Multi-equipment cooperative control method and system for coal mine work

Abstract

The invention relates to the field of coal mine equipment cooperative control, in particular to a multi-equipment cooperative control method and system for coal mine work. The method comprises the following steps: collecting mine multi-dimensional environment monitoring parameters in real time; environment change trend sensing and environment area distribution positioning are carried out, and an environment trend sensing distribution diagram is constructed; real-time operation state parameters of the drilling machine are obtained, maximum safe mining rate calculation and drilling power adjustment are carried out according to the environment trend perception distribution diagram, and maximum drilling power parameters are generated; according to the maximum drilling power parameter, the ore removal amount in the period is predicted, synchronous carrying coordination is carried out, and a dumper synchronous carrying coordination parameter is obtained; and calculating the latest depth of the drilling machine according to the environment trend perception distribution diagram, and performing environment air quality evaluation to generate a position air quality evaluation value of the drilling machine. By intelligently coordinating a plurality of pieces of coal mine equipment, the overall operation efficiency and safety of a coal mine are improved.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control of coal mine equipment, and particularly to a multi-device collaborative control method and system for coal mine operations. Background Art

[0002] With the continuous development of modern coal mine industry and the in-depth application of information technology, the intelligent and automated level of coal mine operations has been increasing day by day. Especially driven by technologies such as big data, artificial intelligence, and the Internet of Things, coal mine production management and equipment control are gradually tending towards digitization and intelligence. The coal mine operation environment is complex and dangerous, and problems such as safety, production efficiency, and environmental protection during the operation process need to be solved urgently. Therefore, how to improve the safety, production efficiency, and resource utilization rate of coal mine operations has become an important issue in the current coal mine industry.

[0003] Traditional coal mine operations mainly rely on manual operation and the single operation mode of mechanical equipment. The cooperation between equipment usually lacks efficient scheduling and coordination. With the expansion of coal mine production scale, the role of a single device is gradually limited. How to achieve collaborative operation between multiple devices has become the key to improving coal mine operation efficiency and safety. Equipment such as drilling machines, material transport vehicles, and ventilation systems play crucial roles in coal mine operations, but their scheduling and control still mainly rely on manual operation, which is difficult to cope with complex and changeable operation environments, and there are problems such as low operation efficiency, resource waste, large equipment wear, and high safety hazards.

[0004] Especially in the harsh mine environment, environmental factors (such as gas concentration, temperature and humidity changes, oxygen deficiency, etc.) have an important impact on the safety and operation efficiency of equipment. At the same time, changes in operation depth, air quality, equipment load, etc. will directly affect the working state of the drilling machine, the transport efficiency of the material transport vehicle, and the operation effect of the ventilation system. Therefore, how to perform precise equipment scheduling and collaborative control according to real-time environmental changes and equipment states to improve operation efficiency, reduce energy consumption, and ensure safe production has become an urgent problem to be solved.

[0005] Traditional coal mine equipment control systems often lack intelligence and automation, only relying on simple sensors and monitoring systems, unable to effectively integrate various equipment and environmental data in the mine, lacking the ability of global optimization and real-time scheduling. Moreover, existing systems often rely on manual intervention, with defects such as slow response speed, inaccurate decision-making, and difficulty in adapting to emergencies. This kind of limitation has led to problems such as uncoordinated equipment operation, energy waste, and low operation efficiency in coal mine production, and in some cases, may even lead to serious safety accidents. In order to cope with the challenges of complex environments and multi-device cooperation in coal mine operations, an intelligent multi-device collaborative control method is urgently needed. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a multi-device collaborative control method and system for coal mine work to solve at least one of the above technical problems.

[0007] To achieve the above object, the present invention provides a multi-device collaborative control method for coal mine work, including the following steps:

[0008] Step S1: Real-time collect multi-dimensional environmental monitoring parameters of the mine; and perform environmental change trend perception and environmental area distribution positioning to construct an environmental trend perception distribution map;

[0009] Step S2: Obtain the real-time operating state parameters of the drilling machine, and calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map to generate the maximum drilling power parameters;

[0010] Step S3: Predict the ore output within a cycle according to the maximum drilling power parameters, and perform synchronous transportation coordination to obtain the synchronous transportation coordination parameters of the transport vehicle;

[0011] Step S4: Calculate the latest depth of the drilling machine according to the environmental trend perception distribution map, and perform environmental air quality assessment to generate the air quality assessment value of the drilling machine position;

[0012] Step S5: Calculate the minimum air flow rate based on the air quality assessment value of the drilling machine position, and then adaptively adjust the real-time rotation speed and air volume output parameters of the ventilation system to obtain the adaptive ventilation adjustment parameters;

[0013] Step S6: Perform multi-device intelligent control optimization according to the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

[0014] By deploying multiple sensors (such as temperature, humidity, gas concentration, and pressure), this system can monitor environmental changes within a mine in real time. This data collection not only enhances the mine's environmental awareness capabilities but also provides reliable data support for subsequent decision-making. Fusion of multi-dimensional environmental data and trend analysis can help managers predict potential environmental changes, such as changes in gas concentration and temperature fluctuations, thereby proactively preventing potential hazards. By locating the specific location of each sensor and combining it with real-time data to construct an environmental trend perception map, the system provides a visual understanding of the environmental conditions in each area of the mine, enabling timely identification of high-risk areas and improving mine safety. By acquiring the drill's real-time operating parameters (such as load and power) and combining them with environmental data, the system can calculate the maximum safe mining rate for the drill under the current conditions, avoiding overload and ensuring safe equipment operation. The system automatically adjusts the drill's power based on environmental changes and the drill's operating status, ensuring efficient operation without exceeding the safe load, thereby improving coal mine production efficiency and reducing equipment wear and tear. If gas concentration increases or temperature fluctuates dramatically within the mine, the system automatically adjusts the drill's operating status to prevent equipment failure or production accidents caused by environmental issues. Based on the maximum drilling power and the actual operating capacity of the drilling rig, the system predicts ore production and accurately estimates future production based on the production plan, avoiding overproduction or inefficient operations. Based on the ore production forecast and the mine's production progress, the system dynamically calculates and adjusts the load, speed, and route of the material transporters to ensure optimal operating efficiency for each vehicle and reduce empty loads, congestion, and delays during transportation. Through synchronized transport coordination, the system rationally allocates tasks among the material transporters, ensuring efficient coordination among all equipment within the mine, thereby improving overall production efficiency. By calculating the drilling rig's depth in real time, the system accurately assesses the air quality at the drilling rig's location, including oxygen levels and hazardous gas concentrations. This step promptly detects whether increasing depth will lead to air quality issues, thereby preventing safety hazards during operations. Based on the air quality assessment of the area where the drilling rig is located, the system provides real-time feedback on whether there is a risk of hazardous gas levels exceeding standards in the mine, allowing for proactive ventilation and adjustments to operational strategies to ensure worker safety. By assessing air quality and generating an air quality assessment value specific to the drilling rig's location, mine managers can make decisions to optimize work schedules and depths, avoiding drilling operations in areas with high concentrations of hazardous gases. By calculating minimum air flow, the mine's ventilation system can be maintained while meeting minimum safety standards, avoiding energy waste caused by over-ventilation and ensuring air quality within the mine. Based on environmental changes within the mine, the system automatically adjusts the fan speed and air volume output to ensure optimal air circulation throughout the mine, thereby ensuring the health and safety of mine workers.By adaptively adjusting the ventilation system, the energy consumption of the mine can be controlled within a reasonable range, which not only improves the economic benefits of mine operations but also protects the environment and reduces carbon emissions. By intelligently coordinating and optimizing the control parameters of various equipment such as drilling machines, material transport vehicles, and ventilation systems, the collaborative efficiency of each equipment can be greatly improved, and conflicts and resource waste between equipment can be avoided. If sudden changes occur in the mine environment (such as sudden increase in gas concentration, equipment failure, etc.), the optimization model can respond in a timely manner, adjust the working state of relevant equipment, and implement emergency plans to ensure the continuity and safety of mine operations. Through the collaborative control model of multiple equipment, the system can dynamically adjust according to real-time data to ensure that the operating state of the equipment is always in the best working condition, greatly improving the intelligent level of coal mine operations.

[0015] In this specification, a multi-equipment collaborative control system for coal mine work is provided, which is used to execute the multi-equipment collaborative control method for coal mine work as described above, including:

[0016] An environment perception module, which is used to collect multi-dimensional environmental monitoring parameters of the mine in real time; and perform environment change trend perception and environmental area distribution positioning, and construct an environment trend perception distribution map;

[0017] A drilling power adjustment module, which is used to obtain the real-time operating state parameters of the drilling machine, and calculate the maximum safe mining rate and adjust the drilling power according to the environment trend perception distribution map to generate the maximum drilling power parameter;

[0018] A transport coordination module, which is used to predict the ore output within a cycle according to the maximum drilling power parameter, and perform synchronous transport coordination to obtain the synchronous transport coordination parameter of the material transport vehicle;

[0019] An air quality assessment module, which is used to calculate the latest depth of the drilling machine according to the environment trend perception distribution map, and perform environmental air quality assessment to generate the air quality assessment value of the drilling machine position;

[0020] An adaptive ventilation adjustment module, which is used to calculate the minimum air flow rate based on the air quality assessment value of the drilling machine position, and then perform adaptive adjustment of the real-time rotation speed and air volume output parameters of the ventilation system to obtain the adaptive ventilation adjustment parameter;

[0021] An intelligent collaborative control module, which is used to perform multi-equipment intelligent control optimization according to the adaptive ventilation adjustment parameter and the synchronous transport coordination parameter of the material transport vehicle, and construct a multi-equipment intelligent collaborative control optimization model.

