Automatic control system for unmanned mining equipment of deep well mine

Abstract

The invention relates to the technical field of automatic control and mining, and discloses an automatic control system for deep well mine unmanned mining equipment, which comprises a sensing module, a decision control module, an execution module and a communication module, the sensing module is used for collecting equipment operation state data and mine environment parameter information; the decision control module is connected with the sensing module, receives and processes data acquired by the sensing module, and generates an equipment navigation path, an operation instruction and a cooperative scheduling strategy; and the execution module is connected with the decision control module, and controls the equipment to complete autonomous navigation, automatic operation and state feedback according to the received instruction. Through automatic path planning and task allocation, the problems of path conflict and resource waste in traditional manual operation are avoided; through real-time state monitoring and exception handling, the equipment fault risk and the accident occurrence probability are reduced; and through multi-device cooperative scheduling and remote monitoring, the overall adaptability and reliability of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of automation control and mining technology, specifically to an automatic control system for unmanned mining equipment in deep mines. Background Technology

[0002] The automatic control system for unmanned mining equipment in deep mines belongs to the field of mining technology. Its main function is to control mining equipment in deep mines through automation and intelligence, thereby achieving unmanned operation and high efficiency. The system mainly includes a sensing module, a decision-making and control module, an execution module, and a communication module. These modules work together to achieve functions such as autonomous navigation, automatic operation, status monitoring, and multi-equipment collaborative scheduling. This automatic control system can significantly improve the safety, efficiency, and accuracy of deep mine operations and adapt to the needs of remote monitoring in complex environments.

[0003] Research revealed that traditional manual operation methods have numerous problems in deep mining environments. For example, deep mines are typically characterized by confined spaces, complex geological conditions, and drastic temperature and humidity fluctuations. These factors make manual operation of equipment vulnerable to safety threats, and also hinder the achievement of ideal work efficiency and accuracy. Furthermore, the coordinated operation of multiple pieces of equipment relies on the experience and judgment of the operators, which may lead to inefficient workflows, task conflicts, or resource waste.

[0004] Furthermore, in terms of equipment condition monitoring, traditional methods often rely on periodic manual inspections or simple sensor feedback. This approach results in a low information collection frequency and cannot reflect the real-time operating status of the equipment. For example, when critical components of the equipment experience slight wear or abnormal vibration, traditional monitoring methods may fail to detect it in time, leading to the escalation of the fault and affecting the overall operation progress. Regarding environmental parameter acquisition, the complex geological conditions and harsh working environment in deep mines place high demands on sensor performance. However, existing sensors may be at risk of data drift or failure under conditions of high temperature, high humidity, or high dust concentration, thus reducing the reliability of the system.

[0005] In terms of decision-making and control, traditional control methods are usually based on preset rules or manual commands, lacking dynamic adjustment capabilities. For example, when mining equipment encounters sudden geological changes or obstacles, traditional control systems may fail to react quickly, causing equipment shutdown or deviation from the planned path. Furthermore, because communication signals are susceptible to interference in deep mines, traditional communication modules may experience delays or data loss during data transmission, further affecting the accuracy and timeliness of control commands.

[0006] In multi-device collaborative scheduling, existing technologies typically employ a centralized scheduling strategy, where a central control unit manages task allocation and path planning for all devices. However, this approach is prone to decreased scheduling efficiency due to excessive computational load when dealing with large-scale device clusters. Furthermore, centralized scheduling places high demands on the stability of the communication network; a network failure can paralyze the entire system. In addition, information exchange and task coordination between different devices still rely on manual intervention, which not only increases operational complexity but also increases the risk of human error. Summary of the Invention

[0007] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an automatic control system for unmanned mining equipment in deep mines, solving the problem of "low working efficiency" mentioned in the background technology.

[0008] (II) Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solution: an automatic control system for unmanned mining equipment in deep mines, comprising a sensing module, a decision control module, an execution module, and a communication module; The sensing module is used to collect equipment operating status data and mine environmental parameter information; The decision control module is connected to the perception module, receives and processes the data collected by the perception module, and generates equipment navigation paths, operation instructions and collaborative scheduling strategies. The execution module is connected to the decision control module and controls the equipment to complete autonomous navigation, automatic operation and status feedback according to the received instructions; The communication module is connected to the sensing module, decision control module, and execution module to realize data transmission, remote monitoring, and information interaction between multiple devices.

