Underground conveyor stepless speed regulation control method realized based on fuzzy control principle

Abstract

The invention discloses an underground conveyor stepless speed regulation control method realized based on a fuzzy control principle. The method comprises the following steps: step 1, realizing dialogue control among three machines through an Internet of Things technology; step 2, realizing dynamic optimization of the front transporter through a fuzzy control principle; 3, speed adjustment of the reversed loader is achieved through operation conditions of a front conveyor, a rear conveyor and a coal mining machine, and the three-loader cooperative control module is used for collecting operation data of the coal mining machine, a hydraulic support and the conveyors and controlling the operation data of the coal mining machine, the hydraulic support and the conveyors; the reversed loader speed adjusting module is used for adjusting the speed of the reversed loader according to the front and rear conveyors and the working condition information; the fuzzy rule base and reasoning module is used for reasoning a fuzzy value of the current coal quantity load through a fuzzy rule in combination with a membership degree of an input variable; the invention has the characteristics of high energy utilization efficiency and capability of effectively reducing invalid wear in equipment operation.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine automation, and specifically to a stepless speed regulation control method for underground conveyors based on the fuzzy control principle. Background Technique

[0002] The speed regulation control of the three machines (transfer machine, front conveyor, and rear conveyor) at the working face during coal mining is one of the core contents of the intelligent development of coal mines. In traditional coal mine production, frequency converters are generally used to drive the underground equipment. Although the frequency converter plays a role in soft starting during equipment startup, its main advantages in speed regulation and energy conservation and consumption reduction have not been fully utilized. Especially during the operation of the shearer, although frequency conversion drive technology has been adopted, due to most of the speed control relying on the experience of the shearer driver and lacking a systematic and intelligent adjustment scheme, the production efficiency is low, and it is difficult to achieve the optimal energy utilization.

[0003] In addition, there is a lack of effective data interaction and information interconnection among the subsystems in the working face centralized control system, resulting in the phenomenon of information islands and making it difficult to coordinate the scheduling of different equipment. The subsystems fail to achieve real-time data sharing and control coordination, further reducing the overall production efficiency. This situation restricts the further development of intelligent management during the coal mining process. There is an urgent need for a new intelligent control technology to improve the coordination efficiency of the equipment on the entire working face, thereby optimizing the coal mine production process, improving energy utilization efficiency, and promoting the in-depth development of coal mine intelligence. Therefore, it is necessary to design a stepless speed regulation control method for underground conveyors based on the fuzzy control principle to improve energy utilization efficiency and reduce ineffective wear of equipment operation. Summary of the Invention

[0004] The purpose of the present invention is to provide a stepless speed regulation control method for underground conveyors based on the fuzzy control principle to solve the problems mentioned in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A stepless speed regulation control system for underground conveyors based on the fuzzy control principle, and the operation method of this system includes the following steps:

[0006] Step 1: Realize the dialogue control among the three machines through Internet of Things technology;

[0007] Step 2: Realize the dynamic optimization of the front conveyor through the fuzzy control principle;

[0008] Step 3: Adjust the speed of the transfer machine according to the operating conditions of the front and rear conveyors and the shearer.

[0009] According to the above technical solution, the step of realizing the dialogue control among the three machines through Internet of Things technology includes:

[0010] Deploy sensors to collect the operation data of shearers, hydraulic supports and conveyors;

[0011] Upload the collected data to the central control room or edge computing device through Wi-Fi or 5G network;

[0012] Perform data preprocessing, including noise reduction and compression, and use the processed data for equipment operation status monitoring and control decision-making.

[0013] According to the above technical solution, the steps of realizing the dynamic optimization of the front conveyor through the fuzzy control principle include:

[0014] Collect the parameters of the mining height, shearer position, cutting current and traction speed of the working face;

[0015] Define the membership function of the input variable and calculate the fuzzy output based on the IF-THEN rule base and the min-max inference method;

[0016] Through centroid defuzzification, convert the fuzzy output into the precise speed command of the conveyor;

[0017] Construct a fuzzy control rule base to map the mining height, cutting current and traction speed to the speed adjustment range of the conveyor;

[0018] Dynamically collect the real-time data of the input parameters and optimize the running speed of the conveyor through the fuzzy inference algorithm;

[0019] Automatically generate speed adjustment commands and send them to the conveyor control subsystem in real time.

