Method for intelligently and autonomously adjusting traction speed of coal mining machine
By using a multi-layer adaptive proportional coefficient optimization method, combined with the current, power and vibration signals of the coal mining machine, the proportional coefficient in the PID control algorithm is dynamically adjusted, which solves the response lag and overshoot problems of the coal mining machine traction control system under complex working conditions, thereby improving stability and safety, and increasing coal mining efficiency and power utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHAWAN ZHIBORIDA ELECTRONIC TECHNOLOGY CO LTD
- Filing Date
- 2026-02-06
- Publication Date
- 2026-04-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing coal mining machine traction control systems cannot match the load status in real time under complex coal seam structures and dynamic load changes in underground working conditions, resulting in overreaction, lag in regulation, and reduced power utilization. Furthermore, a single control strategy is difficult to balance regulation sensitivity and system stability, and is prone to overshoot, oscillation, and mechanical shock.
By using a multi-layer adaptive proportional coefficient optimization method, combined with the current, power and vibration signals of the coal mining machine, the proportional coefficient in the PID control algorithm is dynamically adjusted to achieve adaptive adjustment of the coal mining machine's traction speed. This includes initial adjustment of the basic proportional coefficient, stability correction and safety correction, ensuring that the system improves dynamic adjustment efficiency under the premise of stability and safety.
It significantly improves the dynamic adjustment efficiency and power utilization of the coal mining machine's traction control, ensuring stable operation and safety under complex working conditions, and extending the service life and operational continuity of the equipment.
Smart Images
Figure CN121827809A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, and in particular to a shearer traction speed intelligent autonomous adjustment method. BACKGROUND
[0002] In modern intelligent mining equipment of coal mines, the shearer traction system is a key execution unit to ensure efficient and stable operation of the working face, and the control performance of the traction speed directly affects the continuity of coal flow, the coordination of equipment and the energy consumption level of the whole machine. The current mainstream shearer traction control system generally uses a PID control algorithm based on a fixed proportional coefficient or a constant power control strategy. By detecting signals such as motor current, traction speed and power, proportional-integral-derivative adjustment is performed on the output of the frequency converter to achieve constant speed or constant power operation of the shearer. This type of control method has the advantages of simple structure, mature implementation and intuitive adjustment. Under the conditions of uniform coal seam structure and small load fluctuation of the working face, it can maintain stable operation of the traction system. In recent years, with the improvement of intelligent construction and remote control level of coal mines, the traction control scheme based on the PID control algorithm with a fixed proportional coefficient has been combined with the monitoring system, the sensing network and the power limiting strategy to form a relatively complete automatic traction speed control system, which lays a foundation for safe and efficient mining operations.
[0003] However, in complex underground working conditions with significant dynamic changes in coal seam structure and load, due to frequent changes in coal seam hardness, cutting resistance and mechanical vibration, the load of the traction system presents strong nonlinear characteristics. The PID control algorithm with a fixed proportional coefficient cannot match the load state in real time, resulting in excessive response and sudden speed drop when the coal seam becomes hard, and adjustment lag and power utilization rate decrease when the coal seam becomes soft. On the other hand, load changes are also accompanied by complex coupling of current fluctuations and machine vibration, and a single control strategy based on power or speed deviation cannot balance the adjustment sensitivity and system stability, which may cause problems such as overshoot, oscillation and mechanical impact, affecting the service life of the equipment and the continuity of the operation.
[0004] Therefore, how to adaptively adjust the proportional coefficient in the PID control algorithm so that the control system can adaptively adjust the response speed and significantly improve the dynamic adjustment efficiency and power utilization rate of the traction control while ensuring stable operation has become a problem to be solved. SUMMARY
[0005] Therefore, the present application provides a shearer traction speed intelligent autonomous adjustment method to adaptively adjust the proportional coefficient in the PID control algorithm so that the control system can adaptively adjust the response speed according to the hardness of the coal seam, load disturbance and vibration state, significantly improve the dynamic adjustment efficiency and power utilization rate of the traction control while ensuring stable operation.
[0006] The embodiment of the application provides a shearer traction speed intelligent autonomous adjustment method, which comprises the following steps:
[0007] In the process of autonomously adjusting the traction speed of the target shearer by using the PID control algorithm, a basic proportional coefficient of the PID control algorithm is acquired, an ideal cutting power of the shearer under standard seam conditions is acquired according to a stable state of the target shearer in a historical operation process, an adjustment coefficient based on the cutting resistance of the target shearer is acquired according to a deviation between a real-time output power of the target shearer at the current moment and the ideal cutting power, a first adaptive proportional coefficient is obtained by preliminarily adjusting the basic proportional coefficient according to the adjustment coefficient.
