Self-adaptive variable-frequency speed regulation device based on scraper conveyor and control method

Abstract

The invention discloses a self-adaptive variable-frequency speed regulation device based on a scraper conveyor and a control method, and relates to the technical field of automatic control, and the method comprises the following steps: collecting real-time operation data of the scraper conveyor through a data sensing module, and determining operation demand information of equipment; the evaluation calculation module sets an operation parameter interval and generates an evaluation index. And the rotating speed correlation analysis module performs rotating speed correlation analysis on the real-time data according to the evaluation indexes, constructs a rotating speed correlation list and generates a correlation learning result. And the dynamic matching module calls the frequency converter for dynamic matching to generate an output frequency parameter group. And the linkage control strategy making module executes the intelligent speed regulation control strategy and adjusts the belt speed of the scraper conveyor. The technical problems that in the prior art, due to the fact that the speed of a scraper conveyor cannot be intelligently adjusted under different load conditions, overload shutdown is caused, the energy efficiency is low, and equipment abrasion is aggravated are solved, and the technical effects of reducing energy consumption, reducing equipment abrasion and prolonging the service life are achieved.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to an adaptive variable frequency speed regulation device and a control method based on a scraper conveyor. Background Art

[0002] As a key equipment in industrial production, scraper conveyor is widely used for material transportation in mining, coal mining and other industries. However, traditional control systems are usually unable to automatically adjust the operating speed of the equipment according to the actual load conditions, resulting in low operating efficiency of the equipment under different load conditions. For example, under no-load or light-load conditions, the conveyor often runs at too high a speed, causing excessive wear of the motor and transmission components, shortening the service life of the equipment; and under heavy load, overload shutdown is prone to occur, resulting in production interruption. In addition, due to the lack of intelligent regulation, problems such as overload shutdown, increased equipment wear and energy consumption often occur, affecting production efficiency and safety. Therefore, how to improve energy efficiency, extend equipment life, and ensure stable operation of the system by intelligently adjusting the operating speed of the equipment without affecting normal production has become a technical problem that needs to be solved urgently.

[0003] In the current relevant technologies, there is a technical problem that the scraper conveyor cannot intelligently adjust the speed under different load conditions, resulting in overload shutdown, low energy efficiency, and increased equipment wear. Summary of the invention

[0004] The present application solves the technical problem in the prior art that the scraper conveyor cannot intelligently adjust the speed under different load conditions, resulting in overload shutdown, low energy efficiency and increased equipment wear by providing an adaptive frequency conversion speed regulation device and control method based on the scraper conveyor.

[0005] The present application provides an adaptive frequency conversion speed regulating device based on a scraper conveyor, comprising: A data sensing module, the data sensing module is used to perform data sensing on multiple sensors arranged on the scraper conveyor, generate multiple real-time operation data, perform demand analysis based on the multiple real-time operation data, and determine the operation demand information of the scraper conveyor; an evaluation and calculation module, the evaluation and calculation module is used to set the operation parameter range according to the operation demand information, and based on the operation parameter range, evaluate and calculate the multiple real-time operation data in combination with the operation demand information, and set multiple evaluation indicators; a speed correlation analysis module, the speed correlation analysis module is used to perform speed correlation analysis on the multiple real-time operation data according to the multiple evaluation indicators, construct a speed correlation list, and perform reinforcement learning based on the speed correlation list to generate an association learning result; a dynamic matching module, the dynamic matching module is used to call the frequency converter of the scraper conveyor and combine it with the speed correlation list for dynamic matching to generate an output frequency parameter group; a linkage control strategy formulation module, the linkage control strategy formulation module is used to perform linkage frequency conversion speed regulation on the output frequency parameter group according to the association learning result, formulate a linkage control strategy, and execute the linkage control strategy to intelligently control the belt speed of the scraper conveyor.

[0006] The present application provides a method for adaptive variable frequency speed regulation control of a scraper conveyor, comprising: Arrange multiple sensors on the scraper conveyor for data sensing to generate multiple real-time operation data, perform demand analysis based on the multiple real-time operation data, and determine the operation demand information of the scraper conveyor; set an operation parameter range according to the operation demand information, evaluate and calculate the multiple real-time operation data in combination with the operation demand information based on the operation parameter range, and set multiple evaluation indicators; perform speed correlation analysis on the multiple real-time operation data according to the multiple evaluation indicators, construct a speed correlation list, perform reinforcement learning based on the speed correlation list, and generate a correlation learning result; call the frequency converter of the scraper conveyor and dynamically match it with the speed correlation list to generate an output frequency parameter group; perform linkage frequency conversion speed regulation on the output frequency parameter group according to the correlation learning result, formulate a linkage control strategy, and execute the linkage control strategy to intelligently control the belt speed of the scraper conveyor.

[0007] The adaptive variable frequency speed regulation device and control method based on the scraper conveyor proposed in this application is firstly to collect the real-time operation data of the scraper conveyor by laying out sensors through the data sensing module, and to perform demand analysis to determine the operation demand information of the equipment. The evaluation and calculation module sets the operation parameter range according to the operation demand information, and evaluates and calculates the real-time data to generate the evaluation index. The speed correlation analysis module performs speed correlation analysis on the real-time data according to the evaluation index, constructs a speed correlation list, and generates the correlation learning result through reinforcement learning. The dynamic matching module calls the frequency converter for dynamic matching according to the speed correlation list to generate the output frequency parameter group. The linkage control strategy formulation module executes the intelligent speed regulation control strategy according to the correlation learning result, adjusts the belt speed of the scraper conveyor, and adjusts the speed of the scraper conveyor according to the real-time operation data under different load conditions. It effectively avoids overload shutdown and achieves the technical effect of reducing energy consumption, reducing equipment wear, and extending service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0009] Figure 1 A schematic diagram of the structure of an adaptive variable frequency speed regulating device for a scraper conveyor provided in an embodiment of the present application; Figure 2 A flow chart of an adaptive variable frequency speed control method for a scraper conveyor provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: data sensing module 10 , evaluation and calculation module 20 , speed correlation analysis module 30 , dynamic matching module 40 , linkage control strategy formulation module 50 . DETAILED DESCRIPTION

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0014] The embodiment of the present application provides an adaptive frequency conversion speed regulation device based on a scraper conveyor, such as Figure 1 As shown, the device comprises: The data sensing module 10 is used to sense data of multiple sensors arranged on the scraper conveyor, generate multiple real-time operation data, perform demand analysis based on the multiple real-time operation data, and determine the operation demand information of the scraper conveyor. Specifically, the data sensing module 10 first selects a suitable sensor according to the operation characteristics of the scraper conveyor and the type of parameters to be monitored, such as current and voltage sensors for monitoring the operation of the motor, tension and speed sensors for understanding the operation status of the scraper chain, and weight or material flow sensors for monitoring the material load; at the same time, the installation position of the sensor is determined, the current and voltage sensors are installed on the motor power supply line, the tension sensor is installed on the key connection part of the scraper chain, the speed sensor is installed near the driving wheel or driven wheel of the scraper chain, and the weight sensor is installed on the load-bearing component such as the middle groove. In the data sensing and collection stage, each sensor collects data according to its own working principle, the current sensor measures the motor current, the voltage sensor obtains the motor voltage, the tension sensor monitors the change of the scraper chain tension, the speed sensor records the running speed of the scraper chain, and the weight sensor reflects the weight of the material load, thereby continuously generating multiple real-time operation data. In the demand analysis stage, analysis is performed based on the collected real-time operation data. The data contains information on key parts and operation links of the scraper conveyor. For example, the motor operating power is understood through the motor current and voltage data, the scraper chain working status is judged based on the scraper chain tension and speed data, and the transportation task volume is known based on the material load weight data. Then, the operation demand information of the scraper conveyor is determined based on the data analysis results. When the motor is under high load and the material load is large, the scraper conveyor needs to maintain a high operating speed. If the material load is light and the motor power is low, the operating speed can be appropriately reduced to save energy. If the scraper chain tension is too large, the operating speed needs to be adjusted or the scraper chain connection needs to be checked. Through the comprehensive monitoring and analysis of the scraper conveyor by the data sensor module 10, its operating status can be grasped and reasonable operating requirements can be determined, providing an important basis for subsequent control and optimization operations.