[0022] This invention monitors the mine's environmental conditions in real time, ensuring that key parameters such as temperature, humidity, and gas concentrations remain within safe ranges. If an anomaly occurs, an immediate alarm is issued and countermeasures are implemented. Real-time analysis of environmental trends can predict potential hazards such as changes in gas concentration and temperature fluctuations, thereby proactively preventing dangerous incidents within the mine. Accurate distribution maps allow managers to clearly identify areas with environmental safety risks, enabling them to proactively formulate operational plans or scheduling strategies and optimize mine operations. By combining data from various sensors, a multi-dimensional analysis of the mine environment is conducted, providing a more comprehensive environmental assessment and contributing to improved intelligence in mine operations. By combining environmental data with the real-time operating status of the drilling rig, the maximum safe mining rate of the drilling rig under the current conditions can be intelligently calculated, effectively preventing accidents caused by environmental fluctuations or equipment overload. The drilling rig's power is adjusted in real time based on environmental changes and the rig's load to ensure it is not overloaded, thereby extending its lifespan and reducing the risk of failure. Precise power regulation ensures that the drilling rig operates at optimal power output, improving operational efficiency and the sustainability of coal mine production. When the mine's temperature, humidity, and gas levels fluctuate, the system automatically adjusts to ensure the drill rig operates safely and efficiently. By combining the drill rig's operating data with its maximum mining rate, it accurately predicts the mine's output over a given period, avoiding overproduction or underproduction and improving production scheduling accuracy. By synchronizing transport coordination parameters, it rationally allocates and schedules material transport vehicles within the mine, reducing inefficient transportation such as empty loads and delays, and improving the efficiency of coal mine resource utilization. Based on the predicted output, the system optimizes vehicle scheduling based on the vehicle's capacity and transport time, improving overall mine efficiency. The transport coordination module ensures efficient coordination between production and transportation processes, avoiding production bottlenecks caused by improper vehicle scheduling and ensuring operational continuity. Air quality assessments at the drill rig's location provide precise information on environmental data such as oxygen levels and hazardous gas concentrations in the operating area, enabling timely identification of potential air quality issues. Real-time assessments of air quality at the drill rig's location enable early detection of changes in hazardous gas concentrations, enabling timely implementation of ventilation and other safety measures to ensure worker safety. Based on the mine's depth and air quality data, operational safety at different depths is assessed to ensure the controllability of the deep working environment. The air quality assessment module can automatically detect safety hazards such as excessive harmful gases and insufficient oxygen, providing timely warnings and emergency response recommendations to reduce the risk of accidents. Through real-time analysis of the mine environment and air quality, the minimum air circulation required to meet safe production needs is calculated, avoiding resource waste and ensuring that the mine environment remains within safety standards. The ventilation system's wind speed and air volume can be automatically adjusted in real time based on changes in air quality, ensuring good air circulation in all working areas within the mine, reducing harmful gas concentrations, and preventing air pollution.By adaptively adjusting the air volume output of the ventilation system, it avoids energy waste caused by excessive ventilation, while ensuring that the air quality in the mine always remains at a safe level, improving the energy utilization efficiency of the coal mine. Through the intelligent collaborative scheduling of multiple devices, it realizes the collaborative work of devices such as drilling machines, material transport vehicles, and ventilation systems, thereby improving the overall operation efficiency and safety of the coal mine. The system can automatically adjust the operation plan and device parameters according to the real-time environment and device status, ensuring the efficient collaboration of each device and reducing the stagnation and conflicts during operation. The intelligent collaborative control model is based on big data analysis and machine learning algorithms, and can optimize the overall operation of the mine, thereby improving the intelligence and automation level of coal mine operations and reducing manual intervention. When an emergency occurs in the mine, the system can quickly respond and adjust the operation process and device operations through intelligent control and scheduling, minimizing the probability of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flow chart of the steps of a multi-device collaborative control method for coal mine work according to the present invention;

[0024] Figure 2 It is a schematic detailed implementation step flow chart of step S1;

[0025] Figure 3 It is a schematic detailed implementation step flow chart of step S2;

[0026] Figure 4 It is a schematic detailed implementation step flow chart of step S3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] This application example provides a multi-device collaborative control method and system for coal mine work. The execution subjects of the multi-device collaborative control method and system for coal mine work include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of this application. The data processing platform includes but is not limited to at least one of: audio and image management systems, information management systems, and cloud data management systems.

[0029] Please refer to Figures 1 to 4 , the present invention provides a multi-device collaborative control method for coal mine work. The multi-device collaborative control method for coal mine work includes the following steps:

[0030] Step S1: Real-time collect multi-dimensional environmental monitoring parameters of the mine; and perform environmental change trend perception and environmental area distribution positioning to construct an environmental trend perception distribution map;

[0031] Step S2: Obtain the real-time operating state parameters of the drilling rig, calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map, and generate the maximum drilling power parameters;

[0032] Step S3: Predict the ore output within a cycle according to the maximum drilling power parameters and conduct synchronous transportation coordination to obtain the synchronous transportation coordination parameters of the ore transport vehicle;

[0033] Step S4: Calculate the latest depth of the drilling rig according to the environmental trend perception distribution map and conduct an environmental air quality assessment to generate the air quality assessment value of the drilling rig location;

[0034] Step S5: Calculate the minimum air flow rate based on the air quality assessment value of the drilling rig location, and then adaptively adjust the real-time rotation speed and air volume output parameters of the ventilation system to obtain the adaptive ventilation adjustment parameters;

[0035] Step S6: Conduct multi-device intelligent control optimization according to the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the ore transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

[0036] By deploying multiple sensors (such as temperature, humidity, gas concentration, and pressure), this system can monitor environmental changes within a mine in real time. This data collection not only enhances the mine's environmental awareness capabilities but also provides reliable data support for subsequent decision-making. Fusion of multi-dimensional environmental data and trend analysis can help managers predict potential environmental changes, such as changes in gas concentration and temperature fluctuations, thereby proactively preventing potential hazards. By locating the specific location of each sensor and combining it with real-time data to construct an environmental trend perception map, the system provides a visual understanding of the environmental conditions in each area of the mine, enabling timely identification of high-risk areas and improving mine safety. By acquiring the drill's real-time operating parameters (such as load and power) and combining them with environmental data, the system can calculate the maximum safe mining rate for the drill under the current conditions, avoiding overload and ensuring safe equipment operation. The system automatically adjusts the drill's power based on environmental changes and the drill's operating status, ensuring efficient operation without exceeding the safe load, thereby improving coal mine production efficiency and reducing equipment wear and tear. If gas concentration increases or temperature fluctuates dramatically within the mine, the system automatically adjusts the drill's operating status to prevent equipment failure or production accidents caused by environmental issues. Based on the maximum drilling power and the actual operating capacity of the drilling rig, the system predicts ore production and accurately estimates future production based on the production plan, avoiding overproduction or inefficient operations. Based on the ore production forecast and the mine's production progress, the system dynamically calculates and adjusts the load, speed, and route of the material transporters to ensure optimal operating efficiency for each vehicle and reduce empty loads, congestion, and delays during transportation. Through synchronized transport coordination, the system rationally allocates tasks among the material transporters, ensuring efficient coordination among all equipment within the mine, thereby improving overall production efficiency. By calculating the drilling rig's depth in real time, the system accurately assesses the air quality at the drilling rig's location, including oxygen levels and hazardous gas concentrations. This step promptly detects whether increasing depth will lead to air quality issues, thereby preventing safety hazards during operations. Based on the air quality assessment of the area where the drilling rig is located, the system provides real-time feedback on whether there is a risk of hazardous gas levels exceeding standards in the mine, allowing for proactive ventilation and adjustments to operational strategies to ensure worker safety. By assessing air quality and generating an air quality assessment value specific to the drilling rig's location, mine managers can make decisions to optimize work schedules and depths, avoiding drilling operations in areas with high concentrations of hazardous gases. By calculating minimum air flow, the mine's ventilation system can be maintained while meeting minimum safety standards, avoiding energy waste caused by over-ventilation and ensuring air quality within the mine. Based on environmental changes within the mine, the system automatically adjusts the fan speed and air volume output to ensure optimal air circulation throughout the mine, thereby ensuring the health and safety of mine workers.By adaptively adjusting the ventilation system, the energy consumption of the mine can be controlled within a reasonable range, which not only improves the economic benefits of mine operations, but also protects the environment and reduces carbon emissions. By intelligently coordinating and optimizing the control parameters of various devices such as drilling machines, material transport vehicles, and ventilation systems, the collaborative efficiency of each device can be greatly improved, avoiding conflicts and resource waste between devices. If sudden changes occur in the mine environment (such as sudden increase in gas concentration, equipment failure, etc.), the optimization model can react in a timely manner, adjust the working state of relevant devices, and implement emergency plans to ensure the continuity and safety of mine operations. Through the collaborative control model of multiple devices, the system can make dynamic adjustments according to real-time data to ensure that the operating state of the devices is always in the best working state, greatly improving the intelligent level of coal mine operations.

[0037] In the embodiment of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of a multi-device collaborative control method for coal mine work according to the present invention. In this example, the steps of the multi-device collaborative control method for coal mine work include:

[0038] Step S1: Real-time collect multi-dimensional environmental monitoring parameters of the mine; and perform environmental change trend perception and environmental area distribution positioning to construct an environmental trend perception distribution map;

[0039] In this embodiment, according to the environmental characteristics and safety requirements of the mine, appropriate sensors are selected to monitor multi-dimensional environmental parameters. Commonly used sensors include temperature sensors, humidity sensors, gas sensors (such as carbon monoxide, methane, oxygen, etc.), and pressure sensors. Each sensor should have high precision and good anti-interference ability. Select a temperature sensor with a measurement range of -20°C to 50°C and an accuracy of ±0.5°C; a humidity sensor with a measurement range of 0 - 100% and an accuracy of ±3%; a carbon monoxide sensor with a measurement range of 0 - 1000 ppm and an accuracy of ±5 ppm. Ensure that the sensors can work properly in the complex environment of the mine. Configure a data acquisition system to transmit the data of all sensors to the central control system in real time through wireless communication (such as LoRa, Zigbee, or Wi-Fi). Ensure that the data acquisition frequency is high enough (such as once per second) to monitor environmental changes in a timely manner. Set the sensors to collect and upload data every second, and at the same time ensure that the data format is unified for subsequent processing. The data format can be set as "timestamp, sensor type, measured value". Receive and store the real-time collected environmental parameter data in the central control system. The data should be stored in an efficient database for subsequent query and analysis. At the same time, set up a data backup mechanism to prevent data loss. Use a relational database (such as MySQL) to store data and create appropriate indexes to improve query efficiency. Regularly back up the data to ensure data integrity. Preprocess the collected environmental data to remove noise and outliers to improve the accuracy of data analysis. Simple statistical methods (such as mean and standard deviation) can be used to judge abnormal data. Set the temperature range from -20°C to 50°C. If a measured value is -25°C or 55°C in a certain measurement, it is marked as abnormal and excluded. At the same time, smooth the data (such as moving average) to reduce the impact of instantaneous fluctuations. Use statistical analysis methods (such as time series analysis, linear regression analysis, etc.) to perform trend analysis on the cleaned environmental data to identify the change trends of environmental parameters. The sliding window technique can be used to analyze the parameter changes in a certain past time period. Set a time window of 10 minutes and perform trend analysis on the temperature and humidity data in the past 10 minutes, calculate their means and change rates, and identify whether there are obvious upward or downward trends. Extract the analyzed trend characteristics and record them in the database for subsequent monitoring and analysis. These characteristics should include the type of trend (such as upward, downward, stable) and its change speed. If the analysis result shows that the temperature has risen by 3°C in the past 10 minutes, it is recorded as "temperature rising, change speed is 0.3°C / min". At the same time, generate a trend report for subsequent analysis. Use the three-dimensional positioning system of the mine (such as GPS, ultra-wideband positioning, etc.) to add spatial coordinate information to the environmental parameter data of each sensor. This step ensures that each data point can correspond to its specific location in the mine.If a certain sensor is located at (100, 200, -50) meters, coordinate information is added to the data record to form "timestamp, sensor type, measurement value, coordinates". Based on the spatially located data, a regional distribution model of environmental parameters is constructed. Interpolation methods (such as Kriging interpolation, inverse distance weighting, etc.) can be used to convert discrete sensor data into a continuous spatial distribution map. Using the Kriging interpolation method, temperature and humidity distribution maps of the entire mine area are generated based on the environmental parameter data of each sensor.