[0008] A stability correction coefficient based on the change of the seam hardness is acquired according to a current transient change of the target shearer, the first adaptive proportional coefficient is corrected in stability by using the stability correction coefficient, and a second adaptive proportional coefficient is obtained.
[0009] A safety correction coefficient based on the operation health condition of the target shearer is acquired according to a change rate of a vibration signal of the target shearer at the current moment, the second adaptive proportional coefficient is corrected in safety by using the safety correction coefficient, and a final adaptive proportional coefficient is obtained.
[0010] The traction speed of the target shearer at the next moment is intelligently and autonomously adjusted by using the PID control algorithm according to the final adaptive proportional coefficient.
[0011] Preferably, the adjustment coefficient based on the cutting resistance of the target shearer is acquired according to the deviation between the real-time output power of the target shearer at the current moment and the ideal cutting power, and comprises the following steps.
[0012] A ratio between the real-time output power and the ideal cutting power is calculated, a difference between a constant 1 and the ratio is taken as an independent variable of a hyperbolic tangent function, and a cutting resistance coefficient is obtained.
[0013] A product between a preset power influence weight and the cutting resistance coefficient is calculated, and the adjustment coefficient based on the cutting resistance of the target shearer is obtained.
[0014] Preferably, the first adaptive proportional coefficient is obtained by preliminarily adjusting the basic proportional coefficient according to the adjustment coefficient, and comprises the following steps.
[0015] A sum between a constant 1 and the adjustment coefficient is calculated, an adjustment factor for preliminarily adjusting the basic proportional coefficient is obtained, a product between the basic proportional coefficient and the adjustment factor is calculated, and the first adaptive proportional coefficient is obtained.
[0016] Preferably, the stability correction coefficient based on the change of the coal seam hardness is obtained according to the current transient change of the current of the target coal mining machine, and the stability correction coefficient based on the change of the coal seam hardness is obtained by:
[0017] The historical current of the target coal mining machine at a previous time point of the current time point is obtained, the current of the target coal mining machine at the current time point is recorded as a real-time current, a difference between the historical current and the real-time current is taken as an independent variable of a sign function, and a stability correction direction characteristic value is obtained;
[0018] An absolute value of the difference between the historical current and the real-time current is calculated, an inverse number of a product between the absolute value and a preset sensitivity coefficient is taken as an independent variable of a natural exponential function, a function value is obtained, and a constant 1 is subtracted from the function value to obtain a current instantaneous change amplitude characteristic value;
[0019] A preset current influence weight is calculated, and a product between the stability correction direction characteristic value and the current instantaneous change amplitude characteristic value is obtained to obtain the stability correction coefficient based on the change of the coal seam hardness.
[0020] Preferably, the first adaptive proportional coefficient is stability corrected by using the stability correction coefficient to obtain a second adaptive proportional coefficient, and the stability correction coefficient is obtained by:
[0021] A sum between the constant 1 and the stability correction coefficient is calculated to obtain a stability correction factor for stability correcting the first adaptive proportional coefficient, and a product between the first adaptive proportional coefficient and the stability correction factor is calculated to obtain the second adaptive proportional coefficient.
[0022] Preferably, the safety correction coefficient based on the running health status of the target coal mining machine is obtained according to a change rate of a vibration signal of the target coal mining machine at the current time point, and the safety correction coefficient based on the running health status of the target coal mining machine is obtained by:
[0023] A vibration amplitude of the vibration signal of the target coal mining machine at the current time point is recorded as a real-time vibration amplitude, a historical vibration amplitude of the target coal mining machine at a previous time point of the current time point is obtained, and a vibration change speed between the current time point and the previous time point of the target coal mining machine is obtained according to the real-time vibration amplitude and the historical vibration amplitude, and the vibration change speed is a vector.
[0024] If the vibration change speed is less than or equal to 0, the safety correction coefficient based on the running health status of the target coal mining machine is set to 0, and if the vibration change speed is greater than 0, the vibration change speed is normalized to obtain the safety correction coefficient based on the running health status of the target coal mining machine.
[0025] Preferably, the second adaptive proportional coefficient is safety corrected by using the safety correction coefficient to obtain a final adaptive proportional coefficient, and the safety correction coefficient is obtained by:
[0026] Subtracting the constant 1 from the safety correction coefficient, a safety correction factor for safety correcting the second adaptive proportional coefficient is obtained, the product between the second adaptive proportional coefficient and the safety correction factor is calculated, and a final adaptive proportional coefficient is obtained.
