Augmented-reality-based data monitoring method and system for rotary drilling rig

Abstract

Disclosed are an augmented-reality-based data monitoring method and system for a rotary drilling rig. The monitoring method comprises: using an EPEC controller and sensor data to perform real-time collection and transmission; outputting, via a communication module, matching data of real-time operation data and a pre-built virtual model to a HoloLens device worn by an operator and a remote device; providing remote expert assistance and annotation; and implementing virtual-real fusion and display. The monitoring method ensures real-time monitoring of the operation state of a rotary drilling rig. By combining an attention mechanism and a feature fusion structure, the accuracy of identifying the operation states of components of the rotary drilling rig is improved. By means of precise virtual-real overlay, the operating efficiency is improved, the error rate is reduced, and the safety and accuracy of operations are also enhanced.

Description

Augmented Reality-Based Data Monitoring Method and System for Rotary Drilling Rigs Technical Field

[0001] This invention belongs to the field of rotary drilling rig data monitoring technology, specifically relating to a rotary drilling rig data monitoring method and system based on augmented reality. Background Technology

[0002] Drilling rigs play a crucial role in mining, construction, and geological exploration. However, in traditional two-dimensional observation interfaces, data correlation is often chaotic and interactivity is poor, leading to low data processing efficiency. Furthermore, the operational status and maintenance management of drilling rigs typically rely on manual judgment, making early warning and maintenance of equipment malfunctions heavily dependent on experienced technicians. For newcomers, understanding and processing complex equipment information is challenging, and in actual operation, long hours and high-intensity work can easily lead to fatigue among maintenance personnel, resulting in missed or false diagnoses and increasing the risk of safety accidents. Technical issues

[0003] To address the aforementioned problems, the present invention aims to provide an augmented reality-based method and system for monitoring rotary drilling rig data. This system enables the identification of operational data and real-time matching with a virtual model, allowing operators to visually overlay real-time operational data with the virtual model on the actual rotary drilling rig. Furthermore, with the assistance of remote experts, it further improves operational efficiency and reduces error rates. Technical solutions

[0004] The present invention provides a data monitoring method for rotary drilling rigs based on augmented reality, comprising the following specific steps:

[0005] S1 Real-time Data Collection and Transmission: The data collection and transmission module collects and transmits the operating data of the rotary drilling rig in real time, providing accurate dynamic data support for subsequent augmented reality environments.

[0006] S2 Real-time matching of data with 3D virtual model: The data collection and transmission module (specifically the EPEC controller of the data collection and transmission module) identifies the operating status of each component of the rotary drilling rig, realizes the matching of real-time operating data with the pre-built virtual model, and outputs the real-time operating data and the pre-built virtual model matching data to the HoloLens device (optical see-through head-mounted display) worn by the operator and remote devices via the communication module.

[0007] S3 Remote Expert Assistance and Annotation: Remote experts communicate with operators via a communication module. Remote experts can view the overlay results of real-time operating data and virtual models, and interact with operators through video, voice, data feedback, and / or remote annotation to guide on-site operations, thereby improving efficiency and reducing error rates.

[0008] S4 Virtual-Real Fusion and Display: Operators use HoloLens devices to determine the world coordinate positions of the components of the real rotary drilling rig on the HoloLens device, and overlay real-time operating data, virtual models, and data feedback and / or remote annotation data at the location of the components to achieve virtual-real fusion.

[0009] Furthermore, it also includes step S5, which is as follows: when the operator needs to interact with the remote expert again, return to step S3.

[0010] Furthermore, in step S1, the data collection and transmission module is an EPEC controller integrated sensor that collects the operating data of the rotary drilling rig in real application scenarios, and uses 5G network and Modbus technology to transmit the operating data to the communication module in real time.

[0011] Furthermore, in step S2, the communication module uses WebRTC technology to achieve real-time remote communication, ensuring efficient interconnection between the EPEC controller and remote devices, and supporting low-latency, high-quality video and data stream transmission.