[0040] Step S2: Obtain the real-time operating state parameters of the drilling rig, calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map, and generate the maximum drilling power parameter;

[0041] In this embodiment, multiple sensors should be installed on the drilling machine to monitor its operating parameters in real time. The main sensors include a rotational speed sensor, a torque sensor, a power sensor, and a pneumatic pressure sensor. High-precision sensors are selected to ensure the reliability of the data. The rotational speed sensor should have a measurement range of 0 - 3000 RPM with an accuracy of ±1 RPM; the torque sensor has a range set at 0 - 500 Nm with an accuracy of ±0.5 Nm. A suitable power sensor is selected to ensure it can operate stably under high load conditions. A real-time data acquisition system is configured to transmit the data of all sensors to the central control system via wireless or wired communication methods. Ensure a high data acquisition frequency (such as once per second) for real-time monitoring of the operating status of the drilling machine. Set the acquisition period of each sensor to 1 second, and the data format is "timestamp, sensor type, measured value". Use the data acquisition module to summarize and send the data to the data center. Combine the real-time environmental parameters (such as temperature, humidity, gas concentration, etc.) with the environmental trend perception distribution map to analyze the impact of the current environmental conditions on the maximum safe mining rate. Ensure that environmental changes can be promptly reflected in the mining rate calculation. If the current environmental trend shows a temperature of 25°C, a humidity of 70%, and a carbon monoxide concentration of 40 ppm, then calculate the mining rate by combining these data. Based on the environmental parameters and the performance parameters of the drilling machine, establish a calculation model for the maximum safe mining rate. This model should consider the ore characteristics, the power output of the drilling machine, and the influence of environmental factors. According to the maximum safe mining rate, formulate a power adjustment strategy for the drilling machine. Ensure that the drilling machine operates within a safe range to avoid equipment damage or safety accidents caused by overload. Set a power adjustment coefficient. If the current mining rate is 150 m³ / h, then adjust the power according to the maximum safe mining rate to ensure that the power output of the drilling machine is between 80% - 100%. Through the real-time monitoring system, monitor the power output of the drilling machine and make dynamic adjustments according to the maximum safe mining rate. Ensure operation within the safe range at all times. If the real-time monitoring shows that the power output of the drilling machine is 360 kW while the maximum safe mining rate is calculated to be 450 kW, then increase the power to 420 kW through the adjustment control system to achieve the best mining effect. Record the calculated maximum drilling power parameters in the database to ensure that all parameters can be traced and analyzed. Generate reports for subsequent evaluation and optimization. Generate reports recording "timestamp, maximum drilling power, actual power output", and set up a regular review mechanism to evaluate the effectiveness of the power adjustment strategy.

[0042] Step S3: Predict the ore output within the period based on the maximum drilling power parameters and perform synchronous transportation coordination to obtain the synchronous transportation coordination parameters of the transport vehicle;

[0043] In this embodiment, real-time maximum drilling power parameter data is collected to ensure accurate reflection of the drill's actual operating status. This data should include maximum power output, actual power output, and various environmental factors (such as ore characteristics and gas concentration). If the maximum drilling power is 450 kW, the actual power is 360 kW, and the ore extraction efficiency is set at 0.5 tons / kW·hour, the mine's production capacity can be effectively calculated. Based on the maximum drilling power parameters and ore extraction efficiency, an ore extraction prediction model is established. This model should consider the drill's operating time and efficiency to ensure accurate ore extraction predictions. By substituting the actual power, ore extraction efficiency, and operating time into the prediction model, the ore extraction rate within a specific period is calculated. Based on the mine's transportation needs and actual conditions, the number of haul trucks required and the load capacity of each truck are estimated to ensure that the ore extraction transportation requirements can be met. A haul truck scheduling plan is developed to ensure that during peak ore extraction periods, all trucks can work efficiently and coordinately to avoid empty or overloaded vehicles. Based on the total transportation time, develop a scheduling plan for transport vehicles to ensure that each vehicle completes its task within the specified time. Configure a real-time monitoring system to track the operating status of the material transport vehicle in real time, including vehicle speed, location, load, and transportation progress. Ensure that the transportation plan can be adjusted in a timely manner. Use the GPS positioning system to monitor the location information of each vehicle, and combine it with the transportation scheduling system to ensure that the dispatcher can grasp the transportation dynamics in real time. Dynamically adjust the scheduling strategy of the transport vehicle based on real-time data. If a vehicle is found to have a malfunction or delay, the task should be reassigned in a timely manner to ensure transportation efficiency. If it is monitored that a vehicle fails to arrive at the processing plant on time due to a malfunction, the scheduling of other vehicles must be adjusted immediately to ensure the timely transportation of the output. Regularly evaluate the effectiveness of the transportation coordination strategy, collect data feedback during the transportation process, and make optimization adjustments. Ensure continuous improvement of transportation efficiency in actual operations.

[0044] Step S4: Calculate the latest depth of the drilling rig based on the environmental trend perception distribution map and perform an environmental air quality assessment to generate an air quality assessment value at the drilling rig location;

[0045] In this embodiment, a high-precision depth sensor is installed on the drilling machine to monitor the drilling depth of the drilling machine in real time. Technologies such as cable sounding, ultrasonic sounding, or laser ranging can be used to ensure stable operation in the complex environment of the mine. A cable sounding device is selected with a measurement range of 0 to 1000 meters and an accuracy of ±0.1 meters to ensure accurate measurement of the depth change of the drilling machine. A data acquisition system is configured to upload the data of the depth sensor to the central control system in real time at regular intervals. The data acquisition frequency is set to once per second to obtain the real-time depth information of the drilling machine in a timely manner. The data format is set to "timestamp, depth value", and the data is transmitted to the control center through wireless communication (such as LoRa or Wi-Fi) to ensure the timeliness and accuracy of the data. After receiving and storing the depth data, the data is analyzed in real time to identify the current latest depth and compared with the historical data to ensure the continuity and accuracy of the depth measurement. The latest depth of the drilling machine is combined with the environmental trend perception distribution map to obtain the environmental parameters (such as temperature, humidity, gas concentration) corresponding to this depth. This step ensures that the air quality assessment can be based on the latest environmental conditions. Using the environmental trend distribution map, if the current depth of the drilling machine is 150 meters, the temperature at this depth is extracted as 22°C, the humidity is 60%, the carbon monoxide concentration is 30 ppm, and the methane concentration is 0.5%. Based on the extracted environmental parameters, an air quality assessment model is established. This model should comprehensively consider factors such as oxygen content, harmful gas concentration, temperature and humidity to ensure the accuracy of the assessment results. The air quality assessment formula is set as:

[0046] AQI = ; where AQI is the air quality index, is the oxygen concentration (%), C(CO) is the carbon monoxide concentration (ppm), C( ) is the methane concentration (%), T is the temperature, H is the humidity. Substitute the extracted environmental parameters into the air quality assessment model to calculate the air quality assessment value of the drilling machine location. Ensure that the assessment result can accurately reflect the current air quality status. If the AQI value calculated using the above formula is 75, it indicates good air quality. Record the calculation result in the database and generate an "Air Quality Assessment Report" containing "timestamp, latest depth, AQI value, relevant environmental parameters". Visualize the air quality assessment result and generate charts for managers to quickly understand the current air quality status. Forms such as heat maps and bar charts can be used to display different air quality levels. Generate an air quality analysis report to show the AQI values at different depths and display them in real time in the monitoring system for decision-makers to take corresponding measures in a timely manner.

[0047] Step S5: Calculate the minimum air flow rate based on the air quality evaluation value at the drill rig location, and then adaptively adjust the real-time rotation speed and air volume output parameters of the ventilation system to obtain the adaptive ventilation adjustment parameters;

[0048] In this embodiment, the air quality evaluation value at the drill rig location is obtained from the previous step, including oxygen concentration, harmful gas concentrations (such as carbon monoxide and methane), and relevant temperature and humidity data. These data are the basis for calculating the minimum air flow rate. Suppose the air quality evaluation results show that the oxygen concentration is 19.2%, the carbon monoxide concentration is 60 ppm, the methane concentration is 0.5% (by volume), and the temperature is 22 °C and the humidity is 70%. Based on the air quality evaluation value and the mine safety standards, a calculation model for the minimum air flow rate is established. This model should take into account the safety threshold of oxygen content and the concentration limit of harmful gases. The calculation formula for the minimum air flow rate is set as: , where is the minimum air flow rate (m³ / h), is the safe oxygen concentration (such as 19.5%), is the current harmful gas concentration, is the safety threshold of harmful gases, and k and m are the corresponding adjustment coefficients. Substitute the collected air quality evaluation data into the model to calculate the minimum air flow rate. Suppose the safety threshold of harmful gases is 50 ppm, and the calculated minimum air flow rate is: Q(min)=100⋅(19.5−19.2)+50⋅(60−50)=30+500=530 m³ / h. Before performing real-time adjustment, the baseline parameters of the ventilation system need to be set, including the maximum air volume, minimum rotation speed, and wind speed of the system. These baseline parameters will be used as a reference for dynamic adjustment. Set the maximum air volume of the ventilation system to 1200 m³ / h and the minimum rotation speed to 600 RPM to ensure that the system operates within a safe range. Configure a real-time monitoring system to track the wind speed, air volume, rotation speed, and other parameters of the ventilation system in real time to ensure that adjustments can be made in a timely manner. The monitoring system should have the ability to collect high-frequency data (such as once per second). Set up air volume sensors and rotation speed sensors to ensure that the current air volume and rotation speed data can be fed back in real time, forming data records of "timestamp, air volume, rotation speed". According to the calculated minimum air flow rate and real-time monitoring data, implement an adaptive adjustment strategy. Ensure that the ventilation system can be dynamically adjusted according to the real-time air quality requirements. If it is real-time monitored that the current air volume is 400 m³ / h while the minimum requirement is 530 m³ / h, the system should automatically increase the air volume and adjust the fan rotation speed to 700 RPM to meet the demand. Record the parameters of each adjustment in the database and generate an adjustment log for subsequent analysis and optimization. Ensure that all adjustment processes are well-documented to facilitate the evaluation of the performance of the ventilation system.

