Rotary kiln temperature gradient optimization method and system based on big data

Abstract

The invention discloses a rotary kiln temperature gradient optimization method and system based on big data, and particularly relates to the technical field of big data, and the method comprises the steps: S1, determining a single sub-time monitoring region and a sub-area monitoring region; s2, monitoring data in the rotary kiln in real time, and collecting a first rotary kiln key information value; s3, constructing a parameter preprocessing model, and performing data preprocessing on the process operation parameters and the equipment state parameters to obtain a second rotary kiln key information value; s4, constructing a data analysis model, and performing comprehensive analysis processing on the second rotary kiln key information value to obtain a third rotary kiln key information value; s5, constructing a temperature gradient optimization model, formulating and optimizing a temperature gradient strategy according to the key information value of the third rotary kiln, and adjusting a set value of temperature control equipment in real time; and S6, displaying various parameter information in the rotary kiln in real time through a visual interface, and synchronously sending the parameter information to a user information end. And the accuracy and the reliability of temperature gradient optimization are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and more specifically, to a rotary kiln temperature gradient optimization method and system based on big data. Background Art

[0002] In the process of industrial production such as heat treatment, material preparation and food processing, temperature gradient is a key factor affecting product quality and performance. Accurate temperature gradient control can ensure product consistency and stability, improve production efficiency and product quality. With the rapid development of information technology, big data technology has been widely used in various fields. Big data technology can process and analyze massive data, mine the laws and patterns in the data, and provide new means and methods for optimizing temperature gradients. Rotary kiln is a key heat treatment equipment in the fields of industrial metallurgy, cement, lime, carbon calcination, etc. The precise control of its internal temperature field directly affects product quality and production efficiency. Traditional rotary kiln temperature control often faces the problems of low accuracy and slow response time, which affects production efficiency and may cause fluctuations in product quality.

[0003] The traditional temperature gradient optimization method and system include data acquisition, data processing and analysis, optimization control strategy formulation, and system implementation and operation. Data acquisition is used to obtain temperature data at different locations in the kiln in real time and perform data preprocessing on the data; data processing and analysis fuses data from different sources and extracts key features from the data set; optimization control strategy formulation is used to establish a mathematical model between temperature gradient and operating parameters based on the results of big data analysis, and formulate a specific temperature gradient optimization control strategy; system implementation and operation optimization control strategy is integrated into the control system of the rotary kiln to achieve precise control of the temperature gradient in the kiln, monitor the temperature in real time, and make timely adjustments. Using big data analysis technology to achieve precise control of temperature gradients, improve production efficiency and product quality, and effectively reduce energy consumption and production costs.

[0004] However, in actual use, it still has some shortcomings, such as the collected data is not comprehensive enough. The data collected by the traditional temperature gradient optimization system in the rotary kiln is not comprehensive enough and cannot accurately reflect the real-time situation in the rotary kiln, resulting in inaccurate optimization of the temperature gradient; the degree of automation is low. The traditional temperature gradient optimization system requires a lot of manual operation and monitoring, which increases labor costs and reduces the stability and reliability of the system. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a rotary kiln temperature gradient optimization method and system based on big data, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solution: a rotary kiln temperature gradient optimization method based on big data, comprising: S1: Determine a single sub-time monitoring area and a sub-area monitoring area, divide the time for monitoring the rotary kiln into single sub-time monitoring areas according to equal time lengths, and mark them as 1, 2, ..., i, ..., n in sequence, and divide the detection area of ​​the rotary kiln into single sub-area monitoring areas according to equal areas, and mark them as 1, 2, ..., j, ..., k in sequence; S2: real-time monitoring of data in the rotary kiln, collecting key information values ​​of the first rotary kiln, wherein the key information values ​​of the first rotary kiln include process operation parameters and equipment status parameters, and uploading them to the system operation database; S3: construct a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, obtain the second rotary kiln key information value, the second rotary kiln key information value includes the kiln temperature evaluation value, operation control evaluation value, equipment vibration evaluation value and equipment performance evaluation value, and upload them to the system operation database; S4: constructing a data analysis model, performing comprehensive analysis and processing on the key information value of the second rotary kiln, wherein the comprehensive analysis and processing is used to obtain the key information value of the third rotary kiln, and uploading it to the system operation database; S5: Construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the key information value of the third rotary kiln, and adjust the set value of the temperature control equipment in real time; S6: Various parameter information inside the rotary kiln is displayed in real time through a visual interface, and is sent to the user information terminal synchronously.

