A coal mine equipment group cooperative control method and system

Abstract

The application discloses a kind of coal mine equipment group cooperative control method and system, it is related to the field of coal mine equipment group cooperative control, including efficiency index calculation acquisition module, mining work judging module and coal mine equipment execution module in system, the system calculates dynamic efficiency index by obtaining multidimensional parameter, accurately judges inefficient rock breaking state, and in the state, to coal winning machine, scraper conveyor, hydraulic support is cooperated, based on the adaptive control of efficiency feedback, effectively solve the problem of insufficient cooperative control of prior art under special working conditions, with the advantages that low-efficiency rock breaking state can be accurately identified, and the coal mine equipment group is cooperated, adaptive control is carried out, improve the working efficiency under special working conditions, reduce energy consumption and equipment loss, improve security.

Description

Technical Field

[0001] This invention relates to the field of collaborative control of coal mine equipment groups, and particularly to a method and system for collaborative control of coal mine equipment groups. Background Technology

[0002] In underground coal mining faces, automated coal mining operations rely on a collaborative control system for coal mine equipment groups. This system controls key equipment such as coal mining machines, hydraulic supports, and scraper conveyors in a coordinated manner to improve production efficiency and ensure operational safety.

[0003] However, the complexity and heterogeneity of underground geological conditions in coal mines pose a severe challenge to coal mining operations. When the cutting drum of the coal mining machine encounters interlayers of gangue that are difficult to break effectively or hard rocks in complex geological structures, the cutting load of the coal mining machine will increase sharply, the traction speed will decrease significantly, and it may even cause the coal mining machine to stop.

[0004] Existing collaborative control systems for coal mine equipment clusters typically detect high-load or obstructed operating conditions by monitoring changes in parameters such as the current, torque, or traction speed of the coal mining machine motor. Their response strategies are often based on fixed adjustment logic triggered by preset thresholds, such as reducing the coal mining machine's traction speed or attempting to increase motor power. However, this simple response method often fails to achieve the desired rock-breaking effect and operational efficiency when dealing with complex and variable geological conditions. Excessively reducing the coal mining machine speed may unnecessarily reduce the overall coal mining output, while simply increasing motor power or maintaining low-speed cutting may lead to accelerated wear and breakage of the cutting teeth, or even damage to the motor and transmission mechanism.

[0005] Therefore, when coal mining machines encounter special working conditions such as interbedded rock layers, resulting in obstructed coal cutting operations and significantly lower coal output than normal standards, the existing coal mine equipment group collaborative control system fails to accurately identify the overall efficiency status during this special operating phase and, accordingly, dynamically and adaptively adjust the operating parameters and collaborative strategies of the scraper conveyor and hydraulic supports. This leads to low operating efficiency of the entire coal mining system under special working conditions, increased energy consumption per unit output, aggravated abnormal equipment wear, and an increased probability of equipment failure and safety accidents.

[0006] Solutions are urgently needed to address the above problems. Summary of the Invention

[0007] The present invention aims to solve the technical problems mentioned in the background art. The purpose of the present invention is to provide a collaborative control method and system for coal mine equipment groups, which has the advantages of being able to accurately identify inefficient rock breaking conditions, and to carry out collaborative and adaptive control of coal mine equipment groups, thereby improving operating efficiency under special working conditions, reducing energy consumption and equipment wear, and enhancing safety.

[0008] To achieve the above objectives, the technical solutions of the present invention are as follows:

[0009] As one aspect of this application, a collaborative control method for a coal mine equipment group is provided, the coal mine equipment group including a coal mining machine, a scraper conveyor, and a hydraulic support controlled by a controller, comprising the following steps:

[0010] S1. Obtain parameters characterizing the rock-breaking effect of the coal mining machine, parameters characterizing coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. Use a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit.

[0011] S2. A baseline efficiency value is preset. By comparing the dynamic operation efficiency index of the mining unit with the baseline efficiency value, it is determined whether the mining face has entered an inefficient rock breaking state. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that its cutting components cannot break, or the coal mining machine encounters a complex geological structure.

[0012] S3. After determining that the mining face has entered a state of inefficient rock breaking, the controller issues a control command to drive the coal mine equipment group to perform the following operations:

[0013] The coal mining machine is driven into a preset working mode for trial operation of operating parameters and optimization of efficiency. In this working mode, the operating parameters of the coal mining machine are adjusted based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time.

[0014] The actual coal flow information is obtained by a distributed load sensor array arranged along the conveying path of the scraper conveyor, and the conveying speed of the scraper conveyor is adjusted based on the actual coal flow information.

[0015] The hydraulic support is automatically moved along with the machine, and the moving operation of the hydraulic support is executed or adjusted based on whether the dynamic operation efficiency index of the mining unit has recovered to the preset level and the judgment result of whether there is a safety risk in the moving space ahead.

[0016] Compared with existing technologies, the collaborative control method for coal mine equipment groups proposed in this application calculates dynamic performance indicators by acquiring multi-dimensional parameters, accurately determines the inefficient rock breaking state, and performs collaborative and performance feedback-based adaptive control of the coal mining machine, scraper conveyor, and hydraulic support under this state. This effectively solves the problem of insufficient collaborative control in existing technologies under special working conditions. It has the advantages of accurately identifying the inefficient rock breaking state, performing collaborative and adaptive control of the coal mine equipment group, improving operating efficiency under special working conditions, reducing energy consumption and equipment wear, and enhancing safety.

[0017] Furthermore, the parameters characterizing the rock-breaking effect of the coal mining machine include the actual effective advance of the coal mining machine and the current and torque of the coal mining machine's cutting motor.

[0018] The parameters characterizing coal output are the coal flow sensor signal at the transfer point of the scraper conveyor and the signals of multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor.

[0019] The parameter characterizing the energy consumption of each piece of equipment in the coal mine equipment group is the main motor current of the coal mining machine and the scraper conveyor.

[0020] In this application, step S1 specifically includes:

[0021] S11. Set the acquisition period and acquire the following data within the acquisition period: effective advance information of the coal mining machine, current and torque of the coal mining machine cutting motor, coal flow sensor signal at the transfer point of the scraper conveyor, signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, operating data of the main motor current of the coal mining machine and the main motor current of the scraper conveyor.

[0022] S12. Construct a mathematical model containing basic parameters such as actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. In the mathematical model, determine the weight coefficients of actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. The actual effective advance is obtained by the actual effective advance of the coal mining machine and is verified by the current and torque of the coal mining machine cutting motor. The comprehensive coal flow assessment is obtained by weighted fusion of the signal data of the coal flow sensor at the transfer point of the scraper conveyor and the signal data of multiple sets of distributed pressure sensors arranged along the length of the conveyor. The energy consumption per unit time of the coal mining machine and the energy consumption per unit time of the scraper conveyor are obtained by the main motor current data of the coal mining machine and the main motor current data of the scraper conveyor.

[0023] S13. Based on the mathematical model and the obtained data on actual effective advance, comprehensive coal flow rate assessment, energy consumption per unit time of the coal mining machine and energy consumption per unit time of the scraper conveyor, calculate and obtain the dynamic operation efficiency index of the mining unit.

[0024] Furthermore, the benchmark performance value is adaptively adjusted based on the historical preset data of the coal mine equipment group and the recent operating data of the coal mine equipment group.

[0025] In this application, step S2 specifically includes:

[0026] S21. A preset benchmark efficiency value is set, which indicates whether the dynamic operation efficiency index of the current mining unit is in a state of inefficient rock breaking.

[0027] S22. Continuously judge the value of the dynamic operation efficiency index of the mining unit and the benchmark efficiency value. If the dynamic operation efficiency index of the mining unit is found to exceed the benchmark efficiency value for a certain period of time, output the judgment result that the mining face has entered an inefficient rock breaking state.

[0028] In this application, step S3, in which the scraper conveyor acquires actual coal flow information by means of a distributed load sensor array arranged along its conveying path, and adjusts the conveying speed of the scraper conveyor based on the actual coal flow information, specifically includes:

[0029] A1. After determining that the mining face has entered a low-efficiency rock breaking state, the conveying speed of the scraper conveyor is controlled to a low speed state, and a load distribution map of the coal flow rate on the conveyor belt is generated by a distributed load sensor array. The load distribution map is used to show the coal conveying status on the conveyor belt.

[0030] A2. A first adjustment benchmark value is preset. When the dynamic operation efficiency index of the current mining unit is judged to rise back to the adjustment benchmark value, the controller dynamically adjusts the conveying speed of the scraper conveyor based on the load distribution map obtained in real time.

