A coal mine underground belt conveyor group cooperative control system and method

Abstract

The application discloses a kind of coal mine underground belt conveyor group cooperative control system and method, belong to coal mine transportation automatic control technical field.The system uses the layered architecture of perception layer, analysis layer, cooperative control layer and execution layer.Perception layer is through multi-source sensor acquisition coal flow and motor state data;Analysis layer is filtered to data, through state estimation model online correction and obtains coal flow spatial distribution state, then based on spatial discrete model prediction drive area future coal amount;Cooperative control layer is according to the deviation and change rate of predicted coal amount and target coal amount, generates target belt speed when meeting event trigger condition, and dynamically distributes torque according to each motor load difference;Execution layer controls multiple drive motors cooperative operation accordingly.The method solves the problem of inaccurate coal flow state perception, control lag and uneven multi-motor load in long-distance transportation, realizes forward-looking, smooth, adaptive intelligent speed regulation and balanced drive, and improves system operation efficiency and reliability.

Description

Technical Field

[0001] This invention belongs to the field of control technology for underground belt conveyors in coal mines, and particularly relates to a collaborative control system and method for underground belt conveyor groups in coal mines. Background Technology

[0002] Belt conveyors are key equipment in underground coal mine material transportation systems, primarily used to continuously transport raw coal from various coal mining faces to the main haulage roadway. With the development of intensive mining production, a conveyor group model often forms where multiple face conveyors supply material to a single main haulage roadway belt conveyor. Main haulage roadway belt conveyors typically feature long distances, large carrying capacities, and multiple motor drives; the quality of their operation and control directly affects the efficiency, energy consumption, and equipment safety of the entire transportation system.

[0003] Currently, underground belt conveyors mostly employ constant speed operation or simple speed control strategies based on local detection signals (such as a single sensor installed at the head or tail of the conveyor). However, due to the significant spatiotemporal dynamic changes in coal flow during long-distance transport, local, single-point detection signals cannot accurately and comprehensively reflect the overall distribution and changing trends of coal flow along the conveyor belt. This control based on incomplete information easily leads to a delayed or premature response of the system to changes in coal flow, causing unnecessary frequent speed adjustments, which not only increases equipment wear and energy consumption but also affects the stability of system operation. Furthermore, for multi-motor driven conveyors, the lack of a coordinated torque distribution mechanism that matches the real-time load distribution easily leads to uneven output of each drive motor, resulting in a phenomenon of "some overloaded and some lightly loaded," reducing the reliability and service life of the transmission system.

[0004] Therefore, existing technologies suffer from problems such as incomplete perception of coal flow status, insufficient basis for control response, and poor coordination among multiple motors. There is an urgent need for a control scheme that can more accurately perceive the global state of coal flow and achieve forward-looking optimized speed regulation and balanced multi-motor drive. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a collaborative control system and method for underground belt conveyor groups in coal mines. This system and method aim to solve problems such as distortion in coal flow state perception, inaccurate speed regulation response, and unbalanced loads caused by control based on local detection signals, thereby improving the stability, economy, and reliability of long-distance, high-capacity belt conveyor group systems.

[0006] To address the aforementioned technical problems, this invention differs from control methods based on fixed conveying delay models or single-point detection signals. Instead, it constructs a spatial distribution model of coal flow and performs dynamic evolution prediction based on this model, thereby achieving a forward-looking description and control of the coal flow change process.

[0007] The present invention proposes a collaborative control system for underground belt conveyor groups in coal mines, comprising a sensing layer, an analysis layer, a collaborative control layer, and an execution layer;

[0008] The sensing layer is used to collect coal flow status data and drive motor operating status data of the belt conveyor group.