[0020] According to the above technical solution, the steps of defining the membership function of the input variable and calculating the fuzzy output based on the IF-THEN rule base and the min-max inference method include:

[0021] Map the input variable to the fuzzy set through the membership function, and set the membership degree as μ x (x), and the membership function of the mining height (h) variable is defined as a triangular membership function:

[0022] Membership degree of mining height:

[0023]

[0024] h low,min is the minimum boundary of the mining height variable h belonging to the fuzzy set of "low mining height",

[0025] After the input variable is defuzzified, use the rule base to define the relationship between the input and the output. The rule adopts the IF-THEN structure, where the premise, that is, the IF part, is the defuzzified combination of the input, and the conclusion, that is, the THEN part, is the fuzzy output

[0026] R k :IF μ h (h) ∧ μ p (p) ∧ μ I (I) ∧ μ vcoal (v coal ) ∧ μ S (S) THEN μ v (v front )。

[0027] According to the above technical solution, the steps of adjusting the speed of the transfer machine by the operating conditions of the front and rear conveyors and the shearer include:

[0028] Estimate the coal quantity of the rear conveyor;

[0029] Construct a membership function and a fuzzy rule base, and calculate the coal load of the transfer machine according to the fuzzy inference algorithm;

[0030] Based on the fuzzy control algorithm of the coal quantity of the rear conveyor, estimate the coal quantity by combining the mining height of the working face, the number of coal caving openings, and the attitude information of the support;

[0031] Construct a dynamic amplitude limiting function and optimize the coal flow transportation efficiency through fuzzy inference and real-time data fusion.

[0032] According to the above technical solution, the steps of estimating the coal quantity of the rear conveyor include:

[0033] The estimation of the coal quantity of the rear conveyor is similar to that of the front conveyor, and both use the self-learning algorithm and the fuzzy control method. However, the rear conveyor needs to additionally consider the data related to the coal caving process. During the implementation, first collect the mining height of the working face, the number of coal caving openings, the coal caving time, and obtain the attitude information of the support tail beam and the plow through the support electro-hydraulic control system to calculate the number of supports where coal is being caved. Combine these data with the previously established control model and use the fuzzy algorithm to estimate the coal load of the rear conveyor. The result will be used as the basis for adjusting the speed of the conveyor.

[0034] According to the above technical solution, the steps of constructing a membership function and a fuzzy rule base and calculating the coal load of the transfer machine according to the fuzzy inference algorithm include:

[0035] In the fuzzy algorithm, the input variables include the coal quantity M1 of the front conveyor, the coal quantity M2 of the rear conveyor, and the operating condition state C of the shearer. Establish a membership function for each variable, where the shearer traction speed v coal has membership functions of "slow", "medium", and "fast", and based on this, establish a fuzzy rule base,

[0036] IF M1 is large AND v coalfor fast THEN MR for big;

[0037] IF M2 is small AND the cutoff current in C is low THEN MR is small;

[0038] IF M1 is medium AND M2 is large THEN MR is large;

[0039] These rules integrate information from different sources through fuzzy reasoning mechanism to obtain the coal quantity MR of the rear conveyor. The coal quantity estimation formula can be expressed as MR=f(M1,M2,C).

[0040] According to the above technical solution, the steps of constructing a dynamic limiting function and optimizing the coal flow transportation efficiency through fuzzy reasoning and real-time data fusion include:

[0041] The calculation formula of the transfer machine speed vR is: vR = g(MR), where g is a function related to the coal quantity MR. Then the speed range constraint needs to be considered. Assuming that the speed range is vmin≤vR≤vmax, the final speed must meet this condition. If it does not meet the interval requirements, it is corrected by the limit function. The speed adjustment relationship can be exemplarily expressed as:

[0042] When MR≤Mlow, vR=vmin;

[0043] When MR≥Mhigh, vR=vmax;

[0044] Mlow <MR<Mhigh时,vR随MR线性变化。

[0045] According to the above technical solution, the system includes:

[0046] Three-machine coordinated control module, used to collect and control the operation data of coal mining machine, hydraulic support and conveyor;

[0047] A fuzzy control module for calculating the conveyor speed using fuzzy control;

[0048] The transfer machine speed adjustment module is used to adjust the speed of the transfer machine according to the front and rear conveyors and working condition information.

