Coal mine equipment cooperative control system based on digital twinning

Abstract

The invention relates to a coal mine equipment cooperative control system based on digital twinning, and belongs to the field of coal mine intellectualization. According to the system, the equipment state, environmental parameters and fault data are acquired in real time through the data acquisition layer; the data processing layer realizes multi-source data fusion and fault prediction by using a deep fusion neural network and a space-time attention network; the digital twinborn layer constructs a three-dimensional dynamic model based on unified geodetic coordinate system coupling equipment and geodetic survey information; and the control layer adopts a distributed stream computing framework to execute collaborative scheduling, active disturbance rejection speed control and fault prediction compensation. The problems that in the prior art, spatial position expression is missing, collaborative delay is high, data are not synchronous, and complex working condition adaptability is poor are solved. The method has the beneficial effects that total-factor high-precision space coupling is realized, the collaborative delay bottleneck is overcome, the adaptability to complex working conditions is enhanced, a closed-loop intelligent optimization system is constructed, and full-chain transparent management and control of the coal mine are promoted.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent coal mines and relates to a coal mine equipment collaborative control system based on digital twins. Background Art

[0002] Intelligent coal mines are the core technological support for the high-quality development of the coal industry. As a multidisciplinary, multi-scale, and multi-physical technology, coal mine digital twin technology has broad application prospects in the field of coal mining. This technology can monitor and simulate physical systems in real time in a virtual environment and simultaneously map the status information of the real system. However, current coal mine digital twin technology still has significant shortcomings:

[0003] Core Issue 1: Lack of Spatial Location Expression and Insufficient Data Collaboration

[0004] The existing system lacks the expression of spatial position relationships based on a unified geodetic coordinate system, which results in the inability to effectively couple equipment operation data with geological survey information and weak three-dimensional dynamic visualization capabilities. For example:

[0005] In fully mechanized mining operations, the coordinated control capabilities of the shearer, scraper conveyor, and hydraulic support are poor;

[0006] The coal-rock identification accuracy is low, making it difficult to adapt to the collaborative constraint relationship under complex coal seam conditions;

[0007] The dust concentration in the excavation working area is high, visibility is low, and spray interference is severe. The video acquisition system cannot intuitively reflect the environment and equipment status.

[0008] Core Issue 2: Collaboration Delay and Data Asynchrony

[0009] The coordination delay of existing equipment collaborative control systems generally exceeds 500 milliseconds, and the data synchronization rate is higher than 15%, which seriously restricts the system response speed and control accuracy. For example:

[0010] The three-machine collaborative control method proposed in patent CN118704951A has deviations in cutting trajectory planning due to insufficient accuracy in digital working surface construction;

[0011] The monitoring system of patent CN113610290A relies on a group of sensors to collect data, but incomplete or distorted information causes the digital twin to deviate from the actual device status.

[0012] Core Issue 3: Control Failure under Complex Working Conditions

[0013] Under complex conditions such as sudden changes in coal seam thickness, hardness, or fluctuations in gas concentration:

[0014] Existing systems have difficulty processing the dynamic coupling relationship between device posture, velocity, and environmental parameters in real time;

[0015] The lack of real-time and stability of data transmission affects the accurate execution of control instructions.

[0016] In summary, there is an urgent need for a coal mine equipment collaborative control system that can achieve high-precision spatial coupling, low-latency collaborative control and adapt to complex working conditions. Summary of the Invention

[0017] Prior art collaborative control systems for coal mining equipment suffer from significant coordination delays, large data asynchrony rates, and a lack of spatial positional relationship representation based on a unified geodetic coordinate system. To address these issues, the present invention provides a collaborative control system for coal mining equipment based on digital twins.

[0018] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a coal mine equipment collaborative control system based on digital twins, which includes a data acquisition layer, a data processing layer, a digital twin layer and a control layer.