[0009] The analysis layer includes a coal flow state estimation module and a coal flow evolution prediction module. The coal flow state estimation module is used to filter the collected data and construct a coal flow state estimation model. It corrects the coal flow state online by the deviation between the measured value and the state estimation value, and obtains state variables that reflect the spatial distribution characteristics of the coal flow on the conveyor belt. The coal flow evolution prediction module is used to construct a spatial discrete model based on the state variables of the coal flow spatial distribution characteristics. It divides the conveyor belt into multiple discrete nodes and describes the coal flow propagation process through the state transmission relationship between the nodes, so as to predict the future coal quantity state of the driving area and generate a predicted coal quantity state quantity.

[0010] The collaborative control layer includes a collaborative decision-making module, which is used to construct an event-triggered control mechanism based on the deviation between the predicted coal quantity and the target coal quantity and the coal flow change rate. When the triggering conditions and speed regulation constraints are met, the target operating speed of the main roadway belt conveyor is generated, and the torque distribution ratio is determined according to the load differences of each drive motor.

[0011] The execution layer includes an execution control module, which controls the coordinated operation of each drive motor according to the target operating speed and torque distribution ratio.

[0012] Preferably, the sensing layer includes:

[0013] A coal flow detection device is used to collect coal flow status data in a belt conveyor group. The coal flow detection device includes:

[0014] The coal flow detection unit installed at the discharge end of each working face conveyor is used to acquire the instantaneous coal flow data of each working face conveyor.

[0015] The dual-sided laser contour scanning device, installed at the entrance of the main roadway belt conveyor, is used to obtain cross-sectional contour information of the coal flow.

[0016] An electronic belt scale is installed at the outlet of the main roadway belt conveyor to obtain data on the total amount of coal output by the machine.

[0017] In addition, laser profile detection devices are arranged at preset intervals along the length of the main roadway belt conveyor to monitor the local coal flow accumulation and load distribution.

[0018] A motor operating status detection device is used to collect operating status data of each drive motor. The motor operating status detection device includes:

[0019] Current transformers installed in the power supply circuit of each drive motor are used to collect the load current signal of the motor.

[0020] Additionally, encoders are installed on the shaft ends of each drive motor to collect the motor's speed signal.

[0021] Preferably, both the dual-sided laser profile scanning device located at the entrance and the laser profile detection device arranged along the length of the main roadway belt conveyor are laser profile scanners.

[0022] Preferably, the recursive correction formula used in the coal flow state estimation module is:

[0023]

[0024] in, for Estimated coal flow state at any given time. for The filtered signal at each moment The state correction gain is calculated using the adaptive gain model: ,in: The gain is adjusted based on the control system convergence design. It is an adaptive adjustment coefficient, calibrated experimentally; the coal flow measurement value The calculation formula is obtained based on the coal flow cross-sectional profile information, conveyor belt speed, and coal bulk density. ,in For conveyor belt speed, For the first The height of each horizontal discrete unit For the corresponding packing density, The horizontal distance. The number of horizontal discrete units; Through the The result is obtained by applying a moving average filter, and the calculation formula is as follows: ,in, This represents the length of the sliding window.

[0025] In the coal flow evolution prediction module, the spatial discrete model describes the state transmission relationship of the coal flow between adjacent discrete nodes as follows:

[0026] ;

[0027]

[0028] in, Representing the Estimated coal flow state values ​​for each node. The node spacing, Sampling time, The transfer coefficient of coal flow varies with belt speed. The diffusion coefficient; the diffusion coefficient It is used to characterize the spatial distribution variation of coal flow. Its value is related to the coal particle size distribution, moisture content and conveyor belt inclination angle, and is obtained through experimental data calibration.

[0029] The predicted coal quantity status quantity Obtained through the following formula:

[0030]

[0031]

[0032] in, From the current moment Starting from the state of the coal flow, after The state of the corresponding node in the driver area after the step is transmitted. The prediction step size, representing the discrete time steps required for the coal flow to travel from its current position to the drive zone, is calculated using the conveying distance, conveying speed, and sampling period, and then rounded to match the discrete model. This represents the conveying distance between the current discrete node of the coal flow and the corresponding discrete node in the driving zone. The sampling period is The trend compensation coefficient, which characterizes the impact of coal flow change trends on prediction results, is determined by experimental calibration.