[0031] Furthermore, in step S3, the automatic following movement of the hydraulic support, and the execution or adjustment of the hydraulic support movement based on whether the dynamic operation efficiency index of the mining unit has recovered to a preset level and the judgment result of whether there is a safety risk in the forward movement space, specifically include:

[0032] B1. After determining that the mining face has entered a state of inefficient rock breaking, the controller controls the hydraulic support to remain in a waiting-to-start state.

[0033] B2. A second adjustment benchmark value is preset. When the current dynamic operation efficiency index of the mining unit is judged to rise to the second adjustment benchmark value, the controller controls the hydraulic support to start.

[0034] B3. Real-time monitoring of the hydraulic pressure of the pushing cylinder in the hydraulic support. When the hydraulic pressure of the pushing cylinder does not exceed the preset safety threshold, the controller controls the hydraulic support to maintain the state of performing the pushing operation.

[0035] Furthermore, in step S3, the step of driving the coal mining machine to enter a preset working mode for trial operation of operating parameters and optimization of efficiency, and adjusting the operating parameters of the coal mining machine based on feedback from the dynamic operating efficiency index of the mining unit obtained through real-time calculation in this working mode, specifically includes:

[0036] C1. After determining that the mining face has entered an inefficient rock-breaking state, the controller controls the coal mining machine to enter the optimization working mode, and records the parameter adjustment schemes that the coal mining machine has tried and the change data of the actual working parameters of the coal mining machine corresponding to each parameter adjustment scheme.

[0037] C2. Analyze the correlation between the recorded parameter adjustment scheme and the dynamic operation efficiency index change data of the mining unit, and record the correlation results. When the correlation result is lower than a preset correlation level, it is determined that the current preset parameter adjustment scheme sequence is not adaptable to the current inefficient rock breaking state.

[0038] C3. In response to the determination of insufficient adaptability, retrieve historical inefficient rock breaking condition data, and extract historical inefficient rock breaking condition data where the similarity of the motor current, drum torque, vibration frequency of the coal mining machine and the coal flow distribution of the scraper conveyor is higher than the preset similarity. From the retrieved historical inefficient rock breaking condition data, extract parameter adjustment schemes that successfully improve the dynamic operation efficiency index of the mining unit under the historical inefficient rock breaking condition, and add the extracted parameter adjustment schemes as new parameter adjustment schemes to the current parameter adjustment scheme sequence.

[0039] C4. For parameter adjustment schemes that have been tried in the current parameter adjustment scheme sequence but have not improved the operation of the coal mining machine, based on the changing trend of the dynamic operation efficiency index of the mining unit under the current scheme, adjust the adjustment range and direction of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the scheme, generate an adjusted parameter adjustment scheme to replace the original parameter adjustment scheme, and form an updated parameter adjustment scheme sequence.

[0040] C5. Based on the updated parameter adjustment scheme sequence and the feedback of the dynamic operation efficiency index of the mining unit, adjust the operating parameters of the coal mining machine.

[0041] Furthermore, step C4 specifically includes:

[0042] C41. Obtain the current parameter values ​​of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the parameter adjustment scheme that has been tried but has not improved the dynamic operation status index of the mining unit, and obtain the changing trend of the dynamic operation efficiency index of the mining unit corresponding to the scheme.

[0043] C42. Based on the changing trends of the dynamic operation efficiency indicators of the mining unit, analyze the independent influence trends of the adjustment of the coal mining machine traction speed, the cutting drum speed, and the cutting depth parameter on the dynamic operation efficiency indicators of the mining unit, and identify the coupling influence relationships between these parameters on the dynamic operation efficiency indicators of the mining unit.

[0044] C43. Based on the analysis of independent influence trends and the identification of coupled influence relationships, determine a coordinated adjustment scheme for the parameters of coal mining machine traction speed, cutting drum speed and cutting depth. The coordinated adjustment scheme includes a combination of adjustment magnitude and adjustment direction for at least two of the three parameters. This combination utilizes the coupled influence relationship to improve the dynamic operation efficiency index of the mining unit.

[0045] C44. Apply the determined collaborative adjustment scheme to the current parameter value to generate the adjusted parameter adjustment scheme, which replaces the original parameter adjustment scheme.

[0046] As a second aspect of this application, a collaborative control system for a coal mine equipment group, the coal mine equipment group including a coal mining machine, hydraulic supports, and a scraper conveyor controlled by a controller, comprising:

[0047] The efficiency index calculation and acquisition module is used to acquire parameters characterizing the rock breaking effect of the coal mining machine, parameters characterizing the coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. It uses a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit.

[0048] The mining operation judgment module is used to preset a benchmark efficiency value and judge whether the mining face has entered an inefficient rock breaking state by comparing the dynamic operation efficiency index of the mining unit with the benchmark efficiency value. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that its cutting components cannot break and the coal mining machine encounters a complex geological structure.

[0049] The coal mine equipment execution module is used to drive the coal mine equipment group to perform the following operations after the mining face is determined to have entered an inefficient rock-breaking state, by issuing control commands from the controller: driving the coal mining machine into a preset working mode for trial operation of operating parameters and efficiency optimization, and adjusting the operating parameters of the coal mining machine based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time calculation; driving the scraper conveyor to obtain the actual coal flow information by the distributed load sensor array arranged along its conveying path, and adjusting the conveying speed of the scraper conveyor based on the actual coal flow information; driving the hydraulic support to automatically follow the machine and, based on the judgment results of whether the dynamic operating efficiency index of the mining unit has recovered to the preset level and whether there is a safety risk in the forward moving space, executing or adjusting the moving operation of the hydraulic support.

[0050] This application discloses a collaborative control system for a coal mine equipment group, comprising an efficiency index calculation and acquisition module, a mining operation judgment module, and a coal mine equipment execution module. This system calculates dynamic efficiency indices by acquiring multi-dimensional parameters, accurately judges inefficient rock-breaking states, and performs collaborative, efficiency-feedback-based adaptive control of the coal mining machine, scraper conveyor, and hydraulic support under these states. This effectively solves the problem of insufficient collaborative control in existing technologies under special working conditions. It has the advantages of accurately identifying inefficient rock-breaking states, performing collaborative and adaptive control of the coal mine equipment group, improving operational efficiency under special working conditions, reducing energy consumption and equipment wear, and enhancing safety.

[0051] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a collaborative control method for a coal mine equipment group in this embodiment;

[0053] Figure 2 This is a flowchart illustrating the instruction step S1 in a collaborative control method for a coal mine equipment group in this embodiment.

[0054] Figure 3 This is a flowchart illustrating step S2 in a collaborative control method for a coal mine equipment group in this embodiment.

[0055] Figure 4 This is a system structure block diagram of a coal mine equipment group collaborative control system in this embodiment.

[0056] Figure reference numerals: 100, Coal mine equipment group collaborative control system; 101, Efficiency index calculation and acquisition module; 102, Mining work judgment module; 103, Coal mine equipment execution module. Detailed Implementation

[0057] To better illustrate the coal mine equipment group system control method and system of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0058] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0059] Based on the understanding of traditional coal mine equipment group collaborative control systems, when the coal mining machine encounters interbedded rock layers that its cutting components cannot break or encounters complex geological structures, the mining face enters an inefficient rock-breaking state. At this time, existing systems have errors in identifying these working conditions, delays in linkage adjustments, or the adjustment strategies fail to achieve the expected results. This leads to the scraper conveyor operating with a material load far below its rated capacity for extended periods, resulting in ineffective energy consumption and increased component wear. Simultaneously, hydraulic supports executing pushing commands under conditions where the coal face is not sufficiently broken can cause damage to their own structural components or coal face instability.

[0060] Faced with the aforementioned problems, this application initially considered using a single parameter, such as the coal mining machine motor current or traction speed, to determine the inefficient rock-breaking state and adjust the coal mining machine parameters accordingly. However, this method is not adaptable to complex geological conditions and fails to consider the overall efficiency of the mining unit as a whole and the need for coordinated adjustments with other equipment.

[0061] Therefore, the technical problem that this application actually solves is how to construct an index that can comprehensively reflect the rock-breaking effect of the coal mining machine, the coal output, and the energy consumption of the equipment, while quantifying the dynamic operating efficiency of the mining unit. Based on this comprehensive efficiency index, it is possible to determine whether the mining face has entered an inefficient rock-breaking state and output instructions to adjust the operating strategies of the coal mining machine, scraper conveyor, and hydraulic support.

[0062] The following is a specific embodiment for illustration. In this embodiment:

[0063] Firstly, such as Figure 1 As shown, a collaborative control method for a coal mine equipment group is provided. The coal mine equipment group includes a coal mining machine, a scraper conveyor, and a hydraulic support controlled by a controller. The method includes the following steps:

[0064] S1. Obtain parameters characterizing the rock-breaking effect of the coal mining machine, parameters characterizing coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. Use a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit.