[0027] Preferably, the ideal cutting power of the shearer under the standard seam condition is obtained according to the steady state of the shearer in the historical operation process, and the ideal cutting power of the shearer under the standard seam condition comprises the following steps:
[0028] The output power of the target shearer at each historical time in a complete production shift before the current time is obtained, the corresponding historical time when the target shearer occurs operation abnormity, shutdown and debugging is eliminated in all historical times, the steady operation historical time is obtained, the mean value of the historical output power at all steady operation historical times is calculated, and the ideal cutting power of the shearer under the standard seam condition is obtained.
[0029] Compared with the prior art, the embodiment of the present application has the following beneficial effects:
[0030] The present application firstly obtains the basic proportional coefficient when the target shearer autonomously adjusts the traction speed by using the PID control algorithm, then introduces the output current of the target shearer to adjust the basic proportional coefficient for the first time to obtain the first adaptive proportional coefficient, so that the proportional coefficient automatically converges or widens with the change of power, thereby realizing the automatic change of the control sensitivity with the hardness of the seam; further, the motor current of the target shearer is introduced to correct the first adaptive proportional coefficient, that is, to adjust the basic proportional coefficient for the second time to obtain the second adaptive proportional coefficient, so as to suppress the transient overshoot and balance the system response speed and stability, find the transient change that cannot be reflected by the macro, so that the control system can quickly fit the local change of the seam while keeping the power balance, and improve the response performance of traction adjustment and the coal mining efficiency under the premise of stable operation; finally, the vibration signal of the target shearer is introduced to reflect the stability and health state of the whole operation of the shearer, and the second adaptive proportional coefficient is corrected, that is, the basic proportional coefficient is adjusted for the third time to obtain the final adaptive proportional coefficient, the mechanical impact and body vibration are feedback compensated, and the dynamic adjustment efficiency of traction control and the power utilization rate are further improved under the premise of safe operation of the target shearer. BRIEF DESCRIPTION OF DRAWINGS
[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0032] Figure 1 is a flow chart of a coal mining machine traction speed intelligent autonomous adjustment method provided by an embodiment of the present application. DETAILED DESCRIPTION
[0033] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present disclosure, and cannot be understood as a limitation of the present disclosure.
[0034] It should be noted that the terms "first", "second", and the like in the specification of the present disclosure and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation described in the following exemplary embodiments does not represent all implementations consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure.
[0035] In order to illustrate the technical solutions of the present application, specific embodiments are described below.
[0036] Referring to Figure 1 is a method flow chart of a coal mining machine traction speed intelligent autonomous adjustment method provided by an embodiment of the present application, as Figure 1 indicated, the method can include:
[0037] Step S101, in the process of autonomously adjusting the traction speed of the target coal mining machine by using the PID control algorithm, obtaining a basic proportional coefficient of the PID control algorithm, obtaining an ideal cutting power of the coal mining machine under standard seam conditions according to a stable state of the target coal mining machine in a historical running process, obtaining an adjustment coefficient based on the cutting resistance of the target coal mining machine according to a deviation between a real-time output power of the target coal mining machine at the current moment and the ideal cutting power, and obtaining a first adaptive proportional coefficient by preliminarily adjusting the basic proportional coefficient according to the adjustment coefficient.
[0038] In the process of realizing the traction control of the coal mining machine by using the PID control algorithm, the proportional coefficient in the PID control algorithm is usually a fixed value, and its main defect is that the fixed proportional coefficient cannot reflect the soft and hard changes of the seam and the load disturbance characteristics in real time, resulting in the coexistence of "response lag" and "adjustment overshoot" of the control system under dynamic working conditions. In view of this problem, the present scheme designs a multi-layer adaptive proportional coefficient optimization method based on multi-source signal fusion, from data association, hierarchical correction to coefficient dynamic evolution, to realize the adaptive optimization of the proportional coefficient in the PID control algorithm.
[0039] Let any coal mining machine whose traction speed needs to be autonomously adjusted be denoted as the target coal mining machine. First, based on the working environment of the target coal mining machine, the critical proportional method and optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) are used to obtain the basic proportional coefficient for autonomously adjusting the traction speed of the target coal mining machine using the PID control algorithm, denoted as . The adaptive proportional coefficient is used to adjust it adaptively to obtain the adaptive proportional coefficient at the current moment. Taking the critical proportional method as an example, the general process of obtaining the basic proportional coefficient is as follows: (1) Under the conditions of no load or simulated load of the coal mining machine, set the PID controller to pure P mode and give a stable small amplitude speed setpoint (for example, the traction speed changes from 0 to 2m / min); (2) Starting from a very small initial proportional coefficient, gradually and slowly increase the initial proportional coefficient, observe the response curve of the actual traction speed under each initial proportional coefficient, and obtain the initial proportional coefficient that causes constant amplitude oscillation, which is recorded as the critical gain value; (3) According to the critical gain value, use the Ziegler-Nichols method to calculate the PID parameters; (4) Put the PID parameters into the PID controller, apply the same step setting again, observe the response, and make fine adjustments according to the actual effect until the performance requirements are met. Using the critical proportional method, optimization algorithms (such as genetic algorithms, particle swarm algorithms) to obtain the proportional coefficient in the PID algorithm is an existing technology, which will not be elaborated here.
