Soil high-fidelity sampling control system and control method

Abstract

The invention discloses a soil high-fidelity sampling control system and method. Comprising a multi-source equipment communication framework, a sensor module, a data processing and fusion module, a comprehensive control center module, a lower computer control module and a state monitoring and early warning module. The communication architecture provides a high-speed data channel to realize real-time interaction of sampling point position, soil environment and drilling machine working condition data; the sensor module collects various parameters; the data processing module cleans and standardizes multi-source data, and realizes deviation judgment and parameter optimization in combination with intelligent analysis; the comprehensive control center carries out matching learning and deviation pre-judgment according to historical working conditions, and an optimization instruction is generated and visually displayed; the lower computer control module executes hydraulic driving and power head deviation correction, and corrects a drilling path and thrust in real time; the state monitoring module detects key parameters and triggers early warning or protection control when the key parameters are abnormal. According to the invention, high-fidelity, intelligent and traceable control of the whole sampling process is realized, and the sampling precision, the automation level and the system reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology, and more specifically, relates to a high-fidelity soil sampling control system and control method. Background Technology

[0002] Existing soil sampling equipment generally faces the problem of insufficient high fidelity in application. Specifically, it is difficult to maintain consistent sampling depth and location accuracy. During drilling, the heterogeneity of the soil layer can easily cause deviation and disturbance, thereby reducing the representativeness and integrity of the sample. Even equipment with a certain level of automation often only achieves mechanical substitution in the propulsion or sampling stages, lacking intelligent control and feedback mechanisms for the entire sampling process.

[0003] In terms of data acquisition and processing, existing sampling equipment largely relies on dispersed sensor units, with inconsistent information collection standards and a lack of unified transmission protocols, making real-time data fusion and sharing difficult. This not only affects the accuracy of dynamic sensing and control during the sampling process but also limits the traceability and reliability of sampling results. Although some mechanized or semi-automated soil sampling equipment has emerged in recent years, capable of replacing manual tasks such as drilling, its overall level of intelligence remains insufficient. The various subsystems of such equipment (such as positioning, drilling, and sampling) mostly operate independently, lacking a unified data bus and communication protocol, resulting in large data transmission delays and poor stability both inside and outside the equipment, making efficient information integration difficult. Furthermore, due to the lack of intelligent collaborative control mechanisms, the system cannot achieve adaptive adjustment in complex geological environments. For example, it struggles to respond promptly to changes in soil hardness or drill bit attitude deviations, easily leading to problems such as stuck drills, deviations, and even sample disturbances, further weakening the high fidelity of the sampling.

[0004] Furthermore, existing sampling equipment still has significant shortcomings in remote monitoring and intelligent decision support. Most systems can only provide basic operational status displays and simple manual intervention, lacking the ability to conduct intelligent analysis, prediction, and optimization based on multi-source data. This makes it impossible for operators to assess sample quality in real time during the sampling process, and also makes it difficult to achieve path optimization, dynamic adjustment, or predictive maintenance.

[0005] Therefore, existing soil sampling technologies have not fully addressed the key issues of achieving high precision, low disturbance, and intelligent control throughout the entire process under complex geological conditions, making it difficult to meet the higher requirements for sampling fidelity, data quality, and intelligence levels in modern environmental monitoring, precision agriculture, and contaminated site remediation. Summary of the Invention

[0006] To address the shortcomings of existing soil sampling equipment, such as insufficient sampling fidelity, limited intelligence, and weak data fusion capabilities, this invention proposes a high-fidelity soil sampling control system and method. The system constructs a multi-source data interaction architecture based on a high-speed communication link, enabling real-time acquisition and transmission of site sampling point data, soil environmental data, and drilling rig operating data. A unified data processing module standardizes and fuses this multi-source heterogeneous information, thereby ensuring the integrity and consistency of information throughout the sampling process.

[0007] At the control level, this invention introduces an intelligent control mechanism for the power head correction operation. By monitoring the drill bit attitude and soil resistance characteristics in real time, it automatically corrects the advance path and thrust distribution, effectively reducing sample disturbance and improving sampling accuracy and representativeness. Simultaneously, the system combines historical working condition data with on-site environmental data for matching and analysis, enabling early prediction and assessment of potential deviations, achieving feedforward optimization of sampling parameters and dynamic path adjustment.