[0049] According to the above technical solution, the three-machine collaborative control module includes:

[0050] Equipment networking and data acquisition module, used to connect coal mining machines, hydraulic supports and conveyors to a unified platform, collect key data through the Internet of Things technology, and realize real-time monitoring and management of working face equipment;

[0051] The data transmission and preprocessing module is used to transmit the collected data to the ground central control room and the downhole edge computing equipment through Wi-Fi or 5G network for noise reduction and compression;

[0052] The system integration and function implementation module is used to realize the coordinated control and information interaction of three machines based on the collected data, construct a fuzzy control path through data sharing, and provide dynamic adjustment capabilities.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By integrating Internet of Things technology, fuzzy control algorithms, and self-learning algorithms, the present invention realizes dynamic coordinated control among the three machines (coal shearer, front conveyor, and rear conveyor) on the working face. The system can accurately adjust the speed based on the key parameters collected in real time to ensure that the conveyor speed matches the coal seam mining conditions, thereby realizing smooth coal flow transportation, reducing coal waste, and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0055] In the drawings:

[0056] Figure 1 is a flowchart of the method for realizing stepless speed regulation control of an underground conveyor based on the fuzzy control principle;

[0057] Figure 2 is a schematic diagram of the module composition of the stepless speed regulation control system of an underground conveyor realized based on the fuzzy control principle;

[0058] Figure 3 is the Internet of Things system architecture of the working face of the present invention;

[0059] Figure 4 is the fuzzy control speed regulation principle of the transfer machine of the present invention, DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1:

[0062] Figure 1 is a flowchart of the method for realizing stepless speed regulation control of an underground conveyor based on the fuzzy control principle provided in Embodiment 1 of the present invention. This embodiment can be applied to the scenarios of coal mine automation and intelligentization. This method can be executed by the stepless speed regulation control system of an underground conveyor realized based on the fuzzy control principle provided in this embodiment, as Figure 1As shown in the figure, the method specifically includes the following steps:

[0063] Step 1: Implement dialogue control among the three machines through Internet of Things technology;

[0064] In the embodiment of the present invention, based on the Internet of Things technology of the working face, the electronic control subsystem of the shearer, the electro-hydraulic control subsystem of the hydraulic support, the control subsystem of the conveyor, and the working face WiFi subsystem are integrated, and various devices are connected to a unified platform to achieve data interaction and information interconnection. By deploying sensors, the shearer can collect data such as coal seam thickness, cutting height, cutting current, and traction speed in real time, and upload the data to the platform through the electronic control subsystem; the hydraulic support monitors the operating states such as lifting, advancing, and coal caving through state sensors, and the data is summarized to the platform through the electro-hydraulic control subsystem; the conveyor collects coal weight or volume information through a weighing sensor or an optoelectronic detector and uploads it to the platform. The data is transmitted to the ground central control room through Wi-Fi or 5G network and preprocessed in the underground edge computing device, including noise reduction and data compression. Based on these real-time data, the system realizes direct or indirect dialogue control among the "three machines", data interaction and sharing, and functional penetration and integration, and constructs a stepless speed regulation path for fuzzy control of the conveyor.

[0065] Step 2: Realize dynamic optimization of the front conveyor through the fuzzy control principle;

[0066] In the embodiment of the present invention, the speed of the front conveyor is dynamically adjusted using the real-time data obtained in Step 1. Since there are complex non-linear relationships among the relevant variables, a fuzzy control algorithm is used to handle these uncertainties and ambiguities;

[0067] Exemplarily, the adjustment of the front conveyor speed is based on multiple input parameters, including the cutting height h of the working face, the position p of the shearer, the cutting current I, the traction speed v coal and the state S of the automated working process. Each input variable is mapped to a fuzzy set through a membership function. The form of the membership function is selected as a trigonometric function. Specifically, the conveyor speed is v front , first the input variables cutting height h, shearer position p, cutting current I, traction speed v coal , and automated working process state S. Subsequently, the input variables are mapped to a fuzzy set through a membership function. Let the membership degree be μ x (x). The membership function of the cutting height (h) variable is defined as a triangular membership function:

[0068] Cutting height membership degree:

[0069]

[0070] The above h low,min is the minimum boundary of the cutting height variable h belonging to the fuzzy set of "low cutting height",

[0071] After the input variables are fuzzified, the rule base is used to define the relationship between the inputs and outputs, forming the key part of the fuzzy inference system. The rules adopt the IF-THEN structure, where the premise (IF part) is the fuzzified combination of the inputs, and the conclusion (THEN part) is the fuzzy output (the speed of the front conveyor):

[0072]

[0073] This rule indicates that the speed v of the front conveyor front is determined by the fuzzy values of the cutting height, the position of the shearer, the cutting current, the traction speed, and the state of the automated working steps. Then, a fuzzy inference algorithm is used to calculate the fuzzy output based on the rule base and the fuzzy membership degrees of the input variables The "minimum-maximum method" is adopted for the calculation, that is, the fuzziness of each rule is calculated by taking the minimum value of the membership degrees of each input, namely:

[0074]

[0075] The output of this step is the fuzzy set of the speed of the front conveyor. Finally, the defuzzification method "centroid method" is used to defuzzify the fuzzy output That is:

[0076]

[0077] Thereby it is converted into a specific numerical value, and this numerical value represents the speed of the front conveyor. The final speed value of the front conveyor is obtained by calculating the centroid position of the fuzzy output curve. The above process is integrated into a functional formula: v front = f(h, p, I, v coal , S),

[0078] where:

[0079]

[0080] Exemplarily, in actual situations, the factors affecting the conveyor speed are defined as the input variables of fuzzy control, including:

[0081] Cutting height: defined as "low", "medium", "high".

[0082] Position of the shearer: defined as "near", "medium", "far" according to the relative distance from the head.

[0083] Cutting current: indicating the load, defined as "light load", "medium load", "heavy load".

[0084] Traction speed: defined as "slow", "medium", "fast".

[0085] Automation process steps: defined as "initial", "transition", "stable".

[0086] Conveyor speed: defined as "slow", "medium", "fast".

[0087] Subsequently, membership functions are designed for each input and output variable. Shearer height membership function:

[0088] "Low": high membership degree when the shearer height is in the range of 0 - 1 meter;

[0089] "Medium": high membership degree when the shearer height is in the range of 1 - 2.5 meters;

[0090] "High": high membership degree when the shearer height is in the range of 2.5 - 4 meters.

[0091] Conveyor speed membership function:

[0092] "Slow": high membership degree when the speed is in the range of 0 - 1 m / s;

[0093] "Fast": high membership degree when the speed is in the range of 3 - 5 m / s.

[0094] Set fuzzy rules:

[0095] Based on production experience and experimental data, construct an "if - then" rule base.

[0096] If the shearer height is "high" and the cutting current is "heavy load", then the conveyor speed is "fast".

[0097] If the shearer position is "far" and the traction speed is "fast", then the conveyor speed is "fast".

[0098] If the shearer height is "low" and the cutting current is "light load", then the conveyor speed is "slow".

[0099] Use the fuzzy inference method to calculate the input variables, and combine the rule base to obtain the fuzzy value of the output variable. If the current shearer height is 2 meters (fuzzified to "medium" membership degree 0.8, "high" membership degree 0.2), and the cutting current is "heavy load" (membership degree 0.9), match the corresponding rules through the rule base to obtain the fuzzy speed value.

[0100] Convert the fuzzy inference result into a specific conveyor speed value, for example:

[0101] Use the weighted average method or the centroid method to combine the membership degrees corresponding to "slow", "medium", "fast" with their specific speed ranges to calculate the accurate conveyor speed. Assume that the current shearer height in the working face is 3 meters, the shearer is 50 meters from the head of the face, the cutting current is 300A, and the traction speed is 2 m / s:

[0102] Fuzzification: For the input mining height, the membership degree of "high" is 0.7; for the position, the membership degree of "far" is 0.9; for the cutting current, the membership degree of "heavy load" is 0.8.

