A precise movement control method and system for a fully continuous mining system of an open-pit coal mine

Abstract

The application discloses a kind of open coal mine full continuous mining system precision movement control method and system, the execution mechanism movement trajectory of this method full continuous movement system conveyor is divided into multiple pose feature measurement points, the comprehensive error and acceleration error of all measurement points are solved and control parameter is further established control parameter fuzzy judgment rule;Then the comprehensive error and acceleration error of measurement point when the execution mechanism certain movement is measured and calculated, and from control parameter fuzzy judgment rule corresponding control parameter interval is obtained and optimization is obtained after optimization control parameter;When execution mechanism passes through each measurement point, optimized control parameter is used to optimize adjustment, realize the precision pose control of execution mechanism;And further realize the composite control of multiple execution mechanisms and the cooperation control between multiple conveyors.The application can realize the mutual cooperation of full continuous movement system, and the automation construction efficiency is high, the operation precision is high, and the rapid completion of transfer operation can be realized.

Description

Technical Field

[0001] This invention belongs to the field of control technology for fully continuous transportation systems in open-pit coal mines, and relates to a precise transportation control method and system for fully continuous mining systems in open-pit coal mines. Background Technology

[0002] Currently, continuous transport systems in open-pit coal mines primarily handle the transfer and transportation of coal after mining. Because multiple sets of actuators are typically used during transfer, errors in position, speed, and direction inevitably occur, straining the main structure of each conveyor and affecting its lifespan. Furthermore, the coordination between different pieces of equipment significantly impacts the overall system's efficiency and accuracy. Currently, each conveyor is typically driven independently and sequentially by multiple actuators. During transfer, each actuator needs individual control; the first actuator advances a certain distance before the second advances the same distance, and so on, until all actuators have advanced the same distance to achieve the transfer of a single conveyor. However, this method suffers from low efficiency, high labor intensity, cumbersome operation, and poor safety. Moreover, synchronous coordination between conveyors is impossible. Achieving coordinated movement of multiple actuators and controlling movement errors is the primary technical challenge to solve for precise transport in a continuous transport system. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a precise transport control method and system for a fully continuous open-pit coal mine mining system, so as to achieve precise control of the fully continuous transport system.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A precise transport control method for a fully continuous open-pit coal mine mining system, which enables precise position and orientation control of each conveyor in the system and precise matching control between conveyors; including:

[0006] a. Precise pose control of the actuator: First, the entire motion trajectory of the conveyor actuator is divided into multiple pose feature measurement points. The comprehensive error and acceleration error of all measurement points, as well as the control parameters of the actuator, are calculated, and a fuzzy judgment rule for the control parameters is established. Then, the comprehensive error and acceleration error of the pose feature measurement points of the actuator during a certain motion are measured and calculated. The corresponding control parameter range is obtained from the fuzzy judgment rule for the control parameters, and the optimized control parameters are obtained through optimization. When the actuator passes through each pose feature measurement point, the optimized control parameters are used for optimization and adjustment, thereby precisely adjusting the control parameters at all pose feature measurement points to achieve precise pose control of the actuator.

[0007] b. Composite control of multiple actuators: The comprehensive error and acceleration error of each actuator are calculated separately, and the discrimination error of each actuator is obtained by weighting. The discrimination error of each actuator is adjusted by using the same discrimination error standard. The adjusted comprehensive error and acceleration error are derived from the adjusted discrimination error. The optimized control parameters of each actuator are obtained from the fuzzy judgment rules of the control parameters, and the corresponding actuators are adjusted to ensure that the actuators move synchronously.

[0008] c. Coordination control between multiple conveyors: Calculate the positional deviation and coal error at the docking points of each conveyor, and obtain the conveyor discrimination error by weighting. When the conveyor discrimination error exceeds the set value, perform composite control adjustment on each actuator of the conveyor to achieve precise matching control between the conveyors.

[0009] The present invention also includes the following technical features:

[0010] Optionally, precise control of the individual actuator includes:

[0011] Step a1: The motion process of a single actuator of the conveyor is subdivided into n pose feature measurement points. The actual pose parameters of the actuator are obtained using a pose parameter sensor. Combined with the theoretical pose parameters of the actuator, the real-time pose error data of the actuator at the measurement point is obtained. The real-time pose error data includes: the displacement error dataset X1 = {x 11 x 12 , ..., x 1k , ..., x 1N}、 Velocity error dataset X2={x 21 x 22 , ..., x 2k , ..., x 2N} and acceleration error dataset X3={x 31 x 32 , ..., x 3k , ..., x 3N}, k = 1, 2, ..., N, where the pose parameter sensor repeatedly measures the measurement point N times;

[0012] Step a2, define the running pose error function y of the measurement point to satisfy:

[0013]

[0014] In the formula, α0, α1, α2, α3, α 11 α 22 α 33 α 12 α 13 α 23These are the coefficients of the pose error variable, all ranging from 0 to 1; α1 = α2 = α3, α 11 =α 22 =α 33 α 12 =α 13 =α 23 And α1 < α 11 <α 12 ;

[0015] Substitute the N sets of displacement error, velocity error and acceleration error in step a1 into the above formula (1). When the minimum value of the running pose error function y is obtained, the corresponding displacement error, velocity error and acceleration error are the optimal pose error.

[0016] Step a3: Repeat step a2 to calculate the optimal pose error for all measurement points, thus obtaining the optimal pose error dataset for n measurement points: Optimal Displacement Error Dataset X 10 ={x 110 x 120 , ..., x 1j0， …, x 1n0}, Optimal velocity error dataset X 20 ={x 210 x 220 , ..., x 2j0 , ..., x 2n0} and the optimal acceleration error dataset X 30 ={x 310 x 320 , ..., x 3j0 , ..., x 3n0}, where j = 1 to n;

[0017] Then, construct the comprehensive error dataset E of the actuator, E = {e1, e2, ..., e...} j , ..., e n}, where e j =u1·x 1j0 +u2·x 2j0 +u3·x 3j0 j = 1 to n; u1, u2, u3 are weighting coefficients, each with a value of 0 to 1, and u1 + u2 + u3 = 1;

[0018] Simultaneously, the second derivative of the data in the comprehensive error dataset B is used to obtain the acceleration error dataset EC;

[0019] Step a4, with e j As input, the optimal control parameters for this actuator are obtained through field testing and the critical proportional method. j The corresponding control parameter is m jj = 1 to n; multiple sets of control parameters constitute a control parameter dataset;

[0020] Step a5: Sort the data in the comprehensive error dataset E from smallest to largest, and then divide the sorted dataset into 7 intervals A1 to A7, namely: [e1, e...]. α ), [e α e 2α ), [e 2α e 3α ), [e 3α e 4α ), [e 4α e 5α ), [e 5α e 6α ), [e 6α e 7α ], where e1 and e 7α These are the minimum and maximum values ​​in the comprehensive error dataset E, respectively. α e 2α e 3α e 4α e 5α e 6α These are the values ​​within the comprehensive error dataset E, where e1 < e α <e 2α <e 3α <e 4α <e 5α <e 6α <e 7α ; α is the integer quotient of the total number of data in the comprehensive error dataset E divided by 7. The number of data in the first 6 intervals is α, and the number of data in the last interval is α or 1 to 6 more or 1 to 6 less than α.

[0021] Sort the data in the acceleration error dataset EC from smallest to largest, and then divide the sorted dataset into 7 intervals B1 to B7, which are: [e″1, e″... β ), [e″ β ,e″ 2β ), [e″ 2β ,e″ 3β ), [e″ 3β ,e″ 4β ), [e″ 4β ,e″ 5β ), [e″ 5β ,e″ 6β ), [e″ 6β ,e″ 7β ], e″1 and e″ 7β These are the minimum and maximum values ​​in the acceleration error dataset EC, respectively, e″ β、e″ 2β 、e″ 3β 、e″ 4β 、e″ 5β 、e″ 6β These are the values ​​within the acceleration error dataset EC, where e″1 < e″. β <e″ 2β <e″ 3β <e″ 4β <e″ 5β <e″ 6β <e″ 7β ; β is the integer quotient of the total number of data in the acceleration error dataset EC divided by 7. The number of data in the first 6 intervals is β, and the number of data in the last interval is β or 1 to 6 more or 1 to 6 less than β.