[0019] The data acquisition layer includes multiple sensors and data acquisition units. It also incorporates a sensor fault diagnosis and dynamic switching mechanism. When the primary sensor fails, the system automatically activates a backup sensor group and uses a data fusion unit to weight the redundant data with confidence, enabling real-time collection of operational status data, environmental parameter data, and fault diagnosis data for coal mine equipment. Operational status data includes information such as the equipment's position, posture, and speed; environmental parameter data includes coal seam geological data, roadway development data, and cut-hole development data; and fault diagnosis data includes equipment vibration signals, acoustic signals, and visual detection data.

[0020] The data processing layer includes a data preprocessing unit, a data fusion unit, and a data analysis unit. The data preprocessing unit filters, calibrates, and standardizes the collected raw data; the data fusion unit uses a deep fusion neural network to fuse different types of high-resolution images, complex sound signals, and multi-dimensional sensor data; and the data analysis unit uses a spatiotemporal attention network to analyze time series data, identify long-term dependencies between devices, and predict faults.

[0021] The digital twin layer constructs a virtual simulation environment, including a digital coal seam slice database, integrated with real-time geological radar scanning data streams, and a coal seam dynamic change prediction model. This model predicts resistance changes along the next five-meter cutting path based on coupled analysis of cutting vibration signals and geological data. It also includes a historical cutting posture database and a digital working face database. Through digital twin technology, real-time equipment status data is mapped to the digital model in the virtual environment, enabling the coupling of equipment and geodetic information and the dynamic expression of three-dimensional data.

[0022] The control layer is built using a distributed stream computing framework (Kafka + Flink) and includes a collaborative control unit, a speed control unit, and a fault prediction control unit. The collaborative control unit, based on a digital working face database, implements coordinated dispatch control of the three machines. It also embeds a safety domain constraint controller: when the coupling between the hydraulic support support force and gas concentration exceeds the limit, an emergency stop protocol is automatically triggered and an obstacle avoidance path is generated. The speed control unit uses an anti-disturbance control system to precisely control the equipment speed. The fault prediction control unit uses a device state prediction and compensation algorithm to adjust the control strategy in real time.

[0023] The system also features a learning and optimization module for analyzing and optimizing system operating data. This module, comprised of a machine learning unit and an artificial intelligence decision-making unit, optimizes control rules and fault diagnosis strategies by learning and analyzing large amounts of historical data.

[0024] During system operation, the data acquisition layer collects real-time data on device status and environmental parameters. After preprocessing and fusion analysis at the data processing layer, the data is sent to the digital twin layer for real-time updates. Based on this real-time data, the digital twin layer dynamically updates the state of the virtual environment. The control layer uses this real-time digital twin data to implement coordinated device control and fault prediction, and performs system optimization through a learning optimization module.

[0025] The beneficial effects of the present invention are:

[0026] 1. A digital twin layer is constructed based on a unified geodetic coordinate system, deeply coupling real-time equipment operating data, geological survey information, and a 3D dynamic model for the first time. This overcomes the limitations of traditional systems that lack spatial position representation, enabling precise mapping of key parameters such as shearer posture and scraper conveyor straightness to the coal seam's geological structure. This provides a realistic and reliable virtual environment for adaptive mining in complex coal seams.

[0027] 2. Utilizing the OPCUA data mapping model and superimposing the device state prediction and compensation algorithm, we reconstructed a real-time data update mechanism. Through the efficient data pipeline of the distributed stream computing framework, we completely addressed the shortcomings of traditional systems, such as high collaborative latency and large data asynchrony rates, significantly improving the response speed and execution accuracy of the three-machine collaborative control.

[0028] 3. A deep fusion neural network and a spatiotemporal attention network collaborate to process multi-source heterogeneous data, effectively counteracting the effects of interference from dust and spray on images and sensor signals from the tunneling face. Combined with an active disturbance rejection speed controller and a fuzzy adaptive algorithm, this system achieves robust position synchronization and speed control under harsh conditions such as sudden changes in coal seam thickness and fluctuating gas concentrations.

[0029] 4. The learning optimization module uses machine learning and AI-powered decision-making to continuously analyze historical operating data and dynamically modify control rules. This forms a closed-loop system of "data acquisition - twin mapping - collaborative control - autonomous evolution," significantly improving fault prediction accuracy and long-term system stability, providing technical support for reducing staff and increasing efficiency in hazardous operating areas of coal mines.