[0015] The evaluation and calculation module 20 is used to set the operation parameter interval according to the operation demand information, and based on the operation parameter interval, the multiple real-time operation data are evaluated and calculated in combination with the operation demand information, and multiple evaluation indicators are set. Specifically, the evaluation and calculation module 20 is first based on the scraper conveyor operation demand information determined by the data sensing module 10. In the case of high-load material transportation requirements, it means that the motor needs large power, the scraper chain needs high speed and high tension, etc.; according to different operation demand scenarios, the corresponding parameter interval is determined, such as the motor speed range can be 1000~1500 rpm during high-load rapid transportation, and 200~500 rpm during low-load no-load trial operation. The tension of the scraper chain is 500~800 Newtons at high load and 100~300 Newtons at low load. The interval determination combines multiple factors such as mechanical structure strength, motor performance and safe operation, providing a reasonable reference for subsequent evaluation and calculation. During the evaluation calculation and evaluation index setting stage, multiple real-time operating data are correlated with operating demand information for analysis. When high-load stable operation is required, focus on checking whether data such as motor current, scraper chain tension and speed are within the high-load operating parameter range. If the motor current is close to or exceeds the upper limit of the range, there is a risk of motor overload. If the scraper chain speed is lower than the lower limit, it may cause material accumulation and affect the conveying efficiency. Set multiple evaluation indicators, such as the motor load rate indicator calculated by the ratio of the actual motor current to the rated current. If it is greater than 0.8, it means that the motor is close to the overload edge. The scraper chain stability index is calculated by the ratio of the scraper chain tension fluctuation range to the average tension. If it is greater than 0.2, it means that the scraper chain is unstable and may have problems such as chain jamming or uneven chain relaxation. The evaluation indicators can quantify the degree of deviation between the operating state and the ideal range, providing an accurate basis for control decisions to ensure that the scraper conveyor is in a safe and efficient operating state.

[0016] In a possible implementation, the evaluation and calculation module 20 further includes: an associated device determination unit, which is used to retrieve multiple operating conditions of the scraper conveyor, traverse the scraper conveyor according to the multiple operating conditions to perform operation association analysis, and determine multiple associated devices, wherein the multiple associated devices include a coal mining machine and a transfer machine, and the scraper conveyor is in communication connection with the coal mining machine and the transfer machine. Specifically, the associated device determination unit retrieves multiple operating condition information from the control system database of the scraper conveyor, and the operating condition information includes various parameter records of the scraper conveyor in different production stages, such as startup, stable operation, full load, and no-load, such as motor speed changes, current fluctuations, and scraper chain force data. According to the operating condition information, the entire operating system of the scraper conveyor is traversed, from the power transmission line to the material transportation process, and the potential association between each link and other equipment is analyzed and obtained. By analyzing the source and destination of the material, it is found that the coal mining machine, as the source of material supply, has a direct impact on the feed speed and load size of the scraper conveyor due to its mining volume and mining speed. At the same time, the transfer machine is the connection hub between the scraper conveyor and the subsequent transportation links, and its operating status plays a key role in the smooth discharge of the scraper conveyor. The coal mining machine and the transfer machine are determined as multiple related equipment, and a stable communication connection is established between the scraper conveyor and them, so as to facilitate the timely interaction of subsequent data and the effective implementation of collaborative operations.

[0017] An operation analysis unit is used to perform operation analysis on the coal mining machine in combination with the operation demand information based on the multiple operation conditions, and determine multiple coal mining operation parameters. Specifically, after the operation analysis unit receives the multiple operation conditions and the operation demand information of the scraper conveyor from the associated device determination unit, the coal mining machine performs operation analysis. Based on different operation conditions, such as the operation condition data under different coal seam thickness, hardness and coal mining process, combined with the requirements of the scraper conveyor for material conveying volume, conveying speed and operation stability. Through the data collected by various sensors installed on the coal mining machine, such as speed sensors, power sensors, load sensors, etc., the operation parameters of the coal mining machine are accurately monitored and analyzed. Specifically, the average cutting speed, instantaneous power peak, load change rate and other parameters of the coal mining machine are calculated to determine multiple coal mining operation parameters. The parameters can accurately reflect the working state of the coal mining machine under different working conditions, and the specific impact of this state on the operation of the scraper conveyor. For example, when the coal mining machine is running at high load, the scraper conveyor may face greater material conveying pressure, which provides a key basis for the subsequent adjustment of the scraper conveyor operation parameters.

[0018] The transfer operation parameter determination unit is used to perform operation analysis on the transfer machine in combination with the operation demand information based on the multiple operation conditions, and determine multiple transfer operation parameters. Specifically, after obtaining multiple operation condition information, the transfer operation parameter determination unit performs operation analysis on the transfer machine in combination with the operation demand of the scraper conveyor. Under different working conditions, such as different feeding speeds of the transfer machine (depending on the discharging speed of the scraper conveyor), different discharging directions and distances, etc., the requirements of the scraper conveyor for the material transfer efficiency and transfer stability of the transfer machine are considered. The sensor data on the transfer machine, including the current and speed of the drive motor, the material stacking height and flow rate detected by the material sensor, etc., are used to determine multiple transfer operation parameters. For example, the material transfer flow change curve of the transfer machine, the torque fluctuation range of the drive motor, the average residence time of the material during the transfer process, and other parameters are calculated. The transfer operation parameters reflect the operation characteristics of the transfer machine in the entire scraper conveyor system, as well as its influence on the operation continuity and stability of the scraper conveyor, and provide important data support for a comprehensive evaluation of the operation environment of the scraper conveyor.

[0019] A multidimensional operation fluctuation analysis unit, the multidimensional operation fluctuation analysis unit is used to use the multiple coal mining operation parameters and the multiple transfer operation parameters as constraint conditions, perform multidimensional operation fluctuation analysis on the scraper conveyor based on the constraint conditions, and draw an operation trend diagram of the scraper conveyor. Specifically, after the multidimensional operation fluctuation analysis unit obtains multiple coal mining operation parameters and multiple transfer operation parameters, it uses them as constraint conditions to start the multidimensional operation fluctuation analysis of the scraper conveyor. In the time dimension, the changes in the operation parameters of the scraper conveyor in each time period from startup to shutdown in a complete production cycle are analyzed. For example, at the beginning of startup, the load of the scraper conveyor gradually increases, and the motor current also rises accordingly; during the peak production period, the parameters remain relatively stable but at a high level; at the end of production, the load is reduced and the parameters fall back accordingly. In the spatial dimension, the operation differences of different parts from the head to the tail of the scraper conveyor are studied. For example, the head part is close to the coal mining machine, the material accumulation speed is fast, and the force is large; the tail part may have the risk of material residue and scraper chain loosening due to the long material transportation distance. In terms of operating parameters, the matching relationship between the motor power of the scraper conveyor and the power of the coal mining machine and the transfer machine, the correlation mechanism between the scraper chain tension and the material flow and transfer speed, etc. By integrating the multi-dimensional analysis results and using professional drawing software to draw the operation trend diagram of the scraper conveyor, the operation change law of the scraper conveyor under different working conditions is intuitively displayed.

