A Cooperative Control Configuration System and Method for Coal Mine Tunneling Faces

Abstract

This application discloses a collaborative control configuration system for coal mine tunneling faces, relating to the field of intelligent mining equipment technology. The collaborative control configuration model includes a collaborative sensing model, a collaborative network model, a collaborative decision-making model, and a collaborative execution model. The collaborative sensing model collects sensor data from each individual device, processes, fuses, and analyzes it to determine the collaborative sensing results. The collaborative decision-making model generates collaborative decision results for each individual device based on the collaborative sensing results, according to collaborative control commands, the coupling and correlation of tunneling face processes, and target constraints. The collaborative execution model transmits the collaborative decision results of each individual device to the corresponding device through a distributed centralized control system. The collaborative control configuration model in this application ensures that the entire working face operates collaboratively according to the optimal solution through multi-module collaborative work, achieving intelligent and efficient management and operation of the working face.

Description

Technical Field

[0001] This application relates to the field of intelligent mining equipment technology, and in particular to a collaborative control configuration system and method for coal mine tunneling faces. Background Technology

[0002] Traditional underground tunneling faces in coal mines rely on production systems, auxiliary production systems, and auxiliary operation systems. The production system consists of geophysical exploration equipment, tunneling equipment, support equipment, anchoring equipment, transportation equipment, and dust removal equipment. The auxiliary production system consists of power supply equipment, ventilation equipment, drainage equipment, environmental monitoring equipment, and gas extraction equipment. The auxiliary operation system consists of material handling equipment, transportation extension equipment, roadway repair equipment, and pipeline layout and connection equipment.

[0003] With the development and application of rapid tunneling equipment in coal mines, current coal mine tunneling faces face challenges such as complex procedures, numerous tasks, and large systems. These often require the use of various different equipment, which, often from different manufacturers, hinders flexibility and efficiency in collaboration. Furthermore, the lack of unified standards, specifications, and models for collaborative control across different manufacturers, systems, and equipment severely restricts the application of collaborative control technology. Therefore, a holistic collaborative control technology is needed to achieve coordinated control between various devices. Currently, collaborative control technology for tunneling faces primarily relies on the modification and upgrading of existing equipment by the mine, resulting in poor reliability, limited collaborative operation effects, and weak intelligence. Summary of the Invention

[0004] The purpose of this application is to provide a collaborative control configuration system and method for coal mine tunneling faces, which can achieve intelligent and efficient management and operation of the working face through the collaborative work of multiple modules.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a collaborative control configuration system for a coal mine tunneling face, including: a collaborative sensing model, a collaborative network model, a collaborative decision-making model, a collaborative execution model, and a distributed centralized control system;

[0007] The collaborative sensing model is used to collect sensor data from each standalone device, and to process, fuse, and analyze the sensor data to determine the collaborative sensing results.

[0008] The collaborative network model is used to construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and to send the collaborative perception results to the collaborative decision model based on the communication network architecture.

[0009] The collaborative decision-making model is used to generate collaborative decision-making results for each individual device based on the collaborative perception results, according to collaborative control instructions, coupling and association of tunneling face processes, and target constraints. The collaborative decision-making results for each individual device include the work tasks of the individual device and the work tasks of other individual devices associated with the individual device.

[0010] The collaborative execution model is used to transmit the collaborative decision-making results of each standalone device generated by the collaborative decision-making model to the corresponding standalone device through a distributed centralized control system.

[0011] Secondly, this application provides a collaborative control method for coal mine tunneling faces, including:

[0012] Collect sensor data from each standalone device, and process, fuse, and analyze the sensor data to determine the collaborative sensing results;

[0013] Construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and send the collaborative perception results to the collaborative decision model based on the communication network architecture;

[0014] Based on the collaborative perception results, collaborative decision results for each individual machine are generated according to collaborative control commands, coupling and association of tunneling face processes, and target constraints. The collaborative decision results for each individual machine include the work tasks of the individual machine and the work tasks of other individual machines associated with the individual machine.