[0030] 5. Digital representation of the entire lifecycle, from geological exploration data to equipment operating status, achieves for the first time "geological transparency, equipment transparency, and process transparency" in fully mechanized mining faces. This provides a unified operating platform for monitoring, simulation, and optimization for intelligent coal mines, laying the technical foundation for high-quality development in the coal industry.

[0031] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0033] Figure 1 This is a system diagram of the present invention. DETAILED DESCRIPTION

[0034] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0035] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0036] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0037] like Figure 1 As shown in the figure, this system adopts a layered closed-loop architecture, which specifically includes the following core modules and data flows:

[0038] 1. Data Collection Layer

[0039] The physical perception network is composed of multiple types of sensors, including:

[0040] Spatial perception equipment: LiDAR (coal mining machine posture scanning), gyroscope (scraper conveyor straightness monitoring);

[0041] Geological detection equipment: coal seam thickness sensor, gas concentration sensor;

[0042] Fault diagnosis equipment: acoustic sensors (tool wear identification), vibration sensors (mechanical anomaly detection);

[0043] The data acquisition unit transmits raw data in real time via industrial Ethernet / CAN bus;

[0044] 2. Data processing layer

[0045] Three-stage data processing pipeline:

[0046] (1) Data preprocessing unit: Kalman filtering / wavelet transform is used to eliminate noise interference;

[0047] (2) Data fusion unit: fuses multispectral images and multidimensional sensor data through deep convolutional neural networks;

[0048] (3) Data analysis unit: Analyzes the long-term dependencies of device operations based on spatiotemporal attention networks;

[0049] 3. Digital Twin Layer

[0050] Core database cluster:

[0051] Digital coal seam slice database (storing geological structure data);

[0052] Historical cutting posture database (recording the historical trajectory of the equipment);

[0053] Digital working surface database (3D dynamic model carrier);

[0054] Realize real-time equipment-geology mapping (accuracy <0.1m) through a unified geodetic coordinate system;

[0055] 4. Control layer

[0056] The distributed stream computing framework (Kafka + Flink) supports real-time control:

[0057] Collaborative control unit: performs fuzzy PID synchronization of three-machine posture (response <100ms);

[0058] Speed ​​control unit: Active disturbance rejection controller dynamically adjusts the cutting speed;

[0059] Fault prediction control unit: Bayesian network diagnosis of equipment abnormalities;

[0060] 5. Learning Optimization Module

[0061] Two-way intelligent optimization mechanism:

[0062] Machine Learning Unit: Analyze historical data to discover control laws;

[0063] Artificial intelligence decision-making unit: Generates optimization strategies and feeds them back to the digital twin layer and control layer.

[0064] Forward control flow: acquisition layer → processing layer → twin layer → control layer → actuator (coal mining machine / hydraulic support, etc.)

[0065] Closed-loop optimization flow: Control layer operation data → Learning optimization module → Dynamic update of twin model and control rules

[0066] This architecture enables real-time interaction between the physical world and virtual models, providing adaptive collaborative control capabilities for complex coal mine working conditions.

[0067] Example 1

[0068] The present invention provides a coal mine equipment collaborative control system based on digital twins, which includes a data acquisition layer, a data processing layer, a digital twin layer and a control layer.

[0069] The data acquisition layer includes multiple sensors and data acquisition units, along with a sensor fault diagnosis and dynamic switching mechanism. When a primary sensor (such as a lidar) fails, the system automatically activates a backup sensor group (such as an ultrasonic array) and uses a data fusion unit to weight the redundant data. This layer collects real-time operational status data, environmental parameter data, and fault diagnosis data for coal mining equipment. Operational status data includes the position of the shearer, drum position, scraper conveyor straightness, and hydraulic support position; environmental parameter data includes coal seam thickness, hardness, and permeability; and fault diagnosis data includes vibration signals and current consumption curves from the shearer.

[0070] The data processing layer includes a data preprocessing unit, a data fusion unit, and a data analysis unit. The data preprocessing unit filters, calibrates, and standardizes the collected raw data; the data fusion unit uses a deep fusion neural network to fuse different types of high-resolution images, complex sound signals, and multi-dimensional sensor data; and the data analysis unit uses a spatiotemporal attention network to analyze time series data, identify long-term dependencies between devices, and predict faults.

