Intelligent linkage control device for rotary drilling rig-mud system based on internet of things
By using IoT technology to achieve real-time collaborative control between rotary drilling rigs and mud systems, the problem of independent operation of rotary drilling rigs and mud pumping stations has been solved, improving construction safety and equipment efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE
- Filing Date
- 2026-03-12
- Publication Date
- 2026-06-05
Smart Images
Figure CN122148276A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of engineering machinery technology, and in particular to an intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things. Background Technology
[0002] During long-casing drilling, the excavation operation of the rotary drilling rig and the adjustment of the mud wall system require a high degree of coordination. Current technology suffers from the following significant drawbacks: rig control and mud parameter adjustment are separated, relying primarily on operator experience and judgment. In this mode, system response is severely delayed. Especially in complex and variable geological formations (such as alternating layers of sand and clay), insufficient mud supply can easily lead to negative pressure within the borehole, causing borehole collapse; or excessive mud supply can cause surface seepage, resulting in environmental pollution. In existing technologies, the drilling rig control system and mud pumping station system operate independently, with no data sharing, making dynamic, real-time coordinated control impossible. This results in significant construction safety and environmental risks, and low equipment utilization efficiency.
[0003] Therefore, there is an urgent need to propose an intelligent control device for long casing drilling construction that enables real-time coordinated linkage between rotary drilling rigs and mud wall protection systems, in order to solve the problem that existing drilling rig control systems and mud pumping station systems operate independently and cannot be coordinated in real time. Summary of the Invention
[0004] The purpose of this invention is to provide an intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things, so as to solve the problem that the existing drilling rig control system and mud pumping station system operate independently and cannot be coordinated and controlled in real time.
[0005] The technical solution adopted by the present invention to solve the above-mentioned technical problems provides an intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things, comprising:
[0006] The sensor monitoring module is used to collect drilling rig operating parameters, casing parameters, and mud state parameters;
[0007] The edge computing module is used to analyze the collected drilling rig operating parameters, casing parameters, and mud state parameters in real time. Based on the preset coupling model between drilling rig operating conditions, mud performance in the casing, and water head difference in the casing, it calculates the pressure imbalance index in real time. When the pressure imbalance index exceeds the corresponding preset threshold, it notifies the linkage control module.
[0008] The linkage control module is used to output corresponding control commands based on the information provided by the edge computing module to automatically adjust the pump speed and / or feed amount of the mud supply pump in the mud station and / or the drilling rig advance speed, so as to realize linkage control.
[0009] The Internet of Things (IoT) communication module is used to establish a real-time data transmission channel between the drilling rig and the mud station, transmitting the collected drilling rig operating parameters, casing parameters, and mud status parameters, as well as the control commands output by the linkage control module.
[0010] The visualization management module is used to display the drilling rig operating parameter curves, mud performance curves, and casing liquid level difference changes during the drilling process, and to generate digital construction logs.
[0011] In some embodiments, to provide a feasible sensing monitoring module, the sensing monitoring module includes:
[0012] Liquid level sensors installed inside and outside the casing are used to monitor the liquid level height inside and outside the casing in real time.
[0013] A pressure sensor installed inside the casing is used to monitor the pressure value inside the casing in real time.
[0014] The drilling pressure sensor, torque sensor and speed sensor installed on the drilling rig are used to monitor the drilling rig's operating parameters in real time.
[0015] Flow meters and viscometers installed at the mud station are used to monitor mud condition parameters in real time.
[0016] In some embodiments, to provide a feasible drill pressure sensor, rotation speed sensor, level sensor, and viscometer, the drill pressure sensor is a strain gauge sensor and / or a piezoelectric sensor; the rotation speed sensor is a magnetoelectric sensor and / or a photoelectric encoder; the level sensor is an ultrasonic level gauge or a radar level gauge; and the viscometer is a rotational viscometer.
[0017] In some embodiments, to provide a feasible coupling model between preset drilling rig conditions, mud properties, and casing head difference, the calculation formula for the coupling model between the preset drilling rig conditions, casing mud properties, and casing head difference includes:
[0018] ;
[0019] Where S refers to the pressure imbalance index, ΔH refers to the casing head difference, ΔH=L_in-L_out, L_in refers to the fluid level height inside the casing, L_out refers to the fluid level height outside the casing, P refers to the drilling pressure, ΔH_set refers to the set value of the casing head difference, P_normal refers to the expected drilling pressure benchmark value under the current hole depth and formation conditions, ρ_target refers to the target mud density, ρ_hole refers to the mud density inside the casing, L_set refers to the set value of the fluid level inside the casing, α, β, γ and δ are weighting coefficients, α+β+γ+δ=1, and α>δ>β>γ.