[0012] Furthermore, in step S2, the operating status of each component of the rotary drilling rig is identified by the improved YOLOv7-tiny image recognition algorithm.

[0013] Furthermore, the improved YOLOv7-tiny image recognition algorithm involves adding an attention mechanism to the original backbone framework, replacing the original ELAN module with an ELAN-ECA module (or ELAN-E module, E-ELAN module), employing a Bifpn structure for feature fusion in the feature fusion region, and again using the ELAN-ECA module for enhanced feature extraction during the feature fusion process. Finally, a cross-stage local network under the CSPAECA structure is used in the output layer for feature output detection.

[0014] Furthermore, in step S3, the operator wears a HoloLens device to communicate with a remote expert using a remote device.

[0015] Furthermore, in step S3, the operator uses the camera integrated into the HoloLens device to capture real-time images and video streams of the real-world scene, and interacts with remote experts through gesture interaction and voice interaction.

[0016] Furthermore, in step S4, after the operator uses the camera of the HoloLens device to acquire images of the actual rotary drilling rig components, the operator uses SLAM and anchor point technology to determine the world coordinate position of the components on the HoloLens device.

[0017] This invention relates to an augmented reality-based rotary drilling rig data monitoring system, comprising a data collection and transmission module, a communication module, a HoloLens device, and a remote device. The data collection and transmission module collects real-time operational data of the rotary drilling rig, identifies the operational status of various components, matches real-time operational data with a pre-built virtual model, and outputs the matching data via the communication module to the HoloLens device worn by the operator and the remote device. The HoloLens device communicates with the remote device via the communication module. Specifically, the operator uses the camera integrated into the HoloLens device to capture real-time images and video streams of the real-world scene, and interacts with a remote expert through gestures and voice. The remote expert views the overlay result of the real-time operational data and the virtual model, and interacts with the operator through video, voice, data feedback, and / or remote annotation to guide on-site operations. The operator uses the HoloLens device to determine the world coordinate position of the actual rotary drilling rig components within the HoloLens device, and overlays the real-time operational data, the virtual model, and the remote expert's data feedback and / or remote annotation data onto the component locations. Beneficial effects

[0018] This invention utilizes EPEC controllers and sensors to collect data in real time and achieves efficient transmission via 5G networks and Modbus technology, ensuring real-time monitoring of the rotary drilling rig's operating status. By employing an improved YOLOv7-tiny image recognition algorithm, combined with an attention mechanism and feature fusion structure, the accuracy of identifying the operating status of rotary drilling rig components is significantly enhanced. Operators wearing HoloLens devices can interact with remote experts in real time, improving operational efficiency and reducing error rates through precise overlay of virtual and real data. Simultaneously, the application of SLAM and anchor point technology allows the virtual model to be accurately superimposed onto the actual component positions of the rotary drilling rig, improving operational safety and accuracy. Overall, this invention not only improves the operational reliability of rotary drilling rigs but also enhances operational efficiency and optimizes their management. Attached Figure Description

[0019] Figure 1 is a flowchart of the augmented reality-based rotary drilling rig data monitoring method of the present invention.

[0020] Figure 2 is a structural block diagram of the rotary drilling rig data monitoring system based on augmented reality of the present invention. The best embodiment of the present invention

[0021] As shown in Figure 1, the augmented reality-based rotary drilling rig data monitoring method includes the following specific steps:

[0022] S1 Real-time Data Collection and Transmission: The data collection and transmission module collects and transmits the operating data of the rotary drilling rig in real time, providing accurate dynamic data support for subsequent augmented reality environments. Specifically, the data collection and transmission module integrates sensors with the EPEC controller to collect operating data of the rotary drilling rig in real-world application scenarios and transmits this data to the communication module in real time using 5G networks and Modbus technology. The communication module uses WebRTC technology to achieve real-time remote communication, ensuring efficient interconnection between the EPEC controller and remote devices, and supporting low-latency, high-quality video and data stream transmission. Operating data includes: current drilling depth, working winch speed, pressurized winch speed, mud or clean water flow rate, bus voltage, motor current, power head motor speed, motor temperature, hydraulic oil temperature, single pile power consumption, total power consumption, main pump pressure, pilot oil pressure, mast tilt angle, and alarm data. Alarm data includes controller communication failure, remote control communication failure, remote control handle signal failure, handle signal failure, frequency converter communication failure, rotary encoder failure, wireless communication module communication failure, and background controller communication failure. Bus voltage, motor current, single pile power consumption, and total power consumption can be calculated by detecting various signals from the EPEC controller, or can be detected using voltmeters, ammeters, and multimeters. Alarm data is obtained by detecting various signals from the EPEC controller. All other operating data can be detected by integrated sensors.

[0023] The data collection and transmission module consists of an EPEC controller integrating multiple sensors for distance (drilling depth detection), speed, temperature, pressure, vibration, flow rate, rotational speed, and tilt angle. High-precision data acquisition is ensured via a standard RS-485 serial interface. Data is encoded using the Modbus protocol, which is based on a master-slave architecture. The EPEC controller, as the master device, requests data from each sensor periodically or on demand, while the sensors, as slave devices, provide measurement values. Internally, the EPEC controller converts this physical data into a standardized format, transmits it in real-time via Modbus TCP / IP, and then sends it to the communication module via a 5G network.

[0024] The communication module works as follows: A Node.js-based signaling server is established via WebRTC, sending a message to the remote expert on the remote device. The remote expert receives the proposal, sets a remote description, responds with an answer, and sends it back to the operator via the signaling server. Both ends then coordinate with a STUN / TURN server to collect and exchange ICE candidates, including network connection information. After ICE candidate confirmation, a point-to-point connection is established between the operator and the remote expert, allowing media streams to be transmitted directly between them, enabling real-time audio and video communication. Text, images, and other data communication are achieved through WebRTC's Data Channel functionality.

[0025] S2 Real-time matching of data with 3D virtual model: The data collection and transmission module identifies the operating status of each component of the rotary drilling rig, realizes the matching of real-time operating data with the pre-built virtual model, and outputs the real-time operating data and the pre-built virtual model matching data to the HoloLens device worn by the operator and remote devices via the communication module.

[0026] Specifically, the improved YOLOv7-tiny image recognition algorithm is used to identify the operating status of various components of a rotary drilling rig. An attention mechanism is added to the original backbone framework, and the original ELAN module is replaced with an ELAN-ECA module. A Bifpn structure is used for feature fusion in the feature fusion region, and the ELAN-ECA module is also used to enhance feature extraction during the feature fusion process. Finally, a cross-stage local network under the CSPAECA structure is used for feature output detection at the output layer.

[0027] Specifically, in the data collection and transmission module, the improved YOLOv7-tiny model is run in real time to input data. An attention mechanism and the ELAN-ECA module are introduced into this model, effectively improving the feature extraction accuracy of various components of the rotary drilling rig. The model then uses a Bifpn structure to fuse the extracted features. The Bifpn structure allows for bidirectional feature fusion at multiple scales, ensuring reasonable integration of multi-scale features and enhancing the ability to identify components of different sizes. The fused information is further optimized by the ELAN-ECA module, improving the recognition effect of components with small targets or complex backgrounds. Finally, the output layer uses a CSPAECA structure combined with a cross-stage local network for target detection and status judgment, matching the identified operating data of various rotary drilling rig components with a pre-built virtual model and feeding it back in real time to the HoloLens device and remote devices, thereby providing operators with real-time equipment operating status and accurate component information.

[0028] S3 Remote Expert Assistance and Annotation: Remote experts communicate with operators via a communication module. Remote experts can view the overlay results of real-time operating data and virtual models, and interact with operators through video, voice, data feedback, and / or remote annotation to guide on-site operations, thereby improving efficiency and reducing error rates.