[0071] The digital twin layer constructs a virtual simulation environment, including a digital coal seam slice database, integrated with real-time geological radar scanning data streams, and a coal seam dynamic change prediction model. This model predicts resistance changes along the next five-meter cutting path based on coupled analysis of cutting vibration signals and geological data. It also includes a historical cutting posture database and a digital working face database. Through digital twin technology, real-time equipment status data is mapped to the digital model in the virtual environment, enabling the coupling of equipment and geodetic information and the dynamic expression of three-dimensional data.

[0072] The control layer, built using a distributed stream computing framework (Kafka + Flink), includes a collaborative control unit, a speed control unit, and a fault prediction control unit. The collaborative control unit, based on a digital working face database, enables coordinated dispatch and control of the three machines. The speed control unit utilizes an active disturbance rejection speed controller for precise speed control. The fault prediction control unit uses a machine state prediction and compensation algorithm to adjust control strategies in real time.

[0073] The system also features a learning and optimization module for analyzing and optimizing system operating data. This module, comprised of a machine learning unit and an artificial intelligence decision-making unit, optimizes control rules and fault diagnosis strategies by learning and analyzing large amounts of historical data.

[0074] During system operation, the data acquisition layer collects real-time data on device status and environmental parameters. After preprocessing and fusion analysis at the data processing layer, the data is sent to the digital twin layer for real-time updates. Based on this real-time data, the digital twin layer dynamically updates the state of the virtual environment. The control layer uses this real-time digital twin data to implement coordinated device control and fault prediction, and performs system optimization through a learning optimization module.

[0075] Example 2

[0076] In the coal mine equipment collaborative control system provided by this invention, the data acquisition layer includes multiple sensors and data acquisition units. The sensors, including lidar, ultrasonic sensors, optical sensors, and gyroscopes, are used to collect real-time data about the environment surrounding the coal mine equipment. The data acquisition units use industrial Ethernet for real-time data collection and transmission.

[0077] The data preprocessing unit in the data processing layer uses the Kalman filter algorithm to filter the collected raw data; the data fusion unit uses a deep convolutional neural network to extract and fuse features of image data; the data analysis unit uses a long short-term memory network (LSTM) to analyze time series data, identify long-term dependencies of equipment, and perform fault prediction.

[0078] The digital coal seam slice database, historical cutting position database, and digital working face database in the digital twin layer are all built using 3D visualization software to achieve 3D visualization. Through digital twin technology, real-time equipment status data is mapped to the digital model in the virtual environment, achieving the coupling of equipment and ground measurement information and 3D dynamic expression.

[0079] The collaborative control unit in the control layer is based on the digital working face database and adopts the fuzzy PID control algorithm to realize the collaborative scheduling control of the three equipment. The speed control unit uses the anti-disturbance speed controller to control the equipment's traction speed, cutting speed and coal transportation speed. The fault prediction control unit uses the Bayesian network for fault diagnosis and prediction based on the equipment status prediction and compensation algorithm.

[0080] The learning optimization module includes a machine learning unit that deploys an incremental federated learning framework: each downhole terminal locally trains the fault diagnosis model, aggregates global parameters in the cloud and sends updates to achieve continuous optimization under privacy protection, and learns and analyzes historical data; the artificial intelligence decision-making unit formulates control rules and fault diagnosis strategies based on machine learning results and expert experience.

[0081] Example 3

[0082] In the coal mine equipment collaborative control system provided by the present invention, the data acquisition layer includes multiple sensors and data acquisition units. The sensors, including coal seam thickness sensors, coal seam hardness sensors, coal seam permeability sensors, and gas concentration sensors, are used to collect parameter data of the coal mine environment in real time. The data acquisition unit uses the CAN bus for real-time data acquisition and transmission.