[0065] S2. A baseline efficiency value is preset. By comparing the dynamic operation efficiency index of the mining unit with the baseline efficiency value, it is determined whether the mining face has entered an inefficient rock breaking state. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that its cutting components cannot break, or the coal mining machine encounters a complex geological structure.

[0066] S3. After determining that the mining face has entered a state of inefficient rock breaking, the controller issues a control command to drive the coal mine equipment group to perform the following operations:

[0067] The coal mining machine is driven into a preset working mode for trial operation of operating parameters and optimization of efficiency. In this working mode, the operating parameters of the coal mining machine are adjusted based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time.

[0068] The actual coal flow information is obtained by a distributed load sensor array arranged along the conveying path of the scraper conveyor, and the conveying speed of the scraper conveyor is adjusted based on the actual coal flow information.

[0069] The hydraulic support is automatically moved along with the machine, and the moving operation of the hydraulic support is executed or adjusted based on whether the dynamic operation efficiency index of the mining unit has recovered to the preset level and the judgment result of whether there is a safety risk in the moving space ahead.

[0070] In the method of this embodiment, some key technical features play an important role.

[0071] Among them, the dynamic operation efficiency index of mining unit refers to the quantitative index that comprehensively reflects the rock breaking, coal production and energy consumption of mining unit within a unit of time. It can be obtained by mathematical model calculation, such as based on weighted summation, fuzzy logic or machine learning model. Its purpose is to comprehensively evaluate the actual working status of mining unit and provide a basis for subsequent judgment and control.

[0072] Inefficient rock breaking state refers to one of the situations where the coal mining machine encounters interbedded rock layers that are difficult for its cutting components to break or encounters complex geological structures. Its purpose is to identify special geological difficulties encountered at the mining face and trigger targeted response strategies.

[0073] The working mode of trial operation parameters and efficiency optimization refers to an operating mode that a coal mining machine enters when it is in a low-efficiency rock breaking state. In this mode, the system will try to adjust the working parameters of the coal mining machine and evaluate the adjustment effect based on the changes in the dynamic operating efficiency index of the mining unit after adjustment, so as to find the parameter combination that can improve efficiency. Its purpose is to improve rock breaking efficiency and adaptability under complex geological conditions by dynamically adjusting the parameters of the coal mining machine.

[0074] A distributed load sensor array is an array of multiple load sensors arranged along the conveying path of a scraper conveyor. This array is used to acquire real-time load distribution information of materials on the conveyor belt, thereby assessing the actual coal flow and material distribution status. Its purpose is to accurately grasp the actual load of the scraper conveyor and provide a basis for adjusting the conveying speed.

[0075] Specifically, the solution in this application achieves coordinated control of coal mine equipment groups through the following process.

[0076] First, the system continuously acquires multi-source parameters characterizing the rock-breaking effect of the coal mining machine, coal output, and equipment energy consumption. These parameters are input into a constructed mathematical model, which comprehensively evaluates these factors and calculates and obtains the dynamic operational efficiency index of the mining unit. This index quantifies the overall working efficiency and status of the current mining unit. Next, the system presets a baseline efficiency value and continuously compares the real-time calculated dynamic operational efficiency index of the mining unit with this baseline efficiency value. If the dynamic operational efficiency index of the mining unit deviates from the baseline efficiency value for a certain period of time, it is determined that the mining face has entered an inefficient rock-breaking state, indicating that the coal mining machine may have encountered difficult-to-break interlayers or complex geological structures. Then, after determining that the mining face has entered an inefficient rock-breaking state, the controller issues control commands to drive the coal mine equipment group to perform targeted coordinated operations. Specifically, the coal mining machine is driven into a preset operating parameter trial and efficiency optimization mode. In this mode, the coal mining machine system attempts to adjust its own operating parameters and evaluates the adjustment effect based on feedback from the dynamic operating efficiency index of the mining unit obtained in real time, further optimizing the parameters. Simultaneously, the scraper conveyor uses a distributed load sensor array arranged along its conveying path to acquire actual coal flow information and dynamically adjusts its conveying speed based on this information to match the actual coal output of the coal mining machine. Furthermore, the hydraulic support is driven to perform automatic following and advancing operations, but the execution or adjustment of its advancing operation is based on whether the dynamic operating efficiency index of the mining unit has recovered to a preset level and the judgment of whether there is a safety risk in the advancing space ahead. Only when the overall efficiency has recovered and the space ahead is safe will the hydraulic support execute or continue advancing operations.

[0077] In this way, the entire equipment group can work together to cope with inefficient rock breaking conditions, avoiding the adverse effects of improper operation of a single piece of equipment on overall efficiency and safety.

[0078] To illustrate with a specific example, the calculation of dynamic operational efficiency indicators for mining units can be based on operational data such as the actual effective advance of the coal mining machine, the current and torque of the coal mining machine's cutting motor, the coal flow sensor signal at the transfer point of the scraper conveyor, the signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, the main motor current of the coal mining machine, and the main motor current of the scraper conveyor. A weighted summation mathematical model is constructed, using the actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor as fundamental parameters, and assigning weight coefficients to these parameters. The actual effective advance can be calculated from the position sensor data of the coal mining machine and corroborated by motor current and torque data. The comprehensive coal flow assessment can be obtained by weighted fusion of coal flow sensor data at the transfer point and distributed pressure sensor data. Energy consumption per unit time can be calculated from the main motor current data. The dynamic operational efficiency indicators are calculated using this model. The preset baseline efficiency value can be dynamically adjusted based on historical normal operating data and recent operational data. A threshold can be set to determine if a rock-breaking state is inefficient. For example, if the dynamic operation efficiency index is continuously below this threshold for a period of time, the state is considered inefficient. In this state, the coal mining machine enters an optimization mode, attempting to adjust parameters such as traction speed, drum speed, and cutting depth. Each adjustment scheme and corresponding efficiency index changes are recorded, and the correlation is analyzed. If the current scheme is not sufficiently adaptable, successful schemes under similar historical conditions can be retrieved and added to the sequence, and ineffective schemes can be adjusted. In the initial stage of the inefficient rock-breaking state, the scraper conveyor can initially reduce to a preset low speed and use a load distribution map generated by a distributed load sensor array to determine the actual coal flow. When the efficiency index recovers to a certain level, the conveying speed is dynamically adjusted based on the load distribution map. The hydraulic support remains in a waiting-to-start state during the inefficient rock-breaking state until the dynamic operation efficiency index of the mining unit recovers to a preset level and the forward pushing space is deemed safe by monitoring the hydraulic pressure of the pushing cylinder, at which point the pushing operation is initiated or maintained.

[0079] Based on the above description, this embodiment of a collaborative control method for coal mine equipment groups can identify inefficient rock-breaking conditions in coal mining units, avoiding the limitations of traditional methods that rely solely on single parameters. Based on feedback from dynamic operational efficiency indicators, collaborative dynamic control of the coal mining machine, scraper conveyor, and hydraulic supports is achieved, improving the overall coping ability of the equipment group under complex geological conditions. Through parameter optimization, the coal mining machine can more effectively break hard rock, reducing cutter wear and equipment overload risks. The scraper conveyor adjusts its speed according to the actual coal flow, avoiding energy consumption and component wear caused by high-speed operation under no-load or light-load conditions. The pushing action of the hydraulic supports is linked to the mining process and safety conditions, preventing equipment damage and coal wall instability caused by improper pushing.

[0080] Furthermore, this embodiment proposes a parameter selection standard for calculating the dynamic operation efficiency index of the mining unit. The parameters characterizing the rock-breaking effect of the coal mining machine include the actual effective advance of the coal mining machine and the current and torque of the coal mining machine cutting motor.

[0081] The parameters characterizing coal output are the coal flow sensor signal at the transfer point of the scraper conveyor and the signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor; the parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group are the main motor current of the coal mining machine and the scraper conveyor.

[0082] The actual effective advance of the coal mining machine refers to the distance the machine advances along the working face per unit time. This can be obtained using displacement sensors or encoders installed on the machine, and its purpose is to directly reflect the coal cutting efficiency. The current and torque of the cutting motor refer to the current consumed and torque output by the cutting motor during operation. This can be obtained using current and torque sensors installed on the motor, and its purpose is to reflect the resistance encountered by the machine during rock breaking. The coal flow sensor signal at the scraper conveyor transfer point refers to the coal flow rate at the scraper conveyor transfer point. The signals output by the quantity sensors are intended to directly reflect the amount of coal passing through the transfer point. The signals from the multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor refer to the collection of signals output by multiple pressure sensors spaced apart along the conveying path of the scraper conveyor. Their purpose is to reflect the distribution and load of coal on the conveyor belt. The main motor current of the coal mining machine and scraper conveyor refers to the current consumed by the main drive motor of the coal mining machine and scraper conveyor during operation. This current can be obtained using a current sensor installed in the main motor power supply circuit, and its purpose is to reflect the real-time power consumption level of the equipment.