[0008] Furthermore, this invention integrates a drilling rig status monitoring and early warning subsystem, which performs real-time detection and health diagnosis of key operating parameters such as hydraulic system pressure, drill bit torque, and equipment vibration, ensuring the safety and stability of equipment operation. Therefore, this invention not only solves the shortcomings of existing sampling equipment in terms of data isolation, control lag, and insufficient adaptability, but also achieves high fidelity, intelligence, and traceability throughout the entire soil sampling process, meeting the demand for high-quality soil samples in modern environmental monitoring, precision agriculture, and contaminated site remediation.

[0009] In a first aspect, the present invention provides a high-fidelity soil sampling and control system, comprising: Multi-source device communication architecture: used to build a high-speed, low-latency data transmission channel to realize real-time interaction of site sampling point data, soil environmental data and drilling rig operating data; Sensor modules: Deployed in the drill rig's power head, hydraulic system, and sampling environment to acquire environmental information such as sampling depth, drill bit attitude, hydraulic pressure, vibration parameters, soil temperature and humidity, hardness, pH, and conductivity; Data processing and fusion module: This includes an OPC server module and an intelligent analysis module. The OPC server module normalizes and encapsulates multi-source heterogeneous data to form standardized sampling parameters. The intelligent analysis module performs deviation judgment and parameter optimization based on the fused data, providing input for subsequent control. Integrated Control Center Module: Used to receive and visualize real-time sampling information, combine it with historical operating data for learning and matching analysis, predict potential deviations in advance, and generate optimized control commands; The lower-level control module includes a hydraulic drive unit and a power head correction unit. The lower-level control module receives control commands from the integrated control center and performs real-time corrections to the drill bit's thrust, direction, and angle, thereby minimizing sampling disturbances and improving position accuracy. Status monitoring and early warning module: Used to monitor the operating status of key components of the drilling rig in real time, including hydraulic system pressure, drill bit torque and equipment vibration characteristics, and issue early warning signals when abnormal trends occur to ensure the stability and safety of the system.

[0010] In the first aspect, when the data processing and fusion module normalizes multi-source data, for data with a clear physical range, it adopts the Min-Max normalization method: x' = (x - xmin) / (xmax - xmin).

[0011] In the first aspect, when the data processing and fusion module normalizes multi-source data, for data without clear boundaries or with unknown distribution, it adopts the Z-Score normalization method: z = (x - μ) / σ, where μ is the mean and σ is the sample standard deviation.

[0012] In the first aspect, the intelligent analysis module employs the Kalman filtering method to fuse multi-source positioning data of sampling points in order to obtain the optimal estimate of the equipment operating status through prediction and update steps.

[0013] In the first aspect, the integrated control center module is further used to visualize the standardized parameters so that the operator can monitor the sampling process and environmental status in real time.

[0014] In the first aspect, the lower-level control module adjusts the thrust through the hydraulic drive unit and corrects the angle and attitude of the drill bit in real time through the power head correction unit.

[0015] In the first aspect, the status monitoring and early warning module triggers an alarm signal when it detects an abnormal trend and takes protective measures, including reducing the advance speed or automatically retracting the drill string.

[0016] In the first aspect, the system further includes a sampling data storage and traceability module for recording and storing raw data, standardized parameters and control instructions during the sampling process to ensure the integrity and traceability of the sampling data.

[0017] In the first aspect, the system is suitable for environmental monitoring, precision agriculture, and contaminated site remediation, enabling high-fidelity acquisition and data management of soil samples. In some implementations, the data processing and fusion module, the integrated control center module, and the lower-level machine control module may further incorporate data standardization algorithms, filtering and fusion methods, and intelligent control strategies. The specific implementation methods will be described in the embodiments section.