[0007] Preferably, the process operating parameters include kiln temperature parameters and operation control parameters. The kiln temperature parameters specifically include real-time temperature, temperature gradient value and heat loss, which are marked as T, ΔT and Q respectively; the operation control parameters specifically include coal feed amount, kiln speed, feed amount and air volume, which are marked as B, v, F and L respectively.

[0008] Preferably, the equipment status parameters include equipment vibration parameters and equipment performance parameters, and the equipment vibration parameters specifically include kiln drum vibration amplitude, kiln drum vibration frequency and supporting roller vibration amplitude, which are marked as A and B respectively. k , f and A r ; Equipment performance parameters include the inner diameter of the cylinder, the length of the cylinder, the slope and the number of supports, which are marked as L i , L c , η and N.

[0009] Preferably, the steps of constructing the parameter preprocessing model and the data analysis model are: A1: Collect multiple sets of rotary kiln thermal data from the rotary kiln database and perform data cleaning on the collected data; A2: Extract the features related to temperature gradient from the collected rotary kiln thermal data and perform feature screening; A3: Normalize, reduce the dimension of the data, and transform the data. Select a model and train it based on the data characteristics and optimization goals.

[0010] Preferably, the kiln temperature evaluation value and the operation control evaluation value are obtained by importing the equipment vibration parameters and the equipment performance parameters into the parameter preprocessing model. The kiln temperature evaluation value is specifically expressed as: , y1 represents the evaluation value of the kiln temperature of the target monitoring rotary kiln, T ij represents the real-time temperature of the jth sub-area monitoring area of ​​the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, ΔT represents the temperature gradient value of the target monitoring rotary kiln, Q represents the heat loss of the target monitoring rotary kiln, ΔTi represents the temperature gradient value of the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, α1, α2 and α3 represent the influence coefficients of the real-time temperature, temperature gradient value and heat loss on the temperature evaluation value in the kiln respectively; the operation control evaluation value is specifically expressed as: , y2 represents the operation control evaluation value of the target monitoring rotary kiln, B represents the coal feeding amount of the target monitoring rotary kiln, v represents the kiln speed of the target monitoring rotary kiln, F represents the feeding amount of the target monitoring rotary kiln, L represents the air volume of the target monitoring rotary kiln, β1, β2, β3 and β4 represent the influence coefficients of coal feeding amount, kiln speed, feeding amount and air volume on the operation control evaluation value respectively, and b1, b2, b3 and b4 are known constants.

[0011] Preferably, the equipment vibration evaluation value and the equipment performance evaluation value are obtained by importing the equipment vibration parameters and the equipment performance parameters into a parameter preprocessing model, and the equipment vibration evaluation value is specifically expressed as: , y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, A k represents the vibration amplitude of the rotary kiln drum of the target monitoring rotary kiln, f represents the vibration frequency of the rotary kiln drum of the target monitoring rotary kiln, A r It indicates the vibration amplitude of the supporting wheel of the target monitoring rotary kiln. ε1, ε2 and ε3 respectively indicate the influence coefficients of the vibration amplitude of the kiln drum, the vibration frequency of the kiln drum and the vibration amplitude of the supporting wheel on the vibration evaluation value of the equipment. The equipment performance evaluation value is specifically expressed as: , y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, L i Indicates the inner diameter of the rotary kiln to be monitored, L crepresents the cylinder length of the target monitoring rotary kiln, η represents the inclination of the target monitoring rotary kiln, N represents the number of supports of the target monitoring rotary kiln, μ1, μ2, μ3 and μ4 represent the influence coefficients of the cylinder inner diameter, cylinder length, inclination and number of supports on the equipment performance evaluation value respectively, and c1, c2, c3 and c4 are known constants.

