Method and System for Synchronous Acquisition and Control of Multi-Field Coupled Test Data in Soil and Rock

Abstract

This invention relates to the field of geotechnical engineering test data acquisition and control technology, and discloses a method and system for synchronous acquisition and control of multi-field coupled test data in geotechnical engineering. The method includes: constructing a unified time reference for multiple field sensors based on the IEEE 1588 protocol, performing parallel conditioning of multi-field signals with opto-isolation and adaptive notch filtering, implementing event-triggered multi-rate adaptive sampling and synchronous reconstruction, calculating multi-field coupling parameters online, and closed-loop feedback collaborative control based on the coupling parameters.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering test data acquisition and control technology, and in particular to a method and system for synchronous acquisition and control of geotechnical multi-field coupled test data for deep geological multi-field coupled simulation tests. Background Technology

[0002] With the rapid development of deep underground engineering, research in geotechnical engineering is increasingly focusing on the coupling mechanisms of multiple physical fields, such as temperature, stress, seepage, and chemical fields, in deep geological environments. In major engineering practices such as deep geological disposal of nuclear waste, development of deep geothermal resources, and deep mining, the environment in which rock masses are located often involves the combined effects of multiple factors, including high temperature, high confining pressure, high seepage, and complex chemical erosion. These factors are coupled and influence each other, constituting a typical thermo-mechanical-hydraulic-chemical (THMC) multi-field coupling problem. In order to deeply reveal the mechanical behavior and damage evolution of geotechnical materials under multi-field coupling conditions, indoor multi-field coupling simulation experiments have become an indispensable research tool.

[0003] Chinese patent CN111829894A discloses a multi-field measurement test system and method for geotechnical engineering. The system includes a test support system, a loading system, and a data acquisition system. The data acquisition system comprises stress data acquisition modules, displacement data acquisition modules, temperature field data acquisition modules, and velocity field data acquisition modules. This scheme acquires the temperature field using an infrared thermal imager and the velocity-displacement field using a CCD camera combined with DIC technology, achieving basic measurement functions for the temperature and velocity fields. However, this scheme has significant shortcomings in the time synchronization accuracy of multi-physics field data acquisition. The lack of a unified clock synchronization mechanism between the acquisition modules means that the data acquisition times of different sensors cannot be precisely aligned, resulting in poor temporal correlation between the data of each physics field during subsequent multi-field coupling analysis, severely affecting the accuracy of coupling parameter extraction. Furthermore, this data acquisition system only supports the measurement of stress and temperature fields, failing to cover the monitoring of seepage and chemical fields, and thus cannot meet the data requirements for fully coupled analysis of temperature, stress, seepage, and chemical fields in deep geotechnical engineering. Regarding sensor anti-interference, the scheme does not disclose any measures to deal with electromagnetic coupling interference between signals from multiple sensors. In the case of densely deployed multiple types of sensors under high temperature and high pressure, electromagnetic crosstalk will inevitably have an adverse effect on measurement accuracy.

[0004] Furthermore, in the collaborative analysis of multi-field data, most existing research adopts an offline post-processing approach to perform correlation analysis on the data of each physical field. That is, after the experiment, the data from each channel is imported into data processing software for manual matching and analysis. This offline approach has the following shortcomings: First, since the clock sources of each acquisition device are independent, clock drift of each device after long-term operation makes it difficult to guarantee the time alignment accuracy between data. Especially in long-term coupling experiments lasting tens of hours or even days, the accumulated time deviation may reach tens of milliseconds, seriously affecting the reliability of transient coupling response analysis. Second, the offline analysis method cannot detect abnormal changes in multi-field coupling behavior in a timely manner during the experiment, and researchers lose the valuable opportunity to dynamically optimize the experimental scheme based on real-time feedback.

[0005] Furthermore, existing geotechnical multi-field coupling testing techniques face the following prominent technical challenges: First, the sensor response timescales for different physical fields span several orders of magnitude. Dynamic impact responses in stress fields require high-speed sampling at the microsecond level, while temperature and chemical field changes are relatively slow, requiring only second-level sampling. This sampling rate difference can reach five to six orders of magnitude. Existing data acquisition systems struggle to achieve multi-rate coordinated sampling across these six orders of magnitude on a unified platform, typically using the highest frequency requirement among all physical fields as the unified sampling rate, resulting in significant data redundancy and wasted storage resources. Second, in high-temperature and high-pressure testing environments, severe electromagnetic coupling interference exists between signals from various heterogeneous sensors. In particular, the crosstalk problem caused by power frequency interference and its harmonics to weak signal channels remains unresolved, directly affecting the signal-to-noise ratio and accuracy of the measurement data. Third, most existing testing systems employ open-loop control, making it impossible to automatically adjust test conditions based on real-time changes in multi-field coupling parameters during the test. This hinders precise programmed control of complex coupling conditions, making it difficult to guarantee the repeatability and comparability of test results.

[0006] Furthermore, existing multi-field coupling experimental data acquisition schemes typically store data for each physical field in separate data files. After the experiment, timestamp matching and data alignment are performed manually. This offline post-processing method is not only inefficient, but also suffers from increasing time alignment errors due to the cumulative effect of clock drift from each acquisition device. In long-term experiments lasting hours or even days, these errors can reach millisecond or even second-level errors, completely failing to meet the accuracy requirements of multi-field coupling transient response analysis. Simultaneously, existing technologies lack the ability to extract multi-field coupling characteristic parameters in real time during the experiment. Researchers cannot obtain crucial information such as the thermo-mechanical coupling coefficient and permeability evolution during the experiment, making it difficult to dynamically adjust the experimental scheme according to the experimental progress. This results in the inefficient use of a large amount of valuable experimental resources.

[0007] Therefore, there is an urgent need for a geotechnical multi-field coupling test data synchronous acquisition and control technology that can achieve microsecond-level time synchronization of multi-physics field data, effectively suppress electromagnetic interference between multiple field sensors, support multi-rate collaborative sampling across time scales, and perform closed-loop feedback control based on online calculation results of multi-field coupling parameters. Summary of the Invention

[0008] To address the technical problems in existing technologies, such as asynchronous data acquisition timing, severe electromagnetic crosstalk between multiple sensors, and lack of closed-loop collaborative control in geotechnical multi-field coupling tests, this invention provides a method and system for synchronous data acquisition and control in geotechnical multi-field coupling tests. The core idea of ​​this invention is to introduce the IEEE 1588 precision clock protocol into the distributed data acquisition system for geotechnical multi-field coupling tests, establishing a sub-microsecond unified time reference. Based on this, a combined anti-interference strategy of opto-isolation and adaptive notch filtering ensures the signal quality of each channel. An event-triggered multi-rate sampling mechanism accommodates the time scale requirements of different physical fields. Precise and programmed management of the test conditions is achieved through online coupling parameter calculation and closed-loop feedback control.