[0103] Inference: According to the rule base, it is inferred that the speed of the conveyor is "fast".

[0104] Defuzzification: The calculated specific speed value is 4.2 m / s.

[0105] Adjustment instruction: Transmit the speed instruction to the conveyor control system to achieve dynamic speed regulation.

[0106] Through fuzzy control, the system can intelligently handle complex multivariable relationships, improve the precise adjustment ability of the conveyor speed, and ensure the stable transmission of the coal flow rate at the same time.

[0107] Step 3: Adjust the speed of the transfer conveyor through the operating conditions of the front and rear conveyors and the shearer;

[0108] In the embodiment of the present invention, the estimation of the coal volume of the rear conveyor is similar to that of the front conveyor, and both use the self-learning algorithm and the fuzzy control method. However, the rear conveyor needs to additionally consider the data related to the coal caving process. During the implementation process, first collect parameters including the mining height of the working face, the number of coal caving openings, the coal caving time, etc., and obtain the attitude information of the support tail beam and the scissor plate through the support electro-hydraulic control system to calculate the number of supports that are currently caving coal. Combining these data with the previously established control model, use the fuzzy algorithm to estimate the coal volume load of the rear conveyor, and the result will be used as the basis for the conveyor speed adjustment.

[0109] In the fuzzy algorithm, the input variables include the coal volume M1 of the front conveyor, the coal volume M2 of the rear conveyor, and the operating condition state C of the shearer (such as cutting current, traction speed, etc.). Establish membership functions for each variable. Among them, the membership function of the shearer traction speed v coal is divided into "slow", "medium", and "fast", and a fuzzy rule base is established based on this.

[0110] IF M1 is large AND v coal is fast THEN MR is large;

[0111] IF M2 is small AND the cutting current in C is low THEN MR is small;

[0112] IF M1 is medium AND M2 is large THEN MR is large.

[0113] These rules integrate information from different sources through a fuzzy inference mechanism to obtain the coal quantity MR of the conveyor. The coal quantity estimation formula can be expressed as MR = f(M1, M2, C), where f is an inference calculation function based on a fuzzy rule base that performs a weighted average calculation on the membership degrees of the input variables. During the speed adjustment process, the formula for the speed vR of the conveyor is: vR = g(MR), where g is a function related to the coal quantity MR. Subsequently, considering the speed range constraint, assuming the speed range is vmin ≤ vR ≤ vmax, the final speed needs to satisfy this condition, and when it does not meet the interval requirements, it is corrected through a clipping function. The speed adjustment relationship can be expressed as:

[0114] When MR ≤ Mlow, vR = vmin;

[0115] When MR ≥ Mhigh, vR = vmax;

[0116] When Mlow < MR < Mhigh, vR changes linearly with MR.

[0117] By collecting the working condition data of the front and rear conveyors and the shearer in real time, and combining fuzzy algorithms and self-learning algorithms for coal quantity estimation, the system can intelligently calculate the load coal quantity of the conveyor and dynamically adjust its operating speed to ensure the smooth transportation of the coal flow.

[0118] Embodiment 2:

[0119] Embodiment 2 of the present invention provides an infinitely variable speed control system for underground conveyors implemented based on the fuzzy control principle. Figure 2 For the schematic diagram of the module composition of the infinitely variable speed control system for underground conveyors implemented based on the fuzzy control principle provided in Embodiment 2 of the present invention, as Figure 2 shown, the system includes:

[0120] A three-machine collaborative control module for collecting the operating data of the shearer, hydraulic support, and conveyor and performing control;

[0121] A fuzzy control module for calculating the conveyor speed using fuzzy control;

[0122] A conveyor speed adjustment module for adjusting the speed of the conveyor according to the front and rear conveyors and the working condition information;

[0123] In some embodiments of the present invention, the three-machine collaborative control module includes:

[0124] An equipment networking and data collection module for connecting the shearer, hydraulic support, and conveyor to a unified platform, collecting key data through Internet of Things technology, and realizing real-time monitoring and management of the working face equipment;

[0125] The data transmission and preprocessing module is used to transmit the collected data to the ground central control room and the underground edge computing device through Wi-Fi or 5G network, and perform preprocessing such as noise reduction and compression to ensure data accuracy and transmission efficiency;