[0083] In this embodiment, the baseline performance value is further explained. The baseline performance value is adaptively adjusted based on the historical working preset data of the coal mine equipment group and the recent operating data of the coal mine equipment group.

[0084] By setting the baseline performance value as an adaptively adjustable parameter instead of a fixed value, the problem that fixed baseline values ​​cannot adapt to complex and variable geological conditions is overcome.

[0085] Furthermore, by utilizing historical working preset data of coal mine equipment groups, an initial setting or adjustment range based on long-term experience can be provided for the benchmark performance value, which makes the benchmark value reasonable from the beginning.

[0086] For example, when recent data shows that coal seam hardness has generally increased and the overall equipment efficiency index has decreased, the adaptive adjustment mechanism can appropriately reduce the baseline efficiency value to avoid misjudging normal efficiency decline as inefficient rock breaking; conversely, when recent data shows that the equipment is operating in a softer coal seam and the efficiency index is generally higher, the adaptive mechanism can increase the baseline efficiency value to ensure that inefficient rock breaking judgment is triggered only when the efficiency is significantly lower than the current normal level.

[0087] This adaptively adjusted baseline efficiency value, when compared with the calculated dynamic operational efficiency index of the mining unit, can significantly improve the accuracy and reliability of judging inefficient rock breaking conditions.

[0088] In the implementation of collaborative control methods for coal mine equipment groups, the calculation of dynamic operational efficiency indicators for mining units relies on the collection and fusion of multiple parameters. These parameters include the effective advance information of the coal mining machine, the current and torque of the coal mining machine's cutting motor, the coal flow sensor signal at the transfer point of the scraper conveyor, the signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, the operating data of the main motor current of the coal mining machine, and the main motor current of the scraper conveyor. Simply superimposing or averaging these data cannot accurately reflect the comprehensive impact of each parameter on the dynamic operational efficiency of the mining unit, and it is difficult to distinguish the importance of different parameters. This may lead to discrepancies between the calculated dynamic operational efficiency indicators of the mining unit and the actual mining results, thereby affecting subsequent control decisions.

[0089] Therefore, as Figure 2 As shown, this embodiment proposes the steps of obtaining parameters characterizing the rock-breaking effect of the coal mining machine, parameters characterizing coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group, and using a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit, specifically including:

[0090] S11. Set the acquisition period and acquire the following data within the acquisition period: effective advance information of the coal mining machine, current and torque of the coal mining machine cutting motor, coal flow sensor signal at the transfer point of the scraper conveyor, signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, operating data of the main motor current of the coal mining machine and the main motor current of the scraper conveyor.

[0091] S12. Construct a mathematical model containing basic parameters such as actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. In the mathematical model, determine the weight coefficients of actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. The actual effective advance is obtained by the actual effective advance of the coal mining machine and is verified by the current and torque of the coal mining machine cutting motor. The comprehensive coal flow assessment is obtained by weighted fusion of the signal data of the coal flow sensor at the transfer point of the scraper conveyor and the signal data of multiple sets of distributed pressure sensors arranged along the length of the conveyor. The energy consumption per unit time of the coal mining machine and the energy consumption per unit time of the scraper conveyor are obtained by the main motor current data of the coal mining machine and the main motor current data of the scraper conveyor.

[0092] S13. Based on the mathematical model and the obtained data on actual effective advance, comprehensive coal flow rate assessment, energy consumption per unit time of the coal mining machine and energy consumption per unit time of the scraper conveyor, calculate and obtain the dynamic operation efficiency index of the mining unit.

[0093] Among them, basic parameters refer to key indicators used to build mathematical models, including actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of coal mining machine and energy consumption per unit time of scraper conveyor. The purpose is to transform the raw operating data into quantitative indicators that can more directly reflect the efficiency and cost of the mining process.

[0094] In a specific example, the acquisition period can be set to 1 second. Within this acquisition period, the system acquires the effective advance information provided by the coal mining machine traction encoder, the signals from the coal mining machine cutting motor current and torque sensors, the signals from the ultrasonic coal flow sensor at the scraper conveyor transfer point, the signals from the distributed pressure sensors arranged every 5 meters along the length of the scraper conveyor, the signals from the coal mining machine main motor current sensor, and the signals from the scraper conveyor main motor current sensor. The constructed mathematical model can be in the form of a weighted summation, for example: Dynamic operation efficiency index of mining unit = W1 * Actual effective advance + W2 * Comprehensive coal flow assessment - W3 * Energy consumption of coal mining machine per unit time - W4 * Energy consumption of scraper conveyor per unit time. The actual effective advance can be calculated from the traction encoder data. When the cutting motor current or torque exceeds a preset threshold and the advance change rate is lower than the normal level, the calculated advance is reduced and corrected. The comprehensive coal flow assessment can be obtained by weighted averaging of the instantaneous flow data at the transfer point and the distributed pressure sensor data; for example, assigning a weight of 0.6 to the transfer point flow data and a weight of 0.4 to the distributed pressure data. The energy consumption per unit time of the coal mining machine and the scraper conveyor can be calculated by multiplying the main motor current by the rated voltage and then by the time interval. The weighting coefficients W1, W2, W3, and W4 can be determined through regression analysis based on historical operational data; for example, W1=10, W2=8, W3=2, W4=1. Based on the above mathematical model and the specific values ​​of actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor obtained and processed within the data collection period, the current dynamic operational efficiency index of the mining unit is calculated and obtained.

[0095] Therefore, by setting a collection cycle to acquire multi-source operational data, a mathematical model is constructed that includes basic parameters such as actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. Weighting coefficients are determined, and dynamic operational efficiency indicators of the mining unit are calculated based on the model and data. This can more comprehensively and accurately reflect the dynamic operational efficiency of the mining unit, distinguish the degree of influence of different parameters, and make the calculated indicators closer to reality. This provides a more reliable basis for subsequent collaborative control, thereby improving the accuracy and effectiveness of control decisions.

[0096] In some of the embodiments described above in this application, it is proposed to determine whether the mining face has entered an inefficient rock-breaking state by comparing the dynamic operation efficiency index of the mining unit with the benchmark efficiency value. Specifically, this determination can be made by simply comparing whether the current dynamic operation efficiency index of the mining unit is lower than the preset benchmark efficiency value. If it is lower, it is determined to be an inefficient rock-breaking state. This can preliminarily identify potential inefficient working conditions. However, in its implementation, it is just a simple comparison of magnitude, which is easily affected by instantaneous factors and leads to misjudgment.

[0097] In this regard, such as Figure 3 As shown in this embodiment, the steps for determining whether the mining face has entered an inefficient rock-breaking state include:

[0098] S21. A preset benchmark efficiency value is set, which indicates whether the dynamic operation efficiency index of the current mining unit is in a state of inefficient rock breaking.

[0099] S22. Continuously judge the value of the dynamic operation efficiency index of the mining unit and the benchmark efficiency value. If it is found that the dynamic operation efficiency index of the mining unit always exceeds the benchmark efficiency value within a certain period of time, output the judgment result that the mining face has entered the inefficient rock breaking state.

[0100] Among them, continuously judging the dynamic operation efficiency index of mining unit refers to continuously or periodically comparing the value of the dynamic operation efficiency index of mining unit with the benchmark efficiency value. Its purpose is to observe the trend of the index over time, rather than focusing only on the instantaneous value.

[0101] First, a baseline efficiency value is preset as the basis for judgment. Then, the numerical relationship between the dynamic operational efficiency index of the mining unit and this baseline efficiency value is continuously monitored. Unlike simple instantaneous comparison, this scheme introduces a time dimension, requiring that the dynamic operational efficiency index of the mining unit must consistently exceed the baseline efficiency value within a preset duration before the system ultimately determines that the mining face has entered an inefficient rock-breaking state. This judgment mechanism based on duration and stability effectively filters out instantaneous anomalies in indicators caused by transient geological changes, brief equipment fluctuations, or other accidental factors, avoiding misjudgments caused by these brief disturbances. Only when the dynamic operational efficiency index of the mining unit is indeed stably above the baseline efficiency value for a long period of time is the mining face considered to have truly entered an inefficient rock-breaking condition requiring intervention. This stable and reliable judgment result provides a solid foundation for subsequent equipment collaborative control.