[0009] The first aspect of this invention provides a method for synchronous acquisition and control of multi-field coupled geotechnical test data, comprising the following steps: a multi-field sensor unified time reference construction step, establishing a sub-microsecond time synchronization mechanism among distributed acquisition nodes based on the IEEE 1588 precision clock protocol, each acquisition node receiving a GPS timing reference signal and determining a unified time reference through master-slave clock negotiation, generating data frames carrying unified timestamps for the temperature field acquisition channel, stress field acquisition channel, seepage field acquisition channel, and chemical field acquisition channel respectively; a multi-field signal parallel conditioning and anti-interference processing step, performing independent signal conditioning operations on the original analog signals of each acquisition channel of the temperature field, stress field, seepage field, and chemical field, using opto-isolation devices to achieve electrical isolation and suppressing common-mode interference through differential input amplifiers, while performing power frequency interference suppression processing based on adaptive notch filtering; event-triggered multi-field ... The process includes: a rate-adaptive sampling step, which configures differentiated sampling rates for different acquisition channels based on the response time characteristics of each physical field, and synchronously reconstructs the sampling data of all acquisition channels based on a unified time reference; a multi-field coupling parameter real-time calculation step, which calculates the thermo-mechanical coupling coefficient, the permeability-stress relationship, and the chemical-mechanical damage evolution parameters online based on the synchronously reconstructed multi-field sampling data; and a closed-loop feedback collaborative control step, which compares the real-time calculation results of the multi-field coupling parameters with the preset target parameters and generates a control deviation signal, thereby adjusting the heating power, servo oil pressure, and fluid pump speed, and feeding the control adjustment results back to the event-triggered multi-rate adaptive sampling step to dynamically adjust the sampling rate configuration.

[0010] The second aspect of this invention provides a synchronous acquisition and control system for multi-field coupled geotechnical test data, comprising: a unified time reference construction module, a multi-field signal parallel conditioning module, a multi-rate adaptive sampling module, a coupling parameter real-time calculation module, and a closed-loop feedback control module. Each module corresponds one-to-one with the corresponding steps in the above-mentioned method, and collaboratively realizes synchronous acquisition of multi-field sampling data, online calculation of coupling parameters, and closed-loop control of test conditions. Specifically, the unified time reference construction module and the multi-field signal parallel conditioning module are connected via a timestamp interface. The multi-field signal parallel conditioning module outputs the conditioned clean signal to the multi-rate adaptive sampling module. The multi-rate adaptive sampling module transmits the synchronously reconstructed data to the coupling parameter real-time calculation module. The output of the coupling parameter real-time calculation module is sent to the closed-loop feedback control module. The sampling rate adjustment command of the closed-loop feedback control module is fed back to the multi-rate adaptive sampling module, thereby forming a complete data acquisition-parameter calculation-condition control closed loop.

[0011] The beneficial effects of this invention are as follows: By constructing a unified time reference based on the IEEE 1588 precision clock protocol, synchronous acquisition of 64-channel multi-physics field signals is achieved with a time synchronization error between channels of less than 1μs, fundamentally solving the problem of coupling analysis distortion caused by misalignment of multi-field data timing in existing technologies; through an anti-interference strategy combining opto-isolation and adaptive notch filtering, electromagnetic crosstalk between multiple sensor signals under high temperature and high pressure environments is effectively suppressed, with temperature measurement accuracy better than ±0.1℃ and stress measurement accuracy better than 0.1% of full scale; through an event-triggered multi-rate adaptive sampling mechanism, multi-timescale collaborative sampling across six orders of magnitude is achieved on a unified platform, taking into account both high-speed transient capture and low-speed steady-state monitoring needs; through the synergistic effect of real-time calculation of multi-field coupling parameters and closed-loop feedback control, precise programmed control of complex multi-field coupling conditions is achieved, which can completely reproduce the material response characteristics and damage evolution laws under the four-field coupling conditions of temperature-stress-seepage-chemical in deep geotechnical engineering. Attached Figure Description

[0012] Figure 1 This is a flowchart of the synchronous acquisition and control method for multi-field coupling test data of soil and rock provided in the embodiments of the present invention;

[0013] Figure 2 This is an architecture diagram of the synchronous acquisition and control system for multi-field coupling test data of soil and rock provided in the embodiments of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0015] In the following description, the test objects are geotechnical material samples, including but not limited to natural or artificial geotechnical engineering materials such as granite, sandstone, shale, salt rock, and concrete. The test environment is a deep geological condition simulation test environment, involving the coupling effects of four typical physical fields: temperature field (thermal field T), stress field (mechanical field M), seepage field (hydraulic field H), and chemical field (C). The synchronous acquisition and control method and system described in this invention are also applicable to other experimental research fields that require synchronous acquisition and closed-loop control of multiple physical fields, such as nuclear waste disposal site safety assessment tests, deep geothermal development reservoir simulation tests, oil and gas reservoir mechanical tests, and carbon dioxide geological storage and injection simulation tests. The specific numerical parameters in the following embodiments are only illustrative examples and should not be construed as limiting the scope of protection of this invention. Those skilled in the art can make adaptive adjustments within the framework of the technical solutions disclosed in this invention according to actual experimental needs and equipment conditions.

[0016] Reference Figure 1 The geotechnical multi-field coupled test data synchronous acquisition and control method provided in this embodiment includes the following steps S1 to S5. These five steps form a deeply coupled closed-loop collaborative architecture: the unified time base in step S1 provides a time reference for all data operations in steps S2 to S5; the clean signal output in step S2 serves as the input source for multi-rate sampling in step S3; the synchronous reconstructed data output in step S3 provides the data basis for the coupling parameter calculation in step S4; the calculation result in step S4 drives the closed-loop control decision in step S5; and the control output in step S5 feeds back to influence the sampling configuration in step S3, thereby achieving a complete data acquisition-parameter calculation-condition control closed loop. The following describes each step in detail with reference to specific embodiments.

[0017] Step S1: Constructing a unified time reference for multiple sensors. In this embodiment, constructing a unified time reference is a fundamental prerequisite for achieving accurate synchronization of multi-field sampling data. Specifically, the system deploys one master clock node and several slave clock nodes. The master clock node is configured with a GPS / BeiDou dual-mode timing receiver as the absolute time reference source. The GPS antenna is installed in an open outdoor location at the test site to ensure stable satellite signal reception. The master clock node integrates a temperature-controlled crystal oscillator (OCXO), whose frequency stability is better than 1×10⁻⁻⁻⁶. 9 It is used to maintain the accuracy of the local time base during brief interruptions of GPS signals.