[0020] In some embodiments, to facilitate control by subsequent linkage control modules, the preset coupling model between drilling rig conditions, casing mud properties, and casing head difference further includes:
[0021] When the pressure imbalance index exceeds the corresponding preset threshold, a corresponding risk conclusion is drawn based on the currently input drilling rig operating parameters, casing parameters, and mud state parameters.
[0022] In some embodiments, to illustrate the corresponding risk conclusion, the corresponding risk conclusion includes the variation range of casing head difference and / or the variation range of drilling pressure and / or the current mud concentration inside the casing.
[0023] In some embodiments, to provide a feasible method for calculating the variation range of casing head difference, the variation range of drilling pressure, and the current mud concentration in the casing, the variation range of casing head difference is calculated based on the liquid level height inside and outside the casing at the previous moment and the liquid level height inside and outside the casing at the current moment, and the rate of change of liquid level inside the casing is also obtained.
[0024] The range of drilling pressure variation is calculated based on the drilling pressure at the previous moment and the drilling pressure at the current moment.
[0025] The current mud concentration inside the casing is calculated based on the current pressure value inside the casing and the location of the pressure value being collected.
[0026] In some embodiments, to provide a feasible linkage control module, the linkage control module outputs corresponding control commands based on the information from the edge computing module, including:
[0027] The notification from the edge computing module includes corresponding risk conclusions;
[0028] The corresponding control commands include querying a preset linkage control strategy table based on the corresponding risk conclusion to obtain the corresponding linkage control strategy, and using a fuzzy control algorithm or an adaptive algorithm to calculate the drilling rig advance speed adjustment amount and / or mud flow rate adjustment amount and / or mud concentration adjustment amount.
[0029] In some embodiments, to provide a feasible method for calculating the drilling rig advance speed adjustment amount, mud flow rate adjustment amount, and mud concentration adjustment amount, the method for calculating the drilling rig advance speed adjustment amount is as follows: the drilling rig advance speed is calculated based on the drilling pressure change range and the preset formation resistance coefficient.
[0030] The method for calculating the mud flow rate adjustment is as follows: the pump speed of the mud supply pump is calculated based on the rate of change of the liquid level inside the casing and the preset casing volume.
[0031] The method for calculating the mud concentration adjustment amount is as follows: the feeding amount is calculated based on the mud state parameters of the mud station, the current mud density in the casing, and the preset sand layer characteristics.
[0032] In some embodiments, to provide a feasible method for optimizing and adjusting the corresponding parameters of the fuzzy control algorithm or adaptive algorithm in the linkage control module, then:
[0033] After outputting the corresponding control command, the linkage control module informs the edge computing module. Upon receiving the notification from the edge computing module that the parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted, the linkage control module optimizes and adjusts the parameters of its own fuzzy control algorithm or adaptive algorithm based on the received risk conclusions.
[0034] After receiving the corresponding control command from the linkage control module, the edge computing module calculates and judges in real time whether the pressure imbalance index has returned to the corresponding preset threshold range. If so, it continues to monitor; otherwise, it outputs the corresponding risk conclusion and informs the linkage control module that the corresponding parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted.
[0035] The beneficial effects of this invention are that, in this solution, the drilling rig and mud station are controlled collaboratively based on the collected drilling rig operating parameters, casing parameters, and mud state parameters. This avoids the problem of the drilling rig control system and mud pump station system operating independently, and eliminates the need for manual adjustments, thus facilitating users. It effectively eliminates the problem of borehole pressure imbalance caused by reliance on manual experience and response lag in traditional separate control modes. This automatic collaborative mechanism significantly improves borehole pressure balance, reducing the risk of borehole collapse; simultaneously, it enables precise dynamic control of mud usage, reducing the risk of mud spillage and environmental pollution. It also significantly reduces downtime caused by manual adjustments, substantially improving equipment utilization efficiency and construction progress. Attached Figure Description
[0036] Figure 1 This is a schematic system block diagram of an intelligent linkage control device for a rotary drilling rig-mud system based on the Internet of Things in an embodiment of the present invention.