[0012] Preferably, the third rotary kiln key information value specifically includes a process operation parameter evaluation value, an equipment status parameter evaluation value and a rotary kiln comprehensive evaluation index, and the process operation parameter evaluation value is specifically expressed as: ,y c represents the process operation parameter evaluation value of the target monitoring rotary kiln, y1 represents the kiln temperature evaluation value of the target monitoring rotary kiln, y2 represents the operation control evaluation value of the target monitoring rotary kiln, and y 1标 Indicates the target monitoring rotary kiln temperature evaluation standard value, y 2标 It represents the operation control evaluation standard value of the target monitoring rotary kiln, ρ1 and ρ2 represent the influence coefficients of the kiln temperature evaluation value and the operation control evaluation value on the process operation parameter evaluation value respectively; the equipment status parameter evaluation value is specifically expressed as: ,y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, and y 3标 Indicates the equipment vibration evaluation standard value of the target monitoring rotary kiln, y 4标 It represents the equipment performance evaluation standard value of the target monitoring rotary kiln, ρ3 and ρ4 represent the influence coefficients of the equipment vibration evaluation value and the equipment performance evaluation value on the equipment status parameter evaluation value respectively; the comprehensive evaluation index of the rotary kiln is specifically expressed as: , φ represents the comprehensive evaluation index of the rotary kiln of the target monitoring rotary kiln, y c represents the process operation parameter evaluation value of the target monitoring rotary kiln, y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y c标 represents the standard value of the process operation parameter evaluation of the target monitoring rotary kiln, y e标 It indicates the standard value of equipment status parameter evaluation of the target monitored rotary kiln.

[0013] Preferably, the specific steps of constructing the temperature gradient optimization model are: B1: Collect the key information value of the first rotary kiln of multiple groups of rotary kilns under standard conditions, and calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value; B2: Compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to determine the operating status of the target monitored rotary kiln; B3: According to the comparison results of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value, the temperature gradient strategy is adjusted in real time.

[0014] Preferably, a rotary kiln temperature gradient optimization system based on big data includes a system operation database, a system central processor and a user information terminal, and also includes: a monitoring area determination module, a data acquisition module, a data preprocessing module, a data analysis module, a temperature gradient optimization setting module and a human-computer interaction module; The system operation database includes all data information of the temperature gradient optimization system and collects data information output by each module in real time; the system central processor is used to centrally control the information text instructions output by each module; the user information terminal is a device for receiving information output by the temperature gradient optimization system; The monitoring area determination module is used to determine a single sub-time monitoring area and a single sub-area monitoring area; The data acquisition module is used to collect process operation parameters and equipment status parameters, including a process operation parameter acquisition unit and an equipment status parameter acquisition unit, and transmits the collected data to the data preprocessing module; The data preprocessing module is used to construct a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, and transmit the results to the data analysis module; The data analysis module is used to construct a data analysis model, analyze the data obtained by the data preprocessing module, and transmit the results to the temperature gradient optimization setting module; The temperature gradient optimization setting module is used to construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the results of data analysis, adjust the setting value of the temperature control device in real time, and transmit it to the human-computer interaction module; The human-computer interaction module is used to provide a visual interface, display various parameter information inside the rotary kiln in real time, and send it to the user information terminal synchronously.

[0015] Preferably, the visualization interface should include a menu bar, a tool bar, a status bar and a work area, wherein the work area should display the parameters and working status of the target monitored rotary kiln in real time, and send the information synchronously to the user information terminal.

[0016] Technical effects and advantages of the present invention: The present invention collects the kiln temperature parameters, operation control parameters, equipment vibration parameters and equipment performance parameters of the target monitoring rotary kiln through the process operation parameter collection unit and the equipment status parameter collection unit of the data collection module, and divides the unit sub-time monitoring area and the unit sub-area monitoring area into the monitoring area through the monitoring area determination module, thereby improving the accuracy of data collection, and more comprehensively collecting the parameters affecting the performance of the rotary kiln, providing effective data support for subsequent analysis and processing; The present invention calculates the temperature evaluation value, operation control evaluation value, equipment vibration evaluation value, equipment performance evaluation value, process operation parameter evaluation value, equipment status parameter evaluation value and rotary kiln comprehensive evaluation index in the kiln through a data preprocessing module and a data analysis module, accurately reflects the real-time status in the rotary kiln, and constructs a temperature gradient optimization model through a temperature gradient optimization setting module, adjusts the temperature gradient according to the real-time status monitored in the rotary kiln, and improves the accuracy and reliability of the temperature gradient optimization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a diagram of the method steps of the present invention.

[0018] Figure 2 It is a system structure diagram of the present invention.

[0019] Figure 3 A diagram showing the steps of the method for constructing a temperature gradient optimization model according to the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0021] As attached Figure 2 The rotary kiln temperature gradient optimization system based on big data shown includes a system operation database, a system central processor and a user information terminal, and also includes: a monitoring area determination module, a data acquisition module, a data preprocessing module, a data analysis module, a temperature gradient optimization setting module and a human-computer interaction module.