[0018] In terms of clock synchronization protocol implementation, this embodiment adopts the IEEE 1588v2 Precision Clock Protocol (PTP). The master clock node broadcasts Sync messages to the Ethernet at a period of 128ms, and the messages carry the precise transmission timestamp of the master clock. Each slave clock node records its local timestamp after receiving the Sync message. Then, the clock node sends a Delay_Req delay request message to the master clock node and records the sending timestamp. After receiving the message, the master clock node records the receiving timestamp. The received timestamp is then transmitted via a Delay_Resp delayed response message. This information is then sent back to the slave clock node. Based on this, the slave clock node can calculate the offset between the master and slave clocks. and network transmission latency : , ,in, The timestamp for the master clock sending the Sync message, in nanoseconds; The timestamp for receiving the Sync message from the clock, in nanoseconds; The timestamp for sending the Delay_Req message from the clock, in nanoseconds; The timestamp for receiving Delay_Req messages from the master clock, in ns; The one-way network transmission delay between master and slave nodes, measured in nanoseconds (ns). This represents the time offset of the slave clock relative to the master clock, in nanoseconds (ns). This calculation is based on the assumption that the uplink and downlink transmission delays are symmetrical and equal.

[0019] Preferably, the calculated offset is clocked using a proportional-integral (PI) servo loop from the clock node, with a proportional gain... Set to 0.7, integral gain Set it to 0.3, and adjust the formula as follows: ,in, For the first The frequency adjustment amount of the next synchronization cycle is expressed in ppb (parts per billion). This is the proportional gain coefficient, dimensionless, with a value ranging from 0.5 to 1.0, preferably 0.7, and larger values ​​are also acceptable. It can accelerate the convergence speed but may cause oscillations; This is the integral gain coefficient, dimensionless, with a value ranging from 0.1 to 0.5, preferably 0.3, used to eliminate steady-state deviation; For the first The offset of the next synchronization cycle, in nanoseconds; This represents the current synchronization cycle number. After the above PI servo adjustment, the steady-state synchronization accuracy between each slave clock node and the master clock can reach within 500ns, meeting the sub-microsecond synchronization requirements.

[0020] In one embodiment of the present invention, the system is configured with 64 data acquisition channels, distributed across 4 slave clock nodes, with each slave clock node managing 16 acquisition channels. When performing A / D conversion on analog signals, each acquisition channel embeds the current local clock value as a timestamp into the data frame header, ensuring that all acquired data carries consistent and accurate time information. Specifically, each data frame structure includes: an 8-byte timestamp field (nanosecond precision), a 2-byte channel identifier field, a 2-byte data length field, and a variable-length data payload field, where the timestamp field records the precise acquisition time of the first sampling point of the frame. Each slave clock node uploads its locally acquired data frames to a data aggregation server via Gigabit Ethernet. The server, based on the timestamp field, sequentially arranges and merges data frames from different nodes and channels, forming a multi-field data stream on a unified timeline.

[0021] In addition, the master clock node outputs a PPS (pulse per second) signal every second. Each slave clock node uses this PPS signal to perform second-level coarse calibration of its local clock. Based on this, sub-microsecond-level fine synchronization is achieved through the PTP protocol, forming a two-level synchronization architecture to improve the system's robustness. Preferably, when the GPS signal is temporarily interrupted due to obstruction or interference, the system relies on the hold mode of the OCXO temperature-controlled crystal oscillator to maintain the time base. Within 4 hours after the GPS interruption, the time drift can still be guaranteed to be less than 1μs, ensuring that the continuity of long-term experiments is not affected by the brief interruption of external time synchronization signals.

[0022] Step S2: Parallel signal conditioning and anti-interference processing for multiple fields. Under the premise of obtaining a unified time reference, this step performs independent signal conditioning and anti-interference processing on the raw analog signals from each physical field sensor. In this embodiment, the system simultaneously supports signal acquisition from four types of physical fields: temperature field, stress field, seepage field, and chemical field. The conditioning scheme for each field acquisition channel is designed differently based on its signal characteristics.

[0023] For the temperature field signal channel, this embodiment employs a dual-redundancy configuration of platinum resistance thermometers (Pt100 / Pt1000) and type K thermocouples. The platinum resistance thermometer uses a four-wire connection to eliminate the influence of lead resistance and is digitized using a 24-bit high-precision Σ-Δ ADC, covering a range of -40℃ to 200℃ with a resolution better than 0.01℃. The type K thermocouple covers the high-temperature range of 200℃ to 300℃, and its cold junction compensation is achieved using a high-precision digital temperature sensor with a compensation accuracy better than ±0.05℃. Preferably, the two sensors are cross-checked in the temperature overlap range (100℃ to 200℃), and a measurement anomaly alarm is triggered if the deviation exceeds 0.5℃. The signal conditioning circuit for each temperature field channel includes a constant current source excitation circuit, an instrumentation amplifier, a low-pass filter, and an ADC. The instrumentation amplifier gain is set to 100 times, and the low-pass filter cutoff frequency is set to 10Hz to filter out high-frequency noise. Regarding the calibration of temperature sensors, this embodiment performs three-point calibration on all platinum resistance and thermocouple sensors before the start of the test. The output characteristics of each sensor are measured under the conditions of freezing point (0℃), boiling point (100℃) and the upper limit of test temperature (300℃). Personalized calibration equations are established and stored in the digital correction module of the ADC to achieve online automatic compensation and eliminate the influence of individual sensor differences on measurement accuracy.

[0024] For the stress field signal channel, this embodiment employs a combined configuration of strain gauge and piezoelectric sensor. The strain gauge (resistive strain gauge) measures quasi-static and low-frequency stress changes, using a full-bridge Wheatstone bridge circuit for signal conversion. The bridge voltage excitation is 5V, and digitization is achieved using a 16-bit SAR-type ADC with a bandwidth covering DC to 1kHz. The piezoelectric sensor captures high-frequency dynamic stress response and impact loads. Its signal is converted into a voltage signal by a charge amplifier and then digitized by a 16-bit high-speed ADC with a bandwidth covering 1Hz to 100kHz. Preferably, the measurement data from the strain gauge and piezoelectric channels are spliced ​​and fused in the frequency domain to achieve seamless coverage of the full-band mechanical response from DC to 100kHz, with a stress measurement accuracy better than 0.1% of full scale.

[0025] For the seepage field signal channel, this embodiment uses a differential pressure sensor to measure the pore water pressure distribution at different locations on the sample, with a pressure range of 0–60 MPa and an accuracy better than 0.05% of full scale. Simultaneously, an electromagnetic flow sensor is used to measure the seepage velocity, with a flow rate range of 0.01–100 mL / min. Regarding the chemical field signal channel, this embodiment uses an electrode array composed of multiple ion-selective electrodes (ISEs) to simultaneously monitor H₂. + Na + K + Ca 2+ Cl- The plasma concentration evolution process, with electrode response range covering 10⁻ 6 Up to 1 mol / L.