[0126] The system integration and function implementation module is used to realize the coordinated control and information interaction of the three machines based on the collected data, construct a fuzzy control path through data sharing, and provide dynamic adjustment capabilities;

[0127] In some embodiments of the present invention, the fuzzy control module includes:

[0128] The fuzzyfication module of variables is used to collect data related to the front conveyor, and fuzzify the data through membership functions to process non-linear and uncertain information;

[0129] The fuzzy inference and rule base construction module is used to define rules based on input variables and calculate the fuzzy set of the conveyor speed through fuzzy inference;

[0130] The defuzzification and speed calculation module is used to convert the fuzzy set into a specific conveyor speed value through the centroid method and send an adjustment instruction to the control system.

[0131] In some embodiments of the present invention, the transfer conveyor speed adjustment module includes:

[0132] The coal quantity estimation module is used to calculate the coal quantity load of the transfer conveyor by using a fuzzy algorithm based on the coal quantity of the front conveyor, the coal quantity of the rear conveyor, and the working condition of the shearer;

[0133] The fuzzy rule base and inference module is used to infer the fuzzy value of the current coal quantity load through fuzzy rules and in combination with the membership degrees of the input variables;

[0134] The speed adjustment and limit correction module is used to adjust the speed of the transfer conveyor according to the coal quantity load, adjust the speed value within the set range, and correct the speed exceeding the interval.

[0135] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0136] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A stepless speed regulation control method for an underground conveyor based on the fuzzy control principle, characterized in that: The method includes the following steps: Step 1: Implement dialogue control among the three machines through Internet of Things technology; Step 2: Achieve dynamic optimization of the front conveyor through the fuzzy control principle; Step 3: Adjust the speed of the transfer conveyor according to the operating conditions of the front and rear conveyors and the shearer.

2. The stepless speed regulation control method for an underground conveyor realized based on the fuzzy control principle according to claim 1, characterized in that: The steps of implementing dialogue control among the three machines through Internet of Things technology include: Deploy sensors to collect the operating data of the shearer, hydraulic support, and conveyor; Upload the collected data to the central control room or edge computing device through Wi-Fi or 5G network; Perform data preprocessing, including noise reduction and compression, and use the processed data for equipment operating status monitoring and control decision-making.

3. The stepless speed regulation control method for an underground conveyor based on the fuzzy control principle according to claim 1, characterized in that: The steps of achieving dynamic optimization of the front conveyor through the fuzzy control principle include: Collect the parameters of the mining height of the working face, the position of the shearer, the cutting current, and the traction speed; Define the membership function of the input variables and calculate the fuzzy output based on the IF-THEN rule base and the min-max inference method; Defuzzify through the centroid method to convert the fuzzy output into the precise speed command of the conveyor; Construct a fuzzy control rule base to map the mining height, cutting current, and traction speed to the speed adjustment range of the conveyor; Dynamically collect the real-time data of the input parameters and optimize the operating speed of the conveyor through the fuzzy inference algorithm; Automatically generate speed adjustment commands and send them to the conveyor control system in real time.

4. The stepless speed regulation control method for an underground conveyor based on the fuzzy control principle according to claim 3, characterized in that: The steps of defining the membership function of the input variables and calculating the fuzzy output based on the IF-THEN rule base and the min-max inference method include: Map the input variables to the fuzzy set through the membership function, and let the membership degree be μ x (x). The membership function of the mining height (h) variable is defined as a triangular membership function: Membership degree of mining height: h low,min is the minimum boundary that the mining height variable h belongs to the fuzzy set of "low mining height". After the input variables are fuzzified, use the rule base to define the relationship between the input and output. The rules adopt the IF-THEN structure, where the premise, that is, the IF part, is the fuzzified combination of the input, and the conclusion, that is, the THEN part, is the fuzzy output. R k :IFμ h (h)∧μ p (p)∧μ I (I)∧μ vcoal (v coal )∧ μ S (S) THEN μ v (v front )。 5. The stepless speed regulation control method for an underground conveyor based on the fuzzy control principle according to claim 1, characterized in that: The steps of adjusting the speed of the transfer conveyor according to the operating conditions of the front and rear conveyors and the shearer include: Estimate the coal quantity of the rear conveyor; Construct the membership function and the fuzzy rule base, and calculate the loaded coal quantity of the transfer conveyor according to the fuzzy inference algorithm; Based on the fuzzy control algorithm of the coal quantity of the rear conveyor, estimate the coal quantity by combining the mining height of the working face, the number of coal caving openings, and the support attitude information; Construct a dynamic amplitude limiting function and optimize the coal flow conveying efficiency through fuzzy inference and real-time data fusion.