[0009] Preferably, the sensing module includes an equipment status sensor, an environmental monitoring sensor, and a positioning device. The equipment status sensor is installed on key components of the equipment and monitors the equipment's operating status in real time through a vibration detection unit, a temperature detection unit, and a pressure detection unit. The environmental monitoring sensor is deployed around the mine working face to collect parameters such as temperature and humidity, dust concentration, and geological stress changes. The positioning device adopts a positioning method based on the fusion of inertial navigation and lidar, and obtains equipment location information through an inertial measurement unit and a laser scanning unit.

[0010] Preferably, the vibration detection unit in the equipment status sensor is a triaxial accelerometer, which is arranged along the main axis of the equipment to detect vibration signals generated during equipment operation. The temperature detection unit is a thermocouple probe, which is installed on the surface of the motor housing to monitor the motor operating temperature. The pressure detection unit is a diaphragm pressure sensor, which is installed at the hydraulic system pipeline interface to detect changes in hydraulic oil pressure.

[0011] Preferably, the temperature and humidity detection unit in the environmental monitoring sensor is a digital temperature and humidity sensor, which is installed on the top of the mine roadway at a height of meters to meters above the ground to collect temperature and humidity data in the roadway. The dust concentration detection unit is a light scattering principle sensor, installed near the ventilation opening of the roadway, to monitor the concentration of particulate matter in the air. The geological stress detection unit is a strain gauge array, attached to the surface of the roadway rock wall, to sense changes in rock stress.

[0012] Preferably, the inertial measurement unit in the positioning device is a six-degree-of-freedom IMU sensor, installed at the center of gravity of the device, to acquire the acceleration and angular velocity information of the device, and the laser scanning unit is a multi-beam lidar, installed at the center of the top of the device, to acquire three-dimensional point cloud data of the surrounding environment through rotational scanning.

[0013] Preferably, the decision control module includes a path planning unit, a task allocation unit, and an anomaly handling unit. The path planning unit generates a device navigation path based on the positioning data and environmental parameters provided by the perception module using an improved A* algorithm. The task allocation unit formulates a multi-device collaborative operation plan according to the device type and task priority. The anomaly handling unit adjusts the task execution order or triggers an emergency response by monitoring the device status and environmental changes in real time.

[0014] Preferably, the path planning unit adopts an improved A* algorithm, specifically including: dividing the mine roadway into a grid map, with each grid node corresponding to a location coordinate, defining a cost function f(n)=g(n)+h(n), where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the heuristically estimated cost from the current node to the target node, and optimizing the path search process through a dynamic weight adjustment strategy.

[0015] Preferably, the task allocation unit allocates tasks according to the device type set D = {d1, d2, ...} … ,d n} and task set T={t1,t2, … ,t m Construct a task allocation matrix M, with matrix elements m. ij Indicates device d i Execute task t jThe suitability is determined, and the optimal task allocation scheme is determined by solving the maximum matching problem.

[0016] Preferably, the communication module includes a data transmission unit, a remote monitoring unit, and a collaborative interaction unit. The data transmission unit uses the Zigbee wireless communication protocol to realize data exchange between the sensing module, the decision control module, and the execution module. The remote monitoring unit is connected to the ground control center via the TCP / IP protocol to display the equipment operating status and environmental parameters in real time. The collaborative interaction unit realizes information sharing and task coordination among multiple devices through a local area network.

[0017] (III) Beneficial Effects This invention provides an automatic control system for unmanned mining equipment in deep mines. It has the following beneficial effects: (1) When the automatic control system for the unmanned mining equipment in the deep mine is in use, it meets the needs of automated operation in the complex environment of the deep mine. Through the collaborative work of the sensing module, decision control module, execution module and communication module, it realizes the autonomous navigation, automatic operation, status monitoring and multi-equipment collaborative scheduling of the equipment. Through the comprehensive application of multiple types of sensors in the sensing module, the equipment status and environmental parameters are collected in a comprehensive manner, providing a reliable data foundation for subsequent decision-making. Through the improved A* algorithm and task allocation strategy of the decision control module, the equipment path planning and task execution efficiency are optimized. Through the precise drive control and operation execution of the execution module, the accuracy and stability of the equipment action are ensured. Through the efficient wireless communication and information sharing mechanism of the communication module, seamless cooperation and remote monitoring between equipment are realized.