[0026] In terms of anti-interference processing, this embodiment designs a three-level interference suppression architecture. The first level is physical layer isolation. Independent shielded cables are used for transmission between each field acquisition channel. The shielding layer uses a single-end grounding method to avoid ground loop interference. A physical distance of no less than 50mm is maintained between signal transmission cables of different physical fields to reduce near-field coupling effects. Electrical isolation is achieved between the signal conditioning circuits of each channel through opto-isolation devices (optical couplers), with an isolation voltage of no less than 2500V. The second level is analog front-end suppression. Each channel uses a differential input instrumentation amplifier with a common-mode rejection ratio (CMRR) of no less than 100dB, which can effectively suppress common-mode signals introduced by environmental electromagnetic interference. The third level is digital domain filtering. This embodiment uses an adaptive notch filter algorithm to achieve precise suppression of power frequency interference and its harmonics.

[0027] Specifically, the transfer function of the adaptive notch filter is: ,in, Let be the Z-domain transfer function of the notch filter, which is dimensionless; Delay operator for units; The center angular frequency of the notch filter is expressed in rad. The calculation method is as follows: , The frequency of the interference to be suppressed (e.g., 50Hz power frequency). This is the sampling frequency of the current channel; This is the pole radius factor, dimensionless, with a value range of 0.9 to 0.999. The closer the value is to 1, the narrower the notch bandwidth and the deeper the attenuation of interference frequencies, but the slower the convergence speed. A value of 0.995 is preferred. This notch filter... A transmission zero point is generated at this point, which can theoretically achieve infinite attenuation of interference signals at this frequency.

[0028] Preferably, in order to track minute fluctuations in the actual power frequency (typically within the range of 49.5–50.5 Hz), this embodiment employs a frequency estimator based on the least mean square (LMS) algorithm to track the precise frequency of the interference signal in real time, and dynamically updates the notch filter center frequency accordingly. The formula for updating the weights of the LMS frequency estimator is: ,in, For the first The adaptive weight coefficient vector at time step; This is the step size factor, dimensionless, ranging from 0.001 to 0.1, preferably 0.01, but larger values ​​are also acceptable. It can speed up tracking but will increase steady-state error; For the first The error signal at time t is the difference between the desired signal and the filter output, and its unit is the same as that of the input signal. For the first The reference input vector at time t. For the 50Hz power frequency and its 2nd to 5th harmonics (100Hz, 150Hz, 200Hz, 250Hz), the system is configured with 5 cascaded adaptive notch filters to suppress them respectively. The measured power frequency interference suppression depth can reach more than 60dB.

[0029] Step S3: Event-triggered multi-rate adaptive sampling step. The multi-field clean signals processed in step S2 enter the multi-rate sampling module. The core innovation of this embodiment lies in the fact that, unlike the traditional uniform sampling rate scheme, this step configures differentiated sampling rates for different types of acquisition channels based on the inherent response time scale characteristics of each physical field, and realizes intelligent switching of sampling modes through an event-triggered mechanism.

[0030] Specifically, the default sampling frequency for the temperature field acquisition channel is set to 1Hz. This is because the time constant of thermal conduction processes in soil and rock materials is typically on the order of minutes to hours, and a sampling rate of 1Hz is sufficient to accurately capture the changing characteristics of the temperature field. The default sampling frequency for the chemical field acquisition channel is also set to 1Hz, as the response timescale of ion diffusion and migration processes is similar to that of the temperature field. The default sampling frequency for the seepage field acquisition channel is set to 100Hz to meet the requirements for monitoring pore water pressure fluctuations. The default sampling frequency for the stress field acquisition channel is set to 1kHz to capture the mechanical response during quasi-static loading processes.

[0031] In one embodiment of the present invention, when the system detects a dynamic response event, the stress field acquisition channel automatically switches to a high-speed sampling mode of 100kHz. The detection of the dynamic response event is based on real-time monitoring of the rate of change of the stress field signal, and the specific event triggering conditions are as follows: ,in, for The stress measurement value at time t, in MPa; The stress signal is expressed as the rate of change over time, in MPa / s, and is approximated by the difference between two adjacent sampling points. This is the event trigger threshold, expressed in MPa / s, with a range of 0.1–100 MPa / s. It is set according to the test object and operating condition. Preferably, it is used for brittle fracture capture in triaxial compression tests of rock. The setting is 10 MPa / s. When the stress change rate of three consecutive sampling points exceeds the threshold, the high-speed sampling mode is triggered. When the stress change rate of ten consecutive sampling points is less than 50% of the threshold, the system returns to the default sampling mode. This hysteresis mechanism can effectively avoid frequent switching of sampling modes.

[0032] Since the sampling frequencies of each channel are different, direct multi-field coupling analysis requires aligning the data from all channels to a unified time axis. This embodiment employs a time alignment method based on cubic spline interpolation for synchronous reconstruction. Specifically, using the unified time reference established in step S1 as a reference, a reference sampling interval for the global time axis is defined. The raw sampled data from each channel is mapped to equally spaced sampling points on the global time axis using cubic spline interpolation. The cubic spline interpolation function is applied to each sub-interval. The above is: ,in, For the first The interpolation function values ​​over each sub-interval have the same physical quantity and unit as the measured quantity of that channel; The time to be interpolated is in seconds (s). For the first Each original sampling time is represented in seconds. , , , For the first The spline coefficients of each subinterval are obtained by simultaneously solving the continuity condition of the function values ​​and first derivatives of adjacent sampling points; where The unit is the same as the measured quantity. The unit is the unit of measurement / second. The unit is the unit of measurement / s 2 , The unit is measured in units of measurement per second (s³). Preferably, the global reference sampling interval is... It can be dynamically adjusted according to the analysis requirements of the current test conditions, in the steady state phase. Set to 100ms to save storage space during the transient response phase. Shortened to 10μs to preserve high-frequency details.

[0033] In one embodiment of the present invention, the multi-rate sampling module internally maintains a circular data buffer for temporarily storing the original sampling data and the synchronously reconstructed data of each channel. The capacity of the circular buffer is configured according to the maximum number of channels and the highest sampling frequency; in this embodiment, it is set to 512MB, which can hold approximately 40 seconds of continuous data from 64 channels at a sampling rate of 100kHz. When the buffer utilization exceeds 80%, the system automatically triggers a data transfer operation, writing the historical data in the buffer in batches to a solid-state drive (SSD) storage array. Preferably, the storage array adopts a RAID5 redundant configuration with a total capacity of 8TB, which can support more than 500 hours of continuous data recording in mixed sampling mode. When the sampling data of each channel is written to storage, it is grouped and organized according to the physical field type. The header of each group of data files contains channel configuration information, sensor calibration parameters, and time reference information, ensuring the self-descriptive integrity of the data files. In addition, during the synchronous reconstruction process, quality checks are performed on the data of each channel, including whether the signal amplitude exceeds the sensor's range, whether there are any abnormalities such as jumps or repetitions in the sampling timestamp. If data quality abnormalities are detected, a quality flag is marked on the corresponding data point for identification and screening during subsequent data analysis.

[0034] Step S4: Real-time calculation of multi-field coupling parameters. Based on the synchronously reconstructed multi-field sampling data output in Step S3, this step calculates the key parameters of multi-field coupling online. Compared with traditional offline post-processing methods, real-time calculation can provide immediate decision-making basis for closed-loop control in Step S5, and at the same time enable researchers to observe the evolution trend of coupling parameters in real time during the experiment.