[0102] Within the overall framework of steps S1 to S4 in this embodiment, the calculated dynamic operational efficiency index of the mining unit is used for stable and reliable inefficiency state judgment. Once the judgment result is stably output, the coordinated adjustment operation of the coal mine equipment group defined in step S3 is triggered. This stable judgment combined with the subsequent coordinated adjustment enables the entire system to more accurately identify and respond to real inefficient operating conditions.

[0103] In this embodiment, combined with Figure 1 As shown below, step S3 will be further described in detail.

[0104] First, if the scraper conveyor continues to adjust its conveying speed according to the preset coal flow information after the mining face enters a low-efficiency rock breaking state, the speed may be higher or lower than the actual demand, resulting in a mismatch between energy consumption and actual conveying volume or material accumulation on the conveyor belt. Therefore, how to adjust and control the conveying speed of the scraper conveyor according to the actual situation under low-efficiency rock breaking state to match the actual coal flow changes is a problem that needs to be solved.

[0105] In this embodiment, the step of acquiring actual coal flow information by a distributed load sensor array arranged along the conveying path of the drive scraper conveyor, and adjusting the conveying speed of the scraper conveyor based on the actual coal flow information, specifically includes:

[0106] A1. After determining that the mining face has entered a low-efficiency rock breaking state, the conveying speed of the scraper conveyor is controlled to a low speed state, and a load distribution map of the coal flow rate on the conveyor belt is generated by a distributed load sensor array. The load distribution map is used to show the coal conveying status on the conveyor belt.

[0107] A2. A first adjustment benchmark value is preset. When the dynamic operation efficiency index of the current mining unit is judged to rise to the adjustment benchmark value, the controller dynamically adjusts the conveying speed of the scraper conveyor based on the load distribution map obtained in real time.

[0108] The load distribution map is a representation used to show the load distribution detected by load sensors arranged along the length of the scraper conveyor belt. It can be displayed in the form of a map or a numerical list to show the distribution of coal flow on the conveyor belt, and its purpose is to provide information about the material distribution.

[0109] After determining that the mining face has entered an inefficient rock-breaking state, the operating speed of the scraper conveyor is first reduced to a low speed. Simultaneously, a load distribution map of the coal flow on the conveyor belt is generated using a distributed load sensor array. This initial speed reduction is taken into account that coal flow typically decreases or even interrupts under inefficient rock-breaking conditions. Reducing the speed avoids the mismatch between energy consumption and actual conveying volume, as well as equipment wear, caused by low-load or no-load operation. The generated load distribution map provides more comprehensive information on the material distribution on the conveyor belt. Subsequently, the normal speed is not immediately restored. Instead, the system waits for the dynamic operating efficiency index of the mining unit to rise back to the preset first adjustment benchmark value, indicating that the coal mining machine may have begun to overcome the inefficient rock-breaking state and the coal flow indication has recovered. At this point, based on the real-time acquired load distribution map, the controller dynamically adjusts the conveying speed of the scraper conveyor. This dynamic adjustment is based on the actual coal flow distribution information and can adjust the conveying speed according to the actual situation, based on the accumulation or distribution of coal on the conveyor belt. In this way, it is possible to cope with changes in coal flow under inefficient rock-breaking conditions and avoid mismatches between speed and actual coal flow.

[0110] For example, after determining that the mining face has entered a state of inefficient rock breaking, the controller can control the conveyor speed of the scraper conveyor to a low speed, such as 0.5 m / s. Simultaneously, a distributed load sensor array arranged along the conveyor path collects load data on the conveyor belt and transmits this data to the controller, which generates a load distribution map. This map can display the load value at regular intervals (e.g., 1 meter) along the length of the conveyor belt, or use color depth to represent load magnitude. The controller monitors the dynamic operational efficiency index of the mining unit, which reflects the rock breaking effect of the coal mining machine, coal output, and equipment energy consumption. A preset first adjustment benchmark value can be set as a single value, such as 30% of the normal operational efficiency index. When the dynamic operational efficiency index of the mining unit is detected to rise from a trough and exceed this benchmark value, the controller begins to dynamically adjust the conveyor speed of the scraper conveyor based on the real-time generated load distribution map. For example, if the load distribution graph shows an increase in load at the front of the conveyor belt (near the coal mining machine), indicating that coal chunks are falling, the controller can increase the conveying speed; if the graph shows a low load in most areas of the conveyor belt, the controller can maintain the current speed or make adjustments. This adjustment can be achieved using a proportional-integral-derivative (PID) control algorithm, taking characteristics of the load distribution graph, such as the total load and the uniformity of the load distribution, as input to calculate and output the target speed of the scraper conveyor.

[0111] Secondly, if the hydraulic support continues to perform the pushing and support cycle according to the preset logic after the mining face enters an inefficient rock-breaking state, it is very easy for the pushing cylinder of the hydraulic support to be overloaded and damaged due to encountering huge resistance exceeding its design capacity, or because there is not enough breaking space in front, the top beam of the hydraulic support cannot effectively contact the roof and form a stable and reliable support. Therefore, how to adjust the operation of the hydraulic support after the mining face enters an inefficient rock-breaking state to avoid the above problems is a problem that needs to be solved.

[0112] In this embodiment, the driving hydraulic support automatically moves with the machine, and based on whether the dynamic operation efficiency index of the mining unit has recovered to a preset level and the judgment result of whether there is a safety risk in the forward moving space, the steps of executing or adjusting the moving operation of the hydraulic support specifically include:

[0113] B1. After determining that the mining face has entered a state of inefficient rock breaking, the controller controls the hydraulic support to remain in a waiting-to-start state.

[0114] B2. A second adjustment benchmark value is preset. When the current dynamic operation efficiency index of the mining unit is judged to rise to the second adjustment benchmark value, the controller controls the hydraulic support to start.

[0115] B3. Real-time monitoring of the hydraulic pressure of the pushing cylinder in the hydraulic support. When the hydraulic pressure of the pushing cylinder does not exceed the preset safety threshold, the controller controls the hydraulic support to maintain the state of performing the pushing operation.

[0116] By maintaining the hydraulic supports in a waiting-to-start state after the mining face has entered a state of inefficient rock breaking, the controller avoids potential equipment overload or support failure caused by blindly pushing the hydraulic supports when the coal mining machine is obstructed and the coal wall is not sufficiently broken. This waiting period allows the system time to assess the overall state of the mining unit, providing a basis for subsequent decision-making. Furthermore, a second adjustment benchmark value is preset. When the current dynamic operating efficiency index of the mining unit is judged to have risen to the second adjustment benchmark value, the controller activates the hydraulic supports. This means that the activation of the hydraulic supports is not based on a fixed time or location, but depends on whether the overall efficiency of the mining unit has recovered to a certain level. The rise in the dynamic operating efficiency index of the mining unit reflects that the coal mining machine may have overcome some obstacles and the coal wall breaking situation has improved. Only when the efficiency index reaches the second adjustment benchmark value is the hydraulic support allowed to activate, ensuring that the movement of the hydraulic supports is carried out under relatively favorable conditions and reducing the risk of equipment damage. Based on this, the hydraulic pressure of the pushing cylinder in the hydraulic support is monitored in real time. When the hydraulic pressure of the pushing cylinder does not exceed a preset safety threshold, the controller controls the hydraulic support to maintain the pushing operation. This means that after the hydraulic support is started, the system will continue to monitor the hydraulic pressure of the pushing cylinder. If the pressure exceeds the preset safety threshold, it indicates that significant resistance has been encountered during the pushing process, potentially posing a safety hazard. Through real-time monitoring and judgment, potential risks can be detected and avoided in a timely manner, ensuring the safe operation of the hydraulic support.

[0117] For example, the hydraulic support's pushing cylinder remains in a retracted state, and the column maintains a certain preload but does not perform a pushing cycle. The controller continuously receives dynamic operation efficiency index data of the mining unit from the efficiency index calculation and acquisition module. A preset second adjustment benchmark value can be stored in the controller or a relevant database. When the controller determines that the current dynamic operation efficiency index value of the mining unit has rebounded and reached or exceeded the preset second adjustment benchmark value, the controller determines that the overall state of the mining unit has improved and that the hydraulic support can be pushed. Therefore, it sends a start command to the hydraulic support, and the hydraulic support begins to perform the pushing operation. During the hydraulic support's pushing operation, the controller receives pressure sensor signals from the hydraulic support's pushing cylinder in real time, monitoring the hydraulic pressure of the pushing cylinder. The preset safety threshold can be determined based on the hydraulic support's design parameters and safety specifications. The controller continuously determines whether the real-time monitored hydraulic pressure of the pushing cylinder exceeds the preset safety threshold. If, during the pushing process, the hydraulic pressure of the pushing cylinder does not exceed the preset safety threshold, the controller allows the hydraulic support to continue performing the pushing operation until the pushing cycle is completed or other control commands are received. If the hydraulic pressure of the push cylinder exceeds the preset safety threshold, the controller can immediately issue a command to stop pushing or perform other safety actions, such as partially retracting the push cylinder, to avoid damage to the equipment.