[0018] Secondly, the present invention provides a high-fidelity soil sampling control method based on the above system, comprising the following steps: S1, Data Acquisition. The sensor module acquires data on sampling point location, soil environmental parameters, and drilling rig operating conditions, including sampling depth, drill bit attitude, hydraulic pressure, vibration characteristics, soil temperature and humidity, hardness, pH, and conductivity, and transmits the data in real time through a multi-source device communication architecture. S2, Data Processing and Standardization. The data processing and fusion module cleans, normalizes, and uniformly encapsulates the multi-source data to form standardized sampling parameters. Preferably, Min-Max normalization is used for data with a defined physical range, and Z-Score normalization is used for data with unknown distribution to improve the compatibility and consistency of data from different sources. S3, Intelligent Analysis and Deviation Prediction. The integrated control center calls the intelligent analysis module to perform fusion analysis on the standardized data and conduct pattern matching and trend recognition in conjunction with historical operating data. In some implementations, the Kalman filter method is used to fuse multi-source positioning data such as GPS / BeiDou, IMU, and wheel speedometer to obtain the optimal estimated state of equipment position and speed; based on this, deviation prediction is performed in advance, optimized control quantities are generated, and the optimized control quantities are sent to the lower-level control module. S4, the lower-level machine performs correction. The lower-level machine control module drives the hydraulic unit and the power head correction unit according to the optimized control values, and adjusts the thrust and drilling angle in real time to correct the drill string path and optimize the thrust distribution, thereby reducing disturbance and improving the integrity and representativeness of the sample; S5, Status Monitoring and Early Warning. The status monitoring and early warning module dynamically detects key operating parameters such as hydraulic system pressure, drill bit torque, and equipment vibration. Once an abnormal trend is detected, it immediately triggers an early warning or executes protective controls to avoid stuck drill, overload, or sampling failure, ensuring the stability and safety of system operation.

[0019] Overall, compared with the prior art, the present invention has the following beneficial effects: 1. By introducing an intelligent correction mechanism for the power head and a real-time adjustment mechanism for hydraulic thrust, this invention can automatically correct the drilling path based on changes in soil hardness and drill bit attitude deviation, thereby reducing sample disturbance and ensuring the accuracy of sampling depth and location. This technical feature directly improves sample integrity and representativeness, overcoming the accuracy deficiencies caused by manual or single-control methods in existing technologies.

[0020] 2. By normalizing and standardizing the data acquisition from multiple sensors and the data processing module, and combining this with the intelligent analysis module and the integrated control center's fusion learning and matching analysis of historical operating data, this invention can predict and anticipate potential deviations before sampling and generate optimized control quantities in advance. This feature enables the sampling process to have feedforward optimization capabilities, avoiding the delays and instabilities caused by relying on simple feedforward corrections, thereby improving the system's adaptability and process robustness.

[0021] 3. By executing hydraulic drive and power head correction through the lower-level control module, this invention can dynamically adjust the propulsion force and drilling angle in complex geological environments, realize closed-loop precise control of the sampling process, and ensure high-fidelity sampling and traceability of sampling results.

[0022] 4. By using a status monitoring and early warning module to detect hydraulic pressure, drill bit torque, and equipment vibration in real time, this invention can issue timely warnings and take protective measures when abnormal trends occur, reducing the risks of stuck drill, overload, and equipment damage. This feature ensures the safety and stability of the sampling equipment under complex working conditions, while improving the system's reliability and engineering application value. Attached Figure Description

[0023] Figure 1 This is a framework diagram of a high-fidelity soil sampling and control system provided in an embodiment of the present invention; Figure 2 A flowchart of a control method based on the system provided in an embodiment of the present invention; Figure 3 A detailed flowchart illustrating the data processing, analysis, and execution monitoring of the control method provided in this embodiment of the invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Equivalent substitutions or modifications made by those skilled in the art without departing from the spirit and substance of this invention should be covered within the scope of protection of this invention. Furthermore, the technical features involved in the various embodiments of this invention can be arbitrarily combined to form more embodiments, as long as they do not constitute a conflict.

[0025] Example 1: See Figure 1This embodiment provides a high-fidelity soil sampling control system, including a multi-source device communication architecture, a sensor module, a data processing and fusion module, a comprehensive control center module, a lower-level control module, and a status monitoring and early warning module. These modules are interconnected and cooperate to achieve high-fidelity, intelligent, and traceable control throughout the entire soil sampling process.

[0026] In this embodiment, sensor modules are deployed in the drilling rig's power head, hydraulic system, and sampling environment to acquire real-time parameters such as drill bit attitude, drilling depth, hydraulic pressure, vibration characteristics, and soil temperature, humidity, hardness, pH, and conductivity. The collected data is transmitted at high speed and low latency through a multi-source device communication architecture and then enters the data processing and fusion module.

[0027] The data processing and fusion module includes an OPC server module and an intelligent analysis module. The OPC server module first cleans and normalizes the multi-source data, and then encapsulates the data from different sources into standardized parameters. In some implementations, for data with a defined physical range, a Min-Max normalization method is used. Where x' is the normalized result, x is the original sensor data, and xmax and xmin are the theoretical maximum and minimum values, respectively.