[0035] In this embodiment, the thermal-mechanical coupling coefficient The calculation employs a least-squares linear regression method within a sliding time window. Specifically, within the time window... Temperature increment after internal collection synchronization and the corresponding thermal stress increment For the data pairs, the slope of their linear correlation is extracted using least-squares fitting as the thermo-mechanical coupling coefficient:

[0036] ,

[0037] in, for The thermo-mechanical coupling coefficient calculated at each time point, in MPa / ℃, represents the increase in thermal stress caused by a unit temperature change, with a typical range of 0.01 to 1.0 MPa / ℃. The total number of data points within the sliding time window, determined by the window length. and reference sampling interval Sure, ; For the first in the window The temperature increment of each data point is expressed in °C and is calculated based on the temperature value at the beginning of the window. The corresponding thermal stress increment, in MPa, is obtained by extracting the thermal stress component after subtracting the mechanical loading contribution from the total stress. The value is the sliding window length, in seconds, ranging from 10 to 600 seconds, with 60 seconds being preferred. The selection of the window length should take into account both time resolution and statistical stability.

[0038] The fitting of the permeability versus stress relationship is based on Darcy's law framework. Given the sample geometry and seepage boundary conditions, the current effective stress state is determined. penetration rate Inversion can be performed using the following formula: ,in, For effective stress Permeability under action, in meters 2 Typical rock permeability ranges from 10⁻²¹ to 10⁻¹² m²; Permeation flow rate, unit: m³ 3 / s, obtained by the electromagnetic flow sensor of the seepage field; This is the dynamic viscosity of the permeating fluid, expressed in Pa·s. For pure water, it is approximately 1.002 × 10⁻³ Pa·s at 20°C, and can be corrected for based on real-time temperature. The length of the sample along the seepage direction is in meters. The cross-sectional area of ​​the sample for seepage is in m². The pressure difference between the two ends of the sample is expressed in Pa and is obtained by a differential pressure sensor. The effective stress is expressed in MPa and is calculated by subtracting the pore water pressure from the total stress. The system will update the current stress value in each calculation cycle. By adding data points to the permeability-stress curve, researchers can observe the complete process of permeability evolution with stress in real time.

[0039] The calculation of chemical-mechanical damage evolution parameters is based on the theoretical framework of damage mechanics. This embodiment defines chemical damage variables. To characterize the degree to which chemical erosion weakens the mechanical properties of rocks: ,in, for The chemical damage variable at time t is dimensionless and ranges from 0 to 1. Indicates no damage. Indicates complete damage; for The elastic modulus of the specimen at time t, in GPa, is extracted in real time from the stress-strain data; The initial elastic modulus of the sample, expressed in GPa, is measured under chemically resistant conditions at the start of the test. Preferably, the real-time extraction of the elastic modulus employs the secant modulus method on the linear segment of the stress-strain curve, calculated from the slope of the unloading segment in each loading-unloading cycle. Changes in ion concentration in the chemical field monitoring data serve as an indicator of the degree of chemical erosion, and are recorded synchronously by the system. The correlation between ion concentration and ion concentration provides experimental basis for establishing a chemical-mechanical coupled constitutive model.

[0040] Furthermore, this embodiment also calculates comprehensive characteristic parameters of multi-field coupling to characterize the degree of synergistic coupling between the temperature field, stress field, seepage field, and chemical field. Specifically, a multi-field coupling strength index is defined. for: ,in, for The multi-field coupling strength index at time t, dimensionless (after standardization of each component). This is the standardized value of the thermo-mechanical coupling coefficient; This represents the sensitivity of permeability to effective stress, i.e., the standardized value of the local slope of the permeability-stress relationship curve; The standardized value of the chemical-mechanical damage evolution rate is defined as follows: The standardized results are as follows. The standardization method for each component is to divide it by its expected maximum value over the entire experimental process, so that the contribution weights of each component are relatively equal. When When the value exceeds a preset threshold (preferably 1.5), it indicates that the sample is in a state of strong multi-field coupling. Based on this, the system reminds researchers to pay attention to the coupling response characteristics of the current experimental stage and automatically increases the sampling frequency of all acquisition channels during that period to a higher level to ensure the integrity of data acquisition. The real-time calculation of this coupling strength index provides a quantitative basis for judging the operating stage of the outer loop controller in step S5.

[0041] Step S5: Closed-loop feedback coordinated control step. This step is based on the real-time calculation results of the multi-field coupling parameters output in step S4, and achieves precise programmed control of the test conditions through closed-loop feedback control. The control architecture of this embodiment adopts a hierarchical control strategy, including two control levels: an inner loop and an outer loop.

[0042] The inner loop control consists of individual PID controllers for each physical field, independently controlling heating power, servo oil pressure, and fluid pump speed. The temperature field controller has a control cycle of 1 second and uses an incremental PID algorithm to adjust the heating power. Its proportional gain 2.0W / ℃, integration time 60s, differential time The heating power adjustment range is 0–5000W. The stress field controller has a control cycle of 10ms and achieves precise control of confining pressure and axial pressure by adjusting the oil pressure of the hydraulic system through a servo valve. The confining pressure control accuracy is better than ±0.01MPa, and the axial pressure control accuracy is better than 0.05% of full scale. The seepage field controller has a control cycle of 100ms and maintains the set seepage boundary conditions by adjusting the pump speed of a high-precision injection pump. The pump speed adjustment range is 0.001–50mL / min, and the flow rate control accuracy is better than ±0.5%.

[0043] The outer loop control is a multi-field coupled coordination controller. Its core function is to determine the current stage of the test condition based on the coupling parameters calculated in real time in step S4, and accordingly coordinate and adjust the target setpoints of each inner loop controller. Preferably, the outer loop controller adopts a condition switching logic based on a finite state machine, pre-defining several test condition stages and their corresponding multi-field boundary condition combinations. When the change in coupling parameters meets the preset stage transition conditions, it automatically switches to the next stage. For example, in a temperature-pressure coupled loading test, when the thermo-mechanical coupling coefficient... When the calculated value exceeds a preset threshold, it indicates that the sample has entered the thermoelastic deformation stage. The outer loop controller adjusts the heating rate of the temperature field controller and the loading path of the stress field controller accordingly. Specifically, the outer loop finite state machine defines the following typical operating stages: initial equilibrium stage (all field parameters stabilize at their initial set values), heating stage (temperature rises at a set rate, confining pressure remains constant), coupled loading stage (temperature and confining pressure change synchronously along a predetermined path), seepage injection stage (seepage is initiated while maintaining temperature and confining pressure), chemical etching stage (chemical solution is injected into the sample and ion concentration evolution is continuously monitored), and unloading recovery stage (all field parameters gradually decrease to their initial values ​​according to a safe sequence). The transition conditions between stages are determined based on the combination criteria of the coupling parameters. For example, the transition condition from the heating stage to the coupled loading stage is that the temperature has reached 95% of the target temperature and the rate of change of the thermo-mechanical coupling coefficient tends to stabilize (rate of change less than 0.01 MPa / ℃ / min).