[0022] Sort all parameters in the actuator's control parameter dataset in ascending order, and then divide the sorted dataset into five intervals C1 to C5, as follows: [m1, m... γ ), [m γ m 2γ ), [m 2γ m 3γ ), [m 3γ m 4γ ), [m 4γ m 5γ ), m1 and m 5γ m represents the minimum and maximum values ​​among all control parameters. γ m 2γ m 3γ m 4γ The values ​​are among all control parameters, and m1 < m. γ <m 2γ <m 3γ <m 4γ <m 5γ ; γ is the integer quotient of the total number of data in all control parameters divided by 5. The number of data in the first 4 intervals is γ, and the number of data in the last interval is γ or 1 to 4 more or 1 to 4 less than γ.

[0023] Step a6, establish fuzzy judgment rules for control parameters:

[0024] (A1, B1, C1), (A1, B2, C1), (A1, B3, C q ), (A1, B4, C q ), (A1, B5, C q ), (A1, B6, C q ), (A1, B7, C q), (A2, B1, C1), (A2, B2, C1), (A2, B3, C q (A2, B4, C) q (A2, B5, C) q (A2, B6, C) q (A2, B7, C) q ), (A3, B1, C1), (A3, B2, C q (A3, B3, C) q (A3, B4, C) q (A3, B5, C) q (A3, B6, C) q (A3, B7, C) q ), (A4, B1, C1), (A4, B2, C q (A4, B3, C) q ), (A4, B4, C q (A4, B5, C) q (A4, B6, C) q ), (A4, B7, C5), (A5, B1, C q (A5, B2, C) q (A5, B3, C) q (A5, B4, C) q (A5, B5, C) q (A5, B6, C) q ), (A5, B7, C5), (A6, B1, C q (A6, B2, C) q (A6, B3, C) q ), (A6, B4, C4), (A6, B5, C q ), (A6, B6, C5), (A6, B7, C5), (A7, B1, C q (A7, B2, C) q (A7, B3, C) q (A7, B4, C) q (A7, B5, C) q ), (A7, B6, C5), (A7, B7, C5);

[0025] Where q = 2 to 4;

[0026] Step a7: During a certain movement, the actuator obtains the displacement error x of the measurement point based on the theoretical pose parameters of the current measurement point and the actual pose parameters collected by the pose parameter sensor. 10 Speed ​​error x 20 and acceleration error x30 ; Calculate the current comprehensive error data e0=|u1·x 10 +u2·x 20 +u3·x 30 The values ​​of u1, u2, and u3 are the same as those in step a3; the second derivative of the current comprehensive error data e0 is obtained to get the current acceleration error data e″0; based on the current comprehensive error data e0 and the current acceleration error data e″0, the corresponding control parameter interval C is selected in the fuzzy judgment rule of control parameters in step a6. q Using the particle swarm optimization algorithm, in the interval C q The internal optimization module obtains optimized control parameters; the parameter optimization module then uses these optimized control parameters to optimize and adjust the actuator when it passes the measurement point.

[0027] Optionally, the actual pose parameters include actual displacement parameters, actual velocity parameters, and actual acceleration parameters, which are measured by the distance sensor, velocity sensor, and acceleration sensor in the pose parameter sensor, respectively; the theoretical pose parameters include theoretical displacement parameters, theoretical velocity parameters, and theoretical acceleration parameters, which are all obtained by physical simulation tests.

[0028] The displacement error is the difference between the actual displacement parameter and the theoretical displacement parameter at the measurement point; the velocity error is the difference between the actual velocity parameter and the theoretical displacement parameter at that moment; and the acceleration error is the difference between the actual acceleration parameter and the theoretical acceleration parameter at that moment.

[0029] Optionally, the composite control of the multiple actuators includes:

[0030] Step b1: When multiple actuators move simultaneously, each actuator, upon reaching its corresponding pose feature measurement point, obtains its own current comprehensive error data e0 and current acceleration error data e″0, and performs a weighted calculation to obtain the discrimination error data w1 for identifying a particular actuator.

[0031] w1=n1·e0+n2·e″0

[0032] In the formula, n1 and n2 are weighting coefficients, both ranging from 0 to 1, and n1 + n2 = 1;

[0033] Step b2: Obtain the discrimination error data of all actuators and form a discrimination error data dataset W1 = {w 11 w 12 ,…,w 1i ,…,w 1M}, i = 1, 2, 3, ..., M, there are M actuators in total; select the largest discrimination error data as the standard, and adjust the discrimination error data of the remaining actuators;

[0034] Step b3: Based on the adjusted discrimination error data, the adjusted current comprehensive error data and acceleration error data are derived. The corresponding optimized control parameters are obtained using the fuzzy judgment rule of the control parameters, and the corresponding actuators are adjusted to ensure that all actuators can move synchronously as required.

[0035] Optionally, the coordination control between the plurality of conveyors includes:

[0036] Step c1: Position detection sensors are installed at the middle of the input port and the middle of the output port of each conveyor, and the position detection sensors of the upper-level conveyor and the lower-level conveyor are kept concentric. The position detection sensors monitor the position deviation of the two relative conveyors in real time and obtain the deviation ε1.

[0037] Step c2: At the same time, flow meters are installed at the input end of each conveyor to measure the coal entering the conveyor and obtain the error ε2 of the coal entering the upper and lower conveyors.

[0038] Step c3: Weight the deviation ε1 and the error ε2 to obtain the discrimination error data w2.

[0039] w2=n3·ε1+n4·ε2

[0040] In the formula, n3 and n4 are weighting coefficients, both ranging from 0 to 1, and n3 + n4 = 1;

[0041] When the discrimination error data w2 between two adjacent conveyors exceeds the set value, the two conveyors are adjusted simultaneously. The adjustment is achieved by the composite control of multiple actuators corresponding to the conveyors, so that the discrimination error data between two adjacent conveyors does not exceed the set value, thus realizing precise matching control between each conveyor.

[0042] Optionally, the conveyors in the fully continuous transport system include a mining double rotary transfer machine, a straight-line transfer machine, an end-side steep-angle belt conveyor, and a moving conveyor; the mined coal can be transported and transferred sequentially through the mining double rotary transfer machine and the straight-line transfer machine to the moving conveyor, and then the end-side steep-angle belt conveyor lifts the coal from the bottom of the pit to the surface.

[0043] Optionally, the actuator of the mining double rotary transfer machine includes a direct-drive cylinder, a horizontal-pull cylinder, a rotary reducer I, a rotary reducer II, and a crawler actuator I; the actuator of the single-line transfer machine includes a side-pull cylinder, a rotary reducer III, and a crawler traveling mechanism II; the actuator of the end-side steep-angle belt conveyor includes a crawler traveling mechanism III, a crawler traveling mechanism IV, and a crawler traveling mechanism V; and the actuator of the shifting conveyor includes a crawler traveling mechanism VI, a rotary reducer IV, and a diagonal support cylinder.

[0044] A precision transport control system for a fully continuous open-pit coal mine mining system, the system being used to implement the precision transport control method for the fully continuous open-pit coal mine mining system, includes a pose parameter sensor, a data decision unit, a parameter optimization module, a position detection sensor, and a flow meter;

[0045] The pose parameter sensors are arranged at fixed positions on each actuator to collect the actual displacement parameters, actual velocity parameters, and actual acceleration parameters of the actuator as it reaches the corresponding measurement point;

[0046] The data decision unit receives information from the pose parameter sensor, performs comprehensive analysis to establish a fuzzy judgment rule for the control parameters, and uses this judgment rule to analyze and calculate the control parameters to obtain optimized control parameters.

[0047] The parameter optimization module uses optimized control parameters to optimize and adjust each actuator and precisely control the movement of each conveyor;

[0048] The position detection sensors are installed on each conveyor to monitor the positional deviation between adjacent conveyors.