[0020] An operation extreme value determination unit is used to perform extreme value analysis based on the operation trend chart to determine multiple operation extreme values, and the multiple operation extreme values ​​include operation maximum values ​​and operation minimum values. Specifically, the operation extreme value determination unit performs extreme value analysis based on the operation trend chart. In the operation trend chart, multiple operation extreme values ​​are determined by data screening and calculation algorithms. For the operation maximum value, the maximum value of the motor power of the scraper conveyor, the maximum tension that the scraper chain can withstand, and the upper limit of the operation speed are found under extreme working conditions such as the maximum mining volume of the coal mining machine, the highest efficiency of the transfer machine, and the longest distance of the scraper conveyor. For the operation minimum value, the minimum value of the motor power, the minimum tension of the scraper chain, and the lowest operation speed are analyzed under working conditions such as low production of the coal mining machine, no load of the transfer machine, and no load trial operation of the scraper conveyor. The operation extreme value clearly defines the performance boundary of the scraper conveyor in different operation scenarios, and provides key data basis for the subsequent construction of a reasonable operation parameter range.

[0021] An operating parameter interval construction unit, the operating parameter interval construction unit is used to set the operating maximum value as the upper limit of the interval, set the operating minimum value as the lower limit of the interval, and construct the operating parameter interval. Specifically, after receiving the operating maximum value and the operating minimum value determined by the operating extreme value determination unit, the operating parameter interval construction unit sets the operating maximum value as the upper limit of the interval and the operating minimum value as the lower limit of the interval to construct the operating parameter interval. For example, if the operating maximum value of the motor power is 100 kilowatts and the operating minimum value is 20 kilowatts, the constructed motor power operating parameter interval is [20,100] kilowatts. After the interval is determined, in actual operation, as long as the motor power of the scraper conveyor fluctuates within this interval, it can ensure normal production while avoiding overheating and damage of the motor due to excessive power or poor material transportation due to too low power. Similarly, corresponding intervals are also constructed for other parameters such as scraper chain tension and operating speed, thereby providing quantitative control standards for the safe and efficient operation of the scraper conveyor.

[0022] In a possible implementation, the evaluation calculation module 20 further includes: a weight allocation unit, which is used to allocate weights to the multiple coal mining operating parameters based on the operating parameter interval to generate multiple coal mining weight coefficients. Specifically, the weight allocation unit analyzes the criticality of each coal mining operating parameter in different intervals based on the operating parameter interval. For example, in the high-load and high-speed interval, the power parameters of the coal mining machine have a significant impact on the operating stability and efficiency of the scraper conveyor, and its weight is increased accordingly; in the low-load interval, the weight is reduced. Through statistical analysis of a large amount of operating data, weights are determined for each coal mining operating parameter, and a coal mining weight coefficient is generated to accurately reflect its relative importance to the evaluation of the scraper conveyor under different working conditions.

[0023] The first indicator calculation formula construction unit is used to construct a first indicator calculation formula based on the multiple coal mining weight coefficients combined with the multiple coal mining operation parameters. Specifically, the first indicator calculation formula construction unit constructs the first indicator calculation formula based on the coal mining weight coefficients. is the shearer speed, for its weight; is the position of the coal mining machine, for its weight; is the coal mining machine current, for its weight; is the scraper conveyor current, is its weight. The formula is ,in , ; ( to are the rated values ​​or extreme values ​​of the speed, position, current of the coal mining machine and the current of the scraper conveyor, respectively). This formula comprehensively considers the coal mining operation parameters and their weights, and is used to quantify the impact of the coal mining process on the operating state of the scraper conveyor.

[0024] The first evaluation index determination unit is used to synchronize the multiple real-time operation data to the first index calculation formula to evaluate and calculate the scraper conveyor and determine the first evaluation index. Specifically, the first evaluation index determination unit obtains the real-time operation data of the scraper conveyor, including the speed, position, current of the coal mining machine and the current of the scraper conveyor. Substitute the data into the first index calculation formula and calculate and The value of A is then used as the first evaluation indicator. If A is close to 1, it indicates that the operation of the coal mining process is close to the ideal state; if the deviation is large, such as A is too large or too small, it may indicate that the coal mining machine is operating abnormally and further analysis and adjustment are required.

[0025] A transfer weight coefficient generation unit is used to assign weights to the multiple transfer operation parameters based on the operation parameter interval to generate multiple transfer weight coefficients. Specifically, the transfer weight coefficient generation unit analyzes the changes in the importance of the transfer machine parameters in different operation intervals based on the operation parameter intervals. In the high material flow interval, the torque parameters of the transfer machine are crucial to the smoothness of the material transfer of the scraper conveyor, and the weight is higher; the weight is reduced in the low flow interval. Using data processing technology, weights are assigned to each transfer operation parameter to generate a transfer weight coefficient to reflect its relative value in the evaluation of the scraper conveyor under different working conditions.

[0026] The second indicator calculation formula construction unit is used to construct a second indicator calculation formula based on the multiple reprint weight coefficients combined with the multiple reprint operation parameters. Specifically, the second indicator calculation formula is constructed according to the reprint weight coefficients. is the transfer machine torque, for its weight; The real-time coal quantity of the transfer machine. is its weight. The formula is ,in , ; ( is the maximum torque of the transfer machine, is the maximum coal quantity of the transfer machine), this formula comprehensively considers the transfer operation parameters and their weights, and is used to measure the effect of the transfer link on the operation of the scraper conveyor.

[0027] The second evaluation index determination unit is used to synchronize the multiple real-time operation data to the second index calculation formula to evaluate and calculate the scraper conveyor and determine the second evaluation index. Specifically, the second evaluation index determination unit collects the real-time torque and real-time coal volume data of the transfer machine and substitutes them into the second index calculation formula. First calculate and The value of is used to obtain the second evaluation index B. If B is within a reasonable range, it means that the transfer link is operating normally; if it exceeds the range, such as B is too large, it may indicate that the transfer machine torque is insufficient or the coal volume is too large, and the relevant parameters need to be adjusted and optimized.

[0028] The evaluation index integration unit is used to integrate the first evaluation index and the second evaluation index to determine multiple evaluation indexes. Specifically, the evaluation index integration unit receives the first evaluation index A and the second evaluation index B. Using the weighted summation method, let the weight of A be , the weight of B is , integrated indicators. According to the actual situation, the maximum value or other logical relationships can be adopted. The determined evaluation indicators can more comprehensively and accurately evaluate the overall operating status of the scraper conveyor and provide a basis for control decisions.

[0029] In a possible implementation, the second indicator calculation formula construction unit further includes: a first indicator calculation formula subunit, and the first indicator calculation formula subunit is used for the first indicator calculation formula: ; , ;in, is the mean value of the first evaluation index A, is the maximum value, They are the speed, position, and current of the coal mining machine and the current of the scraper conveyor; for The corresponding weights; They are the maximum traction speed of the coal mining machine, the number of brackets, the rated current of the coal mining machine, and the rated current of the scraper conveyor. Specifically, the first indicator calculation formula subunit builds an evaluation system for the scraper conveyor, the coal mining machine speed Weight =0.22, its speed affects the material mining rate and the material input and conveying rhythm of the scraper conveyor. If it is fast, the scraper conveyor may run at high load, and if it is slow, it may cause insufficient material supply and affect system efficiency; coal mining machine position Weight =0.13, its position is related to the force distribution of the scraper conveyor and the change of the material loading point. Different positions affect the force uniformity of the scraper chain and the wear of the middle groove; coal mining machine current Weight =0.26, which can reflect the working load of the coal mining machine. The higher the load, the higher the drive requirements of the scraper conveyor. The current change can indicate the hidden dangers of coal mining machine failure, which indirectly affects the stable operation of the scraper conveyor. The current of the scraper conveyor Weight =0.39, reflecting its own operating energy consumption and load conditions. The increase in current may be due to factors such as material load, scraper chain friction or motor efficiency, which is critical to evaluating its operating status and whether it is overloaded or faulty. Calculate the mean When ,like , , , hour, =11.71. Determine the maximum value in accordance with , assuming , , , ,but =18. Finally, the formula Calculate the first evaluation index A, and present the degree of closeness to the theoretical maximum operating state in percentage. If it is close to 100%, it means that it is close to the limit and there is a risk of overload. If it is lower, it may not be fully utilized or there may be potential problems. This provides a basis for subsequent analysis and adjustment to optimize operation and improve collaborative efficiency.