[0037] Figure 2 This is a schematic diagram showing the installation positions of each sensor in the sensing and monitoring module of this invention.
[0038] Among them, 1 is a torque sensor, 2 is a drilling pressure sensor, 3 is a speed sensor, 4 is a flow meter, 5 is a viscometer, 6 is a liquid level sensor installed inside the casing, 7 is a pressure sensor installed inside the casing, 8 is a main motor current sensor, 9 is a main hoist height sensor, 10 is a liquid level sensor installed outside the casing, and 11 is a pressure sensor installed outside the casing. Detailed Implementation
[0039] The technical solution of the present invention will now be described in detail with reference to the embodiments and accompanying drawings.
[0040] like Figure 1 As shown in the embodiment of the present invention, an intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things is provided, comprising:
[0041] The sensor monitoring module is used to collect drilling rig operating parameters, casing parameters, and mud state parameters;
[0042] The edge computing module is used to analyze the collected drilling rig operating parameters, casing parameters, and mud state parameters in real time. Based on the preset coupling model between drilling rig operating conditions, mud performance in the casing, and water head difference in the casing, it calculates the pressure imbalance index in real time. When the pressure imbalance index exceeds the corresponding preset threshold, it notifies the linkage control module.
[0043] The linkage control module is used to output corresponding control commands based on the information provided by the edge computing module to automatically adjust the pump speed and / or feed amount of the mud supply pump in the mud station and / or the drilling rig advance speed, so as to realize linkage control.
[0044] The Internet of Things (IoT) communication module is used to establish a real-time data transmission channel between the drilling rig and the mud station, transmitting the collected drilling rig operating parameters, casing parameters, and mud status parameters, as well as the control commands output by the linkage control module.
[0045] The visualization management module is used to display the drilling rig operating parameter curves, mud performance curves, and casing liquid level difference changes during the drilling process, and to generate digital construction logs.
[0046] In the above embodiments, the sensor monitoring module collects drilling rig operating parameters, casing parameters, and mud state parameters respectively, performs unified edge calculations, and then implements linkage control. This integrates the previously separate drilling rig and mud station into a unified system for coordinated control. Because the computer performs real-time calculations and responses, the response speed is fast, effectively eliminating the problem of borehole pressure imbalance caused by reliance on manual experience and response lag in traditional separate control modes. This automatic coordination mechanism significantly improves borehole pressure balance, reducing the risk of borehole collapse; it also enables precise dynamic control of mud usage, reducing the risk of mud spillage and environmental pollution; and it greatly reduces downtime caused by manual adjustments, significantly improving equipment utilization efficiency and construction progress.
[0047] In some embodiments, to provide a feasible sensing monitoring module, the sensing monitoring module may include:
[0048] Liquid level sensors (including liquid level sensor 6 inside the casing and liquid level sensor 10 outside the casing) installed inside and outside the casing are used to monitor the liquid level height inside and outside the casing in real time.
[0049] The pressure sensor 7, installed inside the casing, is used to monitor the pressure value inside the casing in real time.
[0050] The drilling pressure sensor 2, torque sensor 1 and speed sensor 3 installed on the drilling rig are used to monitor the drilling rig's operating parameters in real time.
[0051] The flow meter 4 and viscometer 5 installed in the mud station are used to monitor the mud state parameters in real time.
[0052] It is understandable that the drilling rig and mud station may also include other sensors for monitoring the drilling rig and mud station, such as main motor current sensors and main winch height sensors. The purpose is to allow the aforementioned IoT-based intelligent linkage control device for the rotary drilling rig-mud system to obtain as much relevant operational information as possible about the drilling rig and mud station. The placement of each sensor is as follows: Figure 2 As shown, Figure 2 In addition, a pressure sensor 11 installed outside the casing has been added, which is also an important piece of relevant operating information. The value it collects can be combined with the pressure value inside the casing to diagnose the stability of the current borehole wall. In addition, a main motor current sensor 8 and a main winch height sensor 9 have also been added, which are both important pieces of relevant operating information in the drilling rig.
[0053] Among them, the drilling pressure sensor can be a strain gauge sensor and / or a piezoelectric sensor; the rotation speed sensor can be a magnetoelectric sensor and / or a photoelectric encoder; the liquid level sensor can be an ultrasonic liquid level gauge or a radar liquid level gauge; and the viscometer can be a rotational viscometer.