[0049] Step S6: Perform multi-device intelligent control optimization based on the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

[0050] In this example, adaptive ventilation control parameters are collected from the ventilation system's real-time monitoring system. These parameters include the current air volume, air speed, rotation speed, and any adjusted set values. This ensures that all data reflects the ventilation system's response under varying environmental conditions. For example, assume the current air volume is 700 m³ / h, the rotation speed is 800 RPM, and the set minimum air volume is 530 m³ / h. This data is recorded and its integrity is ensured in the database for subsequent analysis. The ventilation control parameters are integrated with parameters of other related equipment (such as the scheduling status and transport capacity of material transport vehicles) to form a comprehensive data set. Visualization tools are used to present this data, helping managers quickly understand the current operating status. A chart is used to display the relationship between air volume and the number of material transport vehicles. If the current air volume meets air quality standards and the transport capacity can meet mine production requirements, the system is in good condition. Real-time status data for each material transport vehicle is collected, including information such as each vehicle's current location, load, and estimated arrival time. This data forms the basis for developing scheduling strategies, ensuring coordinated operation and avoiding empty or overloaded vehicles. If the hauler monitoring system reports that 30 vehicles are currently operating, including 10 loading, 15 transporting, and 5 on standby, this information can be used to make reasonable scheduling decisions. The real-time status of the haulers can be combined with ventilation control parameters to analyze their impact on the overall mine efficiency. Optimization algorithms (such as linear programming or genetic algorithms) can be used to develop an optimal scheduling plan to ensure coordination between transport and ventilation. Linear programming can be used to calculate how to schedule haulers within a specific time period to maximize transport efficiency and ensure compliance with air quality standards. Based on adaptive ventilation control parameters and hauler synchronization coordination parameters, a framework for a multi-device intelligent control optimization model is designed. The model should be able to receive real-time status information from each device and dynamically adjust according to pre-set objectives. The model objective is set as "maximizing ore output and transport efficiency while ensuring safe air quality," and weights for each parameter are defined to facilitate subsequent optimization. An appropriate optimization algorithm is selected to ensure the model can effectively handle the coordinated operation of multiple devices. Constraints (such as air flow rate and maximum hauler load) are incorporated into the model to ensure the feasibility of the solution. A genetic algorithm is used to solve the model, searching for the optimal ventilation and transportation coordination solution through iterative optimization. A fitness function is set to evaluate each generation of solutions, ensuring that they gradually approach the optimal solution. The optimization model is run and its output is verified to ensure that the generated scheduling plan and ventilation control strategy meet actual operational requirements. The model's accuracy and efficiency are evaluated by comparing it with historical data. If the model recommends adjusting the air volume to 750 m³ / h and scheduling material transport trucks under this condition, and actual monitoring shows that air quality meets standards and transportation efficiency improves, the model is proven to be effective. The optimization model's results are recorded in a database to ensure that all decisions are traceable.Establish a feedback mechanism, collect data regularly, evaluate the performance of the model, and make corresponding adjustments and optimizations. Generate a report recording "timestamp, optimization results, implementation effects", and adjust the model parameters according to the feedback to ensure efficient operation in a changing environment.

[0051] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0052] Real-time collect multi-dimensional environmental monitoring parameters of the mine based on multi-sensor nodes;

[0053] Perform three-dimensional spatial positioning on each multi-sensor node, and extract the position coordinates of each sensor node;

[0054] Perform multi-gas type identification based on the multi-dimensional environmental monitoring parameters of the mine to obtain various gas types in the mine;

[0055] Calculate the real-time gas concentration change according to various gas types in the mine, and generate a concentration change curve for each gas type;

[0056] Identify the air temperature and humidity fluctuation characteristics of the multi-dimensional environmental monitoring parameters of the mine;

[0057] Perceive the environmental change trend for the air temperature and humidity fluctuation characteristics and the concentration change curve of each gas type to generate environmental change trend characteristics;

[0058] Perform environmental area distribution positioning on the environmental change trend characteristics based on the position coordinates of each sensor node, and construct an environmental trend perception distribution map.

[0059] In this embodiment, a variety of sensor nodes are installed at different positions in the mine to monitor environmental parameters in real time. The types of sensors should include temperature, humidity, gas concentration (such as methane, carbon monoxide, oxygen, etc.), and barometric pressure sensors. Each sensor node should use wireless communication technology (such as LoRa or Zigbee) for data transmission. A sensor node is installed every 10 meters in the key areas of the mine to ensure that the coverage of environmental parameters and the frequency of data collection can meet the monitoring requirements. It is set that each node collects data every 10 seconds to ensure timely update of the environmental status. Configure the real-time data collection mechanism of the sensor node to ensure that data can be uploaded to the central monitoring system in a timely and accurate manner. The sensors should be self-tested regularly to confirm their working status and troubleshoot faults. After each sensor node collects data, set the data packet format as "timestamp, temperature, humidity, gas concentration", and transmit it to the data center through the wireless network. Receive and store the data from all sensor nodes in the central monitoring system. Conduct preliminary processing on the received data, including denoising, outlier detection, and data format conversion, to ensure the accuracy of subsequent analysis. Use a filtering algorithm to smooth the collected temperature and humidity data, and set a threshold range (such as the temperature is between -10°C and 50°C) to identify and remove outliers. Adopt three-dimensional positioning technology (such as ultra-wideband positioning, Bluetooth positioning, or inertial navigation) to perform spatial positioning on each sensor node. By setting up base stations in the mine and using the signals of sensor nodes, calculate the specific position coordinates of each node. Set the base station spacing to 20 meters to ensure signal coverage. Use the time difference positioning algorithm to calculate the distances between each sensor node and multiple base stations, so as to obtain its three-dimensional coordinates (X, Y, Z). Integrate the positioning results with the sensor data to ensure that the environmental monitoring data of each sensor node corresponds one-to-one with its spatial coordinates. Perform position correction to improve the positioning accuracy. Adopt the Kalman filtering method to correct the positioning data, and combine the moving state and environmental conditions of the sensor to reduce the positioning error. Use data mining and machine learning algorithms (such as support vector machines, neural networks, etc.) to analyze the gas concentration data collected by the sensors to identify various gas types in the mine. Set a training set containing data of known gas concentrations and types, use the algorithm to train the model, and set the target recognition accuracy to be above 90%. By continuously monitoring the gas concentration changes, generate the concentration change curves of each gas type. Compare the real-time data with the historical data to analyze the fluctuations of the gas concentration. Set the concentration data of each gas to be recorded in hours, and generate a concentration change curve graph to facilitate observing the short-term and long-term trends of the gas concentration. Record the concentration change curves of each gas, identify the significant change points (such as peaks and valleys) of the concentration, and analyze their possible causes (such as mine operation activities or natural gas releases). Display the concentration change data of each gas in the form of a chart, and mark important change events in the chart for subsequent analysis.Statistically analyze the collected temperature and humidity data to identify its fluctuation characteristics. Use time series analysis methods to calculate the hourly temperature and humidity changes and generate fluctuation curves. Set the standard fluctuation ranges for temperature and humidity. If the temperature fluctuation exceeds ±2°C or the humidity exceeds ±5%, it is recorded as an abnormal fluctuation. Extract the main characteristics of temperature and humidity fluctuations, such as mean, standard deviation, maximum value, and minimum value, and analyze their impact on the mine environment. Combine with the gas concentration changes to identify their correlation. If there is a significant correlation between the temperature and humidity fluctuations and the changes in a certain gas concentration, record this discovery for in-depth analysis. Generate a comprehensive environmental change trend feature based on the air temperature and humidity fluctuation characteristics and the gas concentration change curve, which will help identify the overall change situation and potential risks in the mine environment. Set the calculation formula for the trend feature, including the weighted average of temperature and humidity and the weighted accumulation of gas concentration, to obtain a comprehensive environmental change index. Based on the position coordinates of the sensor nodes, locate the spatial distribution of the environmental change trend feature to identify the environmental change situations in different regions. Combine with a 3D model to display the environmental status in different regions. Use heat maps or contour maps to show the spatial distribution of the environmental change index and distinguish high-risk areas from normal areas. Combine the environmental change trend feature with the spatial position of the sensor nodes to construct an environmental trend perception distribution map for easy monitoring and decision-making. Generate a comprehensive distribution map of the mine environmental status, marking the regions with different gas concentrations and temperature and humidity fluctuations, so as to facilitate the mine management personnel to take measures in a timely manner.

[0060] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0061] Obtain the real-time operating state parameters of the drilling machine; calculate the operating power and maximum operating load of the real-time operating state parameters of the drilling machine;

[0062] Track the real-time position of the drilling machine and mark the environmental trend perception distribution map in real time to obtain the real-time position map of the drilling machine;

[0063] Calculate the maximum safe mining rate of the current mine area based on the real-time position map of the drilling machine to obtain the maximum safe mining rate;

[0064] Based on the maximum safe mining rate and the maximum operating load, adjust the operating power to generate the maximum drilling power parameter.