[0049] The flow meters are installed on each conveyor to measure the error in the coal conveying between adjacent conveyors.

[0050] Optionally, the attitude parameter sensors on the mining double-rotor transfer machine are respectively arranged at the left root of the direct-push cylinder, the lower root of the transverse-pull cylinder, the upper connecting plate of the first rotary reducer, the upper connecting plate of the second rotary reducer, and the side connecting plate of the first track walking mechanism; the attitude parameter sensors on the single-line transfer machine are respectively arranged at the upper root of the side-pull cylinder, the upper connecting plate of the third rotary reducer, and the side connecting plate of the second track walking mechanism; the attitude parameter sensors on the end-side large-angle belt conveyor are respectively arranged... The sensors are located on the side connecting plates of track walking mechanism three, track walking mechanism four, and track actuator five; the position parameter sensors on the movable conveyor are respectively arranged on the side connecting plate of track walking mechanism six, the upper connecting plate of rotary reducer four, and the root of the upper end of the inclined support cylinder; the position detection sensors are located in the middle of the input port and the middle of the output port of each conveyor, and the position detection sensors of the upper level conveyor and the lower level conveyor are concentric; the flow meter is located at the input end of each conveyor.

[0051] Optionally, the data decision unit includes an input module, a decision module, and an output module; the input module is used to receive information from the pose parameter sensor; the decision module is used to analyze the comprehensive data to complete the precise pose control and basic function implementation of each conveyor; and the output module is used to output instructions to the parameter optimization module.

[0052] Compared with the prior art, the present invention has the following technical effects:

[0053] This invention first divides the entire motion process of the actuator into n pose feature measurement points. By repeatedly measuring the motion trajectory parameters and corresponding control parameters of the entire pose feature points, and using an optimization method, the optimal motion pose error parameters and corresponding control parameters are obtained. This establishes the relationship between the motion pose error parameters and the corresponding control parameters, and derives fuzzy judgment rules to guide the trajectory control of the actuator during actual motion. The control parameters are precisely adjusted at all pose feature points, thereby achieving precise pose control of the actuator.

[0054] This invention enables precise control of the actuators, allowing them to reach a steady state quickly during positioning. The stabilization adjustment time is short, requiring no manual intervention, ensuring precise control of each conveyor, high automated construction efficiency, and high operational accuracy. It also enables the coordinated operation of a fully continuous transport system, rapidly completing transfer operations. This provides a theoretical basis for improving the overall safety and efficiency of transfer and transportation operations in open-pit coal mines. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the components of a fully continuous transport system;

[0056] Figure 2 A schematic diagram of a mining double rotary transfer machine and its actuator;

[0057] Figure 3 A schematic diagram of a single-line transfer machine and its actuator;

[0058] Figure 4 A schematic diagram of an end-side inclined belt conveyor and its actuator;

[0059] Figure 5 This is a schematic diagram of a movable belt conveyor and its actuator.

[0060] Figure 6 The comprehensive error diagram for the positional feature point j=80 of the tracked walking mechanism three when the method of the present invention was not used;

[0061] Figure 7 The comprehensive error diagram of the method of the present invention is shown when the position feature point j=80 of the tracked walking mechanism three.

[0062] The meanings of the labels in the diagram are as follows:

[0063] 1. Mining double rotary transfer conveyor; 2. Single-line transfer conveyor; 3. End-side steep-angle belt conveyor; 4. Transfer conveyor; 11. Direct push cylinder; 12. Horizontal pull cylinder; 13. Rotary reducer one; 14. Rotary reducer two; 15. Track actuator one; 21. Side pull cylinder; 22. Rotary reducer three; 23. Track walking mechanism two; 31. Track walking mechanism three; 32. Track walking mechanism four; 33. Track walking mechanism five; 41. Track walking mechanism six; 42. Rotary reducer four; 43. Inclined brace cylinder. Detailed Implementation

[0064] The fully continuous transport system for open-pit coal mines mainly includes a mine double-rotor transfer conveyor, a single-line transfer conveyor, an end-side steep-angle belt conveyor, and a shift conveyor. It is responsible for transporting coal after mining. The mined coal is sequentially transported via the mine double-rotor transfer conveyor and the single-line transfer conveyor to the shift conveyor, and then lifted from the bottom of the pit to the surface by the end-side steep-angle belt conveyor. Because each piece of equipment typically uses multiple sets of actuators during transport, errors in position, speed, and direction inevitably occur, causing strain on the main structure of each conveyor and affecting its service life. Furthermore, the coordination between different pieces of equipment significantly impacts the overall system's transport efficiency and accuracy. To address this issue, this invention provides a precise transport control method and system for a fully continuous open-pit coal mine mining system. The system deploys posture parameter sensors, a data decision unit, a parameter optimization module, position detection sensors, and flow meters. The method divides the motion trajectory of the actuators of the conveyor in the fully continuous transport system into multiple posture feature measurement points. It calculates the comprehensive error and acceleration error of all measurement points, as well as the control parameters, and then establishes fuzzy judgment rules for the control parameters. Next, it measures and calculates the comprehensive error and acceleration error of the measurement points during a certain movement of the actuator, obtains the corresponding control parameter range from the fuzzy judgment rules, and optimizes the control parameters. When the actuator passes through each measurement point, the optimized control parameters are used for further optimization and adjustment, achieving precise posture control of the actuator. Furthermore, it realizes composite control of multiple actuators and coordinated control between multiple conveyors. This enables mutual coordination within the fully continuous transport system, resulting in high automated construction efficiency, high operational accuracy, and rapid completion of transfer operations.

[0065] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments. All equivalent modifications made based on the technical solutions of this application fall within the protection scope of the present invention.

[0066] This invention provides a precise transport control method for a fully continuous open-pit coal mine mining system. This method enables precise position and orientation control of each conveyor in the fully continuous mining system and precise matching control between conveyors; it includes:

[0067] a. Precise pose control of the actuator: First, the entire motion trajectory of the conveyor actuator is divided into multiple pose feature measurement points. The comprehensive error and acceleration error of all measurement points, as well as the control parameters of the actuator, are calculated, and a fuzzy judgment rule for the control parameters is established. Then, the comprehensive error and acceleration error of the pose feature measurement points of the actuator during a certain motion are measured and calculated. The corresponding control parameter range is obtained from the fuzzy judgment rule for the control parameters, and the optimized control parameters are obtained through optimization. When the actuator passes through each pose feature measurement point, the optimized control parameters are used for optimization and adjustment, thereby precisely adjusting the control parameters at all pose feature measurement points to achieve precise pose control of the actuator.

[0068] b. Composite control of multiple actuators: The comprehensive error and acceleration error of each actuator are calculated separately, and the discrimination error of each actuator is obtained by weighting. The discrimination error of each actuator is adjusted by using the same discrimination error standard. The adjusted comprehensive error and acceleration error are derived from the adjusted discrimination error. The optimized control parameters of each actuator are obtained from the fuzzy judgment rules of the control parameters, and the corresponding actuators are adjusted to ensure that the actuators move synchronously.

[0069] c. Coordination control between multiple conveyors: Calculate the positional deviation and coal error at the docking points of each conveyor, and obtain the conveyor discrimination error by weighting. When the conveyor discrimination error exceeds the set value, perform composite control adjustment on each actuator of the conveyor to achieve precise matching control between the conveyors.

[0070] Precise control of a single actuator includes:

[0071] Step a1: Perform N measurements on a single measurement point and obtain the error dataset: Subdivide the motion process of a single actuator of the conveyor into n pose feature measurement points. Use a pose parameter sensor to obtain the actual pose parameters of the actuator. Combine this with the theoretical pose parameters of the actuator to obtain the real-time pose error data of the actuator at the measurement point. The real-time pose error data includes: the displacement error dataset X1 = {x 11 x 12 , ..., x 1k , ..., x 1N}、 Velocity error dataset X2={x 21 x 22 , ..., x 2k , ..., x 2N} and acceleration error dataset X3={x 31 x 32 , ..., x 3k , ..., x 3N}, k = 1, 2, ..., N, where the pose parameter sensor repeatedly measures the measurement point N times; the actual pose parameter dataset consists of N data points (multiple repeated tests, each measurement point was measured N times in total, resulting in N actual displacement parameters, N actual velocity parameters, and N actual acceleration parameters). Dimensionless processing of the spatial displacement error data, spatial velocity error data, and spatial acceleration error data yields the aforementioned standardized dataset.

