Intelligent control method for hydraulic loading pump of coal mill

Abstract

The invention provides an intelligent control method for a hydraulic loading pump of a coal mill, and belongs to the technical field of intelligent control. The method comprises the steps that 1, the working state of the coal mill is collected in real time through a sensor, and original state parameter data are obtained; 2, standardizing the original state parameter data to obtain standard state parameter data, and inputting the standard state parameter data into a fuzzy controller as an input variable; 3, designing a fuzzy controller based on a fuzzy algorithm, performing fuzzy algorithm analysis on the input variables according to a preset fuzzy rule base, and generating fuzzy control decision output; 4, the fuzzy control decision is output and sent to corresponding equipment, and a control instruction is executed; and step 5, configuring the fuzzy controller to a real environment, obtaining real operation data after execution control, and optimizing the fuzzy rule base according to the real operation data. The hydraulic efficiency and stability of the coal mill are improved, operation of the coal mill is optimized, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, and in particular to an intelligent control method for a hydraulic loading pump of a coal mill. Background Art

[0002] At present, coal mill is a widely used equipment in industries such as electricity and steel, mainly used for the preparation of coal powder. In the operation of coal mill, hydraulic loading pump plays a key role, mainly used to control the pressure and load of coal mill, so as to ensure that coal mill can work stably and efficiently. Traditional hydraulic loading pump control methods mostly rely on manual adjustment and simple PID control. Traditional control systems have a long response time and cannot adapt to load changes in time, which easily leads to overload or underload of coal mill.

[0003] Therefore, the present invention proposes an intelligent control method for a hydraulic loading pump of a coal mill. Summary of the invention

[0004] The present invention provides an intelligent control method for a hydraulic loading pump of a coal mill, which is used to collect coal mill working status data in real time through sensors, and input the data into a fuzzy controller after standardized processing; make decisions according to preset rules, generate control instructions and execute them; the data after execution is fed back to the system to optimize the fuzzy rule base, thereby improving the operating efficiency and stability of the coal mill and achieving precise control and self-optimization.

[0005] On the one hand, the present invention provides an intelligent control method for a hydraulic loading pump of a coal mill, comprising:

[0006] Step 1: Collect the working status of the coal mill in real time through sensors to obtain original state parameter data;

[0007] Step 2: Standardize the original state parameter data to obtain the standard state parameter data, and input it into the fuzzy controller as an input variable;

[0008] Step 3: Design a fuzzy controller based on fuzzy algorithm, perform fuzzy algorithm analysis on input variables according to the pre-set fuzzy rule base, and generate fuzzy control decision output;

[0009] Step 4: Send the fuzzy control decision output to the corresponding device and execute the control instruction;

[0010] Step 5: Configure the fuzzy controller to the real environment, obtain the real operating data after executing the control, and optimize the fuzzy rule base based on the real operating data.

[0011] On the other hand, the working status of the coal mill is collected in real time through sensors to obtain the original status parameter data, including:

[0012] According to the operation target requirements of the coal mill hydraulic loading pump, determine the monitoring working status parameter indicators;

[0013] Obtain the factory design drawing of the coal mill, determine the possible installation positions of the sensors according to the working status parameter indicators, and generate a sensor layout diagram;

[0014] The matching degree of each possible installation position is calculated based on the sensor type and working status parameter index, and the possible installation positions of sensors of any sensor type are sorted according to the matching degree, and the possible installation position of the sensor with the highest matching degree is regarded as the optimal position.

[0015] On the other hand, it also includes:

[0016] According to the sensor layout diagram, all optimal positions are assigned unique first numbers, all sensors are assigned unique second numbers, and sensor installation configuration is implemented according to a one-to-one correspondence between the first number and the second number;

[0017] Based on the installed and configured sensors, the original state parameter data of the working state of the coal mill is obtained in real time, and the original interface data is transmitted through the data center interface according to the data protocol.

