cone tip resistance at the current depth point and the real-time penetration rate obtained by the encoder differentiation at the same time are taken. The real-time speed ratio is calculated by the ratio of cone tip resistance to penetration rate. The real-time speed ratio represents the control parameter of the formation response intensity at the current depth point.

4. The intelligent speed ratio control method for seabed probe penetration according to claim 3, characterized in that, In step S2, the geological characteristics include at least four typical types: soft clay, hard clay, loose sand, and dense sand.

5. The intelligent speed ratio control method for seabed probe penetration according to claim 4, characterized in that, In step S2, the real-time speed ratio, speed ratio comparison deviation, sidewall friction change gradient, and pore water pressure offset are used to form a real-time decision vector. The input of the real-time decision vector is a preset formation characteristic identification logic matrix, and the output is the preliminary identification result of the formation characteristics at the current penetration point. The gradient of sidewall frictional resistance represents the result of the sidewall frictional resistance ratio analysis relative to the preset short-time window; the pore water pressure offset represents the degree of deviation between pore water pressure and hydrostatic pressure. The stratigraphic characteristic identification logic matrix represents the correspondence between different parameter combinations and four typical stratigraphic types: soft clay, hard clay, loose sand, and dense sand.

6. The intelligent speed ratio control method for seabed probe penetration according to claim 5, characterized in that, The formation characteristic identification logic matrix includes rule one, rule two, rule three, and rule four; Rule 1 states that if the real-time speed ratio is lower than the first speed ratio threshold and the pore water pressure offset is positive, it is initially identified as a soft clay soil layer. Rule 2 states that if the real-time speed ratio is higher than the second threshold of the speed ratio, and the gradient of the sidewall frictional resistance change is greater than or equal to the gradient threshold, then it is initially identified as a hard clay layer. Rule 3 states that if the real-time speed ratio is within the speed ratio threshold range, the speed ratio comparison deviation is greater than or equal to the speed ratio comparison deviation threshold, and the pore water pressure offset is close to zero, then it is initially identified as a loose sandy stratum. The speed ratio threshold range refers to the closed interval formed by the first speed ratio threshold and the second speed ratio threshold. Rule 4 states that if the real-time speed ratio is higher than the second speed ratio threshold and the absolute value of the sidewall friction increases synchronously, it is initially identified as a dense sand formation.

7. The intelligent speed ratio control method for seabed probe penetration according to claim 1, characterized in that, In step S3, the fuzzy PID controller dynamically adjusts the proportional coefficient, integral coefficient, and derivative coefficient based on the speed ratio comparison deviation and its rate of change, specifically as follows: If the absolute value of the speed ratio comparison deviation is greater than or equal to the speed ratio comparison deviation reference value, the proportional coefficient adjustment amount is increased to improve the response speed, while the integral coefficient adjustment amount is limited to avoid integral saturation. If the absolute value of the speed ratio comparison deviation is less than the speed ratio comparison deviation benchmark value and the deviation change rate is greater than or equal to the deviation change rate benchmark value, then increase the differential coefficient adjustment amount to suppress overshoot.

8. The intelligent speed ratio control method for seabed probe penetration according to claim 7, characterized in that, In step S3, the specific steps for calculating the penetration rate adjustment are as follows: Based on the real-time proportional coefficient adjustment, integral coefficient adjustment, and derivative coefficient adjustment after dynamic adjustment by the fuzzy PID controller, the penetration rate adjustment is calculated, and the specific expression is as follows: ， in, , as well as This indicates the real-time adjustment amount of the proportional coefficient, integral coefficient, and derivative coefficient after dynamic adjustment; This indicates the speed ratio deviation in the current control cycle; This indicates the speed ratio deviation in the previous control cycle; This indicates the speed ratio deviation between the previous two control cycles; This indicates the amount of adjustment to the penetration rate of the output.

9. A method for intelligent speed ratio control for seabed probe penetration according to claim 8, characterized in that, In step S3, the step of performing a safety correction on the target speed ratio based on the real-time pore water pressure offset by comparing the real-time pore water pressure offset with a preset pressure offset threshold range involves the following steps: If the real-time pore water pressure offset is less than the lower limit threshold of the pressure offset, it is determined to be a safe state, and the target speed ratio is the preset standard target speed ratio. If the real-time pore water pressure offset exceeds the upper limit threshold of the pressure offset, it is determined to be a dangerous state. The target speed ratio is set to zero, and the penetration rate emergency reduction procedure is triggered to protect the probe safety and ensure the validity of the data. If the real-time pore water pressure offset is within the pressure offset threshold range, it is determined to be in an early warning state. Based on the linear or nonlinear interpolation of the real-time pore water pressure offset within the pressure offset threshold range, the preset standard target speed ratio based on the current real-time identified formation characteristics is reduced to obtain a reduction coefficient. The preset standard target speed ratio and the reduction coefficient are interactively processed to obtain the corrected target speed ratio. The pressure offset threshold range represents a closed interval between the lower limit threshold and the upper limit threshold of the pressure offset.

10. The intelligent speed ratio control method for seabed probe penetration according to claim 1, characterized in that, In step S4, the specific steps for adjusting the penetration rate in a closed loop are as follows: S41. The calculated penetration rate adjustment is superimposed with the actual penetration rate of the previous cycle to generate the current target penetration rate command, and the target penetration rate command is sent to the servo controller of the penetration drive mechanism in real time via the fieldbus. S42. After receiving the target penetration rate command, the servo controller drives the motor and uses an S-curve acceleration and deceleration algorithm to control the actual output of the drive mechanism, so that the penetration rate smoothly transitions from the actual penetration rate of the previous cycle to the current target penetration rate. At the same time, the servo controller monitors the drive pressure in real time. If the drive pressure is greater than the drive pressure safety threshold, the safety speed limit protection is executed first. S43. After completing this penetration rate adjustment, return to steps S1 to S3 and continue running until the penetration depth reaches the preset final penetration depth value, or an external stop command is received, or an emergency stop protection is triggered.

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