[0072] Step a2, obtain the optimal pose error for this single measurement point: Define the running pose error function y for this measurement point to satisfy:

[0073]

[0074] In the formula, α0, α1, α2, α3, α 11 α 22 α 33 α 12 α 13 α 23 These are the coefficients of the pose error variable, all ranging from 0 to 1; α1 = α2 = α3, α 11 =α 22 =α 33 α 12 =α 13 =α 23 And α1 < α 11 <α 12 In the specific scheme, the specific values ​​of this set of coefficients are determined according to the required operating accuracy of the actuator; the higher the accuracy requirement, the smaller the value.

[0075] Substituting the N sets of displacement errors, velocity errors, and acceleration errors from step a1 into equation (1) above, when the minimum value of the running pose error function y is obtained, the corresponding displacement error x 1j0 Speed ​​error x 2j0 and acceleration error x 3j0 This represents the optimal pose error.

[0076] Step a3, calculate the comprehensive error of all measurement points: Repeat step a2 to calculate the optimal pose error of all measurement points, thus obtaining the optimal pose error dataset of n measurement points: Optimal displacement error dataset X 10 ={x 110 x 120 , ..., x 1j0 , ..., x 1n0}, Optimal velocity error dataset X 20 ={x 210 x 220 , ..., x 2j0 , ..., x 2n0} and the optimal acceleration error dataset X 30 ={x 310 x 320 , ..., x 3j0 , ..., x 3n0}, where j = 1 to n;

[0077] Then, construct the comprehensive error dataset E of the actuator, E = {e1, e2, ..., e...} j , ..., e n}, where e j =u1·x 1j0 +u2·x 2j0 +u3·x 3j0 j = 1 to n; u1, u2, u3 are weighting coefficients, each with a value of 0 to 1, and u1 + u2 + u3 = 1;

[0078] Simultaneously, the second derivative of the data in the comprehensive error dataset E is obtained to yield the acceleration error dataset EC;

[0079] Step a4, obtain the control parameters through comprehensive error: e j As input, the optimal control parameters for this actuator are obtained through field testing and the critical proportional method. j The corresponding control parameter is m j j = 1 to n; multiple sets of control parameters constitute a control parameter dataset;

[0080] Step a5: Establish intervals for the overall error, acceleration error, and control parameters respectively.

[0081] Sort the data in the comprehensive error dataset E from smallest to largest, and then divide the sorted dataset into 7 intervals A1 to A7, namely: [e1, e...]. α ), [e α e 2α ), [e 2α e 3α ), [e 3α e 4α ), [e 4α e 5α ), [e 5α e 6α ), [e 6α e 7α ], where e1 and e 7α These are the minimum and maximum values ​​in the comprehensive error dataset E, respectively. α e 2α e 3α e 4α e 5α e 6αThese are the values ​​within the comprehensive error dataset E, where e1 < e α <e 2α <e 3α <e 4α <e 5α <e 6α <e 7α α is the integer quotient of the total number of data points in the comprehensive error dataset E divided by 7, where the number of data points in the first 6 intervals is 7. α The number of data points in the last interval is α, or 1 to 6 more than α, or 1 to 6 less than α;

[0082] Sort the data in the acceleration error dataset EC from smallest to largest, and then divide the sorted dataset into 7 intervals B1 to B7, which are: [e″1, e″... β ), [e″ β ,e″ 2β ), [e″ 2β ,e″ 3β ), [e″ 3β ,e″ 4β ), [e″ 4β ,e″ 5β ), [e″ 5β ,e″ 6β ), [e″ 6β ,e″ 7β ], e″1 and e″ 7β These are the minimum and maximum values ​​in the acceleration error dataset EC, respectively, e″ β 、e″ 2β 、e″ 3β 、e″ 4β 、e″ 5β 、e″ 6β These are the values ​​within the acceleration error dataset EC, where e″1 < e″. β <e″ 2β <e″ 3β <e″ 4β <e″ 5β <e″ 6β <e″ 7β ; β is the integer quotient of the total number of data in the acceleration error dataset EC divided by 7. The number of data in the first 6 intervals is β, and the number of data in the last interval is β or 1 to 6 more or 1 to 6 less than β.

[0083] Sort all parameters in the actuator's control parameter dataset in ascending order, and then divide the sorted dataset into five intervals C1 to C5, as follows: [m1, m... γ ), [m γ m 2γ ), [m2γ m 3γ ), [m 3γ m 4γ ), [m 4γ m 5γ ), m1 and m 5γ m represents the minimum and maximum values ​​among all control parameters. γ m 2γ m 3γ m 4γ The values ​​are among all control parameters, and m1 < m. γ <m 2γ <m 3γ <m 4γ <m 5γ ; γ is the integer quotient of the total number of data in all control parameters divided by 5. The number of data in the first 4 intervals is γ, and the number of data in the last interval is γ or 1 to 4 more or 1 to 4 less than γ.

[0084] Step a6, establish fuzzy judgment rules for control parameters:

[0085] (A1, B1, C1), (A1, B2, C1), (A1, B3, C q ), (A1, B4, C q ), (A1, B5, C q ), (A1, B6, C q ), (A1, B7, C q ), (A2, B1, C1), (A2, B2, C1), (A2, B3, C q (A2, B4, C) q (A2, B5, C) q (A2, B6, C) q (A2, B7, C) q ), (A3, B1, C1), (A3, B2, C q (A3, B3, C) q (A3, B4, C) q (A3, B5, C) q (A3, B6, C) q (A3, B7, C) q ), (A4, B1, C1), (A4, B2, C q (A4, B3, C) q ), (A4, B4, C q (A4, B5, C) q (A4, B6, C) q ), (A4, B7, C5), (A5, B1, C q(A5, B2, C) q (A5, B3, C) q (A5, B4, C) q (A5, B5, C) q (A5, B6, C) q ), (A5, B7, C5), (A6, B1, C q (A6, B2, C) q (A6, B3, C) q ), (A6, B4, C4), (A6, B5, C q ), (A6, B6, C5), (A6, B7, C5), (A7, B1, C q (A7, B2, C) q (A7, B3, C) q (A7, B4, C) q (A7, B5, C) q ), (A7, B6, C5), (A7, B7, C5);

[0086] Where q = 2 to 4;

[0087] Step a7: Obtain optimized control parameters based on the fuzzy judgment rule for control parameters and perform precise control: During a certain movement, the actuator obtains the displacement error x of the measurement point based on the theoretical pose parameters of the current measurement point and the actual pose parameters collected by the pose parameter sensor. 10 Speed ​​error x 20 and acceleration error x 30 ; Calculate the current comprehensive error data e0=|u1·x 10 +u2·x 20 +u3·x 30 The values ​​of u1, u2, and u3 are the same as those in step a3; the second derivative of the current comprehensive error data e0 is obtained to get the current acceleration error data e″0; based on the current comprehensive error data e0 and the current acceleration error data e″0, the corresponding control parameter interval C is selected in the fuzzy judgment rule of control parameters in step a6. q With the goal of minimizing the overall simulation error data, a particle swarm optimization algorithm is employed within the interval C. q Internal optimization is performed to obtain optimized control parameters; the simulation comprehensive error data is obtained by using the calculation method of the current comprehensive error data in steps a1 to a3 based on the actual pose data obtained from the theoretical motion trajectory and system simulation calculation; the parameter optimization module optimizes and adjusts the actuator by using the optimized control parameters when it passes through each measurement point.