[0118] Third, how to effectively adjust the operating parameters of the coal mining machine to cope with inefficient rock breaking, and avoid blind trial and error during the adjustment process, so as to improve the adjustment efficiency and adaptability, is a problem that needs to be solved at present.

[0119] In this embodiment, the step of driving the coal mining machine into a preset working mode for parameter exploration and efficiency optimization, and adjusting the working parameters of the coal mining machine based on feedback from the dynamic working efficiency index of the mining unit obtained through real-time calculation in this working mode, specifically includes:

[0120] C1. After determining that the mining face has entered an inefficient rock-breaking state, the controller controls the coal mining machine to enter the optimization working mode, and records the parameter adjustment schemes that the coal mining machine has tried and the change data of the actual working parameters of the coal mining machine corresponding to each parameter adjustment scheme.

[0121] C2. Analyze the correlation between the recorded parameter adjustment scheme and the dynamic operation efficiency index change data of the mining unit, and record the correlation results. When the correlation result is lower than a preset correlation level, it is determined that the current preset parameter adjustment scheme sequence is not adaptable to the current inefficient rock breaking state.

[0122] C3. In response to the determination of insufficient adaptability, retrieve historical inefficient rock breaking condition data, and extract historical inefficient rock breaking condition data where the similarity of the motor current, drum torque, vibration frequency of the coal mining machine and the coal flow distribution of the scraper conveyor is higher than the preset similarity. From the retrieved historical inefficient rock breaking condition data, extract parameter adjustment schemes that successfully improve the dynamic operation efficiency index of the mining unit under the historical inefficient rock breaking condition, and add the extracted parameter adjustment schemes as new parameter adjustment schemes to the current parameter adjustment scheme sequence.

[0123] C4. For parameter adjustment schemes that have been tried in the current parameter adjustment scheme sequence but have not improved the operation of the coal mining machine, based on the changing trend of the dynamic operation efficiency index of the mining unit under the current scheme, adjust the adjustment range and direction of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the scheme, generate an adjusted parameter adjustment scheme to replace the original parameter adjustment scheme, and form an updated parameter adjustment scheme sequence.

[0124] C5. Based on the updated parameter adjustment scheme sequence and the feedback of the dynamic operation efficiency index of the mining unit, adjust the operating parameters of the coal mining machine.

[0125] The correlation between the parameter adjustment scheme and the dynamic operation efficiency index change data of the mining unit refers to the relationship or degree of influence between the implementation of the parameter adjustment scheme and the change of the dynamic operation efficiency index of the mining unit, which can be calculated through statistical analysis, regression analysis or machine learning models.

[0126] Historical data on inefficient rock breaking conditions refers to the operating parameters, environmental parameters, and corresponding processing results recorded by the coal mining machine under inefficient rock breaking conditions in the past. This data can be stored in a database or data lake.

[0127] A parameter adjustment scheme sequence refers to a set of parameter adjustment schemes arranged in a specific order or logical sequence.

[0128] Adjustment magnitude and adjustment direction refer to the magnitude of parameter changes and the direction of increase or decrease.

[0129] After determining that the mining face has entered an inefficient rock-breaking state, the controller controls the coal mining machine to enter the optimization working mode and records the parameter adjustment schemes that have been tried and the corresponding actual parameter change data, providing basic data for subsequent analysis and learning, and making subsequent adjustments traceable.

[0130] Based on the correlation analysis between recorded parameter adjustment schemes and dynamic operational efficiency index changes in mining units, the effectiveness of the current parameter adjustment scheme sequence can be evaluated. If the correlation is lower than the preset level, it indicates that the current scheme is insufficiently adaptable to the current inefficient rock breaking conditions, and a new adjustment scheme needs to be introduced. In response to the determination of insufficient adaptability, historical data on inefficient rock breaking conditions is retrieved, and parameter adjustment schemes that successfully improved efficiency indexes under similar historical conditions are extracted. By drawing on historical experience, effective adjustment schemes can be quickly found. For parameter adjustment schemes that have been tried but have not been effective, the adjustment range and direction of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters are adjusted according to the current trend of efficiency index changes. This adjustment method does not simply abandon the original scheme, but optimizes it based on existing information, making full use of existing trial results and improving the targeting and effectiveness of the adjustment. By adjusting the adjustment range and direction, parameter changes can be controlled more precisely, thereby better adapting to the current working conditions. Based on the updated parameter adjustment scheme sequence and feedback from the dynamic operational efficiency indexes of mining units, the operating parameters of the coal mining machine are adjusted. This means that the entire adjustment process is a closed-loop feedback control process, which can continuously optimize the adjustment plan based on the actual results, and ultimately improve the dynamic operation efficiency of the mining unit.

[0131] For example, when the controller receives a signal that the mining face has entered an inefficient rock-breaking state, it can activate the coal mining machine's optimization mode. In this mode, the controller can gradually adjust parameters such as the traction speed, cutting drum speed, and cutting depth of the coal mining machine according to a preset or dynamically generated parameter adjustment scheme sequence. After each adjustment, the controller can record the specific content of the adjustment scheme and the changes in the actual operating parameters of the coal mining machine (such as motor current, torque, and traction speed). Simultaneously, the controller can continuously receive real-time dynamic operational efficiency index data of the mining unit provided by the efficiency index calculation module. The controller can analyze the correlation between the recorded parameter adjustment schemes and the efficiency index changes, for example, using a regression model. If the calculated correlation is lower than a preset threshold, the controller can determine that the current parameter adjustment scheme sequence is insufficiently adaptable to the current working conditions. At this time, the controller can send a retrieval request to the historical data storage system, which stores past operating data and successful parameter adjustment schemes under different inefficient rock-breaking conditions. The retrieval can be based on the similarity between the characteristic parameters of the current working conditions (such as motor current, drum torque, vibration frequency, and coal flow distribution) and historical data. Historically successful solutions can be extracted and added to the current parameter adjustment solution sequence. Furthermore, for solutions already tried but with limited effectiveness in the current sequence, the controller can adjust the adjustment step size and direction of traction speed, drum speed, and cutting depth parameters based on the changing trends of performance indicators—for example, whether the performance indicators are continuously decreasing, stagnating, or slowly recovering. This generates new optimized solutions to replace the original solutions, forming an updated solution sequence. Finally, based on this updated parameter adjustment solution sequence and combined with real-time performance indicator feedback, the controller dynamically controls the operating parameters of the coal mining machine, such as changing the motor speed by controlling the frequency converter and changing the cutting depth by controlling the hydraulic valves.

[0132] This method avoids blind trial and error by recording and analyzing the adjustment process; improves the efficiency of adjustments by drawing on historical experience; and enhances the pertinence and adaptability of adjustments by optimizing existing solutions. These measures work together to enable coal mining machines to more effectively cope with complex geological conditions and ultimately improve the dynamic operational efficiency of mining units.

[0133] Furthermore, in this embodiment, the step of adjusting the parameters of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the current parameter adjustment scheme sequence, which have been tried but have not significantly improved the operation of the coal mining machine, based on the changing trend of the dynamic operation efficiency index of the mining unit under the current scheme, and generating an adjusted parameter adjustment scheme to replace the original parameter adjustment scheme, thus forming an updated parameter adjustment scheme sequence, specifically includes:

[0134] C41. Obtain the current parameter values ​​of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the parameter adjustment scheme that has been tried but has not improved the dynamic operation status index of the mining unit, and obtain the changing trend of the dynamic operation efficiency index of the mining unit corresponding to the scheme.

[0135] C42. Based on the changing trends of the dynamic operation efficiency indicators of the mining unit, analyze the independent influence trends of the adjustment of the coal mining machine traction speed, the cutting drum speed, and the cutting depth parameter on the dynamic operation efficiency indicators of the mining unit, and identify the coupling influence relationships between these parameters on the dynamic operation efficiency indicators of the mining unit.

[0136] C43. Based on the analysis of independent influence trends and the identification of coupled influence relationships, determine a coordinated adjustment scheme for the parameters of coal mining machine traction speed, cutting drum speed and cutting depth. The coordinated adjustment scheme includes a combination of adjustment magnitude and adjustment direction for at least two of the three parameters. This combination utilizes the coupled influence relationship to improve the dynamic operation efficiency index of the mining unit.