[0029] Specifically, after the communication module establishes stable communication via WebRTC, the operator uses the HoloLens device to overlay real-time running data with pre-built virtual model matching data, providing intuitive operational feedback. In the Unity 3D development platform, through the fusion of the virtual model and real-time running data, remote experts can clearly see the current operating status of the working component and identify potential problems. Remote experts can communicate with the operator in real-time via video and voice, providing operational guidance or technical support. In the Unity environment, the remote expert first selects 2D coordinates and shapes on the Canvas for annotation. This annotation information is then transmitted to the HoloLens device in real-time via the WebRTC Data Channel function. After receiving these 2D coordinates, the HoloLens device analyzes the data and the camera spatial matrix data at the annotation time point to calculate the camera position C1 at the annotation time, and calculates the position C2 of the projection surface annotation point based on the aspect ratio of the Canvas. After completing the positioning at position C2, a ray is emitted from camera position C1 to C2, and the HoloLens device's collision detection function is used to search for the intersection point (position C3) between the ray and the virtual model or actual component. Once C3 is identified, the HoloLens device will register the annotation at that location. By annotating the virtual model in real time (such as with arrows, boxes, or text annotations), remote experts can precisely guide on-site personnel to perform operations, while providing real-time data feedback and suggestions for improvement through remote annotation.

[0030] S4 Virtual-Real Fusion and Display: Operators use HoloLens devices to determine the world coordinate positions of the components of the real rotary drilling rig on the HoloLens device, and overlay real-time operating data, virtual models, and data feedback and / or remote annotation data at the location of the components to achieve virtual-real fusion.

[0031] Specifically, the process is as follows: First, the operator uses the HoloLens device's camera to acquire images of the components of the actual rotary drilling rig. Then, using the HoloLens's SLAM technology, a 3D map of the environment is constructed through real-time scanning, simultaneously determining the HoloLens device's position and orientation. Combined with the HoloLens's spatial anchoring technology, components are identified through target detection, and their specific 2D coordinates within the component image are determined. These 2D coordinates are then converted to HoloLens camera coordinates, and further converted to HoloLens world position coordinates. Collision detection is used to search for virtual anchor points generated on the components of the actual rotary drilling rig, ensuring the virtual model is precisely aligned with them. After the operator wears the HoloLens device, the virtual model is displayed in their field of view, synchronized with the components of the actual rotary drilling rig. Utilizing SLAM and anchoring technology, the virtual model updates its position in real-time, maintaining precise alignment with the actual rotary drilling rig and ensuring that the virtual model is correctly superimposed on the actual rig from any angle. Operators can interact directly with the virtual model during operation, such as rotating, zooming in and out, or viewing the specific operating status of the rotary drilling rig. Embodiments of the present invention

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Example

[0033] As shown in Figure 1, the augmented reality-based rotary drilling rig data monitoring method includes the following specific steps:

[0034] S1 Real-time Data Collection and Transmission: The data collection and transmission module collects and transmits the operating data of the rotary drilling rig in real time, providing accurate dynamic data support for subsequent augmented reality environments. Specifically, the data collection and transmission module integrates sensors with the EPEC controller to collect operating data of the rotary drilling rig in real-world application scenarios and transmits this data to the communication module in real time using 5G networks and Modbus technology. The communication module uses WebRTC technology to achieve real-time remote communication, ensuring efficient interconnection between the EPEC controller and remote devices, and supporting low-latency, high-quality video and data stream transmission. Operating data includes: current drilling depth, working winch speed, pressurized winch speed, mud or clean water flow rate, bus voltage, motor current, power head motor speed, motor temperature, hydraulic oil temperature, single pile power consumption, total power consumption, main pump pressure, pilot oil pressure, mast tilt angle, and alarm data. Alarm data includes controller communication failure, remote control communication failure, remote control handle signal failure, handle signal failure, frequency converter communication failure, rotary encoder failure, wireless communication module communication failure, and background controller communication failure. Bus voltage, motor current, single pile power consumption, and total power consumption can be calculated by detecting various signals from the EPEC controller, or can be detected using voltmeters, ammeters, and multimeters. Alarm data is obtained by detecting various signals from the EPEC controller. All other operating data can be detected by integrated sensors.