[0088] In this embodiment, the actual pose parameters include actual displacement parameters, actual velocity parameters, and actual acceleration parameters, which are measured by the distance sensor, velocity sensor, and acceleration sensor in the pose parameter sensor, respectively; the theoretical pose parameters include theoretical displacement parameters, theoretical displacement parameters, and theoretical acceleration parameters, which are all obtained by physical simulation tests.

[0089] The displacement error is the difference between the actual displacement parameter and the theoretical displacement parameter at the measurement point; the velocity error is the difference between the actual velocity parameter and the theoretical displacement parameter at that moment; and the acceleration error is the difference between the actual acceleration parameter and the theoretical acceleration parameter at that moment.

[0090] Complex control involving multiple actuators includes:

[0091] Step b1: When multiple actuators move simultaneously, each actuator, upon reaching its corresponding pose feature measurement point, obtains its own current comprehensive error data e0 and current acceleration error data e″0, and performs a weighted calculation to obtain the discrimination error data w1 for identifying a particular actuator.

[0092] w1=n1·e0+n2·e″0

[0093] In the formula, n1 and n2 are weighting coefficients, both ranging from 0 to 1, and n1 + n2 = 1;

[0094] Step b2: Obtain the discrimination error data of all actuators and form a discrimination error data dataset W1 = {w 11 w 12 ,…,w 1i ,…,w 1M}, i = 1, 2, 3, ..., M, there are M actuators in total; due to the precise control of each individual actuator, the discrimination error data w of each actuator is already the minimum error for that actuator. Here, we select the largest discrimination error data w. 1i As a standard, the discrimination error data of the remaining actuators are adjusted; for example, when the conveyor moves in parallel, the discrimination error data w1 of all actuators needs to be adjusted to w 1i When the conveyor rotates, the discrimination error data w of all actuators needs to be adjusted to be proportional.

[0095] Step b3: Based on the adjusted discrimination error data, the adjusted current comprehensive error data and acceleration error data are derived. The corresponding optimized control parameters are obtained using the fuzzy judgment rule of the control parameters, and the corresponding actuators are adjusted to ensure that all actuators can move synchronously as required.

[0096] Coordination control between multiple conveyors includes:

[0097] Step c1: Position detection sensors are installed at the middle of the input port and the middle of the output port of each conveyor, and the position detection sensors of the upper-level conveyor and the lower-level conveyor are kept concentric. The position detection sensors monitor the position deviation of the two relative conveyors in real time and obtain the deviation ε1.

[0098] Step c2: At the same time, flow meters are installed at the input end of each conveyor to measure the coal entering the conveyor and obtain the error ε2 of the coal entering the upper and lower conveyors.

[0099] Step c3: Weight the deviation ε1 and the error ε2 to obtain the discrimination error data w2.

[0100] w2=n3·ε1+n4·ε2

[0101] In the formula, n3 and n4 are weighting coefficients, both ranging from 0 to 1, and n3 + n4 = 1;

[0102] When the discrimination error data w2 between two adjacent conveyor stages exceeds the set value, both conveyor stages are adjusted simultaneously. This adjustment is achieved through composite control of multiple actuators corresponding to the conveyors, ensuring that the discrimination error data between adjacent conveyor stages does not exceed the set value, thus realizing precise matching control between the conveyors. Specifically, the discrimination error data corresponding to the three docking points of the four conveyors—the mining double-rotor transfer conveyor, the single-line transfer conveyor, the end-side steep-angle belt conveyor, and the shifting conveyor—are w2, respectively. 21 w 22 w 23 When the discrimination error data w2 is greater than the required cutoff error (set value), it is necessary to adjust both the upper-level belt conveyor and the lower-level belt conveyor at the same time. This adjustment is achieved through the composite control of multiple actuators corresponding to the belt conveyor to ensure that the discrimination error data w2 between the two conveyors meets the requirements.

[0103] In this embodiment, such as Figures 1 to 5 As shown, the conveyors in the fully continuous transport system include a mining double rotary transfer conveyor, a straight-line transfer conveyor, an end-side steep-angle belt conveyor, and a moving conveyor. The mined coal can be transported and transferred sequentially through the mining double rotary transfer conveyor and the straight-line transfer conveyor to the moving conveyor, and then the end-side steep-angle belt conveyor lifts the coal from the bottom of the pit to the surface.

[0104] In another embodiment, the fully continuous transport system has two sets of the aforementioned conveyors for simultaneous layered mining. Specifically, the shifting conveyor is located at the top of the lower coal seam and along the mining length of the coal seam working face. On one side of the shifting conveyor, there is a set of mining double rotary transfer machines and a straight transfer machine. The mining double rotary transfer machine is located on the bottom of the coal seam, and the straight transfer machine is located at the junction of the bottom of the coal seam and the lower coal seam. On the other side of the shifting conveyor, there is also a set of mining double rotary transfer machines located at the top of the lower coal seam. The conveyor on one side of the shifting conveyor can transfer and transport coal cut and mined along the lower coal seam, and the conveyor on the other side of the shifting conveyor can transfer and transport coal cut and mined along the bottom of the upper coal seam. The shifting conveyors on both sides reciprocate to cut and mined the coal face, forming simultaneous continuous mining of the upper and lower coal seams.

[0105] The mining double-rotor transfer machine 1 includes a receiving arm, a discharging arm, a slewing support, and an actuator. The receiving arm is mainly used for transporting and transferring coal. The discharging arm transports and transfers coal from the receiving arm to the single-line transfer machine. The main function of the slewing support device is to achieve a certain angle between the receiving arm and the discharging arm, thereby increasing the working range. The angle range is 0°–90°. The actuator of the mining double-rotor transfer machine includes a direct-drive cylinder 11, a horizontal-pull cylinder 12, a first slewing reducer 13, a second slewing reducer 14, and a crawler actuator 15. The direct-drive cylinder is connected to the receiving arm, the horizontal-pull cylinder is connected to the discharging arm, the crawler actuator 1 is a double-rotor walking track, and the first slewing reducer and the second slewing reducer are respectively installed at the slewing support.

[0106] The single-type transfer conveyor 2 includes a belt conveyor, a slewing support, and an actuator; the belt conveyor is mainly used for transferring coal materials to achieve continuous transportation; the actuator of the single-type transfer conveyor includes a side-pulling cylinder 21, a slewing reducer 3 22, and a crawler walking mechanism 23; the side-pulling cylinder is connected to the belt conveyor and the slewing support, the slewing reducer 3 is located on the slewing support, and the crawler walking mechanism 2 can drive the single-type transfer conveyor to move.

[0107] The actuators of the inclined belt conveyor 3 include crawler walking mechanism 31, crawler walking mechanism 42, and crawler walking mechanism 53. The crawler walking mechanism 31, crawler walking mechanism 42, and crawler walking mechanism 53 are laid out in sequence along the undulating terrain of the coal mining face, mainly used to lift coal from the bottom of the pit to the surface and then transfer it to the fixed belt conveyor.

[0108] The movable conveyor comprises a head section, a lifting section, a transport section, a tail section, and an actuator arranged sequentially. The head section primarily provides power to the movable conveyor and is equipped with a ground anchor at its bottom for stability. The lifting section raises the coal to a certain height, facilitating connection with the inclined belt conveyor at the end sides for continuous transport. The transport section transfers the coal unloaded from the tracked unloading vehicle to the inclined belt conveyor at the end sides; the belt support in the transport section is designed in 30m sections for easy relocation and installation of the movable conveyor. The tail section is also equipped with a ground anchor at its bottom for overall stability, improving transport reliability and stability. The actuator of the movable conveyor 4 includes a tracked walking mechanism 41, a rotary reducer 42, and a slant support cylinder 43.

[0109] The present invention also provides a precision transport control system for a fully continuous open-pit coal mine mining system. This system can be used to implement the above-mentioned precision transport control method for a fully continuous open-pit coal mine mining system, and includes a pose parameter sensor, a data decision unit, a parameter optimization module, a position detection sensor, and a flow meter.