[0044] In one embodiment of the present invention, the closed-loop feedback collaborative control step further includes an adaptive adjustment function for the sampling rate. Specifically, when the outer loop controller detects a phased change in the test conditions, such as a transition from a steady-state loading phase to a dynamic impact phase, the control module sends a sampling mode switching command to the multi-rate adaptive sampling module in step S3, switching the stress field acquisition channel from the default 1kHz sampling mode to a high-speed 100kHz sampling mode, while simultaneously increasing the sampling frequency of the seepage field acquisition channel from 100Hz to 1kHz to ensure that sufficiently detailed multi-field coupling data can be acquired during critical periods of condition changes. When the conditions return to steady state, the sampling mode automatically reverts to the default configuration to save storage resources and reduce system power consumption.

[0045] In addition, the control system is equipped with multiple safety protection mechanisms. When any physical field parameter exceeds a preset safety threshold, the system automatically enters protection mode, gradually reducing the loading level of each field according to a predefined safety unloading procedure to prevent damage to the test equipment and samples. Specifically, the temperature safety threshold is set at 350℃, the confining pressure safety threshold is set at 90% of the equipment's rated pressure, and the pore water pressure safety threshold is set at 60MPa. After the protection mode is activated, the system immediately switches all acquisition channels to the highest sampling rate to fully record the process data of abnormal events, providing detailed raw data for subsequent analysis.

[0046] Reference Figure 2 The geotechnical multi-field coupling test data synchronous acquisition and control system provided in this embodiment of the invention includes a unified time reference construction module 1, a multi-field signal parallel conditioning module 2, a multi-rate adaptive sampling module 3, a coupling parameter real-time calculation module 4, and a closed-loop feedback control module 5.

[0047] The unified time reference construction module 1 corresponds to step S1 above. Its hardware architecture includes a GPS / BeiDou dual-mode timing receiver, an OCXO oven-controlled crystal oscillator, an IEEE 1588 PTP protocol processing engine, and a PPS second pulse distributor. In one embodiment of the present invention, the PTP protocol processing engine uses an FPGA to implement hardware timestamp marking, with a timestamp resolution better than 8ns, far superior to the μs-level resolution of software implementation schemes. The timing accuracy of the GPS / BeiDou dual-mode timing receiver is better than 30ns (1σ), and the frequency stability of the OCXO oven-controlled crystal oscillator is better than 1×10⁻⁶. -9 The unified time base construction module 1 distributes the synchronization signal to each slave clock node via gigabit Ethernet, and simultaneously outputs the second pulse signal through a dedicated PPS signal line.

[0048] The multi-field parallel conditioning module 2 corresponds to step S2 and is designed as a modular plug-in card in terms of physical structure, including four types: temperature field conditioning plug-in card, stress field conditioning plug-in card, seepage field conditioning plug-in card, and chemical field conditioning plug-in card. Each plug-in card supports 16 independent channels, and the system can be configured with up to 4 plug-in cards of the same type, for a total of 64 channels. The plug-in cards are physically isolated from each other using fully shielded metal partitions. The plug-in cards communicate with the main backplane through opto-isolated digital interfaces, thereby cutting off the electrical crosstalk path between the field acquisition channels at the hardware level. Each conditioning plug-in card integrates a digital signal processor (DSP) to execute an adaptive notch filter algorithm, which can perform power frequency interference suppression processing locally before uploading clean data to the main control system, effectively reducing the computational load of the main control system.

[0049] The multi-rate adaptive sampling module 3 corresponds to step S3. Its core is a high-performance FPGA controller, which is responsible for coordinating and managing the sampling timing of all acquisition channels. The FPGA controller internally implements multiple independent sampling timers. The counting period of each timer can be flexibly configured by the main control system through register writing, supporting a sampling frequency range from 0.1Hz to 200kHz. The FPGA controller also integrates event detection logic, performing real-time differential calculations on the ADC output data of the stress field channel. When the stress change rate is detected to exceed a preset threshold, the sampling timer of that channel is automatically switched to high-speed mode. Preferably, the FPGA controller also includes a cubic spline interpolation calculation engine, which can efficiently complete the time alignment reconstruction calculation of multi-channel data at the hardware level, with a processing latency of less than 50μs for a single 64-channel synchronous reconstruction.

[0050] The real-time coupling parameter calculation module 4 corresponds to step S4. It is implemented using an embedded industrial control computer, configured with a multi-core processor and large-capacity memory to meet the computing power requirements for real-time calculation. This module runs a real-time operating system and receives synchronous reconstruction data from the multi-rate adaptive sampling module 3 in each calculation cycle. It then sequentially executes the calculation of the thermo-mechanical coupling coefficient, permeability inversion, and extraction of chemical damage parameters according to a preset calculation process. In one embodiment of the invention, the calculation module adopts a pipelined parallel architecture, distributing the calculation tasks of the three coupling parameters to different processor cores for parallel execution. The time consumed in a single calculation cycle is less than 100ms, meeting the real-time requirements. The calculation results are transmitted to the closed-loop feedback control module 5 through a shared memory interface and simultaneously pushed to the host computer monitoring software for real-time display and storage via an Ethernet interface.

[0051] The closed-loop feedback control module 5 corresponds to step S5 and is implemented using a combined architecture of a programmable logic controller (PLC) and a motion controller. The PLC is responsible for executing the outer-loop working condition coordination control logic and safety protection logic, while the motion controller is responsible for executing the inner-loop PID control algorithm and driving the confining pressure loading hydraulic cylinder, electric heater, and high-precision injection pump through a servo driver, power regulator, and pump controller, respectively. Preferably, the PLC and motion controller communicate via EtherCAT real-time industrial Ethernet with a communication cycle of 1ms to ensure low-latency transmission of control commands. After receiving the coupling parameter data from the coupling parameter real-time calculation module 4, the closed-loop feedback control module 5 determines the current working condition stage through a finite state machine, generates target setpoint update commands for each inner-loop controller, and simultaneously generates sampling rate switching commands to send to the multi-rate adaptive sampling module 3, forming a complete acquisition-calculation-control closed loop. Preferably, the closed-loop feedback control module 5 also integrates a data logger function, which can synchronously record all control commands, working condition switching events, and safety alarm information to non-volatile memory for easy retrospective analysis of the control process after testing.