[0015] The collaborative decision-making results generated by the collaborative decision-making model for each standalone device are transmitted to the corresponding standalone device through a distributed centralized control system.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0017] This application provides a collaborative control configuration system and method for coal mine tunneling faces. The collaborative control configuration system includes: a collaborative sensing model, a collaborative network model, a collaborative decision-making model, a collaborative execution model, and a distributed centralized control system. The collaborative sensing model is used to collect sensor data from each individual device, process, fuse, and analyze the sensor data to determine the collaborative sensing results. The collaborative network model is used to construct the communication network architecture in the collaborative control configuration system for coal mine tunneling faces, and to send the collaborative sensing results to the collaborative decision-making model based on the communication network architecture. The collaborative decision-making model is used to generate collaborative decision-making results for each individual device based on the collaborative sensing results, according to collaborative control commands, coupling and correlation of tunneling face operations, and target constraints. The collaborative execution model is used to transmit the collaborative decision-making results of each individual device generated by the collaborative decision-making model to the corresponding individual device through the distributed centralized control system.

[0018] The collaborative sensing model in this application integrates data from different sensors, providing a more comprehensive and accurate view of the working face. The collaborative network model ensures smooth and real-time information flow between various models to support collaborative decision-making and control of the entire working face. The collaborative decision-making model formulates the optimal decision-making scheme based on preset collaborative control commands, the coupling and correlation of tunneling face processes, and target constraints, ensuring that the entire working face works collaboratively according to the optimal scheme. Therefore, the collaborative control configuration model of the tunneling face achieves intelligent and efficient management and operation of the working face through multi-module collaborative work. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a structural diagram of a collaborative control configuration system for a coal mine tunneling face according to one embodiment of this application;

[0021] Figure 2 This is a schematic diagram of a collaborative network model provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the collaborative decision-making relationship of a tunneling face provided in one embodiment of this application;

[0023] Figure 4 This is a schematic diagram of a distributed centralized control system provided in an embodiment of this application;

[0024] Figure 5A flowchart of a collaborative control method for a coal mine tunneling face is provided as another embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0026] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Currently, the collaborative control of coal mine tunneling faces faces faces several problems: There is a lack of inherent correlation between the actions of various mechanisms within each individual machine, leading to numerous procedures and low efficiency; there is a lack of effective coordination between cutting, support, and transportation equipment; there is a lack of overall collaborative control, and a lack of environmental analysis and collaborative control of each process (cutting, support, and transportation), resulting in complex operation and low efficiency; no active protection system for workers at the working face has been established; and the personnel positioning system is not linked to individual machines. To address the common problems of high labor intensity, large number of personnel at the working face, and low work efficiency and safety in current collaborative control configuration systems for coal mine tunneling faces, this application provides a collaborative control configuration system and method for coal mine tunneling faces.

[0028] In one exemplary embodiment, such as Figure 1 As shown, a collaborative control configuration system for a coal mine tunneling face is provided. In this embodiment, the collaborative control configuration system for a coal mine tunneling face includes: a collaborative perception model, a collaborative network model, a collaborative decision-making model, a collaborative execution model, and a distributed centralized control system.

[0029] The collaborative sensing model is used to collect sensor data from each individual device, and to process, fuse, and analyze this sensor data to determine the collaborative sensing results. There are many individual devices at the tunneling face, and the automatic operation of each device relies on sensors for perception. For collaboration between individual devices, sensor data from different devices needs to be fused. The sensor data collected from each individual device is uniformly transmitted to a distributed centralized control system. Within the control system, a collaborative sensing model is established according to device type, and unified classification and address editing are performed.

[0030] Furthermore, the collaborative sensing model includes a data acquisition unit, a data processing unit, a data fusion unit, and a data analysis unit. The data acquisition unit is used to collect sensor data and corresponding equipment information from each individual device. The data processing unit is used to preprocess the sensor data to obtain preprocessed target sensor data. The preprocessing operations include data filtering, data correction, data anomaly detection, and data conversion. The data fusion unit is used to perform data fusion operations on the target sensor data according to sensor functions and collaborative operation principles to obtain data fusion results. The data analysis unit is used to analyze the collaborative action state based on the data fusion results and the current tunneling face operation, using an artificial neural network analysis method, and to determine the collaborative sensing results based on the collaborative action state. The artificial neural network has self-learning, self-organizing, and adaptive capabilities.

[0031] Furthermore, the data correction involves time synchronization of sensor data based on sensor type and acquisition time. For quantitative or qualitative sensor data, the data needs to be sent to the data processing unit along with the independently acquired timestamp information. It also involves spatial synchronization of sensor data based on sensor type and reference coordinate system. If the time difference between sensor data acquired by different types of devices is within a reasonable range, it is processed directly. If it exceeds the reasonable range, the delayed sensor data needs to be corrected. For example, when the walking speed of the previous device is transmitted to the next device via the distributed centralized collaborative control system, the walking speed of the next device needs to be equal to the speed of the previous device plus the corrected speed. For position sensing or ranging sensors, they need to be converted to the same coordinate system. The conversion process is as follows:

[0032] S i+1 =S i +ΔS;

[0033] Among them, S i+1 S represents the coordinate data of the current coordinate system. i The coordinate data is in another coordinate system, and ΔS is the coordinate transformation variable.