[0065] In this embodiment, a variety of sensors are installed on the drilling machine, including a rotational speed sensor, a torque sensor, and a pressure sensor, to collect its operating state parameters in real time. These sensors should have high precision and response speed to ensure the timeliness and accuracy of the data. The rotational speed sensor should be able to accurately measure in the range of 0 - 2000 RPM, and the measurement range of the torque sensor is set to 0 - 500 Nm to adapt to the working conditions of the drilling machine. The pressure sensor is set within the normal pressure range to monitor pressure changes. Design a real-time data acquisition system to aggregate the data of each sensor and transmit it to the central control system regularly. Use wireless communication technologies (such as LoRa or Wi-Fi) to facilitate data transmission in the complex environment of the mine. Set each sensor to collect data once per second and send the data to the data center through the wireless network to ensure real-time update of the operating state of the drilling machine. After obtaining the data, perform preliminary data processing, including denoising, outlier detection, and data format standardization, to ensure the accuracy of subsequent calculations. Set data cleaning criteria to mark data outside the set range (such as rotational speed higher than 2000 RPM or torque higher than 500 Nm) as abnormal, and record and eliminate it. Adopt a power calculation formula to calculate the real-time operating power of the drilling machine in combination with sensor data. P = T×ω / 1000, where P is the power (kW), T is the torque (Nm), and ω is the angular velocity (rad / s). If the torque recorded by the sensor is 300 Nm and the rotational speed is 1500 RPM (converted to ω = 1500×2π / 60≈157.08 rad / s), the calculated power result is: P = 300×157.08 / 1000≈47.124 kW. According to the design parameters and historical operating data of the drilling machine, evaluate its maximum operating load, which should take into account safety factors and be adjusted in combination with the actual working conditions. Through historical data analysis, determine that the maximum load of the drilling machine under the most severe conditions is 600 Nm, and make appropriate corrections in combination with the current working environment. Record the calculated real-time power and maximum operating load in the database for subsequent analysis and comparison. Ensure the integrity and real-time update of the data. Generate a data table containing "timestamp, real-time power, maximum operating load" and update it regularly for subsequent performance analysis. Install a GPS or other positioning system on the drilling machine to obtain its three-dimensional position coordinates in real time. Ensure that the positioning system can provide accurate position data in the complex environment of the mine. Set the GPS positioning accuracy to ±5 meters, or use ultra-wideband positioning technology to improve the positioning accuracy to ensure accurate tracking of the real-time position of the drilling machine. Transmit the obtained position information to the central control system and update the environmental trend perception distribution map in real time in combination with environmental monitoring data. Use data visualization technology to display the operating trajectory of the drilling machine. Set the positioning data to be updated once per second and mark the position of the drilling machine on the environmental trend distribution map to form a dynamically updated visualization interface. Update the environmental trend perception distribution map in combination with the real-time position of the drilling machine.The figure should show the changes in environmental parameters within the mine, such as gas concentration, temperature, humidity, etc. Use heat maps or contour maps to display the changes in gas concentration, temperature, and humidity in different areas, and mark the real-time position and operating trajectory of the drilling machine on the map for easy analysis and decision-making. Calculate the maximum safe mining rate for the current mine area based on the maximum operating load of the drilling machine, geological conditions, and safety standards. The mining rate should take into account equipment performance and environmental factors. Set the formula for calculating the safe mining rate as: Vmax = P(max) / k, where P(max) is the maximum allowable power and k is the mining efficiency factor (set to 0.8 for example). If the maximum operating load is 600 Nm and the calculated maximum allowable power is 50 kW, then the maximum mining rate is: V max = 50 / 0.8 = 62.5 m / h. During the mining process, monitor the operating parameters of the drilling machine in real time and dynamically adjust the mining rate according to environmental changes and equipment status to ensure safe operation. If an abnormal increase in gas concentration is detected, the mining rate should be reduced to ensure the safety of the equipment and personnel. Develop a power adjustment strategy for the drilling machine based on the maximum safe mining rate and the current maximum operating load. Ensure that the drilling machine operates within a safe range and avoid overloading. Set the maximum drilling power to 80% of the current maximum operating load to ensure the safety and reliability of the equipment. Generate the maximum drilling power parameter according to the calculation result and set it as the operating benchmark for the monitoring system. Monitor and adjust the power output of the drilling machine in real time to match the actual working requirements. If the maximum operating load is 600 Nm, the maximum drilling power is set to 480 kW to ensure safe excavation within this power range. Implement a real-time monitoring mechanism to dynamically adjust the power output based on the current operating status of the drilling machine. Ensure operation within a safe range at all times and provide timely feedback and adjustment according to environmental changes. Set up a real-time feedback system that automatically issues an alarm and adjusts the power when the power output exceeds the set range.

[0066] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of the said step S3 include:

[0067] Predict the ore output within the cycle based on the maximum drilling power parameter to obtain the predicted ore output value;

[0068] Obtain the mine material transportation log; calculate the number of transport vehicles in the area and the carrying capacity of each transport vehicle based on the mine material transportation log;

[0069] Calculate the round-trip distance of material transportation and the average carrying duration based on the mine material transportation log;

[0070] Conduct dynamic transportation demand scheduling for the predicted ore output value to generate the material transportation demand value;

[0071] Synchronously coordinate the material transportation demand value based on the round-trip distance of material transportation, the average transportation duration, the number of material transportation vehicles in the area, and the transportation capacity of each material transportation vehicle to obtain the synchronous transportation coordination parameters of the material transportation vehicles.

[0072] In this embodiment, an ore output prediction model is established based on historical data and the maximum drilling power parameter. This model should consider the efficiency of the drilling machine, ore characteristics, and the working cycle. Methods such as linear regression or time series analysis can be used for modeling. The ore output prediction formula is set as: Q = P(max) × t / E, where Q is the ore output (tons), P(max) is the maximum drilling power (kW), t is the working time (hours), and E is the ore mining efficiency (tons / kW·hour). Assuming the maximum drilling power is 480 kW, the expected working time is 8 hours, and the ore mining efficiency is 0.5 tons / kW·hour, the calculated ore output is: 𝑄 = 480 × 8 / 0.5 = 7680 tons. Collect relevant historical data, including past ore output, the power usage of the drilling machine, and ore characteristics, and input them into the model for prediction. Ensure the integrity and accuracy of the data to improve the reliability of the prediction. Collect the ore output data for the past quarter, and statistically analyze its average value and fluctuations to provide sufficient reference information for the model. Collect the material transportation logs in the mine, including information such as the time of each transportation, the number of transport vehicles, the carrying capacity, the starting point, and the ending point. Ensure the comprehensiveness of the log records for subsequent analysis. Set the log record format as "time, vehicle number, carrying capacity, starting point, ending point" to ensure the clarity and standardization of the information. Sort out and clean the collected transportation logs, remove duplicate records and outliers to ensure the accuracy and usability of the data. Check the timestamp and vehicle status of each record, and eliminate the transportation records during non-working hours (such as at night or on rest days). Store the cleaned transportation logs in the database and set appropriate indexes for quick query and analysis. Ensure the security and integrity of the data. Use a relational database management system to store the data and create indexes to speed up subsequent querying. According to the transportation logs, count the number of transport vehicles for each transportation. Based on this, calculate the total number of transport vehicles in a specific area. Set a statistical time period (such as one day), extract all the transportation records within this time period from the logs, and accumulate the number of transport vehicles. Assume the total number of transport vehicles is 20. Calculate the average carrying capacity of each transport vehicle based on the carrying capacity information recorded in the transportation logs. Ensure that the calculation result can reflect the actual transportation capacity. If 20 vehicles transported a total of 400 tons of materials in one day, the average carrying capacity of each vehicle is: average carrying capacity = 400 tons / 20 vehicles = 20 tons / vehicle. According to the transportation logs, define the transportation routes of the transport vehicles and calculate the round-trip distance of each route. Ensure the accuracy of the route information to reflect the actual transportation situation. If the one-way distance from the mine to the processing plant for the transportation route is 5 kilometers, the round-trip distance is 10 kilometers. Record the start time and end time of each transportation and calculate the average transportation duration. Combine the time information in the transportation logs for statistical analysis.If the total carrying time of the transportation records of 20 vehicles in one day is 10 hours, then the average carrying time per vehicle is: Average carrying time = 10 hours / 20 vehicles = 0.5 hours / vehicle. According to the predicted ore output value and the current transportation capacity, a dynamic carrying demand calculation model is established. This model should consider the number of transport vehicles, carrying capacity, and average carrying time. Set the carrying demand calculation formula as: D = Q / C, where D is the carrying demand (vehicles), Q is the predicted ore output value, and C is the carrying capacity per vehicle (tons). If the predicted ore output value is 7,680 tons and the carrying capacity per vehicle is 20 tons, then the carrying demand is: D = 7,680 / 20 = 384 vehicles. According to the real-time data and calculation results, dynamically adjust the scheduling strategy of transport vehicles to ensure transportation efficiency and safety. Optimization algorithms (such as genetic algorithms or linear programming) can be used for scheduling optimization. If there are currently 30 available vehicles, then determine the best vehicle allocation plan through the scheduling optimization algorithm to meet the carrying demand. According to the round-trip distance of transporting materials, average carrying time, and transportation demand, establish a synchronous carrying coordination mechanism for material transport vehicles. Ensure that multiple material transport vehicles can work together to improve the overall transportation efficiency. Set the coordination parameter as the "starting departure time interval" to ensure that each vehicle departs within a specific time interval to avoid traffic conflicts. By calculating the carrying demand and transportation capacity, allocate the carrying tasks and time for each vehicle. Ensure that the task allocation is fair and efficient. If the total carrying demand is 384 vehicles and there are currently 30 available vehicles, then set the task volume per vehicle as: Task volume = 384 / 30 ≈ 12.8 vehicles. Record the generated synchronous carrying coordination parameters of the material transport vehicles in the database and set a dynamic adjustment mechanism to make timely adjustments according to the actual transportation situation. If a vehicle fails to perform its task due to a breakdown, the system automatically reallocates its task to ensure the continuity of the overall transportation.

[0073] In this embodiment, step S4 includes the following steps:

[0074] Calculate the latest depth of the drilling rig according to the real-time position map of the drilling rig;

[0075] Identify the oxygen content based on the latest depth to obtain the real-time oxygen content at the position of the drilling rig;

[0076] Analyze the change in air temperature and humidity according to the latest depth to generate the change characteristics of air temperature and humidity;

[0077] Identify the trend of harmful gas concentration in the real-time position map of the drilling rig to generate the trend characteristics of harmful gas concentration;

[0078] Evaluate the environmental air quality based on the real-time oxygen content at the position of the drilling rig, the change characteristics of air temperature and humidity, and the trend characteristics of harmful gas concentration to generate the air quality evaluation value at the position of the drilling rig.