[0083] The data preprocessing unit in the data processing layer uses the wavelet transform algorithm to filter the collected raw data; the data fusion unit uses the Deep Generative Adversarial Network (DGAN) to fuse different types of data; the data analysis unit uses the Transformer network to analyze time series data, identify the long-term dependencies of equipment, and perform fault prediction.

[0084] The digital coal seam slice database, historical cutting position database, and digital working face database in the digital twin layer are all built using Pro / E software to achieve 3D visualization. Through digital twin technology, real-time equipment status data collected is mapped to the digital model in the virtual environment, achieving the coupling of equipment and ground measurement information and 3D dynamic expression.

[0085] The collaborative control unit in the control layer is based on the digital working face database and adopts a fuzzy adaptive control algorithm to achieve collaborative scheduling control of the three equipment. The speed control unit uses an anti-disturbance speed controller to control the equipment's traction speed, cutting speed, and coal transportation speed. The fault prediction control unit uses a probabilistic graphical model for fault diagnosis and prediction based on the equipment status prediction and compensation algorithm.

[0086] In the system's learning optimization module, the machine learning unit uses a transfer learning algorithm to learn and analyze historical data; the artificial intelligence decision-making unit formulates control rules and fault diagnosis strategies based on the machine learning results and expert experience.

[0087] Example 4

[0088] 1. Data collection:

[0089] The laser radar scans the position of the coal mining machine drum in real time, the gyroscope monitors the straightness of the scraper conveyor, and the pressure sensor collects the support force of the hydraulic support.

[0090] The coal seam thickness sensor and the gas concentration sensor upload environmental parameters synchronously.

[0091] 2. Data processing and twin mapping:

[0092] The data preprocessing unit uses Kalman filtering to eliminate vibration noise and calibrate posture data.

[0093] The digital twin layer maps the processed data to a unified geodetic coordinate system, dynamically updates the three-dimensional coal seam model (with an accuracy error of 0.08 meters), and displays the cutting trajectory of the coal mining machine and the changes in coal seam thickness in real time.

[0094] 3. Collaborative control execution:

[0095] The Kafka message queue receives the scraper conveyor straightness deviation data, and the Flink stream engine calculates the hydraulic support leveling compensation amount.

[0096] Based on the fuzzy PID algorithm, the collaborative control unit synchronously adjusts the posture of the three machines within a 50-millisecond period to ensure that the coal mining machine drum adaptively cuts along the geological interface of the coal seam.

[0097] Under complex coal seam conditions, the three-machine posture synchronization error is reduced by 60%, and the cutting trajectory fits the geological structure.

[0098] Example 5

[0099] 1. Anti-interference data collection:

[0100] Optical sensors and ultrasonic sensors work together to collect images of tunnel boring machine tools, and deep convolutional neural networks fuse multispectral images to eliminate interference from dust and water mist.

[0101] The acoustic sensor captures the abnormal sound of tool wear and transmits it synchronously with the vibration signal.

[0102] 2. Fault prediction and dynamic control:

[0103] The data analysis unit uses the Transformer network to identify the long-term dependencies of tool vibration signals and predict the remaining life.

[0104] The fault prediction control unit calculates the fault probability through the Bayesian network. When the wear probability exceeds the threshold, the self-disturbance rejection controller is triggered to reduce the excavation speed and an alarm is issued to prompt the tool to be replaced.

[0105] The accuracy of equipment status recognition in dusty environments has been improved to an industry-leading level, avoiding unplanned downtime.

[0106] Example 6

[0107] 1. Historical data drives decision making:

[0108] The machine learning unit analyzed the historical cutting posture database and found that reducing the cutting speed by 15% under hard coal seam conditions can reduce tool wear.

[0109] The artificial intelligence decision-making unit generates a new control rule: when the hardness of the coal seam suddenly increases, the shearer traction speed is automatically limited.

[0110] 2. Real-time optimization execution:

[0111] The OPCUA data mapping model sends the new rules to the control layer, and the self-disturbance rejection speed controller dynamically adjusts the cutting motor power.

[0112] The digital twin layer compares the real-time load cutting with historical data, verifies the effectiveness of the rules and provides feedback to the learning optimization module.