[0018] (2) The automatic control system for the unmanned mining equipment in this deep mine avoids path conflicts and resource waste problems in traditional manual operation through automated path planning and task allocation; it reduces the risk of equipment failure and the probability of accidents through real-time status monitoring and anomaly handling; and it enhances the overall adaptability and reliability of the system through multi-equipment collaborative scheduling and remote monitoring. This system is suitable for the complex environment of deep mines and can be widely used in ore mining, tunnel excavation and geological exploration. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the system operation process of the present invention.

[0020] In the diagram: 1. Sensing module; 2. Decision control module; 3. Execution module; 4. Communication module; 5. Equipment status sensor; 6. Environmental monitoring sensor; 7. Positioning device; 8. Path planning unit; 9. Task allocation unit; 10. Anomaly handling unit; 11. Data transmission unit; 12. Remote monitoring unit; 13. Collaborative interaction unit. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 - Figure 2 This invention provides an automatic control system for unmanned mining equipment in deep mines, including a sensing module 1, a decision control module 2, an execution module 3, and a communication module 4. Specifically, the sensing module 1 is the foundation of the entire system, consisting of equipment status sensors 5, environmental monitoring sensors 6, and a positioning device 7. The equipment status sensors 5 are installed on key components of the mining equipment and include vibration detection units, temperature detection units, and pressure detection units. The vibration detection unit uses triaxial accelerometers arranged along the main axis of the equipment to collect vibration signals generated during equipment operation; the temperature detection unit uses thermocouple probes installed on the surface of the motor housing to monitor the motor's operating temperature; and the pressure detection unit uses diaphragm pressure sensors installed at the hydraulic system pipeline interfaces to detect changes in hydraulic oil pressure. The environmental monitoring sensors 6 are deployed at the top and surrounding areas of the mine roadway and include temperature and humidity detection units, dust concentration detection units, and geological stress detection units. The temperature and humidity detection unit uses digital temperature and humidity sensors deployed at the top of the tunnel, 2 to 3 meters above the ground, to collect temperature and humidity data within the tunnel. The dust concentration detection unit uses a light scattering sensor installed near the tunnel ventilation opening to monitor the concentration of particulate matter in the air. The geological stress detection unit uses a strain gauge array attached to the surface of the tunnel rock wall to sense changes in rock stress. The positioning device 7 uses a fusion of inertial navigation and lidar to acquire equipment position information. The inertial measurement unit uses a six-degree-of-freedom IMU sensor installed at the equipment's center of gravity to obtain acceleration and angular velocity information, while the laser scanning unit uses a multi-beam lidar installed at the center of the top of the equipment and acquires three-dimensional point cloud data of the surrounding environment through rotational scanning. All the above sensors and devices transmit data to the sensing module 1 via wired or wireless connections.

[0023] The decision control module 2 is connected to the perception module 1 and receives the data it collects. Internally, it includes a path planning unit 8, a task allocation unit 9, and an exception handling unit 10. The path planning unit 8 generates a navigation path for the equipment based on the positioning data and environmental parameters provided by the perception module 1, using an improved A* algorithm for path search. Specifically, the mine roadway is first divided into a grid map, with each grid node corresponding to a location coordinate; then, a cost function f(n) = g(n) + h(n) is defined, where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the heuristically estimated cost from the current node to the target node; finally, a dynamic weight adjustment strategy is used to optimize the path search process to ensure the safety and efficiency of path planning. The task allocation unit 9 allocates the path based on the equipment type set D = {d1, d2, ...}. … ,d n} and task set T={t1,t2, … ,t m Construct a task allocation matrix M, with matrix elements m. ij Indicates device d i Execute task t j The system assesses the suitability of the device and determines the optimal task allocation scheme by solving the maximum matching problem. The exception handling unit 10 monitors device status and environmental changes in real time. When any status parameter or environmental parameter exceeds a preset threshold, exception handling logic is triggered to replan the path or suspend task execution. For example, a device status threshold set S = {s1, s2, ...} is set. … The set of environmental parameter thresholds E={e1,e2,s1} and s1} … ,e m If a parameter exceeds the corresponding threshold, the exception response mechanism will be activated immediately.