[0018] On the other hand, the working status of the coal mill is collected in real time through sensors to obtain the original status parameter data, including:

[0019] Convert the original interface data into preset standard format data based on the parsing library to obtain the first parameter data under any parameter type;

[0020] Formatting the first parameter data according to a corresponding standard unit interval to obtain second parameter data, and standardizing the second parameter data based on a dimensional range to obtain standard parameter data of the parameter type;

[0021] According to the input format of the fuzzy controller, the standard parameter data is converted and filled in to obtain the standard input variables and input them into the fuzzy controller.

[0022] On the other hand, according to the pre-set fuzzy rule base, the input variables are analyzed by fuzzy algorithm, including:

[0023] Get the historical input variables from the history library, and define the fuzzy membership of any historical input variable according to the triangle membership function:

[0024] Among them, mu neg Indicates the membership of the historical input variable in the negative fuzzy set, mu zero represents the membership of the historical input variable in the zero fuzzy set, mu posrepresents the membership degree of the historical input variable in the positive fuzzy set, [T 1 ,T 2 ] represents the standard range of triangular membership, t represents any historical input variable;

[0025] According to the three membership degrees of historical input variables, the fuzzy rules of the three historical output variables are matched, and the historical output variables and the if-then rules of the historical output variables are adjusted and constructed based on the experience knowledge of the expert database;

[0026] A fuzzy rule base is constructed based on if-then rules, where each row represents a fuzzy rule, and the fuzzy membership of the historical input variable of each fuzzy rule and the fuzzy membership of the corresponding historical output variable are defined.

[0027] On the other hand, the fuzzy controller based on fuzzy algorithm is designed. According to the preset fuzzy rule base, the input variables are analyzed by fuzzy algorithm to generate fuzzy control decision output, including:

[0028] Inputting input variables into the fuzzy controller, and comparing the input rules of the fuzzy rule base, determining the membership range to which the input variables belong, obtaining the conclusions of the fuzzy rule base accordingly, generating an output fuzzy set, and obtaining the membership of any conclusion in the output fuzzy set through fuzzy operation;

[0029] Defuzzify the output variable: First, determine the centroid of the output fuzzy set based on the fuzziness of the output variable and convert it into a clear output signal;

[0030] Among them, y represents the clear output signal, x i represents the membership value of the i-th conclusion in the output fuzzy set, x ei0 Represents the minimum error membership value, Δ f represents the error coefficient, n represents the output fuzzy set has a total of n conclusions, m represents the centroid conversion coefficient, represents the center of gravity of the output fuzzy set, b represents the weight coefficient of the right value of the fuzzy set range, a represents the weight coefficient of the left value of the fuzzy set range; C represents the adjustment coefficient;

[0031] Generate fuzzy control decision output of corresponding equipment according to clear output signal.

[0032] On the other hand, the fuzzy control decision output is sent to the corresponding device and the control instructions are executed, including:

[0033] Matching the fuzzy control decision output to a corresponding device, using preset standard PID parameters in the fuzzy controller to adjust the fuzzy control decision to obtain a control signal;

[0034] The control signal is sent to the device according to the communication protocol and executed, feedback information is monitored in real time, PID parameters are adjusted according to the feedback information, and the final PID parameters of the fuzzy controller for the device are obtained.

[0035] On the other hand, the fuzzy controller is configured in the real environment to obtain the real operation data after execution control, and the fuzzy rule base is optimized according to the operation data, including:

[0036] Set the initial parameters of the fuzzy controller, configure the fuzzy controller in the real environment, start the fuzzy controller, and control the hydraulic loading pump of the coal mill in the real environment;

[0037] Record the response of the fuzzy controller to the target setting value and record the actual operation data;

[0038] Define the control performance index according to the real operation data, determine whether the control performance index is within the standard index range, and obtain the defect factors of the fuzzy controller;

[0039] Adjust fuzzy rules and PID parameters according to the defect factors of fuzzy controller, and optimize fuzzy rule base and PID algorithm.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides an intelligent control method for a hydraulic loading pump of a coal mill, which is used to collect coal mill working status data in real time through sensors, and input the data into a fuzzy controller after standardized processing; make decisions according to preset rules, generate control instructions and execute them; the data after execution is fed back to the system to optimize the fuzzy rule base, thereby improving the operating efficiency and stability of the coal mill and achieving precise control and self-optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of an intelligent control method of a coal mill hydraulic loading pump provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment 1:

[0046] like Figure 1 As shown, an intelligent control method for a coal mill hydraulic loading pump provided by an embodiment of the present invention includes:

[0047] Step 1: Collect the working status of the coal mill in real time through sensors to obtain original state parameter data;

[0048] Step 2: Standardize the original state parameter data to obtain the standard state parameter data, and input it into the fuzzy controller as an input variable;

[0049] Step 3: Design a fuzzy controller based on fuzzy algorithm, perform fuzzy algorithm analysis on input variables according to the pre-set fuzzy rule base, and generate fuzzy control decision output;

[0050] Step 4: Send the fuzzy control decision output to the corresponding device and execute the control instruction;

[0051] Step 5: Configure the fuzzy controller to the real environment, obtain the real operating data after executing the control, and optimize the fuzzy rule base based on the real operating data.

[0052] In this embodiment, the sensor is a device used to monitor and collect physical quantities or environmental parameters in real time, and the types include: pressure, temperature, flow, vibration, etc.

[0053] In this embodiment, the coal mill is a device used for crushing and grinding coal. Its function is to crush raw coal into fine powder. It consists of the following main parts: a grinding part, a transmission system, a wind selection system, and a slag discharge system.

[0054] In this embodiment, the working state refers to various key parameters and indicators of the coal mill during operation, including: operating speed, pressure, particle size, temperature, load power, etc.

[0055] In this embodiment, the original state parameter data refers to various monitoring data collected in real time by sensors during the operation of the coal mill, including: operating speed, pressure, particle size, temperature, load power, etc.

[0056] In this embodiment, standardization is to convert data of different scales into a unified scale, including steps such as data cleaning, unit formatting, and data scaling.

[0057] In this embodiment, the standard state parameter data refers to the coal mill working state parameter data after standardization.

[0058] In this embodiment, the fuzzy controller is a control system based on fuzzy logic, which is used to deal with situations of uncertainty, ambiguity or incomplete information.

[0059] In this embodiment, the input variables are real-time parameter data reflecting the working status of the coal mill, including key performance indicators such as temperature, pressure, vibration, rotation speed, load, and coal quality.

[0060] In this embodiment, the fuzzy algorithm is a calculation method based on fuzzy set theory and fuzzy logic, which is achieved by expanding traditional precise logic operations into fuzzy logic operations.

[0061] In this embodiment, the fuzzy rule base is one of the core components in the fuzzy control system, consisting of a set of fuzzy rules that describe the relationship between the system input and output, such as IF input variable 1 is A1 AND input variable 2 is A2 THEN output variable is B.

[0062] In this embodiment, the fuzzy control decision output is a specific adjustment value generated by the fuzzy controller through a fuzzy reasoning process according to input variables and a preset fuzzy rule base.

[0063] In this embodiment, the equipment refers to a coal mill and its related actuators or control systems, such as a motor, a fan, a valve, a cooling system, and the like.

[0064] In this embodiment, the control instruction is the output result of the fuzzy controller after processing according to the fuzzy algorithm, and specifically instructs the actuator how to adjust certain parameters of the system to achieve a predetermined control target.

[0065] In this embodiment, the real environment refers to the physical environment in which the coal mill and its control system actually operate.

[0066] In this embodiment, the real operation data refers to the data reflecting the current operation status of the equipment collected by other monitoring equipment during the actual operation of the coal mill, including: temperature, pressure, speed, load, vibration, etc.

[0067] The working principle and beneficial effects of the above technical solution are: collecting data through sensors and inputting them into the fuzzy controller, using fuzzy algorithms to generate control decisions and execute them, optimizing the rule base through real-time feedback, achieving efficient and stable operation of the coal mill, improving control accuracy, adapting to complex working conditions, and having self-optimization and energy-saving effects.