[0022] The output end of the monitoring area determination module is connected to the input end of the data acquisition module, the output end of the data acquisition module is connected to the input end of the data preprocessing module, the output end of the data preprocessing module is connected to the input end of the data analysis module, the output end of the data analysis module is connected to the input end of the temperature gradient optimization setting module, the output end of the temperature gradient optimization setting module is connected to the input end of the human-computer interaction module, the output end of the system central processing unit is connected to the input end of each module, and the output end of each module is connected to the input end of the system operation database.

[0023] The system operation database includes all data information of the temperature gradient optimization system and collects data information output by each module in real time; the system central processor is used to control the information text instructions output by each module; the user information terminal is a device for receiving information output by the temperature gradient optimization system; The monitoring area determination module is used to determine a single sub-time monitoring area, divide the time for monitoring the rotary kiln into single sub-time monitoring areas according to equal time lengths, and mark them as 1, 2, ..., i, ..., n in sequence, and divide the detection area of ​​the rotary kiln into single sub-area monitoring areas according to equal areas, and mark them as 1, 2, ..., j, ..., k in sequence.

[0024] The data acquisition module is used to collect process operation parameters and equipment status parameters, including a process operation parameter acquisition unit and an equipment status parameter acquisition unit, and transmits the collected data to the data preprocessing module.

[0025] Furthermore, the process operation parameters include kiln temperature parameters and operation control parameters. The kiln temperature parameters specifically include real-time temperature, temperature gradient value and heat loss, which are marked as T, ΔT and Q respectively; the operation control parameters specifically include coal feed amount, kiln speed, feed amount and air volume, which are marked as B, v, F and L respectively.

[0026] What needs to be specifically explained in this embodiment is that a temperature sensor is installed in the rotary kiln to monitor the real-time temperature in the rotary kiln in real time, and the temperature gradient value in the rotary kiln is calculated using the real-time temperature of each sub-unit monitoring area, and the heat loss in the rotary kiln is detected using thermal imaging technology; the kiln speed of the rotary kiln is measured by measuring the inclination angle of the rotating drum, and the wind speed is determined by using an anemometer to sense the resistance of the airflow to the measuring probe.

[0027] Furthermore, the equipment status parameters include equipment vibration parameters and equipment performance parameters. The equipment vibration parameters specifically include the vibration amplitude of the kiln drum, the vibration frequency of the kiln drum and the vibration amplitude of the supporting wheel, which are marked as A and B respectively. k , f and A r ; Equipment performance parameters include the inner diameter of the cylinder, the length of the cylinder, the slope and the number of supports, which are marked as L i , L c , η and N.

[0028] The data preprocessing module is used to build a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, obtain kiln temperature evaluation values, operation control evaluation values, equipment vibration evaluation values ​​and equipment performance evaluation values, and pass the results to the data analysis module.

[0029] Furthermore, the kiln temperature evaluation value and the operation control evaluation value are obtained by importing the equipment vibration parameters and equipment performance parameters into the parameter preprocessing model. The kiln temperature evaluation value is specifically expressed as: , y1 represents the evaluation value of the kiln temperature of the target monitoring rotary kiln, T ij represents the real-time temperature of the jth sub-area monitoring area of ​​the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, ΔT represents the temperature gradient value of the target monitoring rotary kiln, Q represents the heat loss of the target monitoring rotary kiln, ΔTi represents the temperature gradient value of the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, α1, α2 and α3 represent the influence coefficients of the real-time temperature, temperature gradient value and heat loss on the temperature evaluation value in the kiln respectively; the operation control evaluation value is specifically expressed as: , y2 represents the operation control evaluation value of the target monitoring rotary kiln, B represents the coal feeding amount of the target monitoring rotary kiln, v represents the kiln speed of the target monitoring rotary kiln, F represents the feeding amount of the target monitoring rotary kiln, L represents the air volume of the target monitoring rotary kiln, β1, β2, β3 and β4 represent the influence coefficients of coal feeding amount, kiln speed, feeding amount and air volume on the operation control evaluation value respectively, and b1, b2, b3 and b4 are known constants.