[0024] The execution module 3 is connected to the decision control module 2 and performs specific operations based on the received instructions. It includes a drive control unit, a work execution unit, and a status feedback unit. The drive control unit adjusts the motor output power through a PID controller to achieve autonomous navigation of the equipment; specifically, it sets a target speed v. target and actual speed v actual Calculate the speed error e = v target - v actual Then through the proportional term Kpe and the integral term K i ∫edt and differential terms A control signal u is generated. The work execution unit completes mining operations by controlling the hydraulic arm and bucket, for example, by setting the target angle θ of the hydraulic arm. target and actual angle θ actral Calculate the angle error Δθ = θ target -θ actralThe hydraulic arm angle is adjusted by regulating the hydraulic oil flow through the hydraulic valve opening driven by a stepper motor. The status feedback unit updates the equipment status information in real time by transmitting data from various sensors and then transmits this information to the decision control module 2 for subsequent decision-making reference.

[0025] The communication module 4 is connected to the sensing module 1, the decision control module 2, and the execution module 3. It includes a data transmission unit 11, a remote monitoring unit 12, and a collaborative interaction unit 13. The data transmission unit 11 uses the Zigbee wireless communication protocol to realize data exchange between nodes, and sets the communication node set N = {n1, n2, ...}. … ,n k Each node corresponds to a device or sensor, and a star network topology is used to establish a master node responsible for data aggregation and forwarding. The remote monitoring unit 12 connects to the ground control center via TCP / IP protocol, sets the data packet format to include device ID, status parameters, environmental parameters, and task progress, and periodically sends data packets to achieve real-time monitoring of device operating status. The collaborative interaction unit 13 achieves information sharing among multiple devices via a local area network, sets the information sharing table structure to include device ID, current location, task status, and resource requirements, and updates the information sharing table via broadcast mechanism to ensure that each device can obtain the latest collaborative information.

[0026] In practical applications, the sensing module 1 first collects equipment operating status data and mine environmental parameter information and transmits this data to the decision control module 2. The decision control module 2 generates equipment navigation paths, work instructions, and collaborative scheduling strategies based on the received data and sends these instructions to the execution module 3. The execution module 3 controls the equipment to complete autonomous navigation, automatic operation, and status feedback according to the instructions, and sends the status information back to the decision control module 2 for subsequent decision-making reference. Simultaneously, the communication module 4 is responsible for data transmission, remote monitoring, and information interaction between multiple devices throughout the process to ensure efficient system operation. For example, in a mining scenario, the sensing module 1 collects information about high temperature and humidity and excessive dust concentration in the tunnel and transmits it to the decision control module 2. The decision control module 2 adjusts the equipment navigation path and reassigns task priorities based on this information to avoid high-risk areas. The execution module 3 adjusts the equipment actions according to the new instructions and completes the mining operation, while simultaneously sending the operation results back to the decision control module 2 through the status feedback unit for subsequent optimization decisions. Throughout the process, the communication module 4 achieves data transmission through wireless communication protocols and ensures seamless collaboration between multiple devices through a local area network.

[0027] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0028] First, once the mining equipment enters the deep mine tunnel, the equipment status sensor 5 in the sensing module 1 begins operation. The vibration detection unit collects vibration signals along the main shaft of the equipment using a triaxial accelerometer and transmits the data to the decision control module 2. Simultaneously, the temperature detection unit monitors the operating temperature of the motor housing surface using a thermocouple probe, while the pressure detection unit detects pressure changes at the hydraulic system pipeline interfaces using a diaphragm pressure sensor. These data collectively constitute the basic information about the equipment's operating status. The environmental monitoring sensor 6 operates synchronously: the temperature and humidity detection unit collects temperature and humidity data within a 2-3 meter range above the ground at the top of the tunnel using a digital sensor; the dust concentration detection unit monitors particulate matter concentration near ventilation openings using a light scattering principle sensor; and the geological stress detection unit senses stress changes on the rock wall surface using a strain gauge array. The inertial measurement unit in the positioning device 7 uses a six-degree-of-freedom IMU sensor to acquire the equipment's acceleration and angular velocity information, and the laser scanning unit acquires three-dimensional point cloud data of the surrounding environment using a multi-beam lidar. All of the above information is transmitted to the sensing module 1 via wired or wireless means and further relayed to the decision control module 2.