[0054] In some embodiments, to provide a feasible coupling model between preset drilling rig conditions, casing mud properties, and casing head difference, the calculation formula in the preset coupling model between drilling rig conditions, casing mud properties, and casing head difference may include:
[0055] ;
[0056] Where S refers to the pressure imbalance index, ΔH refers to the casing head difference, ΔH=L_in-L_out, L_in refers to the fluid level height inside the casing, L_out refers to the fluid level height outside the casing, P refers to the drilling pressure, ΔH_set refers to the set value of the casing head difference, P_normal refers to the expected drilling pressure benchmark value under the current hole depth and formation conditions, ρ_target refers to the target mud density, ρ_hole refers to the mud density inside the casing, L_set refers to the set value of the fluid level inside the casing, α, β, γ and δ are weighting coefficients, α+β+γ+δ=1, and α>δ>β>γ.
[0057] Here, ΔH_set, the set value of the casing head difference, can be dynamically set according to the formation conditions and groundwater level, and is generally taken as 1.5 to 2.5 meters; P_normal, the expected drill pressure benchmark value under the current hole depth and formation conditions, can be generated by fitting historical drilling data; L_set, the set value of the fluid level inside the casing, is based on the principle of preventing mud overflow, and is usually taken as 0.5 to 1.0 meters below the top opening of the casing; ρ_target, the target mud density, can be calculated in real time according to the actual situation, and is mainly related to the hole depth and the pressure difference between the inside and outside of the casing; α, β, γ and δ can be calibrated by the analytic hierarchy process or regression analysis of construction data, and the purpose of α > δ > β > γ is to reflect the principle of casing priority control.
[0058] Understandably, the pre-defined coupling model between drilling rig conditions, casing mud properties, and casing head difference requires coupling between these factors to inform the subsequent linkage control module which direction to adjust. Therefore, it's necessary to first identify whether a problem has occurred before determining its root cause. This section presents a calculation method for determining whether a problem has occurred.
[0059] In some embodiments, to facilitate control by the subsequent linkage control module, the preset coupling model between drilling rig conditions, casing mud properties, and casing head difference may further include:
[0060] When the pressure imbalance index exceeds the corresponding preset threshold, a corresponding risk conclusion is drawn based on the currently input drilling rig operating parameters, casing parameters, and mud state parameters.
[0061] Understandably, in the above embodiments, it is necessary to inform the linkage control module which direction the problem occurred in.
[0062] In addition, as can be seen from the above embodiments, the mud state parameters are the state parameters of the output mud of the mud station. However, in actual use, there are situations where drilling and mud filling are carried out simultaneously. Therefore, the input data in this coupled model should also include the mud state parameters.
[0063] The corresponding risk conclusions may include the variation range of the casing head difference and / or the variation range of the drilling pressure and / or the current mud concentration inside the casing.
[0064] The variation range of the water head difference in the casing can be calculated based on the liquid level height inside and outside the casing at the previous moment and the liquid level height inside and outside the casing at the current moment, and the rate of change of the liquid level inside the casing is also obtained.
[0065] The range of change in drilling pressure can be calculated based on the drilling pressure at the previous moment and the drilling pressure at the current moment;
[0066] The current mud concentration inside the casing can be calculated based on the current pressure value inside the casing and the location of the pressure measurement point. For example, the static pressure formula ρ=P / gh can be used, where ρ is the current mud density inside the casing, P is the current pressure value inside the casing, g is the acceleration due to gravity, and h is the height of the pressure measurement point from the bottom of the mud, i.e., the location of the pressure measurement point. Since the pressure measurement can be performed using a pressure sensor fixed inside the casing, and the location of the pressure sensor inside the casing is known in advance, the h value can be directly calculated.
[0067] The division of the previous moment can be set according to the actual situation, which will not be elaborated here.
[0068] In some embodiments, to provide a feasible linkage control module, the linkage control module may include the following when outputting corresponding control commands based on the information from the edge computing module:
[0069] The notification from the edge computing module includes corresponding risk conclusions;
[0070] The corresponding control commands include querying a preset linkage control strategy table based on the corresponding risk conclusion to obtain the corresponding linkage control strategy, and using a fuzzy control algorithm or an adaptive algorithm to calculate the drilling rig advance speed adjustment amount and / or mud flow rate adjustment amount and / or mud concentration adjustment amount.