[0068] Embodiment 2:

[0069] On the basis of the above-mentioned embodiment 1, the working state of the coal mill is collected in real time by sensors to obtain original state parameter data, including:

[0070] According to the operation target requirements of the coal mill hydraulic loading pump, determine the monitoring working status parameter indicators;

[0071] Obtain the factory design drawing of the coal mill, determine the possible installation positions of the sensors according to the working status parameter indicators, and generate a sensor layout diagram;

[0072] The matching degree of each possible installation position is calculated based on the sensor type and working status parameter index, and the possible installation positions of sensors of any sensor type are sorted according to the matching degree, and the possible installation position of the sensor with the highest matching degree is regarded as the optimal position.

[0073] In this embodiment, the operation target requirement refers to the specific performance target that the system needs to achieve, including: stability, security, efficiency, etc.

[0074] In this embodiment, the coal mill hydraulic loading pump is an important hydraulic driving device in the coal mill system, which is mainly used to provide the hydraulic power required by the coal mill to realize the compacting, loading and regulating functions of the coal mill.

[0075] In this embodiment, the working status parameter index refers to the key physical quantities used to monitor and evaluate the system performance, health status and working quality in the control system of the coal mill hydraulic loading pump, including: temperature, pressure, flow, vibration, etc.

[0076] In this embodiment, the factory design drawing refers to a detailed engineering drawing produced by the manufacturer or design unit of the equipment according to design requirements and standards, which includes detailed information such as the structure, layout, component size, connection method, electrical configuration, and installation location of the equipment.

[0077] In this embodiment, the possible installation position refers to an area or location where the sensor can be installed in the factory design drawing of the device or in the actual operation of the device.

[0078] In this embodiment, the sensor layout diagram is a diagram that determines the installation location, type, and configuration of the sensors according to the design requirements and monitoring objectives of the equipment.

[0079] In this embodiment, the matching degree refers to the degree of adaptation between the sensor installation position and the working status parameter index.

[0080] In this embodiment, the optimal position refers to the most suitable installation position selected based on the matching degree calculation between the working state parameter index and the sensor type.

[0081] The working principle and beneficial effects of the above technical solution are: by analyzing the operating requirements of the hydraulic loading pump of the coal mill, determining the key monitoring parameters, and optimizing the sensor layout based on the design diagram. Using statistical methods to calculate the matching degree and sort the sensor positions, accurate monitoring can be achieved, improving system performance and data acquisition accuracy.

[0082] Embodiment 3:

[0083] Based on the above embodiment 2, it also includes:

[0084] According to the sensor layout diagram, all optimal positions are assigned unique first numbers, all sensors are assigned unique second numbers, and sensor installation configuration is implemented according to a one-to-one correspondence between the first number and the second number;

[0085] Based on the installed and configured sensors, the original state parameter data of the working state of the coal mill is obtained in real time, and the original interface data is transmitted through the data center interface according to the data protocol.

[0086] In this embodiment, the first number is a unique number used to identify the installation location of the sensor.

[0087] In this embodiment, the second number is a unique number used to identify the sensor.

[0088] In this embodiment, the data center interface refers to an interface for communicating with the sensor system installed in the coal mill monitoring system, which is used to receive, process and transmit the original working status parameter data collected by the sensor. The protocols used include: API, MQTT protocol, OPC UA, etc.

[0089] The working principle and beneficial effects of the above technical solution are: by configuring a unique number for each sensor, a one-to-one correspondence between the sensor and the optimal position is achieved, ensuring accurate installation and data collection. The sensor obtains the working status data of the coal mill in real time and transmits it to the data center through the data protocol, improving the accuracy and real-time performance of data collection.

[0090] Embodiment 4:

[0091] On the basis of the above-mentioned embodiment 2, the working state of the coal mill is collected in real time by sensors to obtain the original state parameter data, including:

[0092] Convert the original interface data into preset standard format data based on the parsing library to obtain the first parameter data under any parameter type;

[0093] Formatting the first parameter data according to a corresponding standard unit interval to obtain second parameter data, and standardizing the second parameter data based on a dimensional range to obtain standard parameter data of the parameter type;

[0094] According to the input format of the fuzzy controller, the standard parameter data is converted and filled in to obtain the standard input variables and input them into the fuzzy controller.