[0029] Subsequently, the decision control module 2 generates navigation paths and work instructions based on the received data. The path planning unit 8, based on an improved A* algorithm, divides the alleyway into a grid map, with each grid node corresponding to a location coordinate. The cost function f(n) = g(n) + h(n) is used for path search, where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the heuristically estimated cost from the current node to the target node. A dynamic weight adjustment strategy optimizes the path search process, ensuring both path safety and efficiency. The task allocation unit 9 allocates tasks based on the equipment type set D = {d1, d2, ...}. … ,d n} and task set D={d1,d2, … ,d n Construct a task allocation matrix M, with matrix elements m. ij Indicates device d i Execute task t j The task allocation unit 9 determines the optimal task allocation scheme by solving the maximum matching problem. The exception handling unit 10 monitors the device status and environmental parameters in real time. If a parameter exceeds a preset threshold, the exception handling logic is triggered to replan the path or suspend task execution.

[0030] Next, execution module 3 performs specific operations based on instructions issued by decision control module 2. The drive control unit adjusts the motor output power through a PID controller to achieve autonomous navigation. The target speed v is set. target and actual speed v actual Calculate the speed error e = v target - vactual Then, through the proportional term Kpe and the integral term K i ∫edt and differential terms A control signal u is generated. The work execution unit drives the hydraulic valve opening via a stepper motor, adjusting the hydraulic oil flow to control the angle adjustment of the hydraulic arm. The target angle θ of the hydraulic arm is set. target and actual angle θ actral Calculate the angle error Δθ = θ target -θ actral Then, precise actions are completed through the hydraulic system. The status feedback unit transmits equipment status information back through various sensors and sends this information to the decision control module 2 for subsequent decision-making reference.

[0031] Throughout the process, communication module 4 is responsible for data transmission, remote monitoring, and information exchange between multiple devices. Data transmission unit 11 uses the Zigbee wireless communication protocol, achieving data aggregation and forwarding between nodes through a star network topology. Remote monitoring unit 12 connects to the ground control center via TCP / IP protocol, sets the data packet format to include device ID, status parameters, environmental parameters, and task progress, and periodically sends data packets to achieve real-time monitoring of device operating status. Collaborative interaction unit 13 achieves information sharing among multiple devices via a local area network, sets the information sharing table structure to include device ID, current location, task status, and resource requirements, and updates the information sharing table through a broadcast mechanism to ensure that each device can obtain the latest collaborative information.

[0032] For example, in a specific scenario, after the sensing module 1 collects information about high temperature and humidity and excessive dust concentration in the tunnel, it transmits this information to the decision control module 2. The path planning unit 8 adjusts the equipment navigation path according to environmental parameters to avoid high-risk areas, and the task allocation unit 9 reallocates task priorities to ensure that critical tasks are executed first. The execution module 3 adjusts the equipment actions according to the new instructions to complete the mining operation, and transmits the operation results back to the decision control module 2 through the status feedback unit to optimize subsequent decisions. Throughout the process, the communication module 4 achieves data transmission through a wireless communication protocol and ensures seamless collaboration between multiple devices through a local area network.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automatic control system for unmanned mining equipment in deep mines, characterized in that: It includes a perception module (1), a decision control module (2), an execution module (3), and a communication module (4); The sensing module (1) is used to collect equipment operating status data and mine environmental parameter information; The decision control module (2) is connected to the sensing module (1), receives and processes the data collected by the sensing module (1), and generates equipment navigation paths, operation instructions and collaborative scheduling strategies; The execution module (3) is connected to the decision control module (2) and controls the equipment to complete autonomous navigation, automatic operation and status feedback according to the received instructions; The communication module (4) is connected to the sensing module (1), the decision control module (2) and the execution module (3) to realize data transmission, remote monitoring and information interaction between multiple devices.