[0034] The collaborative network model is used to construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and to send the collaborative perception results to the collaborative decision model based on the communication network architecture.

[0035] Furthermore, the mathematical model formula for the cooperative network model is as follows:

[0036] N = k1V1 + |k2V2 + ... + k n V n ;

[0037] Where N represents the transmission rate of a single byte of information in the collaborative control configuration model of the tunneling face, and V n This represents the transmission rate of various network types in the communication network architecture, including CAN communication, RS485 communication, and TCP / IP communication; k n This represents the weight value calculated based on the installation location of the communication device, the amount of data transmitted, and the transmission distance.

[0038] Furthermore, such as Figure 2 As shown, the collaborative network model is a converged communication network architecture of ground cloud-underground edge-face. Communication protocols and methods are designed according to communication distance, controller type of each individual device, and safety requirements. Specifically, sensors of individual devices in the tunneling face, including cameras, personnel protection sensors, environmental sensors, ranging sensors, and navigation systems, use a fieldbus communication network primarily based on CAN communication to upload sensor data to the controller of each individual device, ensuring the accuracy and real-time nature of the collaborative sensing data. Data interaction and fusion are achieved between individual devices using industrial Ethernet networks such as ModbusTCP and ADS, ensuring rapid interaction of execution control information. The collaborative sensing model transmits data to the ground or underground server for collaborative decision-making via high-speed Ethernet methods such as TCP / IP and EtherCAT. High-speed Ethernet is used between the collaborative decision-making model and the execution model for efficient data processing and analysis. Communication between control information in the collaborative execution model and collaborative decision-making results in the collaborative decision-making model relies on Ethernet switches. The overall communication rate of the collaborative network model is guaranteed to be above gigabits per second.

[0039] The collaborative decision-making model is used to generate collaborative decision-making results for each individual device based on the collaborative perception results, according to collaborative control instructions, coupling and association of tunneling face processes, and target constraints. The collaborative decision-making results for each individual device include the work tasks of the device itself and the work tasks of other individual devices associated with it.

[0040] Furthermore, the collaborative decision-making results of each individual device include advanced detection collaborative decision-making results, cutting collaborative decision-making results, transportation collaborative decision-making results, support collaborative decision-making results, walking collaborative decision-making results, dust removal collaborative decision-making results, power supply collaborative decision-making results, ventilation collaborative decision-making results, drainage collaborative decision-making results, human-machine collaborative decision-making results, and environmental collaborative decision-making results.

[0041] Specifically, the collaborative decision-making relationships at the tunneling face are as follows: Figure 3As shown, the collaborative decision-making results of advanced exploration are obtained based on the advanced exploration collaborative correlation decision-making model C1 in the collaborative decision-making model. Advanced exploration is divided into drilling and geophysical exploration, which detects hydrology, geological structure, and gas. The geological and environmental data monitored by advanced exploration are fed back to the tunneling equipment in real time, guiding the equipment to carry out planned work in combination with the exploration data for cutting, walking, and support. The advanced exploration collaborative correlation decision-making model C1 = [G1 + G2 + E], where G1 is the coal seam hardness, G2 is the coal seam strike and coal seam boundary data, and E is the gas and hydrological content in the coal seam.

[0042] The cutting collaborative decision-making result is obtained based on the cutting collaborative association decision model C2 in the collaborative decision-making model. Mechanized operations at the tunneling face rely on tunneling equipment for cutting operations. The cutting mechanism includes mechanical, hydraulic, and electrical components. It generally consists of a cutting drum, cutting motor, reducer, hydraulic cylinder, electrical control system, and sensors. The cutting boom, cutting drum, and machine body work together to automatically form the cutting section. The cutting collaborative control model C2 = [Icut + Xcut + Xzt + Ci], where Icut is the cutting collaborative control command, Xcut is the control system of the cutting equipment, Xzt is the machine body attitude data of the cutting equipment, and Ci is the data related to the control of other individual equipment and the cutting collaborative equipment, including drilling, support, transportation, geological environment, dust removal, and ventilation.