[0035] The data collection and transmission module consists of an EPEC controller integrating multiple sensors for distance (drilling depth detection), speed, temperature, pressure, vibration, flow rate, rotational speed, and tilt angle. High-precision data acquisition is ensured via a standard RS-485 serial interface. Data is encoded using the Modbus protocol, which is based on a master-slave architecture. The EPEC controller, as the master device, requests data from each sensor periodically or on demand, while the sensors, as slave devices, provide measurement values. Internally, the EPEC controller converts this physical data into a standardized format, transmits it in real-time via Modbus TCP / IP, and then sends it to the communication module via a 5G network.

[0036] The communication module works as follows: A Node.js-based signaling server is established via WebRTC, sending a message to the remote expert on the remote device. The remote expert receives the proposal, sets a remote description, responds with an answer, and sends it back to the operator via the signaling server. Both ends then coordinate with a STUN / TURN server to collect and exchange ICE candidates, including network connection information. After ICE candidate confirmation, a point-to-point connection is established between the operator and the remote expert, allowing media streams to be transmitted directly between them, enabling real-time audio and video communication. Text, images, and other data communication are achieved through WebRTC's Data Channel functionality.

[0037] S2 Real-time matching of data with 3D virtual model: The data collection and transmission module identifies the operating status of each component of the rotary drilling rig, realizes the matching of real-time operating data with the pre-built virtual model, and outputs the real-time operating data and the pre-built virtual model matching data to the HoloLens device worn by the operator and remote devices via the communication module.

[0038] Specifically, the improved YOLOv7-tiny image recognition algorithm is used to identify the operating status of various components of a rotary drilling rig. An attention mechanism is added to the original backbone framework, and the original ELAN module is replaced with an ELAN-ECA module. A Bifpn structure is used for feature fusion in the feature fusion region, and the ELAN-ECA module is also used to enhance feature extraction during the feature fusion process. Finally, a cross-stage local network under the CSPAECA structure is used for feature output detection at the output layer.

[0039] Specifically, in the data collection and transmission module, the improved YOLOv7-tiny model is run in real time to input data. An attention mechanism and the ELAN-ECA module are introduced into this model, effectively improving the feature extraction accuracy of various components of the rotary drilling rig. The model then uses a Bifpn structure to fuse the extracted features. The Bifpn structure allows for bidirectional feature fusion at multiple scales, ensuring reasonable integration of multi-scale features and enhancing the ability to identify components of different sizes. The fused information is further optimized by the ELAN-ECA module, improving the recognition effect of components with small targets or complex backgrounds. Finally, the output layer uses a CSPAECA structure combined with a cross-stage local network for target detection and status judgment, matching the identified operating data of various rotary drilling rig components with a pre-built virtual model and feeding it back in real time to the HoloLens device and remote devices, thereby providing operators with real-time equipment operating status and accurate component information.

[0040] S3 Remote Expert Assistance and Annotation: Remote experts communicate with operators via a communication module. Remote experts can view the overlay results of real-time operating data and virtual models, and interact with operators through video, voice, data feedback, and / or remote annotation to guide on-site operations, thereby improving efficiency and reducing error rates.