[0033] Preferably, the event-triggered control mechanism in the collaborative control layer must simultaneously satisfy the following formula for triggering:

[0034] ;

[0035] ;

[0036] ;

[0037] in, This is a reference coal quantity, set based on the system's rated conveying capacity. The deviation threshold, The rate of change threshold, For the minimum adjustment time interval, This is the last time the speed was adjusted.

[0038] Preferably, the deviation threshold Set to 5% to 10% of the rated conveying capacity, with a change rate threshold. The minimum speed regulation time interval is set to 2% / s to 5% / s. Set to 30-120 seconds.

[0039] The collaborative control layer includes a speed regulation control loop and a torque distribution control loop;

[0040] The speed regulation control loop generates the target operating speed based on the deviation between the predicted coal quantity status and the target coal quantity;

[0041] The torque distribution control loop calculates the total torque demand of the system based on the predicted coal flow rate and performs adaptive torque distribution based on the operating status of each drive unit.

[0042] The total required torque is:

[0043]

[0044] in, It is the coal flow-torque conversion coefficient, which is obtained through load test calibration of the conveying system under different coal flow conditions, and is used to establish the mapping relationship between coal flow quality and driving torque;

[0045] The weighting function is:

[0046]

[0047] in, , These are the speed deviation weighting adjustment coefficient and the load weighting adjustment coefficient, respectively, obtained through experimental calibration. This is the actual speed of the motor. Reference speed; Indicates the first The actual output torque of the drive motor;

[0048] The torque of each drive motor is:

[0049]

[0050] Torque limiting is implemented to prevent overload of a particular motor.

[0051]

[0052] When a drive unit reaches its torque limit, its torque is restricted to the maximum allowable value, and the unallocated remaining torque is redistributed according to the weight ratio of each drive unit that has not reached its limit. The redistribution process maintains the same weight calculation method as the initial allocation.

[0053] Preferably, the execution control module controls the operation of multiple drive motors and reducers connected to the drive drum through a PLC controller and a frequency converter.

[0054] This application also provides a method for coordinated control of underground belt conveyor groups in coal mines using the aforementioned system, comprising the following steps:

[0055] S1: Collect multi-source coal flow data and operating status data of each drive motor along the main roadway belt conveyor;

[0056] S2: Filter the collected coal flow data and correct the coal flow state online based on the coal flow state estimation model and the deviation between the measured value and the estimated value;

[0057] S3: Construct a spatial discrete model based on the corrected spatial distribution state variables of coal flow, and predict the forward evolution of coal flow state through the state transfer relationship between nodes;

[0058] S4: Based on the evolution prediction results, generate the future predicted coal quantity state quantity of the driving zone;

[0059] S5: Based on the deviation between the predicted coal quantity and the target coal quantity and the coal flow change rate, determine whether the event triggering conditions and speed regulation time constraints are met.

[0060] S6: When the triggering condition is met, the target running speed of the main road belt conveyor is generated;

[0061] S7: Then calculate the initial torque command for the motor according to the total required torque and the distribution ratio;

[0062] S8: Output the target running speed and initial torque command to control the coordinated operation of each drive motor.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. Global State Awareness and Accurate Estimation: By deploying multi-source sensors at the inlet, along the line and at the outlet of the conveyor, and combining them with state estimation algorithms, a global, dynamic and accurate estimation of the spatial distribution of coal flow during long-distance transportation is achieved, overcoming the limitations of single-point detection.

[0065] 2. Forward-looking predictive control: By establishing a coal flow spatial evolution model, the coal quantity status of the drive zone at future moments can be predicted, making speed regulation decisions forward-looking, improving the system's response time and control accuracy to changes in coal flow, and avoiding passive response based on lag signals.

[0066] 3. Intelligent event-triggered speed regulation: An event-triggered mechanism based on prediction deviation and rate of change is introduced, which avoids frequent speed regulation caused by small fluctuations in coal flow, significantly reduces the speed regulation frequency and the number of actuator actions, improves the stability of system operation, and reduces mechanical and electrical shocks.