[0095] In this embodiment, the parsing library refers to a tool for parsing data, which decodes and disassembles the original data according to preset rules or formats and converts the original data into data in a structured format.

[0096] In this embodiment, the preset standard format data refers to the data format after parsing and conversion when processing the original interface data.

[0097] In this embodiment, the first parameter data refers to a parameter extracted from the original interface data and its value after preliminary analysis.

[0098] In this embodiment, the standard unit interval is a standard interval used when performing unit conversion and dimension standardization during data processing.

[0099] In this embodiment, unit formatting is a process of converting the value of a parameter in the original data from one unit system to another standard unit.

[0100] In this embodiment, the second parameter data refers to data obtained after the unit formatting step.

[0101] In this embodiment, the dimension range refers to the numerical range used to normalize data in the standardization process of a specific physical quantity or parameter.

[0102] In this embodiment, the input format refers to the form of standardized data accepted by the fuzzy controller.

[0103] The working principle and beneficial effect of the above technical solution are: by parsing the original data and performing unit formatting and standardization processing, the data is converted into standard input variables that meet the input requirements of the fuzzy controller. Through this data processing process, the accuracy and consistency of the data are ensured, and the control accuracy and stability of the system are effectively improved.

[0104] Embodiment 5:

[0105] On the basis of the above-mentioned embodiment 1, the pre-set fuzzy rule base performs fuzzy algorithm analysis on the input variables, including:

[0106] Get the historical input variables from the history library, and define the fuzzy membership of any historical input variable according to the triangle membership function:

[0107] Among them, mu neg Indicates the membership of the historical input variable in the negative fuzzy set, mu zero represents the membership of the historical input variable in the zero fuzzy set, mu pos represents the membership degree of the historical input variable in the positive fuzzy set, [T 1 ,T 2 ] represents the standard range of triangular membership, t represents any historical input variable;

[0108] According to the three membership degrees of historical input variables, the fuzzy rules of the three historical output variables are matched, and the historical output variables and the if-then rules of the historical output variables are adjusted and constructed based on the experience knowledge of the expert database;

[0109] A fuzzy rule base is constructed based on if-then rules, where each row represents a fuzzy rule, and the fuzzy membership of the historical input variable of each fuzzy rule and the fuzzy membership of the corresponding historical output variable are defined.

[0110] In this embodiment, the history base refers to a collection for storing historical input and output data of the fuzzy rule base.

[0111] In this embodiment, the historical input variable refers to the input signal of the system in the past period of time, such as temperature, pressure, speed, humidity, flow rate, etc.

[0112] In this embodiment, the triangle membership function is a membership function commonly used in fuzzy set theory, and is used to represent the membership of an element in a fuzzy set.

[0113] In this embodiment, the fuzzy membership degree indicates the strength of the membership relationship of the element to the fuzzy set.

[0114] In this embodiment, the negative fuzzy set refers to a membership set describing a certain input variable in a "negative" state, and the elements reflect the membership of the input variable in a lower range.

[0115] In this embodiment, the zero fuzzy set refers to a fuzzy set describing an intermediate, balanced state, representing the degree of membership of a variable in a "neutral" or "average" state.

[0116] In this embodiment, the positive fuzzy set is a set describing the membership of a certain input variable in a "positive" state, and the elements reflect the membership of the input variable in a higher range.

[0117] In this embodiment, the if-then rule consists of a condition part and a conclusion part, which describes how to derive the membership degree of the historical output variable according to the membership degree of the historical input variable.

[0118] The working principle and beneficial effects of the above technical solution are as follows: the solution fuzzifies the historical input variables through the triangular membership function, adjusts the if-then rules in combination with expert knowledge, and builds a fuzzy rule base. In this way, the historical input-output relationship is accurately matched, the control strategy of the hydraulic loading pump of the coal mill is optimized, and the adaptive ability and stability of the system are improved.