[0041] Specifically, after the communication module establishes stable communication via WebRTC, the operator uses the HoloLens device to overlay real-time running data with pre-built virtual model matching data, providing intuitive operational feedback. In the Unity 3D development platform, through the fusion of the virtual model and real-time running data, remote experts can clearly see the current operating status of the working component and identify potential problems. Remote experts can communicate with the operator in real-time via video and voice, providing operational guidance or technical support. In the Unity environment, the remote expert first selects 2D coordinates and shapes on the Canvas for annotation. This annotation information is then transmitted to the HoloLens device in real-time via the WebRTC Data Channel function. After receiving these 2D coordinates, the HoloLens device analyzes the data and the camera spatial matrix data at the annotation time point to calculate the camera position C1 at the annotation time, and calculates the position C2 of the projection surface annotation point based on the aspect ratio of the Canvas. After completing the positioning at position C2, a ray is emitted from camera position C1 to C2, and the HoloLens device's collision detection function is used to search for the intersection point (position C3) between the ray and the virtual model or actual component. Once C3 is identified, the HoloLens device will register the annotation at that location. By annotating the virtual model in real time (such as with arrows, boxes, or text annotations), remote experts can precisely guide on-site personnel to perform operations, while providing real-time data feedback and suggestions for improvement through remote annotation.

[0042] S4 Virtual-Real Fusion and Display: Operators use HoloLens devices to determine the world coordinate positions of the components of the real rotary drilling rig on the HoloLens device, and overlay real-time operating data, virtual models, and data feedback and / or remote annotation data at the location of the components to achieve virtual-real fusion.

[0043] Specifically, the process is as follows: First, the operator uses the HoloLens device's camera to acquire images of the components of the actual rotary drilling rig. Then, using the HoloLens's SLAM technology, a 3D map of the environment is constructed through real-time scanning, simultaneously determining the HoloLens device's position and orientation. Combined with the HoloLens's spatial anchoring technology, components are identified through target detection, and their specific 2D coordinates within the component image are determined. These 2D coordinates are then converted to HoloLens camera coordinates, and further converted to HoloLens world position coordinates. Collision detection is used to search for virtual anchor points generated on the components of the actual rotary drilling rig, ensuring the virtual model is precisely aligned with them. After the operator wears the HoloLens device, the virtual model is displayed in their field of view, synchronized with the components of the actual rotary drilling rig. Utilizing SLAM and anchoring technology, the virtual model updates its position in real-time, maintaining precise alignment with the actual rotary drilling rig and ensuring that the virtual model is correctly superimposed on the actual rig from any angle. Operators can interact directly with the virtual model during operation, such as rotating, zooming in and out, or viewing the specific operating status of the rotary drilling rig. Example

[0044] As shown in Figure 2, the augmented reality-based rotary drilling rig data monitoring system includes a data collection and transmission module, a communication module, a HoloLens device, and a remote device. The data collection and transmission module collects the operating data of the rotary drilling rig in real time, identifies the operating status of the rotary drilling rig and its various components, matches the real-time operating data with a pre-built virtual model, and outputs the real-time operating data to the HoloLens device worn by the operator via the communication module. The matching data between the real-time operating data and the pre-built virtual model is also output to the remote device via the communication module. The HoloLens device communicates with remote devices via a communication module. Specifically, the operator uses the camera integrated into the HoloLens device to capture real-time images and video streams of the real-world scene, and interacts with remote experts through gestures and voice. The remote expert views the overlay result of real-time operating data and the virtual model, and interacts with the operator through video, voice, data feedback, and / or remote annotation to guide on-site operations. The operator uses the HoloLens device to determine the world coordinate position of the components of the real rotary drilling rig on the HoloLens device, and overlays the real-time operating data, the virtual model, and the data feedback and / or remote annotation data from the remote expert at the location of the component. Industrial applicability

[0045] Type the industrial utility description paragraph here. Sequence List Free Content

[0046] Type the free content description paragraph for the sequence list here.