[0040] Then, the relevant data required for adaptive adjustment of the basic proportional coefficient is obtained: using the current sensor installed on the traction motor of the target coal mining machine, the power detection module and vibration sensor installed on the target coal mining machine body, the motor current, output power and body vibration signal of the target coal mining machine at the current moment are collected respectively. At a fixed period of 50ms (i.e. 20Hz, which is not limited here and can be set by the implementer according to the specific scenario), the signals and data collected by each sensor are time-stamped and resampled through a timestamp synchronization mechanism. Then, the collected data is preprocessed to obtain the real-time current, real-time output power and real-time vibration signal at the current moment. The data preprocessing includes, but is not limited to: (1) Filtering and denoising: Since the signal in the downhole environment is greatly affected by electromagnetic interference and mechanical vibration, the collected data is filtered by moving average or wavelet denoising algorithm to eliminate transient spikes and high-frequency noise, and obtain smooth and effective power, current and vibration signals; (2) Outlier identification and removal: Threshold range (such as physical possible range) and mutation rate constraints are applied to the denoised data to identify and remove obvious outliers; For the missing data caused by short-term communication interruption, linear interpolation or rolling mean of the nearest time is used to fill in the missing data smoothly; (3) Data normalization: The data is dimensionless and mapped to the interval [0, 1] to eliminate the difference in dimensions. Data preprocessing is an existing technology and will not be described in detail here.
[0041] Considering that in the existing coal mining machine traction control system, power constant control or PID strategy with power feedback as a single adjustment basis is mostly used. Although this kind of strategy can maintain traction power balance when the coal seam load is stable, when the coal seam hardness suddenly changes or the cutting resistance increases sharply, the power signal fluctuates significantly, and the fixed proportional coefficient cannot respond in time, often leading to the problem that the control system has a sudden speed drop in the high load area and energy waste in the low load area, and the adjustment sensitivity and power utilization rate are difficult to balance. Therefore, after obtaining the basic proportional coefficient, first, the power is used to adjust the basic proportional coefficient to obtain the first adaptive proportional coefficient, so that the proportional coefficient automatically converges or widens with the change of power: when it is detected that the coal seam becomes hard and the power rises, the proportional coefficient is reduced to prevent system overshoot; when the coal seam becomes soft and the power decreases, the proportional coefficient is moderately increased to speed up the response speed.
[0042] Among them, the first adaptive proportional coefficient is obtained in the following way:
[0043] (1) According to the stable state of the target coal mining machine in the historical operation process, the ideal cutting power of the coal mining machine under standard coal seam conditions is obtained.
[0044] Specifically: the output power of the target coal mining machine at each historical time in a complete production shift before the current time is obtained, and the historical time corresponding to the running abnormality, shutdown and debugging of the target coal mining machine is excluded among all historical times to obtain the stable running historical time. The mean value of the historical output power (the data is pre-processed data, i.e. normalized data) at all stable running historical times is calculated to obtain the ideal cutting power of the target coal mining machine under standard coal seam conditions, which is used to represent the ideal cutting power level of the target coal mining machine under standard coal seam conditions.
[0045] (2) According to the deviation between the real-time output power of the target coal mining machine at the current time and the ideal cutting power, the basic proportional coefficient is adjusted to obtain the first adaptive proportional coefficient.
[0046] If the real-time output power of the target coal mining machine at the current time is greater than the ideal cutting power, it means that more energy than expected is needed to collect coal mine at this time, which reflects that the current coal seam is relatively hard, so the proportional coefficient needs to be appropriately adjusted to reduce the response and prevent overshoot of rapid speed reduction; if the real-time output power is less than the ideal cutting power, it means that the expected power can be easily collected at this time, which reflects that the current coal seam is relatively soft, and the proportional coefficient needs to be appropriately adjusted to speed up the response, and then more smoothly improve the traction speed and speed up the coal mining speed.