[0137] C44. Apply the determined collaborative adjustment scheme to the current parameter value to generate the adjusted parameter adjustment scheme, which replaces the original parameter adjustment scheme.

[0138] Among them, the coupling effect relationship refers to the nonlinear or synergistic effect on the dynamic operation efficiency index of the mining unit when at least two of the parameters of the coal mining machine traction speed, cutting drum speed and cutting depth change simultaneously.

[0139] The coordinated adjustment scheme refers to a combination of adjustment magnitude and direction for at least two of the parameters, namely the traction speed of the coal mining machine, the rotation speed of the cutting drum, and the cutting depth. This combination utilizes the coupling influence relationship to improve the dynamic operation efficiency index of the mining unit.

[0140] Specifically, the first step is to obtain the current parameter values ​​of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters under this scheme, as well as the changing trends of the corresponding dynamic operation efficiency indicators of the mining unit. Based on the obtained changing trends, the independent influence trends of adjusting the coal mining machine traction speed, cutting drum speed, and cutting depth parameters on the dynamic operation efficiency indicators of the mining unit are analyzed, and the coupled influence relationships between these parameters on the dynamic operation efficiency indicators of the mining unit are identified. It is precisely because the coupling relationships between parameters can be identified that a coordinated adjustment scheme for the coal mining machine traction speed, cutting drum speed, and cutting depth parameters can be determined based on the analyzed independent influence trends and the identified coupled influence relationships.

[0141] For example, the current parameter values ​​of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in a parameter adjustment scheme that has been tried but has not significantly improved the dynamic operation efficiency index of the mining unit, and the changing trend data of the dynamic operation efficiency index of the mining unit corresponding to this scheme during the trial period, can be obtained. Based on the obtained changing trend data of the dynamic operation efficiency index of the mining unit, multiple regression analysis or machine learning models based on historical data can be used to analyze the independent impact trends of each adjustment of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters on the dynamic operation efficiency index of the mining unit. For example, it can be determined whether increasing the traction speed usually leads to a decrease in efficiency, and whether increasing the cutting drum speed usually leads to an increase in efficiency. At the same time, the coupled influence relationships between these parameters on the dynamic operation efficiency index of the mining unit can be identified. For example, it may be found that when the traction speed and cutting depth increase simultaneously, the decrease in efficiency is greater than the sum of their independent effects, or that when the traction speed decreases while the cutting drum speed increases, the efficiency improvement effect is better than adjusting either parameter alone. Based on the analyzed independent influence trends and identified coupled influence relationships, optimization algorithms, such as genetic algorithms, can be employed. With the objective function of maximizing the dynamic operational efficiency index of the mining unit, and starting from the current parameter values, a coordinated adjustment scheme for the coal mining machine traction speed, cutting drum speed, and cutting depth parameters can be searched. This scheme includes a combination of adjustment magnitudes and directions for at least two of these three parameters. The design of this combination utilizes the coupled influence relationships between the parameters; for example, reducing the traction speed decreases rock-breaking resistance while simultaneously increasing the cutting drum speed improves crushing efficiency. These two actions synergistically improve the dynamic operational efficiency index of the mining unit. Finally, the determined coordinated adjustment scheme is applied to the current parameter values ​​to generate an adjusted parameter adjustment scheme, replacing the original scheme, for subsequent parameter testing.

[0142] As a second aspect of this application, such as Figure 4 As shown, a collaborative control system 100 for a coal mine equipment group includes a coal mining machine, hydraulic supports, and a scraper conveyor controlled by a controller.

[0143] The efficiency index calculation and acquisition module 101 is used to acquire parameters characterizing the rock breaking effect of the coal mining machine, parameters characterizing the coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. It uses a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit.

[0144] The mining operation judgment module 102 is used to preset a benchmark efficiency value and judge whether the mining face has entered an inefficient rock breaking state by comparing the dynamic operation efficiency index of the mining unit with the benchmark efficiency value. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that is difficult for its cutting components to break and the coal mining machine encounters a complex geological structure.

[0145] The coal mine equipment execution module 103 is used to drive the coal mine equipment group to perform the following operations after the mining face is determined to have entered an inefficient rock-breaking state, by issuing control commands from the controller: driving the coal mining machine to enter a preset working mode for trial operation of operating parameters and efficiency optimization, and adjusting the operating parameters of the coal mining machine based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time calculation in this working mode; driving the scraper conveyor to obtain the actual coal flow information by the distributed load sensor array arranged along its conveying path, and adjusting the conveying speed of the scraper conveyor based on the actual coal flow information; driving the hydraulic support to automatically follow the machine and, based on the judgment results of whether the dynamic operating efficiency index of the mining unit has recovered to the preset level and whether there is a safety risk in the forward moving space, executing or adjusting the moving operation of the hydraulic support.

[0146] This embodiment of a coal mine equipment group collaborative control system includes an efficiency index calculation and acquisition module 101, a mining operation judgment module 102, and a coal mine equipment execution module 103. The system calculates dynamic efficiency index by acquiring multi-dimensional parameters, accurately judges the inefficient rock breaking state, and performs collaborative and adaptive control of the coal mining machine, scraper conveyor, and hydraulic support based on efficiency feedback under this state. It effectively solves the problem of insufficient collaborative control in special working conditions in the prior art. It has the advantages of being able to accurately identify the inefficient rock breaking state, perform collaborative and adaptive control of the coal mine equipment group, improve the operating efficiency under special working conditions, reduce energy consumption and equipment wear, and improve safety.

[0147] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. A method for coordinated control of a coal mine equipment group, wherein the coal mine equipment group includes a coal mining machine, a scraper conveyor, and hydraulic supports controlled by a controller, characterized in that, Includes the following steps: S1. Obtain parameters characterizing the rock-breaking effect of the coal mining machine, parameters characterizing coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. Use a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit. S2. A baseline efficiency value is preset. By comparing the dynamic operation efficiency index of the mining unit with the baseline efficiency value, it is determined whether the mining face has entered an inefficient rock breaking state. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that its cutting components cannot break, or the coal mining machine encounters a complex geological structure. S3. After determining that the mining face has entered a state of inefficient rock breaking, the controller issues a control command to drive the coal mine equipment group to perform the following operations: The coal mining machine is driven into a preset working mode for trial operation of operating parameters and optimization of efficiency. In this working mode, the operating parameters of the coal mining machine are adjusted based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time. The actual coal flow information is obtained by a distributed load sensor array arranged along the conveying path of the scraper conveyor, and the conveying speed of the scraper conveyor is adjusted based on the actual coal flow information. The hydraulic support is automatically moved along with the machine, and the moving operation of the hydraulic support is executed or adjusted based on whether the dynamic operation efficiency index of the mining unit has recovered to the preset level and the judgment result of whether there is a safety risk in the moving space ahead. Step S1 specifically includes: S11. Set the acquisition period and acquire the following data within the acquisition period: effective advance information of the coal mining machine, current and torque of the coal mining machine cutting motor, coal flow sensor signal at the transfer point of the scraper conveyor, signals from multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, operating data of the main motor current of the coal mining machine and the main motor current of the scraper conveyor. S12. Construct a mathematical model containing basic parameters such as actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. In the mathematical model, determine the weighting coefficients for actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. Among these, the actual effective advance... The energy consumption per unit time of the coal mining machine and the energy consumption per unit time of the scraper conveyor are obtained from the actual effective advance of the coal mining machine, supplemented by the current and torque of the cutting motor of the coal mining machine, and the comprehensive coal flow assessment is obtained by weighted fusion of the signal data of the coal flow sensor at the transfer point of the scraper conveyor and the signal data of multiple sets of distributed pressure sensors arranged along the length of the conveyor. It was obtained from the main motor current data of the coal mining machine and the main motor current data of the scraper conveyor; S13. Based on the mathematical model and the obtained data on actual effective advance, comprehensive coal flow rate assessment, energy consumption per unit time of the coal mining machine and energy consumption per unit time of the scraper conveyor, calculate and obtain the dynamic operation efficiency index of the mining unit.

2. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that: The parameters characterizing the rock-breaking effect of the coal mining machine include the actual effective advance of the coal mining machine and the current and torque of the coal mining machine's cutting motor. The parameters characterizing coal output are the coal flow sensor signal at the transfer point of the scraper conveyor and the signals of multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor. The parameter characterizing the energy consumption of each piece of equipment in the coal mine equipment group is the main motor current of the coal mining machine and the scraper conveyor.

3. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that, The benchmark performance value is adaptively adjusted based on the historical preset data of the coal mine equipment group and the recent operating data of the coal mine equipment group.

4. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that... Step S2 specifically includes: S21. A preset benchmark efficiency value is set, which indicates whether the dynamic operation efficiency index of the current mining unit is in a state of inefficient rock breaking. S22. Continuously judge the value of the dynamic operation efficiency index of the mining unit and the benchmark efficiency value. If the dynamic operation efficiency index of the mining unit is found to exceed the benchmark efficiency value for a certain period of time, output the judgment result that the mining face has entered an inefficient rock breaking state.

5. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that, In step S3, the step of acquiring actual coal flow information by means of a distributed load sensor array arranged along the conveying path of the drive scraper conveyor, and adjusting the conveying speed of the scraper conveyor based on the actual coal flow information, specifically includes: A1. After determining that the mining face has entered a state of inefficient rock breaking, the conveying speed of the scraper conveyor is controlled to a low speed, and a load distribution map of the coal flow rate in the conveyor belt is generated by a distributed load sensor array. The diagram is used to show the coal conveying status on the conveyor belt; A2. A first adjustment benchmark value is preset. When the dynamic operation efficiency index of the current mining unit is judged to rise to the adjustment benchmark value, the controller dynamically adjusts the conveying speed of the scraper conveyor based on the load distribution map obtained in real time.

6. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that, In step S3, the hydraulic support automatically moves with the machine, and based on whether the dynamic operation efficiency index of the mining unit has recovered to a preset level and the judgment result of whether there is a safety risk in the forward moving space, the steps of executing or adjusting the moving operation of the hydraulic support are specifically included: B1. After determining that the mining face has entered a state of inefficient rock breaking, the controller controls the hydraulic support to remain in a waiting-to-start state. B2. A second adjustment benchmark value is preset. When the current dynamic operation efficiency index of the mining unit is judged to rise to the second adjustment benchmark value, the controller controls the hydraulic support to start. B3. Real-time monitoring of the hydraulic pressure of the pushing cylinder in the hydraulic support. When the hydraulic pressure of the pushing cylinder does not exceed the preset safety threshold, the controller controls the hydraulic support to maintain the state of performing the pushing operation.

7. The collaborative control method for coal mine equipment groups according to claim 1, characterized in that, In step S3, the coal mining machine enters a preset working mode for parameter exploration and efficiency optimization. The step of adjusting the working parameters of the coal mining machine based on feedback from the dynamic operational efficiency indicators of the mining unit obtained through real-time calculation within this working mode specifically includes: C1. After determining that the mining face has entered an inefficient rock-breaking state, the controller controls the coal mining machine to enter the optimization working mode, and records the parameter adjustment schemes that the coal mining machine has tried and the change data of the actual working parameters of the coal mining machine corresponding to each parameter adjustment scheme. C2. Analyze the correlation between the recorded parameter adjustment scheme and the dynamic operation efficiency index change data of the mining unit, and record the correlation results. When the correlation result is lower than a preset correlation level, it is determined that the current preset parameter adjustment scheme sequence is not adaptable to the current inefficient rock breaking state. C3. In response to the judgment of insufficient adaptability, retrieve historical inefficient rock breaking condition data, and extract historical inefficient rock breaking condition data with similarity of motor current, drum torque, vibration frequency of coal mining machine and coal flow distribution of scraper conveyor higher than the preset similarity. From the retrieved historical inefficient rock breaking condition data, extract parameter adjustment schemes that successfully improved the dynamic operation efficiency index of mining unit under the historical inefficient rock breaking condition, and add them as new parameter adjustment schemes to the current parameter adjustment scheme sequence. C4. For parameter adjustment schemes that have been tried in the current parameter adjustment scheme sequence but have not improved the operation of the coal mining machine, based on the changing trend of the dynamic operation efficiency index of the mining unit under the current scheme, adjust the adjustment range and direction of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the scheme, generate an adjusted parameter adjustment scheme to replace the original parameter adjustment scheme, and form an updated parameter adjustment scheme sequence. C5. Based on the updated parameter adjustment scheme sequence and the feedback of the dynamic operation efficiency index of the mining unit, adjust the operating parameters of the coal mining machine.

8. The collaborative control method for coal mine equipment groups according to claim 7, characterized in that, Step C4 specifically includes: C41. Obtain the current parameter values ​​of the coal mining machine traction speed, cutting drum speed, and cutting depth parameters in the parameter adjustment scheme that has been tried but has not improved the dynamic operation indicators of the mining unit, and obtain the changing trend of the dynamic operation efficiency indicators of the mining unit corresponding to the scheme. C42. Based on the changing trends of the dynamic operation efficiency indicators of the mining unit, analyze the independent influence trends of the adjustment of the coal mining machine traction speed, the cutting drum speed, and the cutting depth parameter on the dynamic operation efficiency indicators of the mining unit, and identify the coupling influence relationships between these parameters on the dynamic operation efficiency indicators of the mining unit. C43. Based on the analysis of independent influence trends and the identification of coupled influence relationships, determine a coordinated adjustment scheme for the parameters of coal mining machine traction speed, cutting drum speed and cutting depth. The coordinated adjustment scheme includes a combination of adjustment magnitude and adjustment direction for at least two of the three parameters. This combination utilizes the coupled influence relationship to improve the dynamic operation efficiency index of the mining unit. C44. Apply the determined collaborative adjustment scheme to the current parameter value to generate the adjusted parameter adjustment scheme, which replaces the original parameter adjustment scheme.

9. A collaborative control system for a coal mine equipment group, the coal mine equipment group including a coal mining machine, hydraulic supports, and scraper conveyors controlled by a controller, comprising: The efficiency index calculation and acquisition module is used to acquire parameters characterizing the rock breaking effect of the coal mining machine, parameters characterizing the coal output, and parameters characterizing the energy consumption of each piece of equipment in the coal mine equipment group. It uses a constructed mathematical model to calculate and obtain the dynamic operation efficiency index of the mining unit. It is also used to set the acquisition cycle, and within the acquisition cycle, it acquires operating data including the effective advance information of the coal mining machine, the current and torque of the coal mining machine cutting motor, the coal flow sensor signal at the transfer point of the scraper conveyor, the signals of multiple sets of distributed pressure sensors arranged along the length of the scraper conveyor, the main motor current of the coal mining machine, and the main motor current of the scraper conveyor. A mathematical model is constructed, incorporating fundamental parameters such as actual effective advance, comprehensive coal flow assessment, energy consumption per unit time of the coal mining machine, and energy consumption per unit time of the scraper conveyor. Weighting coefficients are determined for these parameters within the mathematical model. Specifically, the actual effective advance... The energy consumption per unit time of the coal mining machine and the energy consumption per unit time of the scraper conveyor are obtained from the actual effective advance of the coal mining machine, supplemented by the current and torque of the cutting motor of the coal mining machine, and the comprehensive coal flow assessment is obtained by weighted fusion of the signal data of the coal flow sensor at the transfer point of the scraper conveyor and the signal data of multiple sets of distributed pressure sensors arranged along the length of the conveyor. It was obtained from the main motor current data of the coal mining machine and the main motor current data of the scraper conveyor; Based on the mathematical model and the obtained data on actual effective advance, comprehensive coal flow rate assessment, energy consumption per unit time of the coal mining machine and energy consumption per unit time of the scraper conveyor, the dynamic operation efficiency index of the mining unit is calculated and obtained. The mining operation judgment module is used to preset a benchmark efficiency value and judge whether the mining face has entered an inefficient rock breaking state by comparing the dynamic operation efficiency index of the mining unit with the benchmark efficiency value. The inefficient rock breaking state indicates one of the following: the coal mining machine encounters a gangue layer that its cutting components cannot break and the coal mining machine encounters a complex geological structure. The coal mine equipment execution module is used to drive the coal mine equipment group to perform the following operations after the mining face is determined to have entered an inefficient rock-breaking state, by issuing control commands from the controller: driving the coal mining machine into a preset working mode for trial operation of operating parameters and efficiency optimization, and adjusting the operating parameters of the coal mining machine based on the feedback of the dynamic operating efficiency index of the mining unit obtained in real time calculation; driving the scraper conveyor to obtain the actual coal flow information by the distributed load sensor array arranged along its conveying path, and adjusting the conveying speed of the scraper conveyor based on the actual coal flow information; driving the hydraulic support to automatically follow the machine and, based on the judgment results of whether the dynamic operating efficiency index of the mining unit has recovered to the preset level and whether there is a safety risk in the forward moving space, executing or adjusting the moving operation of the hydraulic support.

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