[0043] The support coordination decision-making result is obtained based on the support coordination association decision model C3 in the collaborative decision-making model. There are multiple support devices on the working face, and each support device has multiple drill arms. The working status and operation data of each drill arm are transmitted to the collaborative decision-making model, and the work of each support device and its drill arm is coordinated according to the support division of labor determined by geological conditions. The support coordination association decision-making model C3 = [Izh + Z11 + Z12 + ... + Zij + Ci], where Izh is the support coordination control command, Zij is i, which is the device number, j is the drill arm number on the corresponding device, and Ci is the data associated with other single devices and support coordination devices.

[0044] The walking coordination decision-making result is obtained based on the walking coordination association decision model C4 in the collaborative decision-making model. There are multiple walking devices on the working face. Walking coordination is carried out step by step, time by time, and area by area according to the tunneling process and the working mechanism of the complete set of equipment, so as to realize the synchronous forward and backward movement of the walking devices, and ensure the consistency of walking direction and the synchronization of walking speed. The walking coordination association decision-making model C4 = [Ixz + X1 + X2 + ... + Xi + Ci], where Ixz is the walking coordination control command, i in Xi is the walking number of the device, and Ci is the data associated with other single devices and walking coordination devices.

[0045] The transportation collaborative decision-making result is obtained based on the transportation collaborative association decision model C5 in the collaborative decision-making model. Each piece of equipment with transportation function can transmit operational data and fault information to the collaborative control system, including control signals and status data. The transportation units are numbered according to the coal flow sequence. The data of each transportation unit includes number, voltage, current, frequency, bus voltage, speed, pressure, running time, running, fault information, start, and stop. The collaborative control model analyzes and judges based on the issued instructions and the status data of each transportation unit to form the collaborative control of multiple transportation units. The transportation collaborative association decision model C5 = [Iys + 1# + 2# + ... + n# + Ci], where Iys is the transportation collaborative control instruction, 1# + 2# + ... + n# are the operational data of each transportation unit, and Ci is the data of other single equipment associated with the transportation collaborative equipment.

[0046] The power supply coordination decision-making result is obtained based on the power supply coordination correlation decision model C6 in the coordination decision-making model. The tunneling face is equipped with a power supply system, mainly composed of mobile substations and combined feeder switches, to provide power and control for the production and auxiliary systems at the working face. The power supply coordination correlation decision-making model C6 = [Igd + 1# + 2# + ... + n# + Ci], where Igd is the power supply coordination control command, 1# + 2# + ... + n# are the data for each mobile substation and combined switch, and Ci is the data associated with other individual devices and the power supply coordination equipment, mainly including protection coordination and power matching.

[0047] The ventilation coordination decision-making result is obtained based on the ventilation coordination correlation decision-making model C7 in the coordination decision-making model. A local fan ventilation system is configured at the tunneling face, consisting of gas sensors at the face, return airway, and mixed air duct, a local fan variable frequency speed control device, and ventilation ducts. The operating status and data of the local fan ventilation system are transmitted to a distributed centralized control system to achieve coordination between ventilation and human-machine systems. The ventilation coordination correlation decision-making model C7 = [Iay + WS1 + WS2 + WS3 + FA + Ci], where Iay is the ventilation coordination control command, WS1, WS2, and WS3 are gas sensor data, FA is the operating and control data of the fan variable frequency speed control device, and Ci is the data associated with other individual equipment and the ventilation coordination equipment, mainly including power supply, personnel, and equipment control data.

[0048] The dust removal collaborative decision-making results are obtained based on the dust removal collaborative correlation decision-making model C8 in the collaborative decision-making model. Dust removal at the tunneling face generally consists of airborne spraying, coal transport spraying, airborne dust removal, and post-dust removal. The working mechanisms of these devices are driven by hydraulics or electric motors. Airborne spraying is located at the tunneling face and works in coordination with the cutting status and dust concentration. Coal transport spraying is located at the junctions of various transport links and works in coordination with the coal cutting volume and transport speed. Airborne dust removal is located on the tunneling equipment and works in coordination with the coal cutting volume and dust concentration. Dust concentration constitutes the collaborative operation; the post-dust removal consists of a windproof hood, a fan body, a dust collector housing, etc., and the dust removal fan operates in variable frequency speed regulation mode; the dust removal collaborative decision-making model C8=[Idt+AS+TS+AD+RD+Ci], where Idt is the dust removal collaborative control command, AS is the onboard spray, TS is the coal transport spray, AD is the onboard dust removal, RD is the post-dust removal; Ci is the data associated with other single equipment and dust removal collaborative equipment, mainly including collaborative data such as cutting, transportation, and support.