[0028] For data without clear boundaries or with unknown distribution, the Z-Score normalization method is used: Where μ is the mean over a time window, and σ is the sample standard deviation. This processing ensures consistency in timestamps, precision, and format across data from different sources.

[0029] After receiving standardized parameters, the intelligent analysis module performs multi-source fusion and trend analysis by combining historical operating data. Ideally, a Kalman filter algorithm is used to fuse and estimate multi-source positioning data (such as GPS / BeiDou, IMU, and wheel speedometer). The Kalman filter includes two steps: prediction and update. Prediction steps: Update steps: Where x̂ represents the state estimate, P is the covariance matrix, A is the state transition matrix, B is the control matrix, u is the control variable, Q is the process noise, H is the observation matrix, R is the observation noise, z is the measured observation, and K is the Kalman gain. This method enables optimal estimation of the equipment's operating state, allowing for early prediction of deviations.

[0030] The integrated control center module is used to visualize standardized data and generate optimized control commands based on intelligent analysis results. These control commands are sent to the lower-level control module, where they are executed jointly by the hydraulic drive unit and the power head correction unit. The hydraulic drive unit adjusts the drill string's propulsion force, while the power head correction unit corrects the drill string's attitude and angle, thereby maintaining a stable drilling path and reducing sample disturbance in complex geological environments.

[0031] The status monitoring and early warning module is used to monitor key operating parameters in real time, including hydraulic system pressure, drill string torque, and equipment vibration characteristics. When an abnormal trend is detected, the system will immediately trigger an early warning and take protective measures, such as slowing down, pausing, or automatically retracting the drill string, to avoid sampling failure or equipment damage.

[0032] Through the above design, the system described in this embodiment can realize real-time acquisition of multi-source data, standardized processing, intelligent analysis and deviation prediction, real-time correction execution by the lower-level machine, and full-process status monitoring and early warning, thereby significantly improving the high fidelity, automation level and process safety of soil sampling.

[0033] Example 2 See Figure 2 , Figure 3 This embodiment provides a control method based on a high-fidelity soil sampling and control system, including the following steps: S1: Data Acquisition. The sensor module acquires real-time data on sampling point location, soil environmental parameters, and drilling rig operating conditions, including sampling depth, drill bit attitude, hydraulic pressure, vibration characteristics, and soil temperature, humidity, hardness, pH, and conductivity. High-speed, low-latency transmission is achieved through a multi-source device communication architecture.

[0034] S2: Data Processing and Standardization. The data processing and fusion module cleans, normalizes, and uniformly encapsulates the multi-source data to form standardized sampling parameters. In some implementations, for data with a defined physical range, the Min-Max normalization method is used. Where x' is the normalization result, x is the original sensor data, and xmax and xmin are the theoretical maximum and minimum values, respectively.

[0035] For data without clear boundaries or with unknown distribution, the Z-Score normalization method is used: Where μ is the mean within a time window, and σ is the sample standard deviation.

[0036] This step ensures consistency in numerical range, format, and precision of data from different sources.

[0037] S3: Intelligent Analysis and Deviation Prediction. The integrated control center invokes the intelligent analysis module to perform fusion analysis on the standardized data and conduct pattern matching and trend identification in conjunction with historical operating data. In the preferred scenario, the Kalman filter method is used to fuse multi-source data such as GPS / BeiDou, IMU, and wheel speedometer to obtain the optimal estimate of equipment position and speed. The Kalman filter includes the following two steps: Prediction steps: Update steps: Where x̂ represents the state estimate, P is the covariance matrix, A is the state transition matrix, B is the control matrix, u is the control input, Q is the process noise, H is the observation matrix, R is the observation noise, z is the observation quantity, and K is the Kalman gain. This method enables dynamic optimization estimation of the equipment's operating state, allowing for early deviation prediction and the generation of optimized control inputs.

[0038] S4: Lower-level machine performs correction. The lower-level machine control module drives the hydraulic unit and the power head correction unit according to the optimized control values, and adjusts the drill bit thrust and drilling angle in real time to achieve drill bit path correction and thrust distribution optimization, thereby reducing disturbance and improving the integrity and representativeness of the sample.

[0039] S5: Status Monitoring and Early Warning. The status monitoring and early warning module dynamically detects key operating parameters such as hydraulic system pressure, drill string torque, and equipment vibration. When abnormal trends are detected, it promptly triggers early warning signals and can implement protective measures, such as reducing the feed speed or automatically retracting the drill string, thereby avoiding stuck drill, overload, or sampling failure, and ensuring the stability and safety of system operation.