[0110] The pose parameter sensors are arranged at fixed positions on each actuator to collect the spatial pose parameters of each actuator of the conveyor, and can collect the pose feature point parameters of each actuator to collect the actual displacement parameters, actual velocity parameters and actual acceleration parameters of the actuator when it reaches the corresponding measurement point.

[0111] The data decision unit receives information from the pose parameter sensor, comprehensively analyzes it to establish fuzzy judgment rules for control parameters, and uses these rules to analyze and calculate the control parameters to obtain optimized control parameters. The data decision unit includes an input module, a decision module, and an output module. The input module receives information from the pose parameter sensor; the decision module analyzes the comprehensive data to achieve precise pose control and basic function implementation for each conveyor; and the output module outputs relevant instructions to the parameter optimization module.

[0112] The parameter optimization module uses optimized control parameters to optimize and adjust each actuator and precisely control the movement of each conveyor; specifically, it outputs the results of each optimized parameter to the actuator of the conveyor for precise control of the movement of each conveyor.

[0113] Position detection sensors are installed on each conveyor to monitor the positional deviation between adjacent conveyor stages.

[0114] Flow meters are installed on each conveyor to measure the error in the amount of coal conveyed between adjacent conveyors.

[0115] More specifically, the attitude parameter sensors on the mining double-rotor transfer machine are respectively located at the left root of the direct-push cylinder, the lower root of the lateral-pull cylinder, the upper connecting plate of the first rotary reducer, the upper connecting plate of the second rotary reducer, and the side connecting plate of the first track walking mechanism; ensuring reliable connection and no interference with other components during operation; the attitude parameter sensors on the single-line transfer machine are respectively located at the upper root of the side-pull cylinder, the upper connecting plate of the third rotary reducer, and the side connecting plate of the second track walking mechanism; ensuring reliable connection and no interference with other components during operation; the attitude parameter sensors on the end-side large-angle belt conveyor ... second rotary reducer, and the side connecting plate of the third track walking mechanism. The sensors are arranged on the side connecting plates of tracked walking mechanism three, tracked walking mechanism four, and tracked actuator five to ensure reliable connection and prevent interference with other components during operation. The position parameter sensors on the movable conveyor are respectively arranged on the side connecting plate of tracked walking mechanism six, the upper connecting plate of rotary reducer four, and the root of the upper end of the inclined support cylinder to ensure reliable connection and prevent interference with other components during operation. Position detection sensors are located in the middle of the input and output ports of each conveyor, with the position detection sensors of the upper and lower conveyors being concentric. Flow meters are located at the input end of each conveyor.

[0116] Example:

[0117] When the fully continuous transport system is tested in an open-pit coal mine, the control of a belt conveyor with a large angle of inclination at the end side is used as an example. The actuators include tracked walking mechanism three, tracked walking mechanism four, and tracked walking mechanism five. Taking tracked walking mechanism three as an example, a total of n=100 pose feature points are set, and each pose feature point is sampled for N=300 actual pose data.

[0118] In Formula 1, the parameters α0, α1, α2, α3, α 11 α 22 α 33 α 12 α 13 α 23 The values ​​are taken as 0.1, 0.12, 0.12, 0.12, 0.17, 0.17, 0.17, 0.23, 0.23, and 0.23 respectively, to obtain the running pose error function y. Substituting the 300 sets of datasets into formula (1) yields the optimal pose error dataset for all 100 pose feature points.

[0119] u1, u2, and u3 are set to values ​​of 0.4, 0.3, and 0.3 respectively, to construct the comprehensive error dataset E of the tracked walking mechanism three. At the same time, the corresponding acceleration error dataset EC is obtained by taking the derivative, and the corresponding control parameter dataset is also obtained.

[0120] Each dataset was sorted and divided into intervals. The seven intervals A1 to A7 of the comprehensive error dataset E are [-5, -3.65), [-3.65, -2.13), [-2.13, -0.58), [-0.58, 0.61), [0.61, 2.21), [2.21, 3.66), [3.66, 5], respectively. The seven intervals B1 to B7 of the acceleration error dataset EC are [-3, -2.15), [-3, -2.13), [-3.65, -2.13), [-3.65, -2.13], ... -2.15, -0.88), [-0.88, -0.15), [-0, 15, 0.17), [0.17, 0.85), [0.85, 2.11), [2.21, 3]; the five intervals C1 to C5 of the control parameter dataset are [-2.77, -1.34), [-1.34, -0.26), [-0, 26, 0.19), [0.19, 1.25), [1.25, 2.68].

[0121] Based on the on-site construction conditions, the value of q is selected, and the fuzzy judgment rules for the control parameters are established as follows:

[0122] (A1, B1, C1), (A1, B2, C1), (A1, B3, C1), (A1, B4, C2), (A1, B5, C2), (A1, B6, C2), ( A1, B7, C3), (A2, B1, C1), (A2, B2, C1), (A2, B3, C2), (A2, B4, C2), (A2, B5, C3), (A 2, B6, C3), (A2, B7, C3), (A3, B1, C1), (A3, B2, C2), (A3, B3, C2), (A3, B4, C2), (A3 ,B5,C3),(A3,B6,C3),(A3,B7,C3),(A4,B1,C1),(A4,B2,C2),(A4,B3,C2),(A4,B 4, C2), (A4, B5, C3), (A4, B6, C4), (A4, B7, C5), (A5, B1, C2), (A5, B2, C2), (A5, B3 , C3), (A5, B4, C3), (A5, B5, C4), (A5, B6, C4), (A5, B7, C5), (A6, B1, C2), (A6, B2, C 3), (A6, B3, C3), (A6, B4, C4), (A6, B5, C4), (A6, B6, C5), (A6, B7, C5), (A7, B1, C2 ), (A7, B2, C3), (A7, B3, C3), (A7, B4, C4), (A7, B5, C4), (A7, B6, C5), (A7, B7, C5);

[0123] When the tracked walking mechanism reaches the pose feature point j=80, the displacement error x of that measurement point is obtained.10 =0.7921, speed error x 20 =0.8572 and acceleration error x 30 =0.5732; the current comprehensive error data e0 = 0.7460 is obtained, and the corresponding acceleration error data e″0 = -0.4368. The corresponding control parameter interval is (A5, B3, C3), and the control parameter interval is selected as [-0, 26, 0.19). Using the particle swarm optimization algorithm, the optimization is performed within the interval C3, and the optimized control parameter is obtained as 0.1542. The optimized control parameter 0.1542 is used to optimize and adjust the tracked walking mechanism.

[0124] When tracked traveling mechanisms three, four, and five move simultaneously, n1 and n2 take values ​​of 0.5 and 0.5 respectively. The discrimination error data W1 = {2.3642, 1.7693, 2.1548} for the three actuators are obtained. Then, using w... 11 Using 2.3642 as the standard, i.e., taking the third track mechanism as the standard, the discrimination errors of the fourth and fifth track mechanisms are adjusted, resulting in comprehensive error data and acceleration error data that need to be adjusted to 2.6458 and 2.0826, respectively. The corresponding control parameter range is then determined to be (A6, B6, C5), with the selected control parameter range being [1, 25, 2.68). Particle swarm optimization is used to find the optimal control parameters within range C5, yielding optimized control parameters of 1.7564 and 2.0659, respectively. These optimized control parameters (1.7564 and 2.0659) are then used to further optimize and adjust the fourth and fifth track mechanisms, ensuring that all three track mechanisms can move synchronously as required.

[0125] When the fully continuous transport system was tested in an open-pit coal mine, the deviations of four conveyors—a mine-use double rotary transfer conveyor, a single-line transfer conveyor, a shifting conveyor, and an end-side steep-angle belt conveyor—were measured. With n3 and n4 being 0.6 and 0.4 respectively, the deviations ε1 were found to be 0.8679, 0.7686, and 0.6956, and the errors ε2 were 1.1259, 1.5869, and 1.0568, respectively. The results showed that w... 21 =0.9711, w 22 =1.0959, w 23 =0.8401, with the cutoff error set to 1, then w 22 If the cutoff error exceeds the required limit, it is necessary to perform combined control on the single-line transfer machine and the shifting conveyor to adjust the error until the discrimination error data w2 between the two conveyors meets the requirements.