[0079] In this embodiment, a depth sensor is installed on the drilling machine to ensure that the drilling depth of the drilling machine can be obtained in real time. Common depth measurement techniques include ultrasonic sounding, laser ranging, or cable sounding, etc. Select a measurement method suitable for the mine environment. If the cable sounding technology is selected, the depth data is transmitted in real time through the cable sensor. Set the depth measurement range to 0 to 1000 meters and ensure the accuracy is within ±0.1 meters. Configure a real-time data acquisition system for the depth sensor to ensure that the depth information is updated once per second and the data is transmitted to the central control system through wireless communication. Set to collect depth data once per second and send the data in the format of "timestamp, depth value" to the data center for real-time monitoring. Receive and store all depth data in the central control system to ensure the integrity and accuracy of the data. Regularly check the data of the depth sensor to ensure the normal operation of the equipment. Generate a data table to record "timestamp, drilling machine depth" and set a regular backup mechanism to ensure that the data is not lost. Install an oxygen sensor inside the drilling machine or near the borehole to monitor the oxygen concentration at the working depth of the drilling machine in real time. Select a sensor suitable for the mine environment to ensure its accuracy at different depths. Set the detection range of the oxygen sensor to 0%-30% (volume ratio) and the accuracy to ±0.1% to ensure that the oxygen content in the mine can be accurately identified. Collect the oxygen content data in real time through the oxygen sensor and combine it with the depth data to generate the oxygen content information corresponding to the depth. Ensure the stability and accuracy of data transmission. Collect oxygen concentration data every 5 seconds and record the corresponding depth value for subsequent analysis. Record the oxygen content and depth data in the database and generate a visualization chart for easy monitoring and analysis. Use a line chart to show the trend of oxygen concentration changing with depth. Install temperature and humidity sensors inside or around the drilling machine to monitor the temperature and humidity of the working environment in real time. Ensure that the sensors can work normally in the high humidity and high pressure environment in the mine. Set the measurement range of the temperature and humidity sensors to -10°C to 50°C for temperature and 0%-100% for humidity, and ensure their accuracy is ±1°C and ±3%. Regularly collect temperature and humidity data and combine it with the latest depth information to analyze the characteristics of air temperature and humidity changes at different depths. Ensure the real-time and accuracy of the data. Set to collect temperature and humidity data once every 5 seconds, record the temperature, humidity, and the corresponding depth value, and perform data cleaning to remove outliers. Generate the characteristics of air temperature and humidity changes through statistical analysis, calculate the mean, standard deviation, and change trend. Record the results in the database and generate a visualization chart. If the temperature range is 15°C to 25°C and the humidity range is 40% to 80% within a specific depth interval, generate a corresponding change characteristic report to help evaluate the environmental conditions. Install harmful gas sensors (such as carbon monoxide, methane, sulfur dioxide, etc.) around the drilling machine to monitor the concentration of harmful gases in the mine in real time. Select highly sensitive sensors suitable for the mine environment.Set the detection range of the carbon monoxide sensor to 0 - 1000 ppm with an accuracy of ±5 ppm to ensure timely identification of changes in the concentration of harmful gases. Collect data on the concentration of harmful gases in real time through the sensor and combine it with the depth information of the drilling rig to analyze the trend of changes in the concentration of harmful gases at different depths. Collect data on the concentration of harmful gases every 5 seconds and record the corresponding depth information to ensure the accuracy of the data. Conduct statistical analysis on the collected harmful gas data, identify the characteristics of concentration fluctuations, and generate corresponding reports. Determine the maximum concentration, minimum concentration, and their change trends of harmful gases. Record that at a certain depth, the concentration of carbon monoxide fluctuates from 50 ppm to 200 ppm, and generate a concentration change curve graph for subsequent analysis. Based on the real-time oxygen content, the change characteristics of air temperature and humidity, and the trend of harmful gas concentration, establish an air quality assessment model. Ensure that the model can comprehensively consider the impact of various factors on air quality. Calculate the air quality assessment value according to the model and record it in the database for subsequent analysis. Ensure that the assessment value can reflect the real situation of the current environment. If the oxygen content is 20%, the temperature is 22°C, the humidity is 60%, and the carbon monoxide concentration is 150 ppm, then calculate the AQI value to be 75, indicating good air quality. Visualize the air quality assessment results to generate charts or reports for easy viewing by management personnel. Regularly update the assessment results and provide suggestions to improve air quality. Generate an air quality assessment report containing "oxygen content, temperature and humidity, harmful gas concentration, AQI value" and provide improvement suggestions for the current environment.

[0080] In this embodiment, step S5 includes the following steps:

[0081] Calculate the minimum air circulation volume based on the air quality assessment value at the location of the drilling rig to obtain the minimum air circulation volume;

[0082] Conduct an analysis of the change in the spatial distribution of temperature and humidity on the change characteristics of air temperature and humidity to generate the change characteristics of the spatial distribution of temperature and humidity;

[0083] Analyze the optimal ventilation wind direction based on the change characteristics of the spatial distribution of temperature and humidity to obtain the optimal ventilation wind direction;

[0084] Adjust the real-time rotation speed and air volume output parameters of the ventilation system adaptively according to the minimum air circulation volume and the optimal ventilation wind direction to obtain the adaptive ventilation adjustment parameters.

[0085] In this embodiment, a calculation model for the minimum air circulation volume is established based on the air quality evaluation value of the mine. This model should consider the influence of factors such as oxygen content, temperature and humidity, and harmful gas concentration on the air circulation volume. Collect real-time air quality evaluation value data, including the current oxygen content, various harmful gas concentrations, and evaluation values, and input them into the calculation model. Ensure the accuracy and timeliness of the data to improve the reliability of the calculation. Assume that the current oxygen concentration is 20.5% and the carbon monoxide concentration is 80 ppm, and the safety threshold of the harmful gas is set at 50 ppm, then the minimum air circulation volume is obtained through model calculation. Calculate the minimum air circulation volume according to the input data and record the calculation results in the database for subsequent analysis and adjustment. Ensure the integrity and traceability of the recorded information. If the minimum air circulation volume obtained through model calculation is 600 m³ / h, record this data in the "Air Circulation Volume Log" for subsequent monitoring and adjustment. Collect the temperature and humidity data at different positions in the current mine to ensure that the data covers all areas of the mine for spatial distribution analysis. Use temperature and humidity sensors to collect data regularly. Set each sensor to collect temperature and humidity data every 5 minutes and record it in the database to ensure the timeliness and accuracy of the data. Conduct a spatial distribution analysis of the collected temperature and humidity data to identify the temperature and humidity change characteristics of each area. Interpolation methods (such as Kriging interpolation) can be used to generate the spatial distribution map of temperature and humidity. If the analysis shows that a certain area has a higher temperature (such as 28°C) while another area has a higher humidity (such as 85%), a corresponding spatial distribution map can be generated to display the temperature and humidity change characteristics. Based on the spatial distribution change characteristics of temperature and humidity, establish an optimal ventilation wind direction analysis model. This model should consider the relationship between wind speed, wind direction, and temperature and humidity to determine the best ventilation strategy. Set the wind direction optimization model as: , is the optimal ventilation wind direction, and are the target temperature and humidity, and is the actual temperature and humidity. Using the current temperature and humidity distribution data, calculate the optimal ventilation direction. By analyzing the temperature and humidity differences in different areas, determine the direction of fresh air to be introduced. If the temperature in a certain area is high and the humidity is low, calculate the best direction to introduce fresh air from the area with low temperature and high humidity. According to the calculated minimum air circulation volume and optimal ventilation direction, formulate an adaptive control strategy for the ventilation system. Ensure that the ventilation system can automatically adjust the wind speed and air volume according to environmental changes. Set that when the air circulation volume is lower than 600 m³ / h, the system automatically increases the fan speed to meet the demand. Configure a real-time monitoring system to monitor the wind speed, air volume and direction of the ventilation system. According to the real-time data, automatically adjust the fan speed and air volume output to meet the requirements of the minimum circulation volume and optimal direction. If the system detects that the current air volume is 500 m³ / h and the target circulation volume is 600 m³ / h, automatically increase the fan speed by 10%. Record the wind speed, air volume and speed parameters during the real-time adjustment process in the database to ensure that there is a complete adjustment history for subsequent analysis and optimization. Generate a record form containing "timestamp, fan speed, air volume output, adjustment status", and regularly review the adjustment effect to optimize the ventilation strategy.

[0086] In this embodiment, the specific steps for calculating the minimum air circulation volume based on the air quality evaluation value of the drilling machine position to obtain the minimum air circulation volume are as follows:

[0087] The drilling machine is equipped with a high-definition camera;

[0088] Obtain real-time mine environment monitoring images according to the high-definition camera;

[0089] Conduct three-dimensional mine structure analysis on the mine environment monitoring images to generate three-dimensional mine structure features;

[0090] Identify multiple internal ventilation paths based on the mine environment monitoring images;

[0091] Conduct three-dimensional ventilation path distribution modeling on the three-dimensional mine structure features and multiple internal ventilation paths to construct a three-dimensional ventilation path distribution model;

[0092] Conduct dynamic air flow simulation on the three-dimensional ventilation path distribution model to generate dynamic air flow simulation data;

[0093] Based on a preset mine area air safety threshold, perform deviation calculation on the air quality evaluation value of the drilling machine position to identify the safety deviation value at the current position;

[0094] Based on the safety deviation value at the current position, calculate the ventilation compensation air volume to obtain the safety deviation compensation air volume;

[0095] Calculate the minimum air flow rate based on the safety deviation compensation air volume for the dynamic air flow simulation data to obtain the minimum air flow rate.