Claims

1. A data monitoring method for rotary drilling rigs based on augmented reality, characterized in that: Includes the following steps: S1 collects and transmits the operating data of the rotary drilling rig in real time through the data collection and transmission module; The S2 data collection and transmission module identifies the operating status of each component of the rotary drilling rig, realizes the matching of real-time operating data with the pre-built virtual model, and outputs the real-time operating data and the matching data of the pre-built virtual model to the HoloLens device worn by the operator and remote devices via the communication module. S3 remote experts communicate with operators via a communication module. Remote experts can view the overlay results of real-time operating data and virtual models, and interact with operators through video, voice, data feedback and / or remote annotation to guide on-site operations. S4 operators use HoloLens devices to determine the world coordinate positions of components of the real rotary drilling rig on the HoloLens device, and overlay real-time operating data, virtual models, and data feedback and / or remote annotation data on the location of the components to achieve virtual-real fusion.

2. The augmented reality-based rotary drilling rig data monitoring method according to claim 1, characterized in that: It also includes step S5: when the operator needs to interact with the remote expert again, return to step S3.

3. The augmented reality-based rotary drilling rig data monitoring method according to claim 1 or 2, characterized in that: In step S1, the data collection and transmission module is an EPEC controller integrated sensor that collects the operating data of the rotary drilling rig in real application scenarios and uses 5G network and Modbus technology to transmit the operating data to the communication module in real time.

4. The augmented reality-based rotary drilling rig data monitoring method according to claim 1 or 2, characterized in that: In step S2, the communication module uses WebRTC technology to achieve real-time remote communication.

5. The augmented reality-based rotary drilling rig data monitoring method according to claim 1 or 2, characterized in that: In step S2, the operating status of each component of the rotary drilling rig is identified using the improved YOLOv7-tiny image recognition algorithm.

6. The augmented reality-based rotary drilling rig data monitoring method according to claim 5, characterized in that: The improved YOLOv7-tiny image recognition algorithm is as follows: an attention mechanism is added to the original backbone network framework, the original ELAN module is replaced with an ELAN-ECA module, a Bifpn structure is used for feature fusion in the feature fusion region, and an ELAN-ECA module is also used to enhance feature extraction during the feature fusion process; finally, a cross-stage local network under the CSPAECA structure is used in the output layer for feature output detection.

7. The augmented reality-based rotary drilling rig data monitoring method according to claim 1 or 2, characterized in that: In step S3, the operator wears a HoloLens device to communicate with a remote expert using a remote device.

8. The augmented reality-based rotary drilling rig data monitoring method according to claim 7, characterized in that: In step S3, the operator uses the camera integrated into the HoloLens device to capture real-time images and video streams of the real-world scene, and interacts with remote experts through gesture interaction and voice interaction.

9. The augmented reality-based rotary drilling rig data monitoring method according to claim 7, characterized in that: In step S4, after the operator uses the camera of the HoloLens device to acquire images of the components of the actual rotary drilling rig, the operator uses SLAM and anchor point technology to determine the world coordinate position of the components on the HoloLens device.

10. An augmented reality-based rotary drilling rig data monitoring system, comprising a data collection and transmission module, a communication module, and remote equipment, characterized in that, It also includes a HoloLens device; the data collection and transmission module collects the operating data of the rotary drilling rig in real time, identifies the operating status of each component of the rotary drilling rig, matches the real-time operating data with a pre-built virtual model, and outputs the matching data of the real-time operating data and the pre-built virtual model to the HoloLens device and the remote device via the communication module; the HoloLens device communicates with the remote device via the communication module, specifically: using the camera integrated on the HoloLens device to capture real-time images and video streams in the real scene, and interacting with the remote device through gesture interaction and voice interaction; viewing the superposition result of the real-time operating data and the virtual model through the remote device, and interacting with the HoloLens device through video, voice, data feedback and / or remote annotation methods to guide on-site operation; using the HoloLens device to determine the world coordinate position of the components of the real rotary drilling rig on the HoloLens device, and superimposing the real-time operating data, the virtual model and the data feedback and / or remote annotation data of the remote device at the location of the component.

Images