[0067] 4. Dynamic load balancing of multiple motors: By monitoring the load of each motor in real time and dynamically adjusting the torque distribution ratio based on the load deviation, it ensures that the output of each motor is uniform in the multi-motor drive system, avoids overload or underload of a single motor, and improves the overall reliability and efficiency of the drive system. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of a scenario for a belt conveyor group collaborative control system according to an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram of the hierarchical structure of the collaborative control system of the present invention.

[0070] Figure 3 This is a schematic diagram illustrating the principle of the coal flow spatial evolution prediction module of the present invention.

[0071] Figure 4 This is a logic diagram of the multi-motor coordinated speed regulation and torque distribution control of the present invention.

[0072] Figure 5 This is a flowchart illustrating the collaborative control method of the present invention.

[0073] Figure label:

[0074] 1. Drive roller; 2. First working face conveyor; 3. Sensor; 4. Main roadway belt conveyor; 5. Tail roller; 6. Output section; 7. Second working face conveyor; 8. Coal flow detection device; 9. Motor operation status detection device; 10. Cooperative control system; 11. Drive motor; 12. Reducer. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. The description of these embodiments is intended to be illustrative and not to limit the scope of protection of the present invention.

[0076] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The coal mine underground belt conveyor group collaborative control system 10 provided by the present invention can be applied to, for example... Figure 1 The image shows a typical underground belt conveyor system in a coal mine.

[0077] This application scenario mainly includes: the first working face conveyor 2, the second working face conveyor 7, and the main roadway belt conveyor 4, which serves as the main transportation equipment. The main roadway belt conveyor 4 includes a drive drum 1, a tail drum 5, a conveyor belt (not labeled in the figure), and a drive unit consisting of drive motors 11 (M1-M4) and reducers 12. Coal flows from the working face conveyor to the main roadway belt conveyor 4 and is finally transported out by the output section 6.

[0078] In this embodiment, the collaborative control system 10 is connected to the following detection and execution devices to form a complete control loop:

[0079] The coal flow detection device 8 comprises multiple detection units: at the entrance of the main roadway belt conveyor 4 (shortly after the coal from the two working faces converges), two sets of laser profile scanners are symmetrically arranged to acquire the full-width cross-sectional profile information of the coal flow. Along the length of the main roadway belt conveyor 4, several laser detection devices are installed at key locations such as straight sections, before bends, and at the starting point of uphill sections at a certain distance from the entrance, to monitor whether the coal flow has deviated, accumulated, or has uneven thickness. At the exit of the main roadway belt conveyor 4 (after the unloading point of the drive drum 1), an electronic belt scale is installed to accurately measure the total amount of coal finally conveyed. All coal flow detection devices 8 are connected to the data acquisition interface of the collaborative control system 10.

[0080] The motor operation status detection device 9 includes a current transformer installed in the power supply circuit of each drive motor 11 and a rotary encoder installed on the shaft end of each motor, which are used to collect the load current and speed signals of the drive motor 11 in real time and transmit these signals to the cooperative control system 10.

[0081] The collaborative control system 10 is the core implementation carrier of the control function of this invention. Its hardware can be built based on an industrial computer or a high-performance PLC, and its internal operation is as follows: Figure 2 The control logic is shown below. The system receives all data from the coal flow detection device 8 and the motor operation status detection device 9 through the communication network. After analysis and decision-making, it sends speed and torque commands to the frequency converter that controls the drive motor 11.

[0082] See Figure 2 The collaborative control system 10 adopts a layered architecture, including a perception layer, an analysis layer, a collaborative control layer, and an execution layer.

[0083] The perception layer includes a data acquisition module, which is responsible for acquiring, formatting, and performing preliminary verification of raw signals from various sensors.