[0049] The drainage collaborative decision-making result is obtained based on the drainage collaborative correlation decision-making model C9 in the collaborative decision-making model. There are two drainage pipelines at the underground tunneling face, with surface water directly discharged to the mining area water sump. The tunneling face drainage system consists of a drainage pump, a magnetic starter, and a water level sensor. When the surface water reaches a certain level, the magnetic starter activates the drainage pump to drain the surface water. The tunneling face advance detection system detects the hydrogeological conditions of the tunneling face, predicting water inflow in advance. Water used for spraying and dust suppression during tunneling is measured using pressure and flow sensors. The drainage collaborative correlation decision-making model C9 = [Idn + SR + MS + Ci], where Idn is the drainage collaborative control command, SR is the sensor, MS is the magnetic starter operating data, and Ci is the data associated with other individual equipment and the drainage collaborative equipment, mainly including collaborative data such as cutting and support.

[0050] The human-machine collaborative decision-making result is obtained based on the human-machine collaborative correlation decision model C10 in the collaborative decision-making model. The operation, maintenance, inspection, and material transportation of equipment at the tunneling face rely on human-machine collaboration. There are generally about 10 operators and assistants at the working face, each with a fixed job and responsibilities. However, the working face environment is complex, labor intensity is high, and safety risks are high. In order to avoid electromechanical accidents and improve the safety of personnel operation, a human-machine collaborative safety protection system has been established at the working face. It mainly consists of a personnel positioning system and a personnel active protection system, which realizes the positioning of personnel at the working face, personnel number statistics and early warning, and collision prevention between personnel and equipment. The human-machine collaborative correlation decision-making model C10 = [Ihm + NPi + PS + Ci], where Ihm is the human-machine collaborative control command, NPi is the number of personnel entering the working face, PS is the working status of personnel at the working face, and Ci is the collaborative positional relationship between other single equipment and personnel.

[0051] The environmental collaborative decision-making result is obtained based on the environmental collaborative correlation decision-making model C11 in the collaborative decision-making model. The environmental sensors at the tunneling face include gas, dust, carbon monoxide, temperature, and humidity, forming an environmental monitoring system at the tunneling face. The environmental collaborative correlation decision-making model C11 = [Ihj + 1# + 2# + ... + n# + Ci], where Ihj is the environmental collaborative control command, 1# + 2# + ... + n# are the data of each sensor, and Ci is the data of other single equipment and environmental collaborative equipment, mainly including cutting, traveling, and ventilation collaborative control equipment.

[0052] The collaborative execution model is used to transmit the collaborative decision-making results of each standalone device generated by the collaborative decision-making model to the corresponding standalone device through a distributed centralized control system.

[0053] Furthermore, the collaborative execution model is used to decompose the collaborative decision-making results of each individual device, forming the work tasks of that individual device and the work tasks of other individual devices associated with it. These tasks are then transmitted to the corresponding individual devices through a distributed centralized control system, which manages and controls each individual device. The distributed centralized control system is as follows: Figure 4 As shown, the distributed centralized control system is placed at the tunneling face. It autonomously connects to various individual devices via the standard communication network of the tunneling face, achieving collaborative control and distributed scheduling management of the tunneling face according to agreed-upon data addresses and defined functions. Each of the tunneling face's functions—advanced detection, cutting, transportation, support, travel, dust removal, power supply, ventilation, drainage, human-machine interaction, and environment—reserves a data interaction area within the distributed centralized control system to store collaborative sensing results for collaborative control of multiple machines at the tunneling face.

[0054] In another exemplary embodiment of this application, such as Figure 5 As shown, this application provides a collaborative control method for coal mine tunneling faces. This method is applied to the aforementioned collaborative control configuration system for coal mine tunneling faces, and includes steps 201 to 204:

[0055] Step 201: Collect sensor data from each standalone device, and process, fuse, and analyze the sensor data to determine the collaborative sensing results.

[0056] Step 202: Construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and send the collaborative perception results to the collaborative decision model based on the communication network architecture.

[0057] Step 203: Based on the collaborative perception results, generate collaborative decision results for each individual device according to the collaborative control instructions, the coupling and association of the tunneling face operations, and the target constraints; the collaborative decision results for each individual device include the work tasks of the individual device and the work tasks of other individual devices associated with the individual device.