[0030] Furthermore, the equipment vibration evaluation value and the equipment performance evaluation value are obtained by importing the equipment vibration parameters and the equipment performance parameters into the parameter preprocessing model. The equipment vibration evaluation value is specifically expressed as: , y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, A k represents the vibration amplitude of the rotary kiln drum of the target monitoring rotary kiln, f represents the vibration frequency of the rotary kiln drum of the target monitoring rotary kiln, A r It indicates the vibration amplitude of the supporting wheel of the target monitoring rotary kiln. ε1, ε2 and ε3 respectively indicate the influence coefficients of the vibration amplitude of the kiln drum, the vibration frequency of the kiln drum and the vibration amplitude of the supporting wheel on the vibration evaluation value of the equipment. The equipment performance evaluation value is specifically expressed as: , y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, L i Indicates the inner diameter of the rotary kiln to be monitored, L c represents the cylinder length of the target monitoring rotary kiln, η represents the inclination of the target monitoring rotary kiln, N represents the number of supports of the target monitoring rotary kiln, μ1, μ2, μ3 and μ4 represent the influence coefficients of the cylinder inner diameter, cylinder length, inclination and number of supports on the equipment performance evaluation value respectively, and c1, c2, c3 and c4 are known constants.

[0031] The data analysis module is used to construct a data analysis model, analyze the data obtained by the data preprocessing module, obtain the process operation parameter evaluation value, equipment status parameter evaluation value and rotary kiln comprehensive evaluation index, and pass the results to the temperature gradient optimization setting module.

[0032] Furthermore, the process operation parameter evaluation value is specifically expressed as: ,y crepresents the process operation parameter evaluation value of the target monitoring rotary kiln, y1 represents the kiln temperature evaluation value of the target monitoring rotary kiln, y2 represents the operation control evaluation value of the target monitoring rotary kiln, and y 1标 Indicates the target monitoring rotary kiln temperature evaluation standard value, y 2标 It represents the operation control evaluation standard value of the target monitoring rotary kiln, ρ1 and ρ2 represent the influence coefficients of the kiln temperature evaluation value and the operation control evaluation value on the process operation parameter evaluation value respectively; the equipment status parameter evaluation value is specifically expressed as: ,y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, and y 3标 Indicates the equipment vibration evaluation standard value of the target monitoring rotary kiln, y 4标 It represents the equipment performance evaluation standard value of the target monitoring rotary kiln, ρ3 and ρ4 represent the influence coefficients of the equipment vibration evaluation value and the equipment performance evaluation value on the equipment status parameter evaluation value respectively; the comprehensive evaluation index of the rotary kiln is specifically expressed as: , φ represents the comprehensive evaluation index of the rotary kiln of the target monitoring rotary kiln, y c represents the process operation parameter evaluation value of the target monitoring rotary kiln, y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y c标 represents the standard value of the process operation parameter evaluation of the target monitoring rotary kiln, y e标 It indicates the standard value of equipment status parameter evaluation of the target monitored rotary kiln.

[0033] The temperature gradient optimization setting module is used to build a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the results of data analysis, adjust the set value of the temperature control equipment in real time, and pass it to the human-computer interaction module.

[0034] Furthermore, key information values ​​of the first rotary kiln of multiple groups of rotary kilns under standard conditions are collected, and the comprehensive evaluation index of the rotary kiln corresponding to the rotary kiln under the standard condition is calculated, which is the comprehensive evaluation warning value of the rotary kiln; the comprehensive evaluation index of the rotary kiln is compared with the comprehensive evaluation warning value of the rotary kiln to judge the operating status of the target monitored rotary kiln; and the temperature gradient strategy is adjusted in real time according to the comparison result of the comprehensive evaluation index of the rotary kiln and the comprehensive evaluation warning value of the rotary kiln.

[0035] In this embodiment, it should be specifically explained that the rotary kiln comprehensive assessment warning value is marked as φ 标 , when φ<φ 标 When φ>φ, it means that the comprehensive evaluation index of the rotary kiln is less than the comprehensive evaluation warning value of the rotary kiln, indicating that the operation status of the target monitored rotary kiln is in normal state and there is no need to adjust the temperature gradient strategy; when φ>φ 标When , it means that the comprehensive evaluation index of the rotary kiln is greater than the comprehensive evaluation warning value of the rotary kiln, which means that the operating status of the target monitored rotary kiln is in an abnormal state, and the temperature gradient strategy needs to be adjusted according to the first rotary kiln key information value of the rotary kiln under the standard state.