2. The automatic control system for unmanned mining equipment in deep mines according to claim 1, characterized in that: The sensing module (1) includes an equipment status sensor (5), an environmental monitoring sensor (6), and a positioning device (7). The equipment status sensor (5) is installed on key components of the equipment and monitors the equipment's operating status in real time through a vibration detection unit, a temperature detection unit, and a pressure detection unit. The environmental monitoring sensor (6) is deployed around the mine working face to collect parameters such as temperature and humidity, dust concentration, and geological stress changes. The positioning device (7) adopts a positioning method based on the fusion of inertial navigation and lidar, and obtains equipment location information through an inertial measurement unit and a laser scanning unit.

3. The automatic control system for unmanned mining equipment in deep mines according to claim 2, characterized in that: The vibration detection unit in the equipment status sensor (5) is a triaxial accelerometer, which is arranged along the main axis of the equipment to detect the vibration signal generated during the operation of the equipment. The temperature detection unit is a thermocouple probe, which is installed on the surface of the motor housing of the equipment to monitor the working temperature of the motor. The pressure detection unit is a diaphragm pressure sensor, which is installed at the interface of the hydraulic system pipeline to detect the change of hydraulic oil pressure.

4. The automatic control system for unmanned mining equipment in deep mines according to claim 2, characterized in that: The temperature and humidity detection unit in the environmental monitoring sensor (6) is a digital temperature and humidity sensor, which is installed on the top of the mine roadway at a height of 2 to 3 meters above the ground to collect temperature and humidity data in the roadway. The dust concentration detection unit is a light scattering principle sensor, which is installed near the ventilation opening of the roadway to monitor the concentration of particulate matter in the air. The geological stress detection unit is a strain gauge array, which is attached to the surface of the roadway rock wall to sense changes in rock stress.

5. The automatic control system for unmanned mining equipment in deep mines according to claim 2, characterized in that: The inertial measurement unit in the positioning device (7) uses a six-degree-of-freedom IMU sensor, which is installed at the center of gravity of the device to obtain the acceleration and angular velocity information of the device. The laser scanning unit uses a multi-beam lidar, which is installed at the center of the top of the device to obtain three-dimensional point cloud data of the surrounding environment through rotation scanning.

6. The automatic control system for unmanned mining equipment in deep mines according to claim 1, characterized in that: The decision control module (2) includes a path planning unit (8), a task allocation unit (9), and an exception handling unit (10). The path planning unit (8) generates a device navigation path based on the positioning data and environmental parameters provided by the perception module (1) using an improved A* algorithm. The task allocation unit (9) formulates a multi-device collaborative operation plan according to the device type and task priority. The exception handling unit (10) adjusts the task execution order or triggers an emergency response by monitoring the device status and environmental changes in real time.

7. The automatic control system for unmanned mining equipment in deep mines according to claim 1, characterized in that: The path planning unit (8) adopts an improved A* algorithm, which specifically includes: dividing the mine roadway into a grid map, with each grid node corresponding to a location coordinate, defining a cost function f(n)=g(n)+h(n), where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the heuristic estimated cost from the current node to the target node, and optimizing the path search process through a dynamic weight adjustment strategy.

8. The automatic control system for unmanned mining equipment in deep mines according to claim 6, characterized in that: The task allocation unit (9) allocates tasks according to the device type set D={d1,d2, … ,d n } and task set T={t1,t2, … ,t m Construct a task allocation matrix M, with matrix elements m. ij Indicates device d i Execute task t j The suitability is determined, and the optimal task allocation scheme is determined by solving the maximum matching problem.

9. The automatic control system for unmanned mining equipment in deep mines according to claim 1, characterized in that: The communication module (4) includes a data transmission unit (11), a remote monitoring unit (12), and a collaborative interaction unit (13). The data transmission unit (11) uses the Zigbee wireless communication protocol to realize data exchange between the sensing module (1), the decision control module (2), and the execution module (3). The remote monitoring unit (12) is connected to the ground control center through the TCP / IP protocol to display the equipment operating status and environmental parameters in real time. The collaborative interaction unit (13) realizes information sharing and task coordination among multiple devices through the local area network.

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