[0052] In one embodiment of the present invention, the overall power supply of the system adopts a dual-redundant uninterruptible power supply (UPS) to ensure that each acquisition and control module can continue to operate for at least 30 minutes when the mains power is interrupted, providing sufficient time margin for the execution of the safe unloading procedure. The interface connection relationship between the functional modules is as follows: the PTP synchronization signal and PPS signal of the unified time base construction module 1 are respectively connected to the clock input terminals of the multi-field signal parallel conditioning module 2 and the multi-rate adaptive sampling module 3; the digital output terminal of the multi-field signal parallel conditioning module 2 is connected to the data input terminal of the multi-rate adaptive sampling module 3; the synchronous reconstruction data output terminal of the multi-rate adaptive sampling module 3 is connected to the data input terminal of the coupling parameter real-time calculation module 4; the calculation result output terminal of the coupling parameter real-time calculation module 4 is connected to the parameter input terminal of the closed-loop feedback control module 5; and the sampling configuration command output terminal of the closed-loop feedback control module 5 is fed back to the configuration input terminal of the multi-rate adaptive sampling module 3.

[0053] In addition, the system is equipped with host computer monitoring software, providing a graphical human-computer interaction interface that supports functions such as editing and downloading experimental schemes, real-time waveform display of multi-field data, plotting trend graphs of coupling parameters, manual / automatic switching of experimental conditions, and playback analysis of historical data. The host computer monitoring software communicates with each hardware module via gigabit Ethernet, using TCP / IP protocol to transmit control commands and UDP protocol to transmit real-time data streams, ensuring both reliability and real-time performance. The host computer software also provides data export functionality, supporting the export of collected data and coupling parameter calculation results as CSV and HDF5 format files, facilitating post-processing with scientific computing platforms such as MATLAB and Python, and also supporting the export of standardized experimental report templates.

[0054] To verify the technical effectiveness of the method and system described in this invention, the following experiments were conducted.

[0055] The experiment used a standard granite cylindrical specimen with a diameter of 50 mm × 100 mm and conducted a temperature-stress-seepage-chemical four-field coupled test on a true triaxial servo testing machine. The test conditions were set as follows: confining pressure was gradually increased from 0 to 40 MPa, temperature was increased from room temperature to 200 °C, seepage pressure was set to 5 MPa, and the chemical etching solution was a 0.1 mol / L NaCl solution. The system was configured with 64 acquisition channels, including 16 channels for the temperature field, 16 channels for the stress field, 16 channels for the seepage field, and 16 channels for the chemical field.

[0056] Regarding time synchronization accuracy, an oscilloscope was used to simultaneously capture the PPS outputs of the master clock node and each slave clock node for direct comparative measurement. During 72 hours of continuous operation, the time synchronization error between each slave clock node and the master clock was less than 800 ns (peak value), with a root mean square error of 120 ns, better than the design specification of 1 μs. Compared to conventional acquisition systems that do not employ this synchronization scheme, the time alignment error of multi-field data was reduced from the original 5–50 ms to within 800 ns, an improvement of more than four orders of magnitude. Furthermore, by simultaneously applying known square wave signals to the stress field channels of different acquisition nodes to verify inter-channel synchronization, the measured maximum sampling time deviation between channels was 680 ns, with an average of 95 ns, confirming the effectiveness of the IEEE 1588 protocol-based synchronization scheme in a distributed multi-node acquisition system.

[0057] In terms of anti-interference performance, under the condition of full-power heater operation (the most severe power frequency electromagnetic interference), the signal-to-noise ratio of each acquisition channel is better than 80dB, and the residual amplitude of power frequency interference is lower than the noise floor level. The measured accuracy of temperature field measurement is ±0.08℃ (better than the design target of ±0.1℃), and the measured accuracy of stress field measurement is 0.08% of full scale (better than the design target of 0.1%). In particular, comparing the temperature channel signals with the adaptive notch filter function turned off and on, the 50Hz power frequency interference component decreased from about 3.2mV (equivalent to about 0.8℃ error) before turning it on to less than 0.02mV (equivalent to less than 0.005℃) after turning it on, showing a very significant interference suppression effect. In addition, the comparison deviation of the platinum resistance and thermocouple dual-redundancy cross-verification function under the 200℃ test condition is less than 0.3℃, and no measurement abnormality alarm is triggered, verifying the consistency of the dual-redundancy scheme.

[0058] In terms of multi-rate sampling performance, the system operates in a hybrid sampling mode (1Hz for temperature field, 1Hz for chemical field, 100Hz for seepage field, and 1kHz for stress field) during the steady-state phase, with a total data throughput of approximately 3.4 Msps for the 64 channels. In simulating dynamic impact events of brittle rock fracture, the stress field channel completed the sampling mode switch from 1kHz to 100kHz within 10μs, successfully capturing the stress wave propagation process lasting approximately 200μs, achieving a transient peak data throughput of 6.4 Msps. During the complete 48-hour experiment, the event triggering mechanism triggered 23 high-speed sampling events, with a cumulative high-speed sampling time of approximately 180s, accounting for only 0.1% of the total experiment time. Compared to the 100kHz sampling scheme throughout, the data storage volume was reduced by approximately 99%, effectively solving the resource pressure problem of massive data storage, while ensuring no transient dynamic events were missed.

[0059] Regarding closed-loop control performance, in the temperature-pressure coupled loading path test, the temperature control deviation was less than ±0.5℃, the confining pressure control deviation was less than ±0.02MPa, and the seepage flow rate control deviation was less than ±1%. The system automatically adjusted the heating rate based on the real-time calculated thermo-mechanical coupling coefficient, ensuring that the thermal stress gradient remained within the preset range, effectively preventing thermal cracking of the sample caused by sudden temperature changes. In terms of real-time calculation of multi-field coupling parameters, the system continuously outputs real-time evolution data of the thermo-mechanical coupling coefficient, permeability-stress relationship curve, and chemical damage variables throughout the entire test, with a calculation delay consistently less than 100ms. The real-time permeability inversion results showed a relative deviation of less than 5% compared to the calculation results from the offline standard analysis method after the test, verifying the accuracy of the online calculation algorithm. The entire four-field coupling test lasted approximately 48 hours, during which the system operated stably without data loss or communication interruption, fully verifying the reliability and practicality of the technical solution of this invention.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Equivalent substitutions and improvements made by those skilled in the art based on the technical solutions of the present invention should all be included within the scope of protection of the claims of the present invention. For example, the embodiments of the present invention use temperature-stress-percolation-chemical four-field coupling as an example for illustration, but the synchronous acquisition and control method and system framework of the present invention are also applicable to multi-field coupling test scenarios involving other types of physical fields (such as electric fields, magnetic fields, sound fields, etc.). For another example, the embodiments of the present invention use IEEE 1588v2 as the clock synchronization protocol, but in application scenarios with higher synchronization accuracy requirements, it can be replaced with sub-nanosecond synchronization protocols such as White Rabbit without departing from the core technical concept of the present invention. Furthermore, the adaptive notch filter in the embodiments of the present invention is implemented using an IIR structure, which can be replaced with an FIR adaptive filter in scenarios requiring strictly linear phase characteristics.