[0036] The human-computer interaction module is used to provide a visual interface, display various parameter information inside the rotary kiln in real time, and send it to the user information terminal synchronously.

[0037] Furthermore, the visualization interface should include a menu bar, a tool bar, a status bar and a work area, wherein the work area should display the parameters and working status of the target monitored rotary kiln in real time, and send the information synchronously to the user information terminal.

[0038] What needs to be specifically explained in this embodiment is that the menu bar of the visualization interface provides user navigation and setting options, including file operations, view control, and file import and export. The toolbar includes commonly used tool buttons to facilitate users to interact with the interface, and display the parameters and working status of the target monitored rotary kiln in real time, including kiln temperature parameters, operation control parameters, equipment vibration parameters, equipment performance parameters, kiln temperature evaluation values, operation control evaluation values, equipment vibration evaluation values, equipment performance evaluation values, process operation parameter evaluation values, equipment status parameter evaluation values ​​and rotary kiln comprehensive evaluation index.

[0039] As attached Figure 1 As shown, this embodiment provides a rotary kiln temperature gradient optimization method based on big data, comprising the following steps: S1: Determine a single sub-time monitoring area and a sub-area monitoring area, divide the time for monitoring the rotary kiln into single sub-time monitoring areas according to equal time lengths, and mark them as 1, 2, ..., i, ..., n in sequence, and divide the detection area of ​​the rotary kiln into single sub-area monitoring areas according to equal areas, and mark them as 1, 2, ..., j, ..., k in sequence; S2: real-time monitoring of data in the rotary kiln, collecting key information values ​​of the first rotary kiln, wherein the key information values ​​of the first rotary kiln include process operation parameters and equipment status parameters, and uploading them to the system operation database; S3: construct a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, obtain the second rotary kiln key information value, the second rotary kiln key information value includes the kiln temperature evaluation value, operation control evaluation value, equipment vibration evaluation value and equipment performance evaluation value, and upload them to the system operation database; S4: constructing a data analysis model, performing comprehensive analysis and processing on the key information value of the second rotary kiln, wherein the comprehensive analysis and processing is used to obtain the key information value of the third rotary kiln, and uploading it to the system operation database; S5: Construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the key information value of the third rotary kiln, and adjust the set value of the temperature control equipment in real time; S6: Various parameter information inside the rotary kiln is displayed in real time through a visual interface, and is sent to the user information terminal synchronously.

[0040] As attached Figure 3 As shown, this embodiment provides a method for constructing a temperature gradient optimization model, comprising the following steps: B1: Collect the key information value of the first rotary kiln of multiple groups of rotary kilns under standard conditions, and calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value; B2: Compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to determine the operating status of the target monitored rotary kiln; B3: According to the comparison results of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value, the temperature gradient strategy is adjusted in real time.

[0041] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rotary kiln temperature gradient optimization method based on big data, characterized in that: include: S1: Determine a single sub-time monitoring area and a sub-area monitoring area, divide the time for monitoring the rotary kiln into single sub-time monitoring areas according to equal time lengths, and mark them as 1, 2, ..., i, ..., n in sequence, and divide the detection area of ​​the rotary kiln into single sub-area monitoring areas according to equal areas, and mark them as 1, 2, ..., j, ..., k in sequence; S2: real-time monitoring of data in the rotary kiln, collecting key information values ​​of the first rotary kiln, wherein the key information values ​​of the first rotary kiln include process operation parameters and equipment status parameters, and uploading them to the system operation database; S3: construct a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, obtain the second rotary kiln key information value, the second rotary kiln key information value includes the kiln temperature evaluation value, operation control evaluation value, equipment vibration evaluation value and equipment performance evaluation value, and upload them to the system operation database; S4: constructing a data analysis model, performing comprehensive analysis and processing on the key information value of the second rotary kiln, wherein the comprehensive analysis and processing is used to obtain the key information value of the third rotary kiln, and uploading it to the system operation database; S5: Construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the key information value of the third rotary kiln, and adjust the set value of the temperature control equipment in real time; S6: Various parameter information inside the rotary kiln is displayed in real time through a visual interface, and is sent to the user information terminal synchronously.

2. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The process operation parameters include kiln temperature parameters and operation control parameters. The kiln temperature parameters specifically include real-time temperature, temperature gradient value and heat loss, which are marked as T, ΔT and Q respectively; the operation control parameters specifically include coal feed amount, kiln speed, feed amount and air volume, which are marked as B, v, F and L respectively.

3. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The equipment status parameters include equipment vibration parameters and equipment performance parameters. The equipment vibration parameters specifically include the vibration amplitude of the kiln drum, the vibration frequency of the kiln drum and the vibration amplitude of the supporting wheel, which are marked as A, A and B respectively. k , f and A r ; Equipment performance parameters include the inner diameter of the cylinder, the length of the cylinder, the slope and the number of supports, which are marked as L i , L c , η and N.

4. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The steps of constructing the parameter preprocessing model and the data analysis model are: A1: Collect multiple sets of rotary kiln thermal data from the rotary kiln database and perform data cleaning on the collected data; A2: Extract the features related to temperature gradient from the collected rotary kiln thermal data and perform feature screening; A3: Normalize, reduce the dimension of the data, and transform the data. Select a model and train it based on the data characteristics and optimization goals.

5. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The kiln temperature evaluation value and the operation control evaluation value are obtained by importing the equipment vibration parameters and the equipment performance parameters into the parameter preprocessing model. The kiln temperature evaluation value is specifically expressed as: , y1 represents the evaluation value of the kiln temperature of the target monitoring rotary kiln, T ij represents the real-time temperature of the jth sub-area monitoring area of ​​the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, ΔT represents the temperature gradient value of the target monitoring rotary kiln, Q represents the heat loss of the target monitoring rotary kiln, ΔTi represents the temperature gradient value of the i-th sub-time monitoring area of ​​the target monitoring rotary kiln, α1, α2 and α3 represent the influence coefficients of the real-time temperature, temperature gradient value and heat loss on the temperature evaluation value in the kiln respectively; the operation control evaluation value is specifically expressed as: , y2 represents the operation control evaluation value of the target monitoring rotary kiln, B represents the coal feeding amount of the target monitoring rotary kiln, v represents the kiln speed of the target monitoring rotary kiln, F represents the feeding amount of the target monitoring rotary kiln, L represents the air volume of the target monitoring rotary kiln, β1, β2, β3 and β4 represent the influence coefficients of coal feeding amount, kiln speed, feeding amount and air volume on the operation control evaluation value respectively, and b1, b2, b3 and b4 are known constants.

6. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The equipment vibration evaluation value and the equipment performance evaluation value are obtained by importing the equipment vibration parameters and the equipment performance parameters into the parameter preprocessing model. The equipment vibration evaluation value is specifically expressed as: , y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, A k represents the vibration amplitude of the rotary kiln drum of the target monitoring rotary kiln, f represents the vibration frequency of the rotary kiln drum of the target monitoring rotary kiln, A r It indicates the vibration amplitude of the supporting roller of the target monitoring rotary kiln. ε1, ε2 and ε3 respectively indicate the influence coefficients of the vibration amplitude of the kiln drum, the vibration frequency of the kiln drum and the vibration amplitude of the supporting roller on the vibration evaluation value of the equipment. The equipment performance evaluation value is specifically expressed as: , y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, L i Indicates the inner diameter of the rotary kiln to be monitored, L c represents the cylinder length of the target monitoring rotary kiln, η represents the inclination of the target monitoring rotary kiln, N represents the number of supports of the target monitoring rotary kiln, μ1, μ2, μ3 and μ4 represent the influence coefficients of the cylinder inner diameter, cylinder length, inclination and number of supports on the equipment performance evaluation value respectively, and c1, c2, c3 and c4 are known constants.

7. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The third rotary kiln key information value specifically includes a process operation parameter evaluation value, an equipment status parameter evaluation value and a rotary kiln comprehensive evaluation index, and the process operation parameter evaluation value is specifically expressed as: ,y c represents the process operation parameter evaluation value of the target monitoring rotary kiln, y1 represents the kiln temperature evaluation value of the target monitoring rotary kiln, y2 represents the operation control evaluation value of the target monitoring rotary kiln, and y 1标 Indicates the target monitoring rotary kiln temperature evaluation standard value, y 2标 It represents the operation control evaluation standard value of the target monitoring rotary kiln, ρ1 and ρ2 represent the influence coefficients of the kiln temperature evaluation value and the operation control evaluation value on the process operation parameter evaluation value respectively; the equipment status parameter evaluation value is specifically expressed as: ,y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y3 represents the equipment vibration evaluation value of the target monitoring rotary kiln