[0040] Through the above steps, the method described in this embodiment realizes full-process control of real-time data acquisition and fusion, intelligent correction and path optimization, and operation status monitoring and early warning, ensuring high fidelity, intelligence and traceability of soil sampling.

[0041] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Equivalent substitutions or modifications made by those skilled in the art without departing from the spirit and essence of the present invention should be included within the scope of protection of the present invention.

Claims

1. A soil high fidelity sampling control system, characterized by, The system comprises: A multi-source device communication architecture for building a high-speed, low-latency data transmission channel to realize real-time interaction of site sampling point data, soil environment data and drilling rig working condition data; A sensor module arranged in the drilling rig power head, hydraulic system and sampling environment for obtaining sampling depth, drilling tool posture, hydraulic pressure, vibration parameters and environmental information such as soil temperature, humidity, hardness, pH and conductivity; A data processing and fusion module including an OPC server module and an intelligent analysis module, wherein the OPC server module is used for cleaning, normalizing and uniformly packaging multi-source heterogeneous data to form standardized sampling parameters; the intelligent analysis module is used for deviation judgment and parameter optimization based on the standardized sampling parameters to provide input for subsequent control; A central control center module for receiving and visually displaying real-time sampling information, learning and matching analysis combined with historical working condition data, calling the intelligent analysis module to predict potential deviations in advance, and generating optimized control instructions; A lower machine control module including a hydraulic drive unit and a power head deviation correction unit for executing real-time correction of drilling tool propulsion force, propulsion direction and angle according to the control instructions issued by the central control center to reduce sampling disturbance and improve position accuracy; A state monitoring and early warning module for real-time monitoring of hydraulic system pressure, drilling tool torque and equipment vibration characteristics, and sending an early warning signal when an abnormal trend occurs to ensure the stability and safety of system operation.

2. The system of claim 1, wherein, When normalizing multi-source data, the data processing and fusion module uses the Min-Max normalization method for data with a clear physical range: x'=(x-xmin) / (xmax-xmin).

3. The system of claim 1, wherein, When normalizing multi-source data, the data processing and fusion module uses the Z-Score normalization method for data without clear boundaries or unknown distribution: z=(x-μ) / σ, where μ is the mean and σ is the sample standard deviation.

4. The system of claim 1, wherein, The intelligent analysis module uses the Kalman filter method to fuse multi-source positioning data of the sampling point to obtain the optimal estimate of the device running state through the prediction step and the update step.

5. The system of claim 1, wherein, The central control center module is further used for visual display of standardized parameters to enable operators to real-time master the sampling process and environmental state.

6. The system of claim 1, wherein, The lower machine control module adjusts the propulsion force through the hydraulic drive unit and real-time corrects the angle and posture of the drilling tool through the power head deviation correction unit.

7. The system of claim 1, wherein, The state monitoring and early warning module triggers an alarm signal when an abnormal trend is detected and takes protective measures including reducing the propulsion speed or automatically retracting the drilling tool.

8. The system of claim 1, wherein, The system further comprises a sampling data storage and traceability module for recording and storing original data, standardized parameters and control instructions during the sampling process to ensure the integrity and traceability of the sampling data.

9. The system of claim 1, wherein, The system is suitable for environmental monitoring, precision agriculture and contaminated site remediation to realize high-fidelity collection and data management of soil samples.

10. A method of soil high fidelity sampling control based on the system of claim 1, characterized by, The method comprises the following steps: S1, acquiring sampling point position data, soil environment parameters and drilling rig working condition information through a sensor module, and transmitting in real time through a multi-source device communication architecture; S2, cleaning, normalizing and standardizing and packaging the multi-source data by a data processing and fusion module to form unified standard sampling parameters; S3, calling the intelligent analysis module by the integrated control center, performing multi-source data fusion and matching analysis based on the standardized sampling parameters and combined with historical working condition data, making a deviation prediction in advance, and generating optimized control quantities; S4, executing power head deviation correction and hydraulic propulsion adjustment according to the optimized control quantities by the lower computer control module, realizing real-time correction of drilling path and thrust to improve sampling accuracy and reduce disturbance; S5, dynamically detecting key operating parameters such as hydraulic pressure, drilling tool torque and equipment vibration by the state monitoring and early warning module, and triggering early warning or protective control when abnormal trends occur to avoid sampling failure or equipment damage.

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