6. The stepless speed regulation control method for an underground conveyor based on the fuzzy control principle according to claim 5, characterized in that: The steps of estimating the coal quantity of the rear conveyor include: The estimation of the coal quantity of the rear conveyor is similar to that of the front conveyor, both using the self-learning algorithm and the fuzzy control method. However, the rear conveyor needs to additionally consider the data related to the coal caving process. During the implementation process, first collect the mining height of the working face, the number of coal caving openings, the coal caving time, and obtain the attitude information of the support tail beam and the plow through the support electro-hydraulic control system to calculate the number of supports that are coal caving. Combine these data with the previously established control model and use the fuzzy algorithm to estimate the coal quantity load of the rear conveyor. The result will be used as the basis for adjusting the speed of the conveyor.

7. The stepless speed regulation control method for an underground conveyor based on the fuzzy control principle according to claim 5, characterized in that: The steps of constructing the membership function and the fuzzy rule base and calculating the loaded coal quantity of the transfer conveyor according to the fuzzy inference algorithm include: In the fuzzy algorithm, the input variables include the coal quantity M1 of the front conveyor, the coal quantity M2 of the rear conveyor, and the operating condition C of the shearer. Membership functions are established for each variable. Among them, the membership function of the shearer traction speed v coal is divided into "slow", "medium", and "fast", and a fuzzy rule base is established based on this, IF M1 is large AND v coal is fast THEN MR is large; IF M2 is small AND the cutting current in the middle cutting is low THEN MR is small; IF M1 is medium AND M2 is large THEN MR is large; These rules integrate information from different sources through a fuzzy inference mechanism to obtain the coal quantity MR of the rear conveyor. The coal quantity estimation formula can be expressed as MR = f(M1, M2, C).

8. The stepless speed regulation control method for the underground conveyor realized based on the fuzzy control principle according to claim 5, characterized in that: The steps of constructing the dynamic amplitude-limiting function and optimizing the coal flow transportation efficiency through fuzzy inference and real-time data fusion include: The calculation formula for the speed vR of the transfer conveyor is: vR = g(MR), where g is a function related to the coal quantity MR. Subsequently, the speed range constraint needs to be considered. Assuming the speed range is vmin ≤ vR ≤ vmax, the final speed needs to meet this condition. When it does not meet the interval requirements, it is corrected through the amplitude-limiting function. The speed adjustment relationship can be exemplarily expressed as: When MR ≤ Mlow, vR = vmin; When MR ≥ Mhigh, vR = vmax; When Mlow < MR < Mhigh, vR changes linearly with MR.

9. An infinitely variable speed control system for an underground conveyor based on the fuzzy control principle, characterized in that: The system includes: A three-machine collaborative control module for collecting the operating data of the shearer, hydraulic support, and conveyor and performing control; A fuzzy control module for calculating the conveyor speed using fuzzy control; A transfer conveyor speed adjustment module for adjusting the speed of the transfer conveyor according to the front and rear conveyors and working condition information.

10. The stepless speed regulation control system of the underground conveyor based on the fuzzy control principle according to claim 9, characterized in that: The three-machine collaborative control module includes: An equipment networking and data collection module for connecting the shearer, hydraulic support, and conveyor to a unified platform, collecting key data through Internet of Things technology, and realizing real-time monitoring and management of the working face equipment; A data transmission and preprocessing module for transmitting the collected data to the ground central control room and underground edge computing equipment through Wi-Fi or 5G network for noise reduction and compression; A system integration and function implementation module for realizing the collaborative control and information interaction of the three machines based on the collected data, constructing a fuzzy control path through data sharing, and providing dynamic adjustment capabilities.

Images