Claims

1. A method for synchronous acquisition and control of multi-field coupled geotechnical test data, characterized in that, Includes the following steps: Steps for constructing a unified time reference for multiple field sensors: A sub-microsecond time synchronization mechanism is established between distributed acquisition nodes based on the IEEE 1588 precision clock protocol. Each acquisition node receives GPS timing reference signals and determines a unified time reference through master-slave clock negotiation. Data frames carrying unified timestamps are generated for the temperature field, stress field, seepage field, and chemical field acquisition channels, respectively. Multi-field signal parallel conditioning and anti-interference processing steps: Independent signal conditioning operations are performed on the original analog signals of each acquisition channel of the temperature field, stress field, seepage field and chemical field. Each acquisition channel uses opto-isolation devices to achieve electrical isolation and suppresses common-mode interference through differential input amplifiers. At the same time, power frequency interference suppression processing based on adaptive notch filtering is performed, and the isolated and filtered multi-field clean signals are output to the event-triggered multi-rate adaptive sampling step. Event-triggered multi-rate adaptive sampling steps: Based on the response time characteristics of each physical field, different sampling rates are configured for different acquisition channels. When a dynamic response event is detected, the stress field acquisition channel switches to high-speed sampling mode, the temperature field and chemical field acquisition channels adopt low-speed sampling mode, and the seepage field acquisition channel adopts medium-speed sampling mode. Based on the unified time reference, the sampling data at different rates are synchronously reconstructed. Real-time calculation steps for multi-field coupling parameters: online calculation of thermo-mechanical coupling coefficient, permeability as a function of stress, and chemical-mechanical damage evolution parameters based on synchronously reconstructed multi-field sampling data; Closed-loop feedback collaborative control steps: Based on the real-time calculation results of multi-field coupling parameters, a control deviation signal is generated by comparing it with the preset target parameters. The heating power, servo oil pressure and fluid pump speed are adjusted accordingly, and the control adjustment results are fed back to the event-triggered multi-rate adaptive sampling step to dynamically adjust the sampling rate configuration.

2. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the multi-field sensor unified time reference construction step, the master-slave clock negotiation includes: the master clock node sends synchronization messages to each slave clock node at a set period, and each slave clock node calculates the clock offset and transmission delay based on the sending timestamp of the synchronization message and the local receiving timestamp, and corrects the local clock accordingly, so that the time synchronization error between each acquisition node is less than 1μs.

3. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the multi-field signal parallel conditioning and anti-interference processing steps, the temperature field acquisition channel adopts a dual-redundant measurement configuration of platinum resistance and thermocouple, covering a measurement range of -40℃ to 300℃, with a temperature measurement accuracy better than ±0.1℃; the stress field acquisition channel adopts a combination configuration of strain gauge sensor and piezoelectric sensor, covering the acquisition of mechanical response across the entire frequency band from static to dynamic.

4. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the multi-field signal parallel conditioning and anti-interference processing steps, the seepage field acquisition channel uses a differential pressure flow sensor and an electromagnetic flow sensor to monitor pore water pressure and seepage velocity; the chemical field acquisition channel uses an ion-selective electrode array to monitor the time evolution of ion concentration in the pore liquid.

5. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the event-triggered multi-rate adaptive sampling step, the sampling frequency of the high-speed sampling mode of the stress field acquisition channel is 100kHz, the sampling frequency of the low-speed sampling mode of the temperature field acquisition channel and the chemical field acquisition channel is 1Hz, and the sampling frequency of the medium-speed sampling mode of the seepage field acquisition channel is 100Hz; the detection of the dynamic response event is based on whether the rate of change of the stress field signal exceeds a preset event triggering threshold.

6. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the event-triggered multi-rate adaptive sampling step, the synchronous reconstruction includes: using the global time axis generated by the unified time reference as a reference, the sampling data of each channel is processed by a time alignment interpolation method based on cubic splines to uniformly map the data of different sampling rates to equally spaced sampling points on the global time axis. The time interval of the equally spaced sampling points is dynamically determined according to the time resolution requirements of the current test conditions.

7. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the real-time calculation step of the multi-field coupling parameters, the calculation of the thermo-mechanical coupling coefficient includes: performing least squares linear regression on the temperature change and stress change after synchronization within the sliding time window, and extracting the temperature-stress correlation slope as the thermo-mechanical coupling coefficient; the fitting of the relationship between permeability and stress change includes: based on the Darcy's law framework, using the synchronized pore water pressure gradient and permeation velocity data to invert the permeability value under the current effective stress state.

8. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the multi-field signal parallel conditioning and anti-interference processing steps, each field acquisition channel uses an independent shielded cable for transmission, and the shielding layer of the independent shielded cable uses a single-end grounding method; the common-mode rejection ratio of the differential input amplifier is not less than 100dB.

9. The method for synchronous acquisition and control of multi-field coupled geotechnical test data according to claim 1, characterized in that, In the closed-loop feedback collaborative control step, the control deviation signal generates control quantities for each actuator through a PID control algorithm, wherein the control cycle for heating power is 1s, the control cycle for servo hydraulic pressure is 10ms, and the control cycle for fluid pump speed is 100ms. When the real-time calculation results of the multi-field coupling parameters indicate a phased change in the test conditions, the sampling rate configuration of each channel in the event-triggered multi-rate adaptive sampling step is switched to a preset configuration scheme that matches the current conditions.

10. A synchronous acquisition and control system for multi-field coupled geotechnical test data, used to implement the synchronous acquisition and control method for multi-field coupled geotechnical test data as described in any one of claims 1-9, characterized in that, include: The unified time reference construction module is used to establish a sub-microsecond time synchronization mechanism between distributed acquisition nodes based on the IEEE 1588 precision clock protocol. It receives GPS timing reference signals and determines a unified time reference through master-slave clock negotiation. It generates data frames carrying unified timestamps for each acquisition channel of the temperature field, stress field, seepage field and chemical field. The multi-field parallel signal conditioning module is used to perform independent signal conditioning operations on the raw analog signals of each acquisition channel of the temperature field, stress field, seepage field and chemical field. It uses opto-isolation devices to achieve electrical isolation and suppresses common-mode interference through differential input amplifiers. At the same time, it performs power frequency interference suppression processing based on adaptive notch filtering. The multi-rate adaptive sampling module is used to configure differentiated sampling rates for different acquisition channels according to the response time characteristics of each physical field, and to synchronously reconstruct the sampling data of all acquisition channels based on the unified time reference. The real-time coupling parameter calculation module is used to calculate the thermo-mechanical coupling coefficient, the relationship between permeability and stress, and the chemical-mechanical damage evolution parameters online based on the synchronously reconstructed multi-field sampling data. The closed-loop feedback control module is used to compare the real-time calculation results of the coupling parameters with the preset target parameters and generate a control deviation signal, thereby adjusting the heating power, servo oil pressure and fluid pump speed, and feeding back the control adjustment results to the multi-rate adaptive sampling module to dynamically adjust the sampling rate configuration.

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