[0071] Understandably, the linkage control strategy table here can be derived from expert experience and historical data analysis. For example, when the change in casing head difference shows a decrease of more than 15% and the change in drilling pressure shows an increase of more than 20%, priority should be given to ensuring borehole stability. The goal is to quickly restore the casing head difference to the preset safe value. Additionally, the stability of the current borehole wall can be diagnosed based on the pressure values inside and outside the casing.
[0072] The method for calculating the drilling rig advance speed adjustment can be as follows: the drilling rig advance speed is calculated based on the change range of drilling pressure and the preset formation resistance coefficient.
[0073] The method for calculating the mud flow rate adjustment is as follows: the pump speed of the mud supply pump is calculated based on the rate of change of the liquid level inside the casing and the preset casing volume.
[0074] The method for calculating the mud concentration adjustment amount is as follows: the feeding amount is calculated based on the mud state parameters of the mud station, the current mud density in the casing, and the preset sand layer characteristics.
[0075] Here, the mud state parameters of the mud station may include the outlet mud density and outlet mud flow velocity output from the mud station to the casing. The outlet mud flow velocity can be obtained by the flow meter installed in the mud station, while the outlet mud density can be obtained by the viscosity value of the viscometer installed in the mud station and then calculated according to the standard viscosity-density conversion curve.
[0076] In some embodiments, to provide a feasible method for optimizing and adjusting the corresponding parameters of the fuzzy control algorithm or adaptive algorithm in the linkage control module, then:
[0077] The linkage control module can, after outputting the corresponding control command, inform the edge computing module. When it receives the notification from the edge computing module that the corresponding parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted, it can optimize and adjust the corresponding parameters of its own fuzzy control algorithm or adaptive algorithm based on the received corresponding risk conclusion.
[0078] The edge computing module can calculate and determine in real time whether the pressure imbalance index has returned to the corresponding preset threshold range after receiving the corresponding control command from the linkage control module. If so, it will continue to monitor; otherwise, it will output the corresponding risk conclusion and inform the linkage control module that the corresponding parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted.
[0079] It is understandable that the optimization and adjustment schemes for the corresponding parameters of the aforementioned fuzzy control algorithm or adaptive algorithm are commonly used in existing technologies, and will not be detailed here. In the initial stage, the corresponding control commands can be used to control the drilling rig's advance speed, mud flow rate, and mud concentration by ±10% or ±5%.
[0080] Furthermore, in the above embodiments, the edge computing module can receive real-time data from the sensing and monitoring module with low latency via an industrial bus (such as a CAN bus or industrial Ethernet). The entire calculation process can be controlled within 50 milliseconds. The linkage control module can use an industrial programmable logic controller (PLC) to receive data from the edge computing module with low latency via an industrial real-time communication network (such as PROFINET). This ensures that the instruction latency between the linkage control module, the edge computing module, the drilling rig, and the mud station is within 20 milliseconds, enabling real-time communication with the IoT communication module. Simultaneously, execution feedback can be added between the linkage control module and the drilling rig and mud station, such as by incorporating cyclic scanning into the linkage control module. The cyclic scanning period is less than or equal to 5 millimeters to ensure the deterministic and real-time processing of control commands.
Claims
1. An intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things, characterized in that, include: The sensor monitoring module is used to collect drilling rig operating parameters, casing parameters, and mud state parameters; The edge computing module is used to analyze the collected drilling rig operating parameters, casing parameters, and mud state parameters in real time. Based on the preset coupling model between drilling rig operating conditions, mud performance in the casing, and water head difference in the casing, it calculates the pressure imbalance index in real time. When the pressure imbalance index exceeds the corresponding preset threshold, it notifies the linkage control module. The linkage control module is used to output corresponding control commands based on the information provided by the edge computing module to automatically adjust the pump speed and / or feed amount of the mud supply pump in the mud station and / or the drilling rig advance speed, so as to realize linkage control. The Internet of Things (IoT) communication module is used to establish a real-time data transmission channel between the drilling rig and the mud station, transmitting the collected drilling rig operating parameters, casing parameters, and mud status parameters, as well as the control commands output by the linkage control module. The visualization management module is used to display the drilling rig operating parameter curves, mud performance curves, and casing liquid level difference changes during the drilling process, and to generate digital construction logs.
2. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 1, characterized in that, The sensing and monitoring module includes: Liquid level sensors installed inside and outside the casing are used to monitor the liquid level height inside and outside the casing in real time. A pressure sensor installed inside the casing is used to monitor the pressure value inside the casing in real time. The drilling pressure sensor, torque sensor and speed sensor installed on the drilling rig are used to monitor the drilling rig's operating parameters in real time. Flow meters and viscometers installed at the mud station are used to monitor mud condition parameters in real time.
3. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 2, characterized in that, The drilling pressure sensor is a strain gauge sensor and / or a piezoelectric sensor; the rotation speed sensor is a magnetoelectric sensor and / or a photoelectric encoder; the liquid level sensor is an ultrasonic liquid level gauge or a radar liquid level gauge; and the viscometer is a rotational viscometer.
4. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 1, characterized in that, The coupling model between the preset drilling rig operating conditions, the mud properties inside the casing, and the water head difference in the casing includes the following calculation formulas: ； Where S refers to the pressure imbalance index, ΔH refers to the casing head difference, ΔH=L_in-L_out, L_in refers to the fluid level height inside the casing, L_out refers to the fluid level height outside the casing, P refers to the drilling pressure, ΔH_set refers to the set value of the casing head difference, P_normal refers to the expected drilling pressure benchmark value under the current hole depth and formation conditions, ρ_target refers to the target mud density, ρ_hole refers to the mud density inside the casing, L_set refers to the set value of the fluid level inside the casing, α, β, γ and δ are weighting coefficients, α+β+γ+δ=1, and α>δ>β>γ.
5. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 1, characterized in that, The pre-defined coupling model between drilling rig conditions, casing mud properties, and casing head difference also includes: When the pressure imbalance index exceeds the corresponding preset threshold, a corresponding risk conclusion is drawn based on the currently input drilling rig operating parameters, casing parameters, and mud state parameters.
6. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 5, characterized in that, The corresponding risk conclusions include the variation range of the casing head difference and / or the variation range of the drilling pressure and / or the current mud concentration inside the casing.
7. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 6, characterized in that, The variation range of the water head difference in the casing is calculated based on the liquid level height inside and outside the casing at the previous moment and the liquid level height inside and outside the casing at the current moment, and the rate of change of the liquid level inside the casing is also obtained. The range of drilling pressure variation is calculated based on the drilling pressure at the previous moment and the drilling pressure at the current moment. The current mud concentration inside the casing is calculated based on the current pressure value inside the casing and the location of the pressure value being collected.
8. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 7, characterized in that, The linkage control module, based on the information from the edge computing module, outputs corresponding control commands, including: The notification from the edge computing module includes corresponding risk conclusions; The corresponding control commands include querying a preset linkage control strategy table based on the corresponding risk conclusion to obtain the corresponding linkage control strategy, and using a fuzzy control algorithm or an adaptive algorithm to calculate the drilling rig advance speed adjustment amount and / or mud flow rate adjustment amount and / or mud concentration adjustment amount.
9. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in claim 8, characterized in that, The method for calculating the drilling rig advance speed adjustment is as follows: the drilling rig advance speed is calculated based on the drilling pressure change range and the preset formation resistance coefficient. The method for calculating the mud flow rate adjustment is as follows: the pump speed of the mud supply pump is calculated based on the rate of change of the liquid level inside the casing and the preset casing volume. The method for calculating the mud concentration adjustment amount is as follows: the mud state parameters of the mud station, the current mud density in the casing, and the preset sand layer characteristics are used to calculate the feeding amount.
10. The intelligent linkage control device for rotary drilling rig-mud system based on the Internet of Things as described in any one of claims 5-9, characterized in that, After outputting the corresponding control command, the linkage control module informs the edge computing module. Upon receiving the notification from the edge computing module that the parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted, the linkage control module optimizes and adjusts the parameters of its own fuzzy control algorithm or adaptive algorithm based on the received risk conclusions. After receiving the corresponding control command from the linkage control module, the edge computing module calculates and judges in real time whether the pressure imbalance index has returned to the corresponding preset threshold range. If so, it continues to monitor; otherwise, it outputs the corresponding risk conclusion and informs the linkage control module that the corresponding parameters of the fuzzy control algorithm or adaptive algorithm need to be adjusted.