[0126] The overall error variation of the tracked traveling mechanism when reaching the pose feature point j=80 is as follows: Figure 6-7 The figures shown are graphs illustrating the changes in overall error when the method of this invention was not used and when the method of this invention was used.

[0127] See Figure 6 As shown, by monitoring the changes in the comprehensive error of the pose feature point j=80, it can be seen from the actual sampling that the comprehensive error vibration of the track walking mechanism three changes significantly, the entire movement process is not stable enough, and it has not reached equilibrium after 1 second.

[0128] See Figure 7 As shown, by using the method of this invention and adjusting the control parameters, the overall error curve changes smoothly, the entire movement process is stable and reliable, and it only takes 0.9s to reach a stable state. The stabilization adjustment time is short, the movement is precise, and there are no step signals during the entire movement process.

Claims

1. A precise transport control method for a fully continuous open-pit coal mine mining system, characterized in that, This method enables precise position and orientation control of each conveyor in a fully continuous mining system and precise matching control between conveyors; it includes: a. Precise pose control of the actuator: First, the entire motion trajectory of the conveyor actuator is divided into multiple pose feature measurement points. The comprehensive error and acceleration error of all measurement points, as well as the control parameters of the actuator, are calculated, and a fuzzy judgment rule for the control parameters is established. Then, the comprehensive error and acceleration error of the pose feature measurement points of the actuator during a certain motion are measured and calculated. The corresponding control parameter range is obtained from the fuzzy judgment rule for the control parameters, and the optimized control parameters are obtained through optimization. When the actuator passes through each pose feature measurement point, the optimized control parameters are used for optimization and adjustment, thereby precisely adjusting the control parameters at all pose feature measurement points to achieve precise pose control of the actuator. b. Composite control of multiple actuators: The comprehensive error and acceleration error of each actuator are calculated separately, and the discrimination error of each actuator is obtained by weighting. The discrimination error of each actuator is adjusted by using the same discrimination error standard. The adjusted comprehensive error and acceleration error are derived from the adjusted discrimination error. The optimized control parameters of each actuator are obtained from the fuzzy judgment rules of the control parameters, and the corresponding actuators are adjusted to ensure that the actuators move synchronously. c. Coordination control between multiple conveyors: Calculate the positional deviation and coal error at the docking points of each conveyor, and obtain the conveyor discrimination error by weighting. When the conveyor discrimination error exceeds the set value, perform composite control adjustment on each actuator of the conveyor to achieve precise matching control between the conveyors.

2. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 1, characterized in that, Precise control of a single actuator includes: Step a1, the single actuator motion process of the conveyor is subdivided into n pose feature measurement points, the actual pose parameters of the actuator are obtained by using the pose parameter sensor, and the real-time pose error data of the actuator at the measurement point is obtained by combining the theoretical pose parameters of the actuator; the real-time pose error data includes: displacement error data set X1={x 11 , x 12 , …, x 1k , …, x 1N}, velocity error data set X2={x 21 , x 22 , …, x 2k , …, x 2N} and acceleration error data set X3={x 31 , x 32 , …, x 3k , …, x 3N}, k=1, 2, …, N, wherein the pose parameter sensor repeatedly measures the measurement point N times; Step a2, define the running pose error function y of the measurement point to satisfy: where α0, α1, α2, α3, α 11 , α 22 , α 33 , α 12 , α 13 , α 23 are the coefficients of the pose error variables, and their values are all 0 to 1; α1 = α2 = α3, α 11 = α 22 = α 33 , α 12 = α 13 = α 23 , and α1 < α 11 < α 12 ; Substitute the N sets of displacement error, velocity error and acceleration error in step a1 into the above formula (1). When the minimum value of the running pose error function y is obtained, the corresponding displacement error, velocity error and acceleration error are the optimal pose error. Step a3: Repeat step a2 to calculate the optimal pose error for all measurement points, thus obtaining the optimal pose error dataset for n measurement points: Optimal Displacement Error Dataset X 10 ={x 110 x 120 , ..., x 1j0 , ..., x 1n0 }, Optimal velocity error dataset X 20 ={x 210 x 220 , ..., x 2j0 , ..., x 2n0 } and the optimal acceleration error dataset X 30 ={x 310 x 320 , ..., x 3j0 , ..., x 3n0 }, where j = 1 to n; Then, construct the comprehensive error dataset E of the actuator, E = {e1, e2, ..., e...} j , ..., e n }, where e j =u1·x 1j0 +u2·x 2j0 +u3·x 3j0 j = 1 to n; u1, u2, u3 are weighting coefficients, each with a value of 0 to 1, and u1 + u2 + u3 = 1; Simultaneously, the second derivative of the data in the comprehensive error dataset B is used to obtain the acceleration error dataset EC; Step a4, with e j As input, the optimal control parameters for this actuator are obtained through field testing and the critical proportional method. j The corresponding control parameter is m j j = 1 to n; multiple sets of control parameters constitute a control parameter dataset; Step a5: Sort the data in the comprehensive error dataset E from smallest to largest, and then divide the sorted dataset into 7 intervals A1 to A7, namely: [e1, e...]. α ), [e α e 2α ), [e 2α e 3α ), [e 3α e 4α ), [e 4α e 5α ), [e 5α e 6α ), [e 6α e 7α ], where e1 and e 7α These are the minimum and maximum values ​​in the comprehensive error dataset E, respectively. α e 2α e 3α e 4α e 5α e 6α These are the values ​​within the comprehensive error dataset E, where e1 < e α <e 2α <e 3α <e 4α <e 5α <e 6α <e 7α ; α is the integer quotient of the total number of data in the comprehensive error dataset E divided by 7. The number of data in the first 6 intervals is α, and the number of data in the last interval is α or 1 to 6 more or 1 to 6 less than α. Sort the data in the acceleration error dataset EC from smallest to largest, and then divide the sorted dataset into 7 intervals B1 to B7, which are: [e″1, e″... β ), [e″ β ,e″ 2β ), [e″ 2β ,e″ 3β ), [e″ 3β ,e″ 4β ), [e″ 4β ,e″ 5β ), [e″ 5β ,e″ 6β ), [e″ 6β ,e″ 7β ], e″1 and e″ 7β These are the minimum and maximum values ​​in the acceleration error dataset EC, respectively, e″ β 、e″ 2β 、e″ 3β 、e″ 4β 、e″ 5β 、e″ 6β These are the values ​​within the acceleration error dataset EC, where e″1 < e″. β <e″ 2β <e″ 3β <e″ 4β <e″ 5β <e″ 6β <e″ 7β ; β is the integer quotient of the total number of data in the acceleration error dataset EC divided by 7. The number of data in the first 6 intervals is β, and the number of data in the last interval is β or 1 to 6 more or 1 to 6 less than β. Sort all parameters in the actuator's control parameter dataset in ascending order, and then divide the sorted dataset into five intervals C1 to C5, as follows: [m1, m... γ ), [m γ m 2γ ), [m 2γ m 3γ ), [m 3γ m 4γ ), [m 4γ m 5γ ), m1 and m 5γ m represents the minimum and maximum values ​​among all control parameters. γ m 2γ m 3γ m 4γ The values ​​are among all control parameters, and m1 < m. γ <m 2γ <m 3γ <m 4γ <m 5γ ; γ is the integer quotient of the total number of data in all control parameters divided by 5. The number of data in the first 4 intervals is γ, and the number of data in the last interval is γ or 1 to 4 more or 1 to 4 less than γ. Step a6, establish fuzzy judgment rules for control parameters: (A1, B1, C1), (A1, B2, C1), (A1, B3, C q (A1, B4, C) q (A1, B5, C) q (A1, B6, C) q (A1, B7, C) q (A2, B1, C1) (A2, B2, C1) (A2, B3, C q (A2, B4, C) q (A2, B5, C) q (A2, B6, C) q (A2, B7, C) q (A3, B1, C1) (A3, B2, C q (A3, B3, C) q (A3, B4, C) q (A3, B5, C) q (A3, B6, C) q (A3, B7, C) q ),(A4,B1,C1),(A4,B2,C q (A4, B3, C) q (A4, B4, C) q (A4, B5, C) q (A4, B6, C) q ),(A4,B7,C5),(A5,B1,C q (A5, B2, C) q (A5, B3, C) q (A5, B4, C) q (A5, B5, C) q (A5, B6, C) q (A5, B7, C5), (A6, B1, C q (A6, B2, C) q (A6, B3, C) q ),(A6,B4,C4),(A6,B5,C q (A6, B6, C5) (A6, B7, C5) (A7, B1, C q (A7, B2, C) q (A7, B3, C) q (A7, B4, C) q (A7, B5, C) q (A7, B6, C5) (A7, B7, C5) Where q = 2 to 4; Step a7: During a certain movement, the actuator obtains the displacement error x of the measurement point based on the theoretical pose parameters of the current measurement point and the actual pose parameters collected by the pose parameter sensor. 10 Speed ​​error x 20 and acceleration error x 30 ; Calculate the current comprehensive error data e0=|u1·x 10 +u2·x 20 +u3·x 30 The values ​​of u1, u2, and u3 are the same as those in step a3; the second derivative of the current comprehensive error data e0 is obtained to get the current acceleration error data e″0; based on the current comprehensive error data e0 and the current acceleration error data e″0, the corresponding control parameter interval C is selected in the fuzzy judgment rule of control parameters in step a6. q Using the particle swarm optimization algorithm, in the interval C q The internal optimization module obtains optimized control parameters; the parameter optimization module then uses these optimized control parameters to optimize and adjust the actuator when it passes the measurement point.

3. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 2, characterized in that, The actual pose parameters include actual displacement parameters, actual velocity parameters, and actual acceleration parameters, which are measured by the distance sensor, velocity sensor, and acceleration sensor in the pose parameter sensor, respectively. The theoretical pose parameters include theoretical displacement parameters, theoretical acceleration parameters, and all of them are obtained from physical simulation tests. The displacement error is the difference between the actual displacement parameter and the theoretical displacement parameter at the measurement point; the velocity error is the difference between the actual velocity parameter and the theoretical displacement parameter at that moment; and the acceleration error is the difference between the actual acceleration parameter and the theoretical acceleration parameter at that moment.

4. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 3, characterized in that, The composite control of the multiple actuators includes: Step b1: When multiple actuators move simultaneously, each actuator, upon reaching its corresponding pose feature measurement point, obtains its own current comprehensive error data e0 and current acceleration error data e″0, and performs a weighted calculation to obtain the discrimination error data w1 for identifying a particular actuator. w1=n1·e0+n2·e″0 In the formula, n1 and n2 are weighting coefficients, both ranging from 0 to 1, and n1 + n2 = 1; Step b2: Obtain the discrimination error data of all actuators and form a discrimination error data dataset W1 = {w 11 w 12 ,…,w 1i ,…,w 1M }, i = 1, 2, 3, ..., M, there are M actuators in total; select the largest discrimination error data as the standard, and adjust the discrimination error data of the remaining actuators; Step b3: Based on the adjusted discrimination error data, the adjusted current comprehensive error data and acceleration error data are derived. The corresponding optimized control parameters are obtained using the fuzzy judgment rule of the control parameters, and the corresponding actuators are adjusted to ensure that all actuators can move synchronously as required.

5. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 4, characterized in that, The coordination control among the multiple conveyors includes: Step c1: Position detection sensors are installed at the middle of the input port and the middle of the output port of each conveyor, and the position detection sensors of the upper-level conveyor and the lower-level conveyor are kept concentric. The position detection sensors monitor the position deviation of the two relative conveyors in real time and obtain the deviation ε1. Step c2: At the same time, flow meters are installed at the input end of each conveyor to measure the coal entering the conveyor and obtain the error ε2 of the coal entering the upper and lower conveyors. Step c3: Weight the deviation ε1 and the error ε2 to obtain the discrimination error data w2. w2=n3·ε1+n4·ε2 In the formula, n3 and n4 are weighting coefficients, both ranging from 0 to 1, and n3 + n4 = 1; When the discrimination error data w2 between two adjacent conveyors exceeds the set value, the two conveyors are adjusted simultaneously. The adjustment is achieved by the composite control of multiple actuators corresponding to the conveyors, so that the discrimination error data between two adjacent conveyors does not exceed the set value, thus realizing precise matching control between each conveyor.

6. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 5, characterized in that, The fully continuous mining system includes a mine double rotary transfer machine, a straight transfer machine, an end-side steep-angle belt conveyor, and a moving conveyor. The mined coal can be transported and transferred sequentially through the mine double rotary transfer machine and the straight transfer machine to the moving conveyor, and then the end-side steep-angle belt conveyor lifts the coal from the bottom of the pit to the surface.

7. The precise transport control method for a fully continuous open-pit coal mine mining system as described in claim 6, characterized in that, The actuator of the mining double rotary transfer machine includes a direct-drive cylinder, a horizontal-pull cylinder, a rotary reducer I, a rotary reducer II, and a crawler actuator I; the actuator of the single-line transfer machine includes a side-pull cylinder, a rotary reducer III, and a crawler traveling mechanism II; the actuator of the end-side steep-angle belt conveyor includes a crawler traveling mechanism III, a crawler traveling mechanism IV, and a crawler traveling mechanism V; the actuator of the shifting conveyor includes a crawler traveling mechanism VI, a rotary reducer IV, and a diagonal support cylinder.

8. A precision transport control system for a fully continuous open-pit coal mine mining system, characterized in that, This system can be used to implement the precise movement control method for the fully continuous open-pit coal mining system as described in claim 7, including a pose parameter sensor, a data decision unit, a parameter optimization module, a position detection sensor, and a flow meter; The pose parameter sensors are arranged at fixed positions on each actuator to collect the actual displacement parameters, actual velocity parameters, and actual acceleration parameters of the actuator as it reaches the corresponding measurement point; The data decision unit receives information from the pose parameter sensor, performs comprehensive analysis to establish a fuzzy judgment rule for the control parameters, and uses this judgment rule to analyze and calculate the control parameters to obtain optimized control parameters. The parameter optimization module uses optimized control parameters to optimize and adjust each actuator and precisely control the movement of each conveyor; The position detection sensors are installed on each conveyor to monitor the positional deviation between adjacent conveyors. The flow meters are installed on each conveyor to measure the error in the coal conveying between adjacent conveyors.

9. The precision transport control system for a fully continuous open-pit coal mine mining system as described in claim 8, characterized in that, The attitude parameter sensors on the mining double rotary transfer machine are respectively arranged at the left root of the direct push cylinder, the lower root of the horizontal pull cylinder, the upper connecting plate of rotary reducer one, the upper connecting plate of rotary reducer two, and the side connecting plate of track walking mechanism one; the attitude parameter sensors on the straight transfer machine are respectively arranged at the upper root of the side pull cylinder, the upper connecting plate of rotary reducer three, and the side connecting plate of track walking mechanism two; the attitude parameter sensors on the end-side large-angle belt conveyor are respectively arranged at... The side connecting plates of track walking mechanism three, track walking mechanism four, and track actuator five are located at the side connecting plates of the moving conveyor; the position parameter sensors on the moving conveyor are respectively arranged on the side connecting plate of track walking mechanism six, the upper connecting plate of rotary reducer four, and the root of the upper end of the inclined support cylinder; the position detection sensors are located in the middle of the input port and the middle of the output port of each conveyor, and the position detection sensors of the upper level conveyor and the lower level conveyor are concentric; the flow meter is located at the input end of each conveyor.

10. The precision transport control system for a fully continuous open-pit coal mine mining system as described in claim 8, characterized in that, The data decision unit includes an input module, a decision module, and an output module; the input module is used to receive information from the pose parameter sensor; the decision module is used to analyze the comprehensive data to complete the precise pose control and basic function implementation of each conveyor; the output module is used to output instructions to the parameter optimization module.

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