[0113] A closed loop of "rule generation - real-time control - effect verification" is formed, extending tool life by 40% and increasing productivity by 18%.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital twin-based coal mine equipment collaborative control system, characterized by: It includes the data acquisition layer, data processing layer, digital twin layer and control layer connected in sequence; The data acquisition layer includes multiple types of sensors and data acquisition units, and is equipped with a sensor fault diagnosis and dynamic switching mechanism: when the main sensor fails, the system automatically activates the backup sensor group and uses the data fusion unit to perform confidence weighting on the redundant data to collect real-time operating status data, environmental parameter data, and fault diagnosis data of coal mine equipment; The data processing layer includes a data pre-processing unit, a data fusion unit and a data analysis unit, wherein: The data preprocessing unit performs filtering, calibration and standardization on the original data; The data fusion unit fuses multi-source data through a deep fusion neural network and introduces an adversarial training module: using DGAN to generate dust / spray interference samples to train the feature extraction network, thereby improving the fusion accuracy under low visibility conditions; The data analysis unit uses a spatiotemporal attention network to analyze time series data to identify long-term device dependencies and fault predictions; The digital twin layer constructs a virtual simulation environment, including a digital coal seam slice database, integrated geological radar real-time scanning data stream, and a coal seam dynamic change prediction model: Based on the coupled analysis of cutting vibration signals and geological data, it predicts the resistance change of the cutting path in the next 5 meters. It also includes a historical cutting posture database and a digital working face database, and realizes real-time mapping and 3D dynamic expression of equipment status data and virtual models through a unified geodetic coordinate system; The control layer is built using a distributed stream computing framework, including: The collaborative control unit performs coordinated dispatching control of three machines based on the digital working face database and is embedded with a safety domain constraint controller. When the coupling between the hydraulic support force and the gas concentration exceeds the limit, the emergency stop protocol is automatically triggered and an obstacle avoidance path is generated. Speed ​​control unit, which uses active disturbance rejection controller to adjust the equipment speed; The fault prediction control unit adjusts the control strategy in real time according to the equipment status prediction compensation algorithm.

2. The digital twin-based coal mine equipment collaborative control system according to claim 1 is characterized by: The sensors include at least three of a laser radar, an ultrasonic sensor, an optical sensor, a gyroscope, a coal seam thickness sensor, a coal seam hardness sensor and a gas concentration sensor.

3. The digital twin-based coal mine equipment collaborative control system according to claim 1 is characterized in that: The deep fusion neural network executed by the data fusion unit includes one of a deep convolutional neural network (DCNN) and a deep generative adversarial network (DGAN), which is used to extract features from image data and fuse multi-dimensional sensor data.

4. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The digital twin layer establishes a coupling relationship between the equipment and the ground measurement information through industrial three-dimensional visualization software, and the spatial position relationship expression accuracy error of the unified geodetic coordinate system is less than 0.1 meters.

5. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The real-time data update mechanism of the control layer adopts the OPC Unified Architecture (OPC UA) data mapping model with an update cycle of 50ms, and superimposes the device status prediction compensation algorithm to reduce the collaborative delay to less than 200ms and the data synchronization rate to less than 5%.

6. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The distributed stream computing framework consists of an Apache Kafka message queue and an Apache Flink stream processing engine, and is used to handle the collaborative constraints of three equipment under complex coal seam conditions.

7. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: It also includes a learning optimization module connected to the control layer, including: Machine learning unit, which uses transfer learning or deep learning algorithms to analyze historical data; Artificial intelligence decision-making unit optimizes control rules and fault diagnosis strategies based on analysis results.

8. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The collaborative control unit adopts a fuzzy PID control algorithm or a fuzzy adaptive control algorithm to achieve synchronous control of the three-machine posture, and the control response time is less than 100ms.

9. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The active disturbance rejection controller of the speed control unit satisfies the following control law: Among them, u is the control output, e is the deviation between the speed setting value and the actual value, k p 、k i 、k d are the proportional, integral, and differential coefficients respectively.

10. The digital twin-based coal mine equipment collaborative control system according to claim 1, characterized in that: The fault prediction control unit uses a Bayesian network or a probabilistic graphical model to perform fault diagnosis.

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