[0058] Step 204: Transmit the collaborative decision-making results of each standalone device generated by the collaborative decision-making model to the corresponding standalone device through the distributed centralized control system.

[0059] This application also provides an application scenario in which the above-described collaborative control method for coal mine tunneling faces is applied. Specifically, the collaborative control method for coal mine tunneling faces provided in this embodiment can be applied in coal mine mining scenarios. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0060] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A collaborative control configuration system for a coal mine tunneling face, characterized in that, The coal mine tunneling face collaborative control configuration system includes: a collaborative perception model, a collaborative network model, a collaborative decision-making model, a collaborative execution model, and a distributed centralized control system; The collaborative sensing model is used to collect sensor data from each standalone device, and to process, fuse, and analyze the sensor data to determine the collaborative sensing results. The collaborative network model is used to construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and to send the collaborative perception results to the collaborative decision model based on the communication network architecture. The collaborative network model is a fusion communication network architecture of ground cloud-underground edge-face end, and the communication protocol and communication method are designed according to the communication distance, the controller type of each single device and the safety requirements. The mathematical model formula for the cooperative network model is as follows: N=k1V1+k2V2+...+k n V n ; Where N represents the transmission rate of a single byte of information in the collaborative control configuration model of the tunneling face, and V n This represents the transmission rate of various network types in the communication network architecture, including CAN communication, RS485 communication, and TCP / IP communication; k n This represents the weight value calculated based on the installation location of the communication device, the amount of data transmitted, and the transmission distance. The collaborative decision-making model is used to generate collaborative decision-making results for each individual machine based on the collaborative perception results, according to collaborative control commands, coupling and association of tunneling face processes, and target constraints. The collaborative decision-making results for each individual machine include its own work tasks and the work tasks of other individual machines associated with it. Furthermore, the collaborative decision-making results for each individual machine include collaborative decision-making results for advanced detection, cutting, transportation, support, travel, dust removal, power supply, ventilation, drainage, human-machine collaboration, and environmental collaboration. The collaborative execution model is used to transmit the collaborative decision results of each single device generated by the collaborative decision model to the corresponding single device through the distributed centralized control system. The distributed centralized control system is placed at the tunneling face and autonomously connects each single device through the standard communication network of the working face. It realizes the collaborative control and distributed scheduling management of the working face according to the agreed data address and its defined function.

2. The collaborative control configuration system for a coal mine tunneling face according to claim 1, characterized in that, The collaborative sensing model includes a data acquisition unit, a data processing unit, a data fusion unit, and a data analysis unit; The data acquisition unit is used to collect sensor data and corresponding device information for each standalone device. The data processing unit is used to perform preprocessing operations on the sensor data to obtain preprocessed target sensor data. The preprocessing operations include data filtering, data correction, data anomaly detection, and data transformation; The data fusion unit is used to perform data fusion operations on the target sensor data according to the sensor function and the principle of collaborative operation to obtain the data fusion result; The data analysis unit is used to analyze the collaborative action status based on the data fusion results and the current tunneling face operation, using artificial neural network analysis methods, and to determine the collaborative perception results based on the collaborative action status.

3. The collaborative control configuration system for a coal mine tunneling face according to claim 2, characterized in that, The artificial neural network has the ability to learn, organize, and adapt.

4. The collaborative control configuration system for a coal mine tunneling face according to claim 1, characterized in that, The collaborative execution model is also used to decompose the collaborative decision-making results of each standalone device into the work tasks of the standalone device and the work tasks of other standalone devices associated with the standalone device, and transmit them to the corresponding standalone devices through a distributed centralized control system.

5. A collaborative control method for a coal mine tunneling face, applied to a collaborative control configuration system for a coal mine tunneling face as described in any one of claims 1-4, characterized in that, include: Collect sensor data from each standalone device, and process, fuse, and analyze the sensor data to determine the collaborative sensing results; Construct the communication network architecture in the collaborative control configuration system of the coal mine tunneling face, and send the collaborative perception results to the collaborative decision model based on the communication network architecture; Based on the collaborative perception results, collaborative decision results for each individual device are generated according to collaborative control commands, coupling and association of tunneling face processes, and target constraints. The collaborative decision results for each individual device include the work tasks of the device itself and the work tasks of other individual devices associated with it. The collaborative decision-making results generated by the collaborative decision-making model for each standalone device are transmitted to the corresponding standalone device through a distributed centralized control system.

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