[0030] The second indicator calculation formula subunit is used for the second indicator calculation formula: ; , ;in, is the mean value of the second indicator B; is the maximum value; , They are the torque of the transfer machine and the real-time coal quantity; , are the maximum torque and the maximum coal quantity respectively. , for , Specifically, the second indicator calculation formula subunit constructs an evaluation system related to the transfer machine and associated with the operation of the scraper conveyor. Weight =0.6, which is dominant in the overall evaluation. Its size affects the efficiency and stability of material transfer. Under high load conditions, large torque can ensure that materials are smoothly transferred to the scraper conveyor. Insufficient torque will cause material accumulation at the transfer point, affect the continuity of material input of the scraper conveyor, and even cause blockage. For example, when a large amount of coal is transferred, sufficient torque can maintain the efficient rhythm of the conveying system. Real-time coal quantity Weight =0.4, its change challenges the load and conveying smoothness of the scraper conveyor. If the amount of coal is too large, the load pressure of the scraper conveyor will be large, the force of the scraper chain will be uneven, and the motor load will be heavier. If the amount of coal is too small, it will lead to insufficient equipment utilization and affect production efficiency. If the real-time coal amount cannot be controlled during the peak period of coal mining, the scraper conveyor will be overloaded and shut down, or energy will be wasted and the equipment will be idle when the coal amount is low. Calculate the mean According to the formula ,like , hour, =80, comprehensively considering the comprehensive influence of torque and coal quantity on the scraper conveyor, the preliminary quantitative level is obtained. Determine the maximum value according to , assuming , ,but =122, determine the boundary of the theoretical limit evaluation value, provide an upper limit reference for judging the impact of the transfer machine on the scraper conveyor, and prevent excessive operation from causing adverse conditions of the scraper conveyor. Finally, through the formula The second evaluation index B is calculated and presented as a percentage to show the degree of closeness to the theoretical maximum operating state. If it is close to 100%, attention should be paid to the stability and safety of the scraper conveyor. If it is lower, it means that the efficiency of the transfer machine is not fully utilized. This provides a basis for adjusting parameters, optimizing the operation of the transfer link, and ensuring the efficiency and stability of the coal transportation system.

[0031] The speed correlation analysis module 30 is used to perform speed correlation analysis on the multiple real-time operating data according to the multiple evaluation indicators, construct a speed correlation list, and perform reinforcement learning based on the speed correlation list to generate correlation learning results. Specifically, the speed correlation analysis module 30 first obtains multiple evaluation indicators after comprehensive evaluation of multiple links such as coal mining and transshipment, as well as multiple real-time operating data covering various operating parameter information of scraper conveyors and their related equipment (such as motor speed, current, equipment position, material flow, etc.), and then performs correlation analysis on the speed-related data and other operating data. By analyzing the correlation between the speed and parameters such as motor current and material flow under different evaluation indicators, a large number of data samples are analyzed and statistically calculated to determine the changing rules and characteristics of the speed under different parameter combinations to construct a speed correlation list, which records the speed and other parameters under various operating conditions and evaluation indicator scenarios. ; then, the speed association list data is used as the state information and the operating environment parameters (such as working temperature, humidity, lane slope, etc.) are introduced to construct the learning state space, and actions such as adjusting the inverter output frequency and the tension of the scraper conveyor are determined to construct the learning action space. According to the reward function designed with the goal of improving transportation efficiency, reducing energy consumption, and reducing equipment wear, the state-action pair is rewarded or punished. In the learning state space and action space, continuous exploration and attempts are made, and the strategy is optimized based on feedback. Finally, the associated learning results containing the optimal action strategies under different operating conditions are generated, which provide decision-making basis for subsequent modules to improve the intelligent operation level of the scraper conveyor system.

[0032] In a possible implementation, the speed correlation analysis module 30 further includes: a speed correlation analysis unit, which is used to perform speed correlation analysis based on the multiple real-time operation data to obtain multiple correlation coefficients. Specifically, the speed correlation analysis unit receives multiple real-time operation data from the data acquisition system, and the data contains various parameter information of the scraper conveyor and its associated equipment (such as coal mining machine, transfer machine) during operation, such as the speed, current, voltage of the motor, the tension and speed of the scraper chain, the flow rate and density of the material, etc. The speed-related data in the analysis data, such as the cutting head speed of the coal mining machine, the driving motor speed of the scraper conveyor, etc., are calculated. A professional data analysis algorithm, such as the Pearson correlation coefficient algorithm, is used to calculate the correlation between the speed data and other types of parameter data. For each combination of speed-related data and other parameter data, a correlation coefficient is calculated. For example, the correlation coefficient between the cutting head speed of the coal mining machine and the motor current is calculated. If the coefficient is positive and the value is large, it means that there is a strong positive correlation between the two, that is, the higher the cutting head speed, the larger the motor current; if the coefficient is close to 0, it means that the correlation between the two is weak. By calculating the correlation between all speed-related data and other parameter data, multiple correlation coefficients are obtained. The correlation coefficients provide a quantitative basis for the subsequent in-depth analysis of the relationship between the speed and other parameters.

[0033] A feature traversal analysis unit, the feature traversal analysis unit is used to perform feature traversal analysis on the multiple real-time operation data based on the multiple correlation coefficients, and generate multiple speed-related features. Specifically, the feature traversal analysis unit obtains multiple correlation coefficients calculated by the speed correlation analysis unit and multiple original real-time operation data. Based on the correlation coefficient, feature traversal analysis is performed on the real-time operation data. For each parameter combination corresponding to the correlation coefficient, the feature information is analyzed. For example, when it is found that the correlation coefficient between the speed of the cutting head of the coal mining machine and the motor current is high, the change characteristics of the motor current in different speed ranges are further analyzed, such as the rate of current rise or fall, the amplitude of current fluctuation, etc.; at the same time, combined with the material flow data of the scraper conveyor, the change characteristics of the speed and current relationship under different material flow conditions are analyzed. By performing such feature traversal analysis on all parameter combinations with high correlation, multiple speed-related features are extracted. Features may include information such as the value range, change trend, and fluctuation frequency of other parameters at a specific speed, reflecting the inherent connection between the speed and other parameters, and providing rich materials for constructing a correlation matrix.

[0034] The correlation matrix construction unit is used to construct a correlation matrix according to the multiple speed correlation features combined with the multiple correlation coefficients. Specifically, after receiving the multiple speed correlation features and the multiple correlation coefficients, the correlation matrix construction unit starts to construct the correlation matrix. The rows and columns of the matrix represent different parameters or parameter combinations, including speed-related parameters and other parameters with high correlation with the speed. The initial weight value of each element in the matrix is ​​determined according to the correlation coefficient. The higher the correlation coefficient, the larger the corresponding weight value. Then, the speed correlation features are integrated into the matrix elements. For example, the characteristic information such as the value range and change trend of other parameters at a certain speed is stored in the corresponding matrix elements in a specific encoding method or numerical form. In this way, a correlation matrix that can fully reflect the complex relationship between the speed and other parameters is constructed. This matrix not only reflects the strength of the correlation between the parameters, but also contains detailed characteristic information under different correlations, providing a powerful tool for subsequent precise matching and analysis.