[0047] According to the above description, the basic proportional coefficient can be adjusted according to the deviation between the real-time output power of the target shearer at the current moment and the ideal cutting power, to obtain a first adaptive proportional coefficient, specifically:
[0048] The ratio between the real-time output power and the ideal cutting power is calculated, and the difference between the constant 1 and the ratio is taken as the argument of the hyperbolic tangent function to obtain the cutting resistance coefficient;
[0049] The product between the preset power influence weight and the cutting resistance coefficient is calculated to obtain an adjustment coefficient based on the cutting resistance of the target shearer, the sum between the constant 1 and the adjustment coefficient is calculated to obtain an adjustment factor for preliminary adjustment of the basic proportional coefficient, and the product between the basic proportional coefficient and the adjustment factor is calculated to obtain the first adaptive proportional coefficient.
[0050] In an embodiment, the calculation formula of the first adaptive proportional coefficient is:
[0051]
[0052] Wherein, represents the first adaptive proportional coefficient, represents the basic proportional coefficient, represents the preset power influence weight, represents the real-time output power of the target shearer at the current moment, represents the ideal cutting power, represents the hyperbolic tangent function.
[0053] It should be noted that, is the adjustment coefficient based on the cutting resistance of the target shearer, is the adjustment factor for preliminary adjustment of the basic proportional coefficient, when the real-time output power is greater than the ideal cutting power, is greater than 1, is less than 0, The greater, the harder the coal seam at the current moment, the greater the cutting resistance of the target shearer, at this time the proportional coefficient should be appropriately reduced, the response is reduced, to prevent the strong collision caused by the fast response, to make it stable speed reduction, the adjustment factor is less than 1, to reduce the basic proportional coefficient, and then The smaller; when the real-time output power is less than the ideal cutting power, is less than 1, is greater than 0, The smaller, the softer the coal seam at the current moment, the smaller the cutting resistance of the target shearer, at this time the proportional coefficient should be appropriately increased, the response is accelerated, to ensure the safety to improve the coal mining efficiency, the adjustment factor is greater than 1, to amplify the basic proportional coefficient, and then The greater the value, the greater the adjustment; when the real-time output power is equal to the ideal cutting power, no adjustment is made, i.e. . Wherein, for the preset power influence weight , for controlling the influence strength of the power deviation on the proportional coefficient, the value range is (0, 1), the greater the value, the greater the influence strength, in the embodiment of the application, the intermediate value 0.5 is taken, i.e. , which is not limited here, and the implementer can set it according to the specific scene.
[0054] At this point, the first layer of adjustment of the basic proportional coefficient is realized through the power deviation, and the first adaptive proportional coefficient is obtained.
[0055] In step S102, a stability correction coefficient based on the change of the hardness of the coal seam is obtained according to the current transient change of the current of the target coal mining machine, the stability correction coefficient is used to correct the stability of the first adaptive proportional coefficient, and the second adaptive proportional coefficient is obtained.
[0056] Through step S101, the first adaptive proportional coefficient based on the power deviation is obtained, so that the traction speed of the target coal mining machine can be adaptively adjusted according to the overall state of the coal seam, but since the change of the hardness of the coal seam has suddenness (the transition zone is short, and the coal is rapidly hardened or softened) and locality (partly hard and partly soft or with gangue), the power change may have a lag due to inertia, which is not sufficient to reflect the instantaneous load impact characteristics. Considering that the current signal can directly reflect the instantaneous fluctuation of the cutting load, when the current rises, it can be determined that the hardness of the coal seam is instantaneously enhanced or gangue is encountered, at this time, the proportional coefficient should be appropriately reduced to suppress excessive regulation and transient impact; when the current decreases, it can be determined that the hardness of the coal seam is instantaneously reduced, at this time, the proportional coefficient should be appropriately increased to reduce the occurrence of regulation lag. Therefore, on the basis of the first adaptive proportional coefficient, the transient change of the current signal is introduced to construct a dynamic stability correction mechanism to correct the stability of the first adaptive proportional coefficient, that is, to perform a second layer of adjustment on the basic proportional coefficient, to obtain the second adaptive proportional coefficient, to suppress transient overshoot and balance the response speed and stability of the system, to discover the transient change that cannot be reflected by the macro, and to enable the control system to maintain power balance while quickly fitting the local change of the coal seam, to improve the response performance and coal mining efficiency of the traction regulation under the premise of stable operation.