[0084] The analysis layer comprises three core modules: data filtering, coal flow state estimation, and coal flow evolution prediction. The raw point cloud data from the inlet laser scanner is processed to obtain the height of the lateral discrete cells. Combined with conveyor belt speed (Available from encoder signals or the main control system) and the bulk density of coal. Calculate the instantaneous volumetric flow rate. The formula for calculating the measured coal flow value is:

[0085]

[0086] in For conveyor belt speed, For the first The height of each horizontal discrete unit For the corresponding packing density, The horizontal distance. The number of lateral discrete units; the bulk density is used to characterize the density change characteristics of coal flow during transportation due to compaction, vibration, and operational disturbances. .in: This is the static packing density. , The above parameters are influence coefficients. , , The signal is obtained through experimental calibration or online identification methods to adapt to different coal qualities and operating conditions. Due to noise in the original signal, it is first subjected to moving average filtering. The smoothed observation values ​​are obtained. The coal flow state estimation module treats the conveyor belt as a dynamic system, whose state is the coal quantity distribution along the length of the conveyor belt. This module establishes a state-space model and utilizes filtered multi-point observations. (Entrance, along the route, exit) for model estimates Continuous adjustments will be made. .in, Let k be the estimated value of the coal flow state at time k. This represents the filtered signal at time k. State correction coefficients. The degree of confidence the model has in the latest observation data is determined and calculated by the adaptive gain model:

[0087] ;

[0088] in: The gain is adjusted based on the control system convergence design. This is an adaptive adjustment coefficient, calibrated experimentally. Through this recursive process, a more accurate state estimate that better reflects the overall spatial distribution of coal flow can be obtained than that from a single observation point.

[0089] See Figure 3The coal flow evolution prediction module uses the state of the conveyor belt, which has been corrected by state estimation and reflects the current coal quantity distribution along the entire conveyor belt, as the starting point for prediction. It discretizes the conveyor belt along its length into a series of continuous nodes (e.g., one node per meter or every few meters). The forward movement of the coal flow is simplified in the model as a node state evolution relationship. Specifically, the transmission process of the coal flow on the conveyor belt is modeled as a discrete dynamic process containing convection and diffusion terms. The convection term reflects the overall migration characteristics of the coal flow with the conveyor belt speed, while the diffusion term describes the spatial redistribution of the coal flow during transmission due to particle interaction and local accumulation. Its core spatial discrete model is:

[0090] ;

[0091] in, Representing the The coal flow status at each node, The node spacing, Sampling time, The transfer coefficient of coal flow varies with belt speed. The diffusion coefficient; the diffusion coefficient It is used to characterize the spatial distribution variation of coal flow. Its value is related to the coal particle size distribution, moisture content and conveyor belt inclination angle, and is obtained through experimental data calibration.

[0092] Based on this model, we can extrapolate forward. After one control cycle, the location of the coal flow currently located in the middle of the conveyor belt will be determined. Specifically, the coal quantity reaching the vicinity of drive roller 1 (drive zone) at a future point in time can be predicted. ,in, From the current moment Starting from the state of the coal flow, after The state of the corresponding node in the driver area after the step is transmitted. The formula for predicting the step size is as follows: This represents the number of discrete time steps required for the coal flow to travel from its current location to the drive zone. It is calculated using the conveying distance, conveying speed, and sampling period, and then rounded to match the discrete model. This represents the conveying distance between the current discrete node of the coal flow and the corresponding discrete node in the driving zone. The sampling period is The trend compensation coefficient, which characterizes the impact of coal flow change trends on prediction results, is determined by experimental calibration.

[0093] The collaborative control layer corresponds to the collaborative decision-making module, and its input is the predicted coal quantity output from the analysis layer. This module uses an event-triggered mechanism to determine whether to update the speed command. For example... Figure 4As shown, a new round of speed regulation calculation will only be triggered when the following three conditions are met simultaneously: 1) the predicted coal quantity and the target coal quantity. The absolute value of the deviation (set according to the system's rated capacity) exceeds the threshold. (e.g., 7% of the rated transport capacity); 2) The predicted rate of change in coal volume exceeds the threshold. (For example, 3% / s); 3) The time interval since the last successful speed adjustment is greater than the minimum set value. (For example, 60 seconds). This ensures that speed regulation only occurs when there is a significant and continuous change in the coal flow, avoiding unnecessary actions. Once triggered, the speed regulation loop will adjust according to... and The deviation is used to calculate the new target belt speed through a predetermined control algorithm (such as PID or fuzzy control).