[0119] Embodiment 6:

[0120] On the basis of the above-mentioned embodiment 5, a fuzzy controller based on a fuzzy algorithm is designed. According to a preset fuzzy rule base, a fuzzy algorithm analysis is performed on the input variables to generate a fuzzy control decision output, including:

[0121] Inputting input variables into the fuzzy controller, and comparing the input rules of the fuzzy rule base, determining the membership range to which the input variables belong, obtaining the conclusions of the fuzzy rule base accordingly, generating an output fuzzy set, and obtaining the membership of any conclusion in the output fuzzy set through fuzzy operation;

[0122] Defuzzify the output variable: First, determine the centroid of the output fuzzy set based on the fuzziness of the output variable and convert it into a clear output signal;

[0123] Among them, y represents the clear output signal, x i represents the membership value of the i-th conclusion in the output fuzzy set, x eio Represents the minimum error membership value, Δ f represents the error coefficient, n represents the output fuzzy set has a total of n conclusions, m represents the centroid conversion coefficient, represents the center of gravity of the output fuzzy set, b represents the weight coefficient of the right value of the fuzzy set range, a represents the weight coefficient of the left value of the fuzzy set range; C represents the adjustment coefficient;

[0124] Generate fuzzy control decision output of corresponding equipment according to clear output signal.

[0125] In this embodiment, the input rule refers to a fuzzy rule for deriving a control output based on the membership degree of the input variable.

[0126] In this embodiment, the conclusion is an output fuzzy result obtained according to the fuzzy rule base.

[0127] In this embodiment, the output fuzzy set is a set consisting of all output fuzzy results.

[0128] In this embodiment, the fuzzy operation is a mathematical operation process used to process fuzzy sets in a fuzzy control system.

[0129] In this embodiment, defuzzification is to convert the fuzzy set (ie, the output of fuzzy reasoning) into an accurate and specific control decision value.

[0130] In this embodiment, the clear output signal refers to an accurate control signal obtained by defuzzifying the output fuzzy set after being processed by the fuzzy controller.

[0131] In this embodiment, the fuzziness refers to the membership distribution of an element in a fuzzy set, reflecting the degree of fuzziness of the element.

[0132] In this embodiment, the centroid position refers to the weighted average position of the output fuzzy set.

[0133] In this embodiment, the working principle and beneficial effects of the above technical solution are: fuzzy processing is performed on the input variables through the fuzzy controller, a fuzzy output set is generated by matching the rule base, a clear signal is obtained through defuzzification, and it is converted into a control decision, the equipment control is accurately adjusted, and the accuracy and stability of the system response are improved.

[0134] Embodiment 7:

[0135] On the basis of the above-mentioned embodiment 1, the fuzzy control decision output is sent to the corresponding device and the control instruction is executed, including:

[0136] Matching the fuzzy control decision output to a corresponding device, using preset standard PID parameters in the fuzzy controller to adjust the fuzzy control decision to obtain a control signal;

[0137] The control signal is sent to the device according to the communication protocol and executed, feedback information is monitored in real time, PID parameters are adjusted according to the feedback information, and the final PID parameters of the fuzzy controller for the device are obtained.

[0138] In this embodiment, the preset standard PID parameters refer to the preliminarily set PID controller parameters, which are used to make preliminary adjustments to the control process. The P parameter determines the response speed or amplitude of the controller, the I parameter is used to eliminate steady-state errors, and the D parameter is used to predict the trend of error changes.

[0139] In this embodiment, the control signal refers to a control command output by the fuzzy controller, which is used to adjust the behavior or state of the controlled device.

[0140] In this embodiment, the communication protocol refers to the rules and standards for transmitting control signals from the fuzzy controller to the device and receiving feedback information from the device.

[0141] In this embodiment, the feedback information refers to the actual output or status data transmitted back from the controlled device, reflecting the current operating status or actual response of the device.

[0142] In this embodiment, the final PID parameters refer to control parameters optimized for device output after the fuzzy controller performs real-time adjustment on the device.