[0096] In this embodiment, a high-definition camera is installed on the drilling machine to ensure its normal operation in the complex mine environment. The camera should have good low-light performance and dust-proof ability to adapt to the working conditions of the mine. Select a camera with a resolution of 1080p and set its field of view to 120 degrees to capture a wider range of the mine environment. Configure the real-time image acquisition system of the camera, set to collect multiple frames of images per second, and transmit the image data to the central control system via a wireless network. Set to transmit 30 frames of images per second and use the H.264 compression format to reduce bandwidth occupancy while ensuring image quality. Receive and store the environmental monitoring images in the central control system to ensure the integrity and traceability of the image data. Regularly back up the image data to prevent loss. Generate an image storage directory structure, classified and stored according to "date / time / mine area" to ensure easy subsequent retrieval and analysis. Preprocess the obtained mine environmental monitoring images, including denoising, enhancing contrast, and edge detection, etc., to improve the accuracy of subsequent 3D analysis. Use Gaussian filtering for denoising and apply the Canny edge detection algorithm to extract the mine structure features to ensure that the effective area is clearly visible. Use computer vision techniques (such as structured light or stereo vision) to perform 3D reconstruction on the processed images to generate a 3D structure model of the mine. Set the parameters of the reconstruction algorithm to ensure that the point cloud density reaches 500 points per cubic meter to improve the fineness of the model. Record the generated 3D mine model and display the mine structure features through 3D visualization software for subsequent analysis and decision-making. Use software such as MeshLab or Blender for model visualization to ensure an intuitive display of the mine structure. Based on the generated 3D mine structure model, identify the internal ventilation paths. Use image processing and machine learning algorithms to analyze the mine structure features and extract possible ventilation paths. Use a deep learning model (such as a convolutional neural network) to train the features for identifying ventilation paths to ensure the accuracy and robustness of the identification. Record the identified multiple ventilation paths to form a ventilation path dataset for subsequent modeling and analysis. Record the starting point, ending point, and path length of each ventilation path to generate a data table for subsequent processing. Overlay the identified ventilation paths on the 3D mine model for visual display to help managers intuitively understand the ventilation layout in the mine. Use different colors to identify different types of ventilation paths (such as main ventilation ducts and auxiliary ventilation ducts) for easy identification. According to the identified multiple internal ventilation paths, establish a 3D ventilation path distribution model. This model should consider the airflow characteristics and resistance losses of each path. Set the flow parameters of each ventilation path and calculate the resistance loss coefficient on the path for subsequent flow simulation. Input the 3D mine structure and ventilation path data into the ventilation simulation software for preliminary modeling. Optimize the model parameters to ensure the accuracy of the simulation results. Input the wind speed, air volume, temperature, and humidity data of each area of the mine and adjust the model parameters according to the actual monitoring data.Verify the established ventilation path distribution model to ensure it conforms to the actual situation. Adjust and calibrate the model through on-site testing or historical data. If there is a deviation between the wind speed calculated by the model and the measured wind speed on-site, adjust the flow rate and resistance parameters in the model to ensure the accuracy of the simulation results. Select a suitable air flow simulation software (such as ANSYS Fluent or OpenFOAM), configure the software for three-dimensional air flow simulation. Set the boundary conditions of the simulation as the inlet and outlet of the mine to ensure that the simulation can reflect the actual air flow situation. Run the air flow simulation, conduct dynamic simulation analysis on the three-dimensional ventilation path distribution model, and generate time-domain and frequency-domain data of air flow. Set the simulation time to 60 minutes, output the air flow state data per minute, and record the changes in wind speed, wind direction, temperature, and humidity. Collect real-time air quality assessment values at the drill rig location, including oxygen content, harmful gas concentration, and environmental temperature and humidity data for deviation calculation. Assume the current oxygen content is 19.2% and the carbon monoxide concentration is 60 ppm, and set the safety threshold for oxygen as 19.5%. Based on the preset air safety threshold in the mine area, establish a deviation calculation model to identify the safety deviation value at the current location. Based on the calculated safety deviation value, calculate the ventilation compensation air volume. Ensure that the compensation air volume can meet the safety air circulation requirements in the mine. Record the calculated minimum air circulation volume in the database and set up a feedback mechanism for dynamic adjustment according to real-time data. Generate a log table to record "timestamp, minimum air circulation volume, compensation air volume", and regularly review the adjustment effect to optimize the ventilation system.

[0097] In this embodiment, step S6 includes the following steps:

[0098] Perform abnormal transient mutation detection based on multi-dimensional environmental monitoring parameters of the mine to identify abnormal mutation environmental parameters;

[0099] Perform abnormal fault prediction on the abnormal mutation environmental parameters to obtain transient mutation fault prediction data;

[0100] Infer the immediate emergency situation based on the transient mutation fault prediction data to obtain the immediate emergency situation;

[0101] Conduct intelligent emergency warning for the immediate emergency situation, and make multi-device emergency control decisions to generate an emergency control strategy;

[0102] Perform multi-device intelligent control optimization on the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the transport vehicle according to the emergency control strategy, and construct a multi-device intelligent collaborative control optimization model.

[0103] In this embodiment, a multi-sensor system is used to collect environmental monitoring parameters in the mine in real time, including temperature, humidity, gas concentration (such as carbon monoxide, methane, etc.), air pressure, etc. Ensure a high data collection frequency (such as once per second) to facilitate the timely detection of abnormal changes. Set the real-time monitoring range of the sensors as follows: temperature -10°C to 50°C, humidity 0% - 100%, carbon monoxide concentration 0 - 1000 ppm, methane concentration 0 - 5%. Ensure the accuracy and comprehensiveness of the data. Use statistical analysis methods or machine learning algorithms (such as anomaly detection algorithms, threshold-based detection methods) to analyze the collected environmental parameters and identify abnormal data points with transient mutations. Set the temperature change threshold as ±5°C and the humidity change threshold as ±10%. If the temperature suddenly rises from 20°C to 30°C at a certain time point, it is identified as an abnormal mutation. Record the identified abnormal mutation environmental parameters and set an alarm mechanism in the central control system for real-time monitoring and response. Generate an abnormal record log, the content of which includes "timestamp, abnormal parameter type, abnormal value", and display it in real time on the monitoring interface for the management staff to handle in a timely manner. Conduct in-depth analysis on the identified abnormal mutation environmental parameters, and use methods such as time series analysis and fault tree analysis to establish a fault prediction model to identify possible fault types. If a sharp rise in carbon monoxide concentration is detected, combined with historical fault data analysis, it is predicted that it may be due to poor ventilation leading to gas accumulation. According to the established prediction model, generate transient mutation fault prediction data, and evaluate the probability of the fault occurring and its possible affected range. If the prediction model shows that the probability of the carbon monoxide concentration exceeding the safety threshold (such as 50 ppm) is 80%, then generate a corresponding fault prediction report, the content of which includes the prediction probability and potential impact. Based on the abnormal mutation environmental parameters and fault prediction data, establish an emergency situation inference model to identify possible emergency situations (such as gas poisoning, fire, etc.). Set model parameters to evaluate the safety of the current environment. If the concentration of harmful gases is detected to exceed the safety value, it is inferred as a situation that requires emergency treatment. Record the inferred immediate emergency situations to ensure that all relevant information can be traced and analyzed for subsequent emergency response. The recorded content includes "timestamp, emergency situation type, relevant environmental parameters", and is updated in real time on the monitoring interface. Once an immediate emergency situation is identified, immediately activate the emergency response mechanism to prepare for subsequent early warning and control decisions. Set the emergency response time to be activated within 5 minutes to ensure rapid response and improve the efficiency of emergency handling. According to the immediate emergency situation, design and implement an intelligent emergency early warning system to send real-time alerts to relevant personnel to ensure that all staff in the mine can timely understand the current situation. Notify on-site personnel through various methods such as text messages, APP push notifications, and alarm sounds, and ensure the accuracy of information transmission. Based on the upcoming emergency situation, formulate multi-device emergency control decisions, including the adjustment of the ventilation system, personnel evacuation plan, etc.If the detected carbon monoxide concentration is too high, the emergency decision-making includes increasing the ventilation volume, starting the emergency fan, and formulating an evacuation plan for personnel. Record the formulated emergency control strategy in the database for subsequent evaluation and adjustment to ensure the effectiveness of the emergency response. Generate an emergency response log to record the "timestamp, emergency control strategy, and implementation status", and conduct regular emergency drills and evaluations. According to the emergency control strategy, optimize the adaptive adjustment parameters of the ventilation system to ensure the best air circulation volume and gas dilution effect in case of emergency. If the current ventilation volume is 500 m³ / h and the strategy is set to increase to 800 m³ / h, ensure that the concentration of harmful gases can be quickly reduced. In case of emergency, adjust the synchronous transportation coordination parameters of the transport vehicle to ensure the material transportation efficiency and safety in the mine. If it is found that a certain transportation route is affected, it is necessary to re-arrange the scheduling of the transport vehicle to ensure that the emergency response is not affected. Combine the control parameters of the ventilation system and the transport vehicle to construct a multi-device intelligent collaborative control optimization model to ensure a coordinated response in case of emergency. Use optimization algorithms (such as genetic algorithms) to comprehensively optimize the scheduling of ventilation and transportation to ensure the coordinated operation of each device and improve the overall efficiency of the emergency response.

[0104] In this embodiment, a multi-device collaborative control system for coal mine work is provided, which is used to execute the multi-device collaborative control method for coal mine work as described above, including:

[0105] An environmental perception module, which is used to collect multi-dimensional environmental monitoring parameters of the mine in real time; and perform environmental change trend perception and environmental area distribution positioning to construct an environmental trend perception distribution map;

[0106] A drilling power adjustment module, which is used to obtain the real-time operating state parameters of the drilling machine, and calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map to generate the maximum drilling power parameters;

[0107] A transportation coordination module, which is used to predict the ore output within a cycle according to the maximum drilling power parameters, and perform synchronous transportation coordination to obtain the synchronous transportation coordination parameters of the transport vehicle;

[0108] An air quality assessment module, which is used to calculate the latest depth of the drilling machine according to the environmental trend perception distribution map, and perform environmental air quality assessment to generate the air quality assessment value of the drilling machine position;

[0109] An adaptive ventilation adjustment module, which is used to calculate the minimum air circulation volume based on the air quality assessment value of the drilling machine position, and then perform real-time rotation speed and air volume output parameter adaptive adjustment on the ventilation system to obtain the adaptive ventilation adjustment parameters;

[0110] The intelligent collaborative control module is used to perform multi-device intelligent control optimization according to the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the material transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