[0094] At the same time, the collaborative decision-making module calculates the total torque required by the system based on the predicted coal quantity and the target belt speed. Total torque demand The calculation formula is: .

[0095] in, It is the coal flow-torque conversion coefficient, which is obtained through load test calibration of the conveying system under different coal flow conditions, and is used to establish the mapping relationship between coal flow quality and driving torque;

[0096] The torque distribution control loop then begins operation. It reads the real-time operating data of each drive motor 11. First, it constructs a weighting function. ,in, , These are the speed deviation weighting adjustment coefficient and the load weighting adjustment coefficient, respectively, obtained through experimental calibration. This refers to the actual speed of the motor. For reference speed, Indicates the first The system calculates the weighting coefficient for each motor by comprehensively considering speed deviation and historical load conditions, ensuring that motors with large speed deviations or excessive loads in the previous moment receive relatively lower weights. Then, it calculates the initial torque command for the motors based on the total required torque and the allocation ratio.

[0097]

[0098] To protect hardware safety, the system limits the output torque: When a drive unit reaches its torque limit, its torque is limited to the maximum allowable value, and the unallocated remaining torque is redistributed according to the initial weight ratio of each unit that has not reached its limit, ensuring that the total drive torque meets the standard without damaging any individual motor.

[0099] Finally, the cooperative control system 10 sends torque commands to the execution layer.

[0100] The execution layer corresponds to the execution control module, mainly composed of a PLC, frequency converter, and drive motor 11. The PLC receives target belt speed and torque commands from the collaborative control layer. The output speed and torque of the four drive motors 11 are controlled by a frequency converter. The power output by the motors is amplified by the reducer 12 and then transmitted to the drive drum 1, thereby driving the main belt conveyor 4 to run smoothly at the set speed, and the load among the four motors is kept dynamically balanced.

[0101] See Figure 5 The process of the method of the present invention is as follows:

[0102] S1: The system powers on and initializes, and begins real-time data acquisition. The acquired data includes: coal flow profile, local accumulation, and total coal quantity signals from each coal flow detection device 8, and current and speed signals from each motor from the motor operation status detection device 9.

[0103] S2: Filter the raw coal flow measurement signal (such as volumetric flow rate) to suppress noise.

[0104] S3: Execute the coal flow state estimation algorithm. Using filtered multi-source measurement data, the state vector representing the coal quantity distribution along the entire conveyor belt is recursively corrected online to obtain a more accurate current coal flow spatial distribution.

[0105] S4: Perform coal flow evolution prediction. Based on the current spatial distribution state obtained in step S3, use a spatial discrete model to extrapolate and predict the coal quantity state when the future coal flow arrives at the driving zone. .

[0106] S5: Event Trigger Judgment. Predicted coal quantity. With target coal quantity The parameters are compared, the deviation and rate of change are calculated, and the time interval since the last speed adjustment is checked. It is then determined whether the aforementioned three triggering conditions are met simultaneously.

[0107] S6: If step S5 determines "yes", then a control decision is made. The new target belt speed is calculated based on the deviation. Simultaneously, the initial motor torque command is calculated according to the total required torque and the allocation ratio. .

[0108] S7: If step S5 determines "no", then the current control command remains unchanged.

[0109] S8: Output control command. This command outputs the target belt speed and torque distribution commands obtained from the decision. It is then sent to the PLC and frequency converter.

[0110] S9: Actuator operation. The frequency converter drives motor 11 to operate according to the new instructions, achieving belt speed regulation and load balancing.

[0111] S10: Update speed adjustment time Current time This is used for the next trigger judgment. Then, the process returns to step S1 to start the next control cycle.