[0143] The working principle and beneficial effects of the above technical solution are: generating control signals through fuzzy control decision-making, using standard PID parameter adjustment, real-time monitoring of feedback information to optimize PID parameters, improving equipment control accuracy and response speed, and enhancing the system's adaptability and robustness.

[0144] Embodiment 8:

[0145] On the basis of the above-mentioned embodiment 1, the fuzzy controller is configured in a real environment to obtain the real operation data after the execution control, and the fuzzy rule base is optimized according to the real operation data, including:

[0146] Set the initial parameters of the fuzzy controller, configure the fuzzy controller in the real environment, start the fuzzy controller, and control the hydraulic loading pump of the coal mill in the real environment;

[0147] Record the response of the fuzzy controller to the target setting value and record the actual operation data;

[0148] Define the control performance index according to the real operation data, determine whether the control performance index is within the standard index range, and obtain the defect factors of the fuzzy controller;

[0149] Adjust fuzzy rules and PID parameters according to the defect factors of fuzzy controller, and optimize fuzzy rule base and PID algorithm.

[0150] In this embodiment, the initial parameters are a set of values ​​that need to be set when the system starts running, and they define the working behavior of the controller, including fuzzy rules and PID parameters.

[0151] In this embodiment, the target setting value refers to the ideal control target achieved by the coal mill hydraulic loading pump, including: pressure, temperature, response time, etc.

[0152] In this embodiment, the control performance index is a standard used to evaluate the response of the control system to the target set value during actual operation, including: deviation, deviation change rate, integral deviation, system stability, etc.

[0153] In this embodiment, the standard index range is a reference value range used to evaluate the performance of the fuzzy controller.

[0154] In this embodiment, the defect factors include: inaccurate fuzzy rule base, unreasonable PID parameters, mismatch of controller response speed, etc.

[0155] The working principle and beneficial effects of the above technical solution are: by applying the fuzzy controller in a real environment to control the coal mill hydraulic loading pump, monitoring and analyzing the control response, identifying and adjusting defect factors, optimizing the fuzzy rules and PID parameters, and improving the control performance and system stability.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent control method for a hydraulic loading pump of a coal mill, characterized in that: include: Step 1: Collect the working status of the coal mill in real time through sensors to obtain original state parameter data; Step 2: Standardize the original state parameter data to obtain the standard state parameter data, and input it into the fuzzy controller as an input variable; Step 3: Design a fuzzy controller based on fuzzy algorithm, perform fuzzy algorithm analysis on input variables according to the pre-set fuzzy rule base, and generate fuzzy control decision output; Step 4: Send the fuzzy control decision output to the corresponding device and execute the control instruction; Step 5: Configure the fuzzy controller to the real environment, obtain the real operating data after executing the control, and optimize the fuzzy rule base based on the real operating data.

2. The intelligent control method of a coal mill hydraulic loading pump according to claim 1 is characterized in that: The working status of the coal mill is collected in real time through sensors to obtain the original status parameter data, including: According to the operation target requirements of the coal mill hydraulic loading pump, determine the monitoring working status parameter indicators; Obtain the factory design drawing of the coal mill, determine the possible installation positions of the sensors according to the working status parameter indicators, and generate a sensor layout diagram; The matching degree of each possible installation position is calculated based on the sensor type and working status parameter index, and the possible installation positions of sensors of any sensor type are sorted according to the matching degree, and the possible installation position of the sensor with the highest matching degree is regarded as the optimal position.

3. The intelligent control method of a coal mill hydraulic loading pump according to claim 2 is characterized in that: Also includes: According to the sensor layout diagram, all optimal positions are assigned unique first numbers, all sensors are assigned unique second numbers, and sensor installation configuration is implemented according to a one-to-one correspondence between the first number and the second number; Based on the installed and configured sensors, the original state parameter data of the working state of the coal mill is obtained in real time, and the original interface data is transmitted through the data center interface according to the data protocol.