[0057] The second adaptive proportional coefficient is obtained in the following manner:
[0058] obtaining a historical current of the target coal mining machine at a previous moment of a current moment, and a real-time current at the current moment (both the historical current and the real-time current are pre-processed data, i.e., normalized data), taking a difference value between the historical current and the real-time current as an independent variable of a sign function, to obtain a stability correction direction characteristic value;
[0059] calculating an absolute value of the difference between the historical current and the real-time current, taking an inverse number of a product between the absolute value and a preset sensitivity coefficient as an independent variable of a natural exponential function, to obtain a function value, and subtracting the function value from a constant 1 to obtain a current instantaneous change amplitude characteristic value;
[0060] calculating a preset current influence weight, and multiplying the stability correction direction characteristic value and the current instantaneous change amplitude characteristic value to obtain a stability correction coefficient based on a change condition of a coal seam hardness;
[0061] calculating a sum between the constant 1 and the stability correction coefficient to obtain a stability correction factor for stability correction of the first adaptive proportional coefficient, and calculating a product between the first adaptive proportional coefficient and the stability correction factor to obtain a second adaptive proportional coefficient.
[0062] In an embodiment, a calculation formula of the second adaptive proportional coefficient is as follows:
[0063]
[0064] wherein, the second adaptive proportional coefficient is denoted as K2, the first adaptive proportional coefficient is denoted as K1, the preset current influence weight is denoted as W, the historical current of the target coal mining machine at the previous moment of the current moment is denoted as I1, a time interval between the current moment and the previous moment is denoted as Δt, and in the embodiment of the present application, Δt is 1 second, , the real-time current of the target coal mining machine at the current moment is denoted as I2, the sign function is denoted as sgn, the natural exponential function is denoted as exp, the preset sensitivity coefficient is denoted as a, the absolute value sign is denoted as | |.
[0065] It should be noted that, the stability correction coefficient based on the change condition of the coal seam hardness is denoted as K, the stability correction factor for stability correction of the first adaptive proportional coefficient is denoted as K1, The larger the value, the greater the instantaneous change in current. In this case, the change in coal seam hardness is more sudden, and the correction to the first adaptive proportional coefficient is greater. When the current increases, it indicates a sudden increase in coal seam hardness or the encounter of interbedded rock. In this case, the proportional coefficient should be appropriately reduced to suppress over-adjustment and transient impacts. The stability correction coefficient is less than 0, and the stability correction factor is less than 1. Comparison The smaller; when When the current decreases, the coal seam hardness decreases instantaneously. At this point, the proportional coefficient should be appropriately increased to reduce adjustment lag, and thus... The stability correction coefficient is greater than 0, and the stability correction factor is greater than 1. Comparison The larger; when When the current remains constant, the proportional coefficient remains constant, and thus... The stability correction coefficient is equal to 0, and the stability correction factor is equal to 1. ;
[0066] For the preset sensitivity coefficient The settings, The influence of the instantaneous change in control current on the correction magnitude of the first adaptive proportional coefficient is measured, with a value range of [value missing]. , The larger the value, the greater the impact of the instantaneous change in current on the correction magnitude of the first adaptive proportional coefficient. In this embodiment of the invention, the following settings are provided: The unit is the negative first power of the current unit, used to eliminate The dimensions of the current are not limited here; implementers can set them according to specific scenarios. (Regarding the preset current influence weight...) The settings, The influence of the current on the proportional coefficient is controlled, and its value ranges from (0, 1). The larger the value, the stronger the influence. In this embodiment of the invention, it is set to... There are no restrictions here; implementers can set them according to the specific scenario.
[0067] Thus, the stability correction of the first adaptive proportional coefficient was achieved through the transient change of current, which is to say, the second layer of adjustment of the basic proportional coefficient was achieved, and the second adaptive proportional coefficient was obtained.
[0068] Step S103: Based on the rate of change of the vibration signal of the target coal mining machine at the current moment, obtain the safety correction coefficient based on the operating health status of the target coal mining machine, and use the safety correction coefficient to perform safety correction on the second adaptive proportional coefficient to obtain the final adaptive proportional coefficient.
[0069] The steps S101 and S102 are both through quantitative analysis on the external working condition of the target coal mining machine, that is, quantitative analysis on the coal seam state, adaptive adjustment of the basic proportional coefficient is realized, and the internal running state of the target coal mining machine, that is, the safety degree of the target coal mining machine itself is ignored. Considering that the vibration signal directly comes from the comprehensive feedback of the machine body structure to the load change, impact action and control response, the stability and health state of the overall operation of the coal mining machine can be truly reflected from the internal monitoring level, therefore, in the embodiment of the present application, the vibration signal is introduced to perform safety convergence processing on the second adaptive proportional coefficient, that is, the third layer adjustment is performed on the basic proportional coefficient, and the final adaptive proportional coefficient is obtained, so as to perform feedback compensation on the mechanical impact and machine vibration, further ensure the smoothness and safety of the traction process, so that under the premise of finally ensuring the safe operation of the coal mining machine, efficient and reliable autonomous traction adjustment is realized.
[0070] The specific way of obtaining the final adaptive proportional coefficient is:
[0071] The vibration amplitude of the vibration signal of the target coal mining machine at the current moment is recorded as a real-time vibration amplitude, a historical vibration amplitude of the target coal mining machine at the previous moment of the current moment is obtained (the real-time vibration amplitude and the historical vibration amplitude are preprocessed data, that is, normalized data), according to the real-time vibration amplitude and the historical vibration amplitude, a vibration change speed between the current moment and the previous moment of the target coal mining machine is obtained, and the vibration change speed is a vector.
[0072] If the vibration change speed is less than or equal to 0, the safety correction coefficient based on the running health status of the target coal mining machine is set to 0, and if the vibration change speed is greater than 0, the vibration change speed is normalized to obtain the safety correction coefficient based on the running health status of the target coal mining machine.
[0073] The safety correction factor for safety correction of the second adaptive proportional coefficient is obtained by subtracting the safety correction coefficient from the constant 1, and the product between the second adaptive proportional coefficient and the safety correction factor is calculated to obtain the final adaptive proportional coefficient.
[0074]
[0075]
[0076] Wherein, The final adaptive proportional coefficient is represented by K, The second adaptive proportional coefficient is represented by K2, The safety correction coefficient based on the running health status of the target coal mining machine is represented by Ks, The real-time vibration amplitude of the target coal mining machine at the current moment is represented by A, a historical vibration amplitude of the target shearer at a previous time point of a current time point, a time interval between the current time point and the previous time point, a normalization function.
[0077] It should be noted that, , the vibration level remains stable or shows a downward trend, indicating that the safety of the target shearer is stable or is developing in a stable direction, the running state is good, and the safety degree of the machine body is high. At this time, no additional adjustment is introduced, that is, ; , the vibration level rises, indicating that the instability degree of the target shearer increases, and there may be potential risks. At this time, the proportional coefficient should be appropriately reduced to adjust the traction speed smoothly to ensure safety, , the greater the vibration change rate, the greater the deterioration of safety, and the greater the convergence amplitude of the second adaptive proportional coefficient, that is, , the smaller the second adaptive proportional coefficient.
[0078] At this point, the safety of the second adaptive proportional coefficient is corrected through the vibration signal, and the final adaptive proportional coefficient is obtained.
[0079] Step S104, according to the final adaptive proportional coefficient, using a PID control algorithm, intelligently and autonomously adjusts the traction speed of the target shearer at the next time point.
[0080] The final adaptive proportional coefficient fully reflects the change characteristics of the coal seam load while introducing the constraint of the machine body vibration as an internal health factor, and can adaptively limit the traction adjustment amplitude on the premise of ensuring system stability. The final adaptive proportional coefficient as a key input parameter of the traction control module, that is, a key input parameter of the PID control algorithm, is used for dynamic correction of the adjustment process of the shearer traction speed. The specific application steps are as follows:
[0081] (1) Obtain the data of the PID control algorithm: the control system obtains the final adaptive proportional coefficient output by the scheme, and then obtains the integral and differential item data calculated by the algorithm itself and the collected data.
[0082] (2) Synthesize control instructions and output: the final adaptive proportional coefficient item, the integral item and the differential item are brought into the PID control algorithm to obtain the traction speed control instruction at the next time point and execute it.
[0083] (3) The traction adjustment cooperates with the running state: in the process of traction adjustment, the system continuously monitors the running parameters (i.e. motor current, output power and machine vibration signal), so as to obtain the new final adaptive proportional coefficient at each moment, and realize the intelligent adjustment of traction speed.
[0084] The intelligent autonomous adjustment of the traction speed of the target shearer at the next moment by using the PID control algorithm is the prior art, which will not be described here.
[0085] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.
Claims
1. A method for intelligent autonomous adjustment of the traction speed of a coal mining machine, characterized in that, The intelligent autonomous adjustment method for the traction speed of a coal mining machine includes: In the process of autonomously adjusting the traction speed of the target coal mining machine using the PID control algorithm, the basic proportional coefficient of the PID control algorithm is obtained. Based on the stable state of the target coal mining machine in the historical operation process, the ideal cutting power of the coal mining machine under standard coal seam conditions is obtained. Based on the deviation between the real-time output power of the target coal mining machine at the current moment and the ideal cutting power, the adjustment coefficient based on the cutting resistance of the target coal mining machine is obtained. The basic proportional coefficient is initially adjusted according to the adjustment coefficient to obtain the first adaptive proportional coefficient. Based on the transient change of the current of the target coal mining machine at the current moment, a stability correction coefficient based on the change of coal seam hardness is obtained. The stability correction coefficient is used to correct the stability of the first adaptive proportional coefficient to obtain the second adaptive proportional coefficient. Based on the rate of change of the vibration signal of the target coal mining machine at the current moment, a safety correction coefficient based on the operating health status of the target coal mining machine is obtained. The second adaptive proportional coefficient is then corrected for safety using the safety correction coefficient to obtain the final adaptive proportional coefficient. Based on the final adaptive proportional coefficient, the traction speed of the target coal mining machine is intelligently and autonomously adjusted at the next moment using a PID control algorithm.
2. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The step of obtaining an adjustment coefficient based on the cutting resistance of the target coal mining machine, based on the deviation between the real-time output power and the ideal cutting power of the target coal mining machine at the current moment, includes: Calculate the ratio between the real-time output power and the ideal cutting power, and use the difference between the constant 1 and the ratio as the independent variable of the hyperbolic tangent function to obtain the cutting resistance coefficient; The product between the preset power influence weight and the cutting resistance coefficient is calculated to obtain the adjustment coefficient based on the cutting resistance of the target coal mining machine.
3. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The step of initially adjusting the basic scaling factor according to the adjustment factor to obtain the first adaptive scaling factor includes: The sum of constant 1 and the adjustment coefficient is calculated to obtain the adjustment factor for preliminary adjustment of the basic proportional coefficient. The product of the basic proportional coefficient and the adjustment factor is calculated to obtain the first adaptive proportional coefficient.
4. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The process of obtaining a stability correction coefficient based on the change in coal seam hardness according to the transient change in current of the target coal mining machine at the current moment includes: Obtain the historical current of the target coal mining machine at the previous moment of the current moment, record the current of the target coal mining machine at the current moment as the real-time current, and use the difference between the historical current and the real-time current as the independent variable of the sign function to obtain the stability correction direction characteristic value. Calculate the absolute value of the difference between the historical current and the real-time current, take the negative of the product between the absolute value of the difference and the preset sensitivity coefficient as the independent variable of the natural exponential function to obtain the function value, and subtract the function value from the constant 1 to obtain the characteristic value of the instantaneous change amplitude of the current. The stability correction coefficient based on the change in coal seam hardness is obtained by multiplying the preset current influence weight by the stability correction direction characteristic value and the instantaneous change amplitude characteristic value.
5. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The step of using the stability correction coefficient to perform stability correction on the first adaptive scaling coefficient to obtain the second adaptive scaling coefficient includes: The sum of constant 1 and the stability correction coefficient is calculated to obtain the stability correction factor for the first adaptive scaling coefficient. The product of the first adaptive scaling coefficient and the stability correction factor is calculated to obtain the second adaptive scaling coefficient.
6. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The step of obtaining a safety correction coefficient based on the operational health status of the target coal mining machine, according to the rate of change of the vibration signal of the target coal mining machine at the current moment, includes: The vibration amplitude of the target coal mining machine at the current moment is recorded as the real-time vibration amplitude. The historical vibration amplitude of the target coal mining machine at the previous moment is obtained. Based on the real-time vibration amplitude and the historical vibration amplitude, the vibration change rate of the target coal mining machine between the current moment and the previous moment is obtained. The vibration change rate is a vector. If the vibration change rate is less than or equal to 0, the safety correction coefficient based on the operational health status of the target coal mining machine is set to 0. If the vibration change rate is greater than 0, the vibration change rate is normalized to obtain the safety correction coefficient based on the operational health status of the target coal mining machine.
7. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The step of using the security correction coefficient to perform security correction on the second adaptive scaling coefficient to obtain the final adaptive scaling coefficient includes: Subtracting the safety correction coefficient from the constant 1 yields a safety correction factor for adjusting the safety of the second adaptive scaling factor. The product of the second adaptive scaling factor and the safety correction factor is then calculated to obtain the final adaptive scaling factor.
8. The intelligent autonomous adjustment method for the traction speed of a coal mining machine according to claim 1, characterized in that, The process of obtaining the ideal cutting power of the coal mining machine under standard coal seam conditions based on the stable state of the target coal mining machine during its historical operation includes: Obtain the output power of the target coal mining machine at each historical moment in a complete production shift before the current moment. Among all historical moments, remove the historical moments corresponding to the operation abnormality, shutdown and debugging of the target coal mining machine to obtain the historical moments of stable operation. Calculate the average of the historical output power at all historical moments of stable operation to obtain the ideal cutting power of the coal mining machine under standard coal seam conditions.