[0112] Through the above system and method, this invention realizes intelligent collaborative control of long-distance, multi-motor driven belt conveyor groups, achieving the technical effects of precise speed regulation, stable operation, and balanced load.

[0113] Through the above structure and method, the present invention can realize dynamic estimation and forward prediction of coal flow state, make timely adjustments when coal flow changes, avoid unnecessary frequent speed adjustment, and improve the load balancing capability of multi-motor, thereby significantly improving the operational stability and reliability of long-distance belt conveyor system.

[0114] This embodiment uses the example of two working face conveyors merging into the main conveyor for illustration. In actual systems, it can be expanded to a conveyor group system in which multiple working face conveyors merge into the same main conveyor.

[0115] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific forms described above. Within the scope of knowledge possessed by those skilled in the art, all equivalent substitutions or modifications made without departing from the concept of the present invention should fall within the protection scope of the claims of the present invention.

Claims

1. A collaborative control system for a group of belt conveyors in an underground coal mine, characterized in that, It includes a perception layer, an analysis layer, a collaborative control layer, and an execution layer; The sensing layer is used to collect coal flow status data and drive motor operating status data of the belt conveyor group. The analysis layer includes a coal flow state estimation module and a coal flow evolution prediction module. The coal flow state estimation module is used to filter the collected data and construct a coal flow state estimation model. It corrects the coal flow state online by the deviation between the measured value and the state estimation value, and obtains state variables that reflect the spatial distribution characteristics of the coal flow on the conveyor belt. The coal flow evolution prediction module is used to construct a spatial discrete model based on the state variables of the coal flow spatial distribution characteristics. It divides the conveyor belt into multiple discrete nodes and describes the coal flow propagation process through the state transmission relationship between the nodes, so as to predict the future coal quantity state of the driving area and generate a predicted coal quantity state quantity. The collaborative control layer includes a collaborative decision-making module, which is used to construct an event-triggered control mechanism based on the deviation between the predicted coal quantity and the target coal quantity and the coal flow change rate. When the triggering conditions and speed regulation constraints are met, the target operating speed of the main roadway belt conveyor is generated, and the torque distribution ratio is determined according to the load differences of each drive motor. The execution layer includes an execution control module, which is used to control the coordinated operation of each drive motor according to the target running speed and torque distribution ratio; The recursive correction formula used in the coal flow state estimation module is as follows: in, for Estimated coal flow state at any given time. for The filtered signal at each moment The state correction gain is calculated using the adaptive gain model: ,in: The gain is adjusted based on the control system convergence design. It is an adaptive adjustment coefficient, calibrated experimentally; coal flow measurement value. The calculation formula is obtained based on the coal flow cross-sectional profile information, conveyor belt speed, and coal bulk density. ,in For conveyor belt speed, For the first The height of each horizontal discrete unit For the corresponding packing density, The horizontal distance. The number of horizontal discrete units; where Through the The result is obtained by applying a moving average filter, and the calculation formula is as follows: ,in, The length of the sliding window; In the coal flow evolution prediction module, the spatial discrete model describes the state transfer relationship of the coal flow between adjacent discrete nodes as follows: ； in, Representing the Estimated coal flow state values ​​for each node. The node spacing, Sampling time, The transfer coefficient of coal flow varies with belt speed. The diffusion coefficient; the diffusion coefficient It is used to characterize the spatial distribution variation of coal flow. Its value is related to the coal particle size distribution, moisture content and conveyor belt inclination angle, and is obtained through experimental data calibration. Predicted coal quantity status Obtained through the following formula: in, From the current moment Starting from the state of the coal flow, after The state of the corresponding node in the driver area after the step is transmitted. The prediction step size, representing the discrete time steps required for the coal flow to travel from its current position to the drive zone, is calculated using the conveying distance, conveying speed, and sampling period, and then rounded to match the discrete model. This represents the conveying distance between the current discrete node of the coal flow and the corresponding discrete node in the driving zone. The sampling period is The trend compensation coefficient, which characterizes the impact of coal flow change trends on prediction results, is determined by experimental calibration. The event-triggered control mechanism in the collaborative control layer must simultaneously satisfy the following formula for triggering: ； ； ； in, This is a reference coal quantity, set based on the system's rated conveying capacity. The deviation threshold, The rate of change threshold, For the minimum adjustment time interval, This is the last time the speed was adjusted.