4. The intelligent control method of a coal mill hydraulic loading pump according to claim 2 is characterized in that: The working status of the coal mill is collected in real time through sensors to obtain the original status parameter data, including: Convert the original interface data into preset standard format data based on the parsing library to obtain the first parameter data under any parameter type; Formatting the first parameter data according to a corresponding standard unit interval to obtain second parameter data, and standardizing the second parameter data based on a dimensional range to obtain standard parameter data of the parameter type; According to the input format of the fuzzy controller, the standard parameter data is converted and filled in to obtain the standard input variables and input them into the fuzzy controller.

5. The intelligent control method of a coal mill hydraulic loading pump according to claim 1, characterized in that: According to the pre-set fuzzy rule base, the input variables are analyzed by fuzzy algorithm, including: Get the historical input variables from the history library, and define the fuzzy membership of any historical input variable according to the triangle membership function: Among them, mu neg Indicates the membership of the historical input variable in the negative fuzzy set, mu zero represents the membership of the historical input variable in the zero fuzzy set, mu pos represents the membership of the historical input variable in the positive fuzzy set, [T1, T2] represents the standard range of triangular membership, and t represents any historical input variable; According to the three membership degrees of historical input variables, the fuzzy rules of the three historical output variables are matched, and the historical output variables and the if-then rules of the historical output variables are adjusted and constructed based on the experience knowledge of the expert database; A fuzzy rule base is constructed based on if-then rules, where each row represents a fuzzy rule, and the fuzzy membership of the historical input variable of each fuzzy rule and the fuzzy membership of the corresponding historical output variable are defined.

6. The intelligent control method of a coal mill hydraulic loading pump according to claim 5, characterized in that: Design a fuzzy controller based on fuzzy algorithm, perform fuzzy algorithm analysis on input variables according to the pre-set fuzzy rule base, and generate fuzzy control decision output, including: Inputting input variables into the fuzzy controller, and comparing the input rules of the fuzzy rule base, determining the membership range to which the input variables belong, obtaining the conclusions of the fuzzy rule base accordingly, generating an output fuzzy set, and obtaining the membership of any conclusion in the output fuzzy set through fuzzy operation; Defuzzify the output variable: First, determine the centroid of the output fuzzy set based on the fuzziness of the output variable and convert it into a clear output signal; Among them, y represents the clear output signal, x i represents the membership value of the i-th conclusion in the output fuzzy set, x ei0 Represents the minimum error membership value, Δ f represents the error coefficient, n represents the output fuzzy set has a total of n conclusions, m represents the centroid conversion coefficient, represents the center of gravity of the output fuzzy set, b represents the weight coefficient of the right value of the fuzzy set range, a represents the weight coefficient of the left value of the fuzzy set range; C represents the adjustment coefficient; Generate fuzzy control decision output of corresponding equipment according to clear output signal.

7. The intelligent control method of a coal mill hydraulic loading pump according to claim 1, characterized in that: Send the fuzzy control decision output to the corresponding device and execute the control instructions, including: Matching the fuzzy control decision output to a corresponding device, using preset standard PID parameters in the fuzzy controller to adjust the fuzzy control decision to obtain a control signal; The control signal is sent to the device according to the communication protocol and executed, feedback information is monitored in real time, PID parameters are adjusted according to the feedback information, and the final PID parameters of the fuzzy controller for the device are obtained.

8. The intelligent control method for a coal mill hydraulic loading pump according to claim 1, characterized in that: Configure the fuzzy controller to the real environment, obtain the real operation data after execution control, and optimize the fuzzy rule base based on the operation data, including: Set the initial parameters of the fuzzy controller, configure the fuzzy controller in the real environment, start the fuzzy controller, and control the hydraulic loading pump of the coal mill in the real environment; Record the response of the fuzzy controller to the target setting value and record the actual operation data; Define the control performance index according to the real operation data, determine whether the control performance index is within the standard index range, and obtain the defect factors of the fuzzy controller; Adjust fuzzy rules and PID parameters according to the defect factors of fuzzy controller, and optimize fuzzy rule base and PID algorithm.

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