[0111] This invention monitors the mine's environmental conditions in real time, ensuring that key parameters such as temperature, humidity, and gas concentrations remain within safe ranges. If an anomaly occurs, an immediate alarm is issued and countermeasures are implemented. Real-time analysis of environmental trends can predict potential hazards such as changes in gas concentration and temperature fluctuations, thereby proactively preventing dangerous incidents within the mine. Accurate distribution maps allow managers to clearly identify areas with environmental safety risks, enabling them to proactively formulate operational plans or scheduling strategies and optimize mine operations. By combining data from various sensors, a multi-dimensional analysis of the mine environment is conducted, providing a more comprehensive environmental assessment and contributing to improved intelligence in mine operations. By combining environmental data with the real-time operating status of the drilling rig, the maximum safe mining rate of the drilling rig under the current conditions can be intelligently calculated, effectively preventing accidents caused by environmental fluctuations or equipment overload. The drilling rig's power is adjusted in real time based on environmental changes and the rig's load to ensure it is not overloaded, thereby extending its lifespan and reducing the risk of failure. Precise power regulation ensures that the drilling rig operates at optimal power output, improving operational efficiency and the sustainability of coal mine production. When the mine's temperature, humidity, and gas levels fluctuate, the system automatically adjusts to ensure the drill rig operates safely and efficiently. By combining the drill rig's operating data with its maximum mining rate, it accurately predicts the mine's output over a given period, avoiding overproduction or underproduction and improving production scheduling accuracy. By synchronizing transport coordination parameters, it rationally allocates and schedules material transport vehicles within the mine, reducing inefficient transportation such as empty loads and delays, and improving the efficiency of coal mine resource utilization. Based on the predicted output, the system optimizes vehicle scheduling based on the vehicle's capacity and transport time, improving overall mine efficiency. The transport coordination module ensures efficient coordination between production and transportation processes, avoiding production bottlenecks caused by improper vehicle scheduling and ensuring operational continuity. Air quality assessments at the drill rig's location provide precise information on environmental data such as oxygen levels and hazardous gas concentrations in the operating area, enabling timely identification of potential air quality issues. Real-time assessments of air quality at the drill rig's location enable early detection of changes in hazardous gas concentrations, enabling timely implementation of ventilation and other safety measures to ensure worker safety. Based on the mine's depth and air quality data, operational safety at different depths is assessed to ensure the controllability of the deep working environment. The air quality assessment module can automatically detect safety hazards such as excessive harmful gases and insufficient oxygen, providing timely warnings and emergency response recommendations to reduce the risk of accidents. Through real-time analysis of the mine environment and air quality, the minimum air circulation required to meet safe production needs is calculated, avoiding resource waste and ensuring that the mine environment remains within safety standards. The ventilation system's wind speed and air volume can be automatically adjusted in real time based on changes in air quality, ensuring good air circulation in all working areas within the mine, reducing harmful gas concentrations, and preventing air pollution.By adaptively adjusting the air volume output of the ventilation system, it avoids energy waste caused by excessive ventilation, while ensuring that the air quality in the mine always remains at a safe level, thus improving the energy utilization efficiency of the coal mine. Through the intelligent collaborative scheduling of multiple devices, it realizes the collaborative work of equipment such as drilling machines, material transport vehicles, and ventilation systems, thereby enhancing the overall operation efficiency and safety of the coal mine. The system can automatically adjust the operation plan and equipment parameters according to the real-time environment and equipment status, ensuring the efficient collaboration of each device and reducing the stagnation and conflicts during operation. The intelligent collaborative control model is based on big data analysis and machine learning algorithms, and can optimize the overall operation of the mine, thereby improving the intelligence and automation level of coal mine operations and reducing manual intervention. When an emergency occurs in the mine, the system can quickly respond and adjust the operation process and equipment operation through intelligent control and scheduling, minimizing the probability of accidents.

[0112] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0113] As described above, these are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A multi-device collaborative control method for coal mine work, characterized in that, Including the following steps: Step S1: Collect multi-dimensional environmental monitoring parameters of the mine in real time; perform environmental change trend perception and environmental area distribution positioning, and construct an environmental trend perception distribution map; Step S2: Obtain the real-time operating state parameters of the drilling machine, calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map, and generate the maximum drilling power parameters; Step S3: Predict the ore output within a cycle according to the maximum drilling power parameters, and perform synchronous transportation coordination to obtain the synchronous transportation coordination parameters of the transport vehicle; Step S4: Calculate the latest depth of the drilling machine according to the environmental trend perception distribution map, and evaluate the environmental air quality to generate the air quality evaluation value of the drilling machine position; Step S5: Calculate the minimum air flow rate based on the air quality evaluation value of the drilling machine position, and then adaptively adjust the real-time rotation speed and air volume output parameters of the ventilation system to obtain the adaptive ventilation adjustment parameters; Step S6: Perform multi-device intelligent control optimization according to the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

2. The multi-device collaborative control method for coal mine work according to claim 1, wherein, The specific steps of Step S1 are: Collect multi-dimensional environmental monitoring parameters of the mine in real time based on multi-sensor nodes; Perform three-dimensional space positioning on each of the multi-sensor nodes, and extract the position coordinates of each sensor node; Perform multi-gas type identification based on the multi-dimensional environmental monitoring parameters of the mine to obtain various gas types in the mine; Calculate the real-time gas concentration change according to various gas types in the mine, and generate a concentration change curve for each gas type; Identify the air temperature and humidity fluctuation characteristics of the multi-dimensional environmental monitoring parameters of the mine; Perform environmental change trend perception on the air temperature and humidity fluctuation characteristics and the concentration change curve of each gas type to generate environmental change trend characteristics; Perform environmental area distribution positioning on the environmental change trend characteristics based on the position coordinates of each sensor node, and construct an environmental trend perception distribution map.

3. The multi-device collaborative control method for coal mine work according to claim 1, wherein The specific steps of Step S2 are: Obtain the real-time operating state parameters of the drilling machine; calculate the operating power and maximum operating load of the real-time operating state parameters of the drilling machine; Track the real-time position of the drilling machine, and perform real-time marking on the environmental trend perception distribution map to obtain the real-time position map of the drilling machine; Calculate the maximum safe mining rate of the current mine area according to the real-time position map of the drilling machine to obtain the maximum safe mining rate; Adjust the operating power to the maximum drilling power based on the maximum safe mining rate and the maximum operating load to generate the maximum drilling power parameters.

4. The multi-device collaborative control method for coal mine work according to claim 1, characterized in that The specific steps of Step S3 are: Predict the ore output within a cycle according to the maximum drilling power parameters to obtain the ore output prediction value; Obtain the mine material transportation log; calculate the number of transport vehicles in the area and the carrying capacity of each transport vehicle based on the mine material transportation log; Calculate the round-trip distance of material transportation and the average carrying duration according to the mine material transportation log; Perform dynamic carrying demand scheduling on the ore output prediction value to generate the material carrying demand value; Synchronously coordinate the material transportation demand value based on the round-trip distance of material transportation, the average transportation duration, the number of material transportation vehicles in the area, and the carrying capacity of each material transportation vehicle to obtain the synchronous transportation coordination parameters of the material transportation vehicles.

5. The multi-device collaborative control method for coal mine work according to claim 1, characterized in that, The specific steps of step S4 are as follows: Calculate the latest depth of the drilling machine according to the real-time position map of the drilling machine; Identify the oxygen content based on the latest depth to obtain the real-time oxygen content at the position of the drilling machine; Analyze the change of air temperature and humidity according to the latest depth to generate the change characteristics of air temperature and humidity; Identify the trend of harmful gas concentration in the real-time position map of the drilling machine to generate the trend characteristics of harmful gas concentration; Evaluate the environmental air quality based on the real-time oxygen content at the position of the drilling machine, the change characteristics of air temperature and humidity, and the trend characteristics of harmful gas concentration to generate the air quality evaluation value at the position of the drilling machine.

6. The multi-device collaborative control method for coal mine work according to claim 1, wherein, The specific steps of step S5 are as follows: Calculate the minimum air flow rate based on the air quality evaluation value at the position of the drilling machine to obtain the minimum air flow rate; Analyze the change of temperature and humidity spatial distribution of the change characteristics of air temperature and humidity to generate the change characteristics of temperature and humidity spatial distribution; Analyze the optimal ventilation wind direction based on the change characteristics of temperature and humidity spatial distribution to obtain the optimal ventilation wind direction; Adaptive adjustment of the real-time rotation speed and air volume output parameters of the ventilation system according to the minimum air flow rate and the optimal ventilation wind direction to obtain the adaptive ventilation adjustment parameters.

7. The multi-device collaborative control method for coal mine work according to claim 1, wherein The specific steps of calculating the minimum air flow rate based on the air quality evaluation value at the position of the drilling machine to obtain the minimum air flow rate are as follows: The drilling machine is equipped with a high-definition camera; Obtain the real-time mine environment monitoring image according to the high-definition camera; Analyze the three-dimensional mine structure of the mine environment monitoring image to generate the three-dimensional mine structure characteristics; Identify multiple internal ventilation paths based on the mine environment monitoring image; Build a three-dimensional ventilation path distribution model by modeling the three-dimensional ventilation path distribution of the three-dimensional mine structure characteristics and multiple internal ventilation paths; Conduct dynamic air flow simulation on the three-dimensional ventilation path distribution model to generate dynamic air flow simulation data; Calculate the deviation based on the preset air safety threshold in the mine area for the air quality evaluation value at the position of the drilling machine to identify the safety deviation value at the current position; Calculate the ventilation compensation air volume based on the safety deviation value at the current position to obtain the safety deviation compensation air volume; Calculate the minimum air flow rate based on the safety deviation compensation air volume for the dynamic air flow simulation data to obtain the minimum air flow rate.

8. The multi-device collaborative control method for coal mine work according to claim 1, wherein The specific steps of step S6 are as follows: Detect abnormal transient mutations according to the multi-dimensional environmental monitoring parameters of the mine to identify abnormal mutation environmental parameters; Predict abnormal faults for the abnormal mutation environmental parameters to obtain transient mutation fault prediction data; Infer the immediate emergency situation according to the transient mutation fault prediction data to obtain the immediate emergency situation; Conduct intelligent emergency warning for the immediate emergency situation and make multi-device emergency control decisions to generate emergency control strategies; Optimize the multi-device intelligent control of the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the material transportation vehicles according to the emergency control strategy to build a multi-device intelligent collaborative control optimization model.

9. A multi-device collaborative control system for coal mine work, characterized in that, A multi-device collaborative control method for coal mine work as claimed in claim 1, comprising: An environmental perception module, configured to collect multi-dimensional environmental monitoring parameters of the mine in real time; and perform environmental change trend perception and environmental area distribution positioning to construct an environmental trend perception distribution map; A drilling power adjustment module, configured to obtain real-time operating state parameters of the drilling machine, and calculate the maximum safe mining rate and adjust the drilling power according to the environmental trend perception distribution map to generate maximum drilling power parameters; A transportation coordination module, configured to predict the ore output within a cycle according to the maximum drilling power parameters, and perform synchronous transportation coordination to obtain synchronous transportation coordination parameters of the ore transport vehicle; An air quality assessment module, configured to calculate the latest depth of the drilling machine according to the environmental trend perception distribution map, and perform environmental air quality assessment to generate an air quality assessment value at the position of the drilling machine; An adaptive ventilation adjustment module, configured to calculate the minimum air flow rate based on the air quality assessment value at the position of the drilling machine, and then perform adaptive adjustment on the real-time rotation speed and air volume output parameters of the ventilation system to obtain adaptive ventilation adjustment parameters; An intelligent collaborative control module, configured to perform multi-device intelligent control optimization according to the adaptive ventilation adjustment parameters and the synchronous transportation coordination parameters of the ore transport vehicle, and construct a multi-device intelligent collaborative control optimization model.

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