2. The collaborative control system for underground belt conveyor groups in coal mines according to claim 1, characterized in that, The sensing layer includes: A coal flow detection device is used to collect coal flow status data of a belt conveyor group. The coal flow detection device includes: The coal flow detection unit installed at the discharge end of each working face conveyor is used to acquire the instantaneous coal flow data of each working face conveyor. The dual-sided laser contour scanning device, installed at the entrance of the main roadway belt conveyor, is used to obtain cross-sectional contour information of the coal flow. An electronic belt scale is installed at the outlet of the main roadway belt conveyor to obtain data on the amount of coal output by the entire machine. In addition, laser profile detection devices are arranged at preset intervals along the length of the main roadway belt conveyor to monitor local coal flow accumulation and load distribution. A motor operating status detection device is used to collect operating status data of each drive motor. The motor operating status detection device includes: Current transformers installed in the power supply circuit of each drive motor are used to collect the load current signal of the motor. Additionally, encoders are installed on the shaft ends of each drive motor to collect the motor's speed signal.

3. The collaborative control system for underground belt conveyor groups in coal mines according to claim 2, characterized in that, The dual-sided laser profile scanning device installed at the entrance and the laser profile detection device arranged along the length of the main roadway conveyor are both laser profile scanners.

4. The collaborative control system for underground belt conveyor groups in coal mines according to claim 1, characterized in that, Deviation threshold Set to 5% to 10% of the rated conveying capacity, with a change rate threshold. Set to 2% / s~5% / s, minimum speed regulation time interval Set to 30-120 seconds.

5. The collaborative control system for underground belt conveyor groups in coal mines according to claim 1, characterized in that, The collaborative control layer includes a speed regulation control loop and a torque distribution control loop; The speed regulation control loop generates the target operating speed based on the deviation between the predicted coal quantity status and the target coal quantity. The torque distribution control loop calculates the total torque demand of the system based on the predicted coal flow rate and performs adaptive torque distribution based on the operating status of each drive unit. The total required torque is: in, It is the coal flow-torque conversion coefficient, which is obtained through load test calibration of the conveying system under different coal flow conditions, and is used to establish the mapping relationship between coal flow quality and driving torque; The weighting function is: in, , These are the speed deviation weighting adjustment coefficient and the load weighting adjustment coefficient, respectively, obtained through experimental calibration. This is the actual speed of the motor. Reference speed; Indicates the first The actual output torque of the drive motor; The torque of each drive motor is: Torque limiting is implemented to prevent overload of a particular motor. 。 6. The collaborative control system for underground belt conveyor groups in coal mines according to claim 1, characterized in that, The execution control module controls the operation of multiple drive motors and reducers connected to the drive drum through a PLC controller and frequency converter.

7. A method for coordinated control of underground belt conveyor groups in coal mines based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: S1: Collect multi-source coal flow data and operating status data of each drive motor along the main roadway belt conveyor; S2: Filter the collected coal flow data and correct the coal flow state online based on the coal flow state estimation model and the deviation between the measured value and the estimated value; S3: Construct a spatial discrete model based on the corrected spatial distribution state variables of coal flow, and predict the forward evolution of coal flow state through the state transfer relationship between nodes; S4: Based on the evolution prediction results, generate the future predicted coal quantity state quantity of the driving zone; S5: Based on the deviation between the predicted coal quantity and the target coal quantity and the coal flow change rate, determine whether the event triggering conditions and speed regulation time constraints are met. S6: When the triggering condition is met, the target running speed of the main road belt conveyor is generated; S7: Then calculate the initial torque command for the motor according to the total required torque and the distribution ratio; S8: Output the target running speed and initial torque command to control the coordinated operation of each drive motor.

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