[0035] A speed association list construction unit, the speed association list construction unit is used to map the multiple evaluation indicators to the association matrix for matching, and construct the speed association list according to the matching results. Specifically, the speed association list construction unit obtains the association matrix and multiple evaluation indicators. The evaluation indicator is a quantitative result of evaluating the overall operating status of the scraper conveyor from multiple dimensions, reflecting the degree of operation of the scraper conveyor under different working conditions. These evaluation indicators are mapped to the association matrix for matching. For example, for an evaluation indicator indicating that the scraper conveyor is under high load and efficient operation, the corresponding combination of speed, current, material flow and other parameters and characteristic information are found in the association matrix. By matching all evaluation indicators one by one, a speed association list is constructed according to the matching results. The list lists in detail the specific correlation between the speed and other parameters under different evaluation indicators, such as the corresponding ideal speed range under a certain evaluation indicator, the reasonable value range of other parameters at this speed, and the relationship between them. This speed association list will serve as an important basis for subsequent reinforcement learning and control strategy formulation, so that the control of the scraper conveyor can more accurately adapt to different operating requirements and changes in working conditions.

[0036] In a possible implementation, the speed association analysis module 30 further includes: a learning state space construction unit, the learning state space construction unit is used to introduce the operating environment parameters of the scraper conveyor, traverse the speed association list and combine the operating environment parameters to perform state analysis, and construct a learning state space. Specifically, the learning state space construction unit first introduces the operating environment parameters of the scraper conveyor, including key information such as speed, load, current, and temperature. Traverse the speed association list, which records in detail the association between the speed and other parameters under different evaluation indicators. Combined with the operating environment parameters, state analysis is performed. For example, for the speed parameter, its state changes under different load, current and temperature conditions are analyzed; for the load parameter, the load distribution and change trend under different speed, current and temperature combinations are studied; when considering the current parameter, its fluctuation characteristics in different speed, load and temperature environments and its relationship with the operating state of the equipment are explored; when analyzing the temperature parameter, its influence on the overall performance of the equipment and other parameters is paid attention to, such as high temperature may cause the equipment resistance to increase, thereby affecting the current and speed. Through a comprehensive analysis of the speed association list and the operating environment parameters, the corresponding equipment state under different parameter combinations and changes is determined, thereby constructing a learning state space. The learning state space covers the state set of the scraper conveyor under various possible operating environments and parameter association conditions, providing a comprehensive state description framework for subsequent reinforcement learning.

[0037] A learning action space construction unit is used to traverse the speed association list and perform action analysis in combination with the multiple real-time operation data to construct a learning action space. Specifically, the learning action space construction unit traverses the speed association list and performs action analysis in combination with multiple real-time operation data. The association information between the speed and other parameters in different states is obtained from the speed association list, and the current actual operation of the device is understood based on the real-time operation data, such as the current speed value, load size, current intensity, etc. On this basis, a set of actions that can be taken is determined. For example, when it is found that the speed is lower than expected and the load is light, the actions that can be taken may include appropriately increasing the output frequency of the inverter to increase the speed; if the current is too large, the actions that may be taken include adjusting the operation mode of the device to reduce current consumption; when the temperature is too high, the action may be to start the cooling system or reduce the load of the device to reduce the temperature. Through comprehensive sorting and analysis of actions that can be taken under different states, a learning action space is constructed. This learning action space clarifies the action strategies that can be taken under different device states, and provides an action basis for decision-making in reinforcement learning.

[0038] A reward parameter generation unit, the reward parameter generation unit is used to set a reward function, map the reward function to the learning state space for state reward, and generate a first positive reward parameter and a first negative reward parameter. Specifically, the reward parameter generation unit sets a reward function, which is based on the performance of the equipment and is intended to give positive or negative rewards to different equipment operating states. When the speed, load, temperature and other parameters of the equipment are kept within a reasonable range, it indicates that the equipment is in a good operating state, and the reward system will give a positive first positive reward parameter. For example, when the speed of the scraper conveyor is stable within a reasonable range of the design speed, the load is between 80% and 120% of the rated load, and the temperature is lower than the maximum temperature allowed by the equipment and remains relatively stable, the reward function will calculate a positive reward value according to a pre-set rule. The reward value reflects the good operating performance of the equipment in this state, which helps to guide the equipment to continue to maintain this state in subsequent operations. On the contrary, when the equipment is overloaded, the temperature is too high or the energy efficiency is low, the system will give a first negative reward parameter. For example, if the load exceeds 150% of the rated load, or the temperature exceeds the maximum temperature allowed by the equipment and continues to rise, or the energy consumption of the equipment exceeds a certain percentage of the normal energy consumption range under the same working conditions, the reward function will calculate a negative reward value, indicating that there is a problem with the operation of the equipment and that adjustment or optimization is required.

[0039] A state reward unit, the state reward unit is used to map the reward function to the learning action space for state reward, and generate a second positive reward parameter and a second negative reward parameter. Specifically, the state reward unit maps the reward function to the learning action space for state reward. When the equipment has a high operating efficiency, a positive second positive reward parameter is given. For example, in a certain operating state, by taking specific actions, such as reasonably adjusting the frequency of the frequency converter to increase the material conveying capacity of the scraper conveyor, while the energy consumption does not increase significantly, the overall operating efficiency of the equipment is improved. At this time, the reward function will calculate a positive reward value based on this efficient operation, and encourage the continuation of such action strategies in similar states. When the equipment has a low operating efficiency, a negative second negative reward parameter is given. For example, after taking a certain action, the material conveying capacity does not improve significantly, but instead leads to an increase in energy consumption or a jam of the equipment, which affects the overall operating efficiency. The reward function will calculate a negative reward value, prompting the user to avoid similar actions in subsequent decisions.

[0040] An associated learning result adding unit is used to associate and integrate the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter, and add the integration result to the associated learning result. Specifically, the associated learning result adding unit associates and integrates the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter. For example, for each state-action pair, the corresponding positive and negative reward parameters are recorded, and the operating performance and trend of the equipment under different reward parameter combinations are analyzed. The integration result is added to the associated learning result, so that the associated learning result is continuously enriched and improved. By integrating and analyzing the reward parameters of a large number of state-action pairs, the associated learning result can gradually determine the optimal action strategy under different operating environments and equipment conditions, that is, which actions can obtain higher positive rewards under what conditions, thereby guiding the operation of the scraper conveyor to develop in a more efficient, more stable, and more energy-saving direction, providing a reliable basis for subsequent equipment control and optimization.

[0041] The dynamic matching module 40 is used to call the frequency converter of the scraper conveyor and combine it with the speed association list to perform dynamic matching and generate an output frequency parameter group. Specifically, after the dynamic matching module 40 is started, it will immediately retrieve relevant information of the scraper conveyor inverter, such as performance parameters, adjustable frequency range, etc., and at the same time obtain a speed association list that records the correlation between the speed and other parameters under different operating conditions such as load, material flow, equipment operating status, and corresponding ideal operating indicators. Then, it accurately matches in the speed association list according to the actual operating requirements and status information of the scraper conveyor. For example, when it is detected that the material flow is large and the load is heavy, the corresponding ideal speed range and related parameter settings are found, and a higher output frequency parameter is determined in combination with the inverter performance parameters. If the material flow is small and the load is light, a lower output frequency parameter is determined. Through matching calculations under different operating conditions, an output frequency parameter group that can be dynamically adjusted as the operating status of the scraper conveyor changes in real time is generated. When the material flow suddenly increases during operation or the equipment fails and causes a load change, the module will re-match in the speed association list according to the new status and update the parameter group in time, so as to ensure that the scraper conveyor works under an ideal operating state, realize efficient and stable material transportation, and avoid problems such as excessive equipment wear, excessive energy consumption or material blockage caused by improper frequency settings.

[0042] The linkage control strategy formulation module 50 is used to perform linkage frequency conversion speed regulation on the output frequency parameter group according to the association learning result, formulate a linkage control strategy, and execute the linkage control strategy to intelligently control the belt speed of the scraper conveyor. Specifically, the linkage control strategy formulation module 50 first obtains the output frequency parameter group generated by the dynamic matching module and the association learning result obtained by reinforcement learning. The former reflects the preliminary frequency setting based on the speed association list and the inverter performance under the current operating state, and the latter contains the optimal action strategy and reward feedback under different conditions. Then, according to the association learning result, the output frequency parameter group is analyzed, and according to the reward of the equipment action under different speed, load, environment and other conditions, such as the impact of frequency adjustment on operating efficiency, energy consumption, wear and reward and punishment value under specific combinations, the parameters in the parameter group are evaluated and adjusted. If increasing the frequency can increase efficiency without excessive energy consumption and wear increase, it is adjusted upward. On the contrary, if reducing the frequency can reduce the risk of failure and does not affect transportation, it is adjusted downward. Based on this, a linkage control strategy is formulated to adjust and control the scraper conveyor belt speed and related equipment actions according to the output frequency parameter group in different scenarios. For example, when the load gradually increases, the motor speed is increased according to the adjustment parameters to maintain the conveying speed. At the same time, the tension of the scraper chain is increased and the temperature is monitored considering stability, and cooling measures are taken in case of overheating. Finally, during the operation of the scraper conveyor, the changes in parameters such as load, flow, temperature, and current are continuously monitored, and the frequency parameter group is updated according to the strategy rules and the belt speed and other equipment parameters are adjusted accordingly to achieve adaptive and intelligent operation under different working conditions, ensure efficient, stable, and safe transportation of materials, reduce energy consumption and wear, and improve system reliability and economy.

[0043] In a possible implementation, the linkage control strategy formulation module 50 further includes: a speed impact analysis unit, which is used to perform speed impact analysis on the output frequency parameter group based on the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter to generate multiple speed impact factors. Specifically, the speed impact analysis unit performs speed impact analysis after receiving the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, the second negative reward parameter, and the output frequency parameter group. Positive reward parameters indicate that the device operates well and the action strategy is correct under the corresponding state, for example, when the speed matches the load, temperature, etc. reasonably (the first positive reward parameter related situation) or the device has a high operating efficiency (the second positive reward parameter related situation). Negative reward parameters correspond to problems with the corresponding device, such as overload, high temperature, low energy efficiency (the first negative reward parameter) or low operating efficiency (the second negative reward parameter). By analyzing the relationship between the reward parameter and the output frequency parameter group, the degree of influence of the frequency on the speed under different reward conditions is determined. For example, if in the good operating state corresponding to the first positive reward parameter, the output frequency is in a stable and appropriate range, so that the speed of the scraper conveyor can stably maintain the efficient transportation of materials, then it can be determined that the frequency range has a positive and stable effect on the speed, and then the corresponding speed influence factor is generated; on the contrary, if in the bad state corresponding to the negative reward parameter, such as the motor overload caused by the output frequency being too high (related to the first negative reward parameter), the frequency at this time has a negative effect on the speed, causing the equipment to be abnormal, and the corresponding speed influence factor will also be generated. The speed influence factor reflects the effect of the output frequency on the speed of the scraper conveyor under different operating conditions, and provides a basis for the subsequent control interval setting.

[0044] A variable frequency control interval setting unit, the variable frequency control interval setting unit is used to use the multiple speed influence factors as indexes, match and retrieve the output frequency parameter group, and set the variable frequency control interval. Specifically, after the variable frequency control interval setting unit obtains the multiple speed influence factors generated by the speed influence analysis unit, it uses them as indexes to match and retrieve the output frequency parameter group. Each speed influence factor corresponds to a different operating state and speed performance. By matching with the output frequency parameter group, the appropriate output frequency range under different speed influence conditions can be determined. For example, when a certain speed influence factor indicates that the speed is in a relatively ideal and stable state under the current load and equipment operating efficiency conditions, the output frequency range that matches it can be used as part of the variable frequency control interval. If in this state, the output frequency range corresponding to the speed influence factor is 40Hz~45Hz, then this range may be included in the variable frequency control interval. By matching and searching multiple speed influencing factors one by one, a complete frequency conversion control range is gradually constructed. This range clearly defines the reasonable range of the inverter output frequency under different operating conditions to ensure stable and efficient operation of the scraper conveyor, avoiding equipment failure, increased energy consumption or poor material transportation due to excessively high or low frequency.

[0045] A dynamic frequency output parameter extraction unit, the dynamic frequency output parameter extraction unit is used to construct a linkage control rule based on the association learning result, dynamically adjust the output frequency parameter group based on the linkage control rule combined with the multiple real-time operation data, and extract the dynamic frequency output parameter. Specifically, the dynamic frequency output parameter extraction unit constructs a linkage control rule based on the association learning result. The association learning result contains the optimal action strategy of the equipment under various operating conditions, and rules such as load and speed linkage, temperature and speed linkage, and current and speed linkage are formulated according to the strategy. For example, when the load increases, according to the load and speed linkage rule, the inverter should automatically reduce the output frequency to avoid motor overload. When the load reaches 80%, the output frequency is reduced from 50Hz to 45Hz; when the temperature of the equipment rises, according to the temperature and speed linkage rule, the inverter should reduce the speed to reduce the power consumption of the motor, such as when the temperature exceeds 60°C, the speed is automatically reduced to 40Hz; when the current is close to the rated value, according to the current and speed linkage rule, the inverter should automatically reduce the output frequency to keep the equipment within a safe range, such as when the current exceeds 8A, the frequency is reduced from 50Hz to 40Hz. Then, the output frequency parameter group is dynamically adjusted in combination with multiple real-time operating data, such as the current load value, temperature value, current value, etc. According to the real-time load conditions, it is determined whether the threshold for adjusting the frequency is reached. If reached, the frequency value in the output frequency parameter group is adjusted according to the linkage control rule; similarly, the temperature and current data are monitored and analyzed, and the frequency is adjusted in real time. During the adjustment process, the dynamic frequency output parameter is extracted. This parameter reflects the output frequency of the inverter after adjustment by the linkage control rule under the current actual operating conditions, which is the key parameter for real-time control of the speed of the scraper conveyor.

[0046] The frequency parameter group updating unit is used to determine whether the dynamic frequency output parameter is in the variable frequency control interval. If the dynamic frequency output parameter is not in the variable frequency control interval, the dynamic frequency output parameter is identified as an abnormal control parameter and fed back to the output frequency parameter group for updating to generate an output frequency update parameter group. Specifically, the frequency parameter group updating unit determines whether the dynamic frequency output parameter is in the variable frequency control interval. If the dynamic frequency output parameter is not in the variable frequency control interval, it means that the current output frequency may be abnormal, causing the equipment to fail to operate normally or be in a non-optimal operating state. For example, if the dynamic frequency output parameter is higher than the upper limit of the variable frequency control interval, the motor may be overloaded and the equipment may be aggravated; if it is lower than the lower limit, the material conveying speed may be too slow, affecting production efficiency. At this time, the dynamic frequency output parameter is identified as an abnormal control parameter and fed back to the output frequency parameter group. Then, the output frequency update parameter group is dynamically adjusted based on the linkage control rule combined with multiple real-time operation data. For example, if the dynamic frequency output parameter increases abnormally due to excessive load, the frequency value in the output frequency parameter group is further reduced according to the load and speed linkage rules; if the abnormality is caused by excessive temperature, the frequency is adjusted according to the temperature and speed linkage rules. After adjustment, the parameters are extracted again for judgment iteration, and the output frequency parameter group is continuously optimized until the dynamic frequency output parameter is in the variable frequency control range to ensure the stability and reliability of equipment operation.

[0047] A linkage control strategy formulation unit, wherein the linkage control strategy formulation unit is used to formulate the linkage control strategy based on the dynamic frequency output parameter if the dynamic frequency output parameter is in the variable frequency control interval. Specifically, after determining that the dynamic frequency output parameter is in the variable frequency control interval, the linkage control strategy formulation unit formulates a linkage control strategy based on the dynamic frequency output parameter. Since the dynamic frequency output parameter at this time is obtained by adjusting the linkage control rules while meeting the requirements of the variable frequency control interval, it can ensure the stable operation of the scraper conveyor under the current operating conditions. For example, if the current dynamic frequency output parameter is 42Hz, and the frequency is determined after considering factors such as load, temperature, and current, then the linkage control strategy will stipulate the coordinated operation mode of related parameters such as the motor speed of the scraper conveyor, the tension of the scraper chain, and the conveying speed of the material under this frequency. At the same time, the strategy will also analyze how to further adjust the dynamic frequency output parameters according to the linkage control rules if the parameters change during operation, such as a sudden increase in load or a rise in temperature, so as to maintain the stable operation of the equipment and realize adaptive and intelligent control of the scraper conveyor under different working conditions, thereby improving the operating efficiency and reliability of the entire scraper conveyor system and ensuring the safe and efficient transportation of materials.

[0048] In the above, refer to Figure 1The invention is described in detail based on the adaptive frequency conversion speed regulation device of the scraper conveyor according to the embodiment of the present invention. Figure 2 The invention describes an adaptive variable frequency speed control method for a scraper conveyor according to an embodiment of the invention.

[0049] Based on the adaptive frequency conversion speed control method of scraper conveyor, such as Figure 2 As shown, the method includes: Arrange multiple sensors on the scraper conveyor for data sensing to generate multiple real-time operation data, perform demand analysis based on the multiple real-time operation data, and determine the operation demand information of the scraper conveyor; set an operation parameter range according to the operation demand information, evaluate and calculate the multiple real-time operation data in combination with the operation demand information based on the operation parameter range, and set multiple evaluation indicators; perform speed correlation analysis on the multiple real-time operation data according to the multiple evaluation indicators, construct a speed correlation list, perform reinforcement learning based on the speed correlation list, and generate a correlation learning result; call the frequency converter of the scraper conveyor and dynamically match it with the speed correlation list to generate an output frequency parameter group; perform linkage frequency conversion speed regulation on the output frequency parameter group according to the correlation learning result, formulate a linkage control strategy, and execute the linkage control strategy to intelligently control the belt speed of the scraper conveyor.

[0050] In a possible implementation, the adaptive variable frequency speed control method based on the scraper conveyor also includes: retrieving multiple operating conditions of the scraper conveyor, traversing the scraper conveyor according to the multiple operating conditions to perform operation association analysis, and determining multiple associated devices, the multiple associated devices include a coal mining machine and a transfer machine, and the scraper conveyor is communicatively connected with the coal mining machine and the transfer machine; based on the multiple operating conditions, an operation analysis is performed on the coal mining machine in combination with the operation demand information to determine multiple coal mining operation parameters; based on the multiple operating conditions, an operation analysis is performed on the transfer machine in combination with the operation demand information to determine multiple transfer operation parameters; using the multiple coal mining operation parameters and the multiple transfer operation parameters as constraints, performing multi-dimensional operation fluctuation analysis on the scraper conveyor based on the constraints, and drawing an operation trend diagram of the scraper conveyor; performing extreme value analysis based on the operation trend diagram to determine multiple operation extreme values, the multiple operation extreme values ​​include an operation maximum value and an operation minimum value; setting the operation maximum value as the upper limit of the interval, setting the operation minimum value as the lower limit of the interval, and constructing the operation parameter interval.

[0051] In a possible implementation, the adaptive variable frequency speed control method for scraper conveyor also includes: weighting the multiple coal mining operating parameters based on the operating parameter range to generate multiple coal mining weight coefficients; constructing a first indicator calculation formula based on the multiple coal mining weight coefficients combined with the multiple coal mining operating parameters; synchronizing the multiple real-time operating data to the first indicator calculation formula to evaluate and calculate the scraper conveyor to determine a first evaluation indicator; weighting the multiple transfer operating parameters based on the operating parameter range to generate multiple transfer weight coefficients; constructing a second indicator calculation formula based on the multiple transfer weight coefficients combined with the multiple transfer operating parameters; synchronizing the multiple real-time operating data to the second indicator calculation formula to evaluate and calculate the scraper conveyor to determine a second evaluation indicator; integrating the first evaluation indicator with the second evaluation indicator to determine multiple evaluation indicators.

[0052] In a possible implementation, the adaptive variable frequency speed control method for a scraper conveyor further includes: the first indicator calculation formula: ; , ;in, is the mean value of the first evaluation index A, is the maximum value, They are the speed, position, and current of the coal mining machine and the current of the scraper conveyor; for The corresponding weights; They are the maximum traction speed of the coal mining machine, the number of supports, the rated current of the coal mining machine, and the rated current of the scraper conveyor; the calculation formula of the second index is: ; , ;in, is the mean value of the second indicator B; is the maximum value; , They are the torque of the transfer machine and the real-time coal quantity; , are the maximum torque and the maximum coal quantity respectively. , for , The weight of .

[0053] In a possible implementation, the adaptive variable frequency speed control method for scraper conveyor also includes: performing speed correlation analysis based on the multiple real-time operation data to obtain multiple correlation coefficients; performing feature traversal analysis on the multiple real-time operation data based on the multiple correlation coefficients to generate multiple speed correlation features; constructing a correlation matrix based on the multiple speed correlation features combined with the multiple correlation coefficients; mapping the multiple evaluation indicators to the correlation matrix for matching, and constructing the speed correlation list based on the matching results.

[0054] In a possible implementation, the adaptive variable frequency speed control method based on the scraper conveyor also includes: introducing the operating environment parameters of the scraper conveyor, traversing the speed association list and combining the operating environment parameters to perform state analysis, and constructing a learning state space; traversing the speed association list and combining the multiple real-time operating data to perform action analysis, constructing a learning action space to set a reward function, mapping the reward function to the learning state space for state reward, and generating a first positive reward parameter and a first negative reward parameter; mapping the reward function to the learning action space for state reward, and generating a second positive reward parameter and a second negative reward parameter; associating and integrating the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter, and adding the integration result to the associated learning result.

[0055] In a possible implementation, the adaptive variable frequency speed control method based on the scraper conveyor also includes: performing speed influence analysis on the output frequency parameter group based on the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter to generate multiple speed influence factors; using the multiple speed influence factors as indexes, matching and retrieving the output frequency parameter group, and setting the variable frequency control range; constructing a linkage control rule based on the associated learning result, dynamically adjusting the output frequency parameter group based on the linkage control rule and the multiple real-time operation data, and extracting the dynamic frequency output parameter; judging whether the dynamic frequency output parameter is in the variable frequency control range, if the dynamic frequency output parameter is not in the variable frequency control range, marking the dynamic frequency output parameter as an abnormal control parameter and feeding it back to the output frequency parameter group for updating, and generating an output frequency update parameter group; if the dynamic frequency output parameter is in the variable frequency control range, formulating the linkage control strategy based on the dynamic frequency output parameter.

[0056] The adaptive frequency conversion speed regulation device based on scraper conveyor provided in the embodiment of the present invention can execute the adaptive frequency conversion speed regulation control method based on scraper conveyor provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0057] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0058] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. Based on the adaptive frequency conversion speed regulation device of scraper conveyor, it is characterized by: The device comprises: A data sensing module, wherein the data sensing module is used to perform data sensing on a plurality of sensors arranged on the scraper conveyor, generate a plurality of real-time operation data, perform demand analysis based on the plurality of real-time operation data, and determine the operation demand information of the scraper conveyor; An evaluation and calculation module, the evaluation and calculation module is used to set an operation parameter interval according to the operation requirement information, and based on the operation parameter interval, evaluate and calculate the multiple real-time operation data in combination with the operation requirement information to set multiple evaluation indicators; A speed correlation analysis module, the speed correlation analysis module is used to perform speed correlation analysis on the multiple real-time operation data according to the multiple evaluation indicators, construct a speed correlation list, perform reinforcement learning based on the speed correlation list, and generate a correlation learning result; A dynamic matching module, which is used to call the frequency converter of the scraper conveyor and combine it with the speed association list for dynamic matching to generate an output frequency parameter group; A linkage control strategy formulation module is used to perform linkage frequency conversion speed regulation on the output frequency parameter group according to the associated learning result, formulate a linkage control strategy, and execute the linkage control strategy to intelligently control the scraper conveyor belt speed.

2. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 1 is characterized in that: The evaluation calculation module includes: An associated device determination unit, the associated device determination unit is used to retrieve multiple operating conditions of the scraper conveyor, traverse the scraper conveyor according to the multiple operating conditions to perform operation association analysis, and determine multiple associated devices, the multiple associated devices include a coal mining machine and a transfer machine, and the scraper conveyor is in communication connection with the coal mining machine and the transfer machine; An operation analysis unit, the operation analysis unit is used to perform operation analysis on the coal mining machine based on the multiple operation conditions and the operation demand information, and determine multiple coal mining operation parameters; a transfer operation parameter determination unit, the transfer operation parameter determination unit being used to perform operation analysis on the transfer machine in combination with the operation demand information based on the multiple operation conditions, and determine multiple transfer operation parameters; A multi-dimensional operation fluctuation analysis unit, wherein the multi-dimensional operation fluctuation analysis unit is used to use the multiple coal mining operation parameters and the multiple transfer operation parameters as constraint conditions, perform multi-dimensional operation fluctuation analysis on the scraper conveyor based on the constraint conditions, and draw an operation trend diagram of the scraper conveyor An operation extreme value determination unit, the operation extreme value determination unit is used to perform extreme value analysis based on the operation trend graph to determine a plurality of operation extreme values, wherein the plurality of operation extreme values ​​include an operation maximum value and an operation minimum value; An operation parameter interval construction unit is used to set the operation maximum value as the interval upper limit and the operation minimum value as the interval lower limit to construct the operation parameter interval.

3. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 2 is characterized in that: The evaluation calculation module includes: A weight allocation unit, the weight allocation unit is used to allocate weights to the plurality of coal mining operation parameters based on the operation parameter interval to generate a plurality of coal mining weight coefficients; A first indicator calculation formula construction unit, the first indicator calculation formula construction unit is used to construct a first indicator calculation formula based on the multiple coal mining weight coefficients combined with the multiple coal mining operation parameters; A first evaluation index determination unit, the first evaluation index determination unit is used to synchronize the plurality of real-time operation data to the first index calculation formula to evaluate and calculate the scraper conveyor, and determine a first evaluation index; a reprint weight coefficient generating unit, the reprint weight coefficient generating unit being used to assign weights to the plurality of reprint operation parameters based on the operation parameter interval to generate a plurality of reprint weight coefficients; A second indicator calculation formula construction unit, the second indicator calculation formula construction unit is used to construct a second indicator calculation formula based on the multiple transfer weight coefficients combined with the multiple transfer operation parameters; A second evaluation index determination unit, the second evaluation index determination unit is used to synchronize the plurality of real-time operation data to the second index calculation formula to evaluate and calculate the scraper conveyor, and determine the second evaluation index; The evaluation index integration unit is used to integrate the first evaluation index and the second evaluation index to determine multiple evaluation indexes.

4. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 3 is characterized in that: The calculation formula of the first index and the calculation formula of the second index are as follows: The first indicator calculation formula subunit, the first indicator calculation formula subunit is used for the first indicator calculation formula: ； ； ； in, is the mean value of the first evaluation index A, is the maximum value, They are the speed, position, and current of the coal mining machine and the current of the scraper conveyor; for The corresponding weights; They are the maximum traction speed of the shearer, the number of supports, the rated current of the shearer, and the rated current of the scraper conveyor; The second indicator calculation formula subunit is used for the second indicator calculation formula: ； ； ； in, is the mean value of the second indicator B; is the maximum value; , They are the torque of the transfer machine and the real-time coal quantity; , are the maximum torque and the maximum coal quantity respectively. , for , The weight of .

5. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 1 is characterized in that: The speed correlation analysis module includes: A rotation speed correlation analysis unit, the rotation speed correlation analysis unit is used to perform rotation speed correlation analysis based on the multiple real-time operation data to obtain multiple correlation coefficients; A feature traversal analysis unit, the feature traversal analysis unit is used to perform feature traversal analysis on the multiple real-time operation data based on the multiple correlation coefficients to generate multiple speed correlation features; A correlation matrix construction unit, the correlation matrix construction unit is used to construct a correlation matrix according to the multiple speed correlation features combined with the multiple correlation coefficients; A rotation speed association list construction unit is used to map the multiple evaluation indicators to the association matrix for matching, and construct the rotation speed association list according to the matching result.

6. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 1 is characterized in that: The speed correlation analysis module includes: A learning state space construction unit, wherein the learning state space construction unit is used to introduce operating environment parameters of the scraper conveyor, traverse the speed association list and perform state analysis in combination with the operating environment parameters to construct a learning state space; A learning action space construction unit, the learning action space construction unit is used to traverse the speed association list and combine the multiple real-time operation data to perform action analysis and construct a learning action space; A reward parameter generating unit, the reward parameter generating unit is used to set a reward function, map the reward function to the learning state space for state reward, and generate a first positive reward parameter and a first negative reward parameter; A state reward unit, the state reward unit is used to map the reward function to the learning action space for state reward, and generate a second positive reward parameter and a second negative reward parameter; An associated learning result adding unit, wherein the associated learning result adding unit is used to associate and integrate the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter, and add the integration result to the associated learning result.

7. The adaptive frequency conversion speed regulating device based on scraper conveyor according to claim 6 is characterized in that: The linkage control strategy formulation module includes: A rotation speed impact analysis unit, the rotation speed impact analysis unit is used to perform a rotation speed impact analysis on the output frequency parameter group based on the first positive reward parameter, the second positive reward parameter, the first negative reward parameter, and the second negative reward parameter to generate a plurality of rotation speed impact factors; A variable frequency control interval setting unit, the variable frequency control interval setting unit is used to use the multiple speed influencing factors as indexes, perform matching retrieval on the output frequency parameter group, and set the variable frequency control interval; A dynamic frequency output parameter extraction unit, the dynamic frequency output parameter extraction unit is used to construct a linkage control rule based on the association learning result, dynamically adjust the output frequency parameter group based on the linkage control rule and the multiple real-time operation data, and extract the dynamic frequency output parameter; A frequency parameter group updating unit, the frequency parameter group updating unit is used to determine whether the dynamic frequency output parameter is in the variable frequency control interval, if the dynamic frequency output parameter is not in the variable frequency control interval, the dynamic frequency output parameter is marked as an abnormal control parameter and fed back to the output frequency parameter group for updating, so as to generate an output frequency update parameter group; A linkage control strategy formulation unit is used to formulate the linkage control strategy based on the dynamic frequency output parameter if the dynamic frequency output parameter is in the variable frequency control interval.

8. Based on the adaptive frequency conversion speed control method of scraper conveyor, it is characterized by: The method is applied to the adaptive variable frequency speed regulation device based on a scraper conveyor according to any one of claims 1 to 7, and the method comprises: Arrange multiple sensors on the scraper conveyor for data sensing, generate multiple real-time operation data, perform demand analysis based on the multiple real-time operation data, and determine the operation demand information of the scraper conveyor; Setting an operation parameter interval according to the operation requirement information, and evaluating and calculating the plurality of real-time operation data in combination with the operation requirement information based on the operation parameter interval to set a plurality of evaluation indicators; Performing speed correlation analysis on the multiple real-time operating data according to the multiple evaluation indicators, constructing a speed correlation list, and performing reinforcement learning based on the speed correlation list to generate a correlation learning result; The frequency converter of the scraper conveyor is retrieved and dynamically matched with the speed association list to generate an output frequency parameter group; The output frequency parameter group is linked with frequency conversion speed regulation according to the associated learning result, a linkage control strategy is formulated, and the linkage control strategy is executed to intelligently control the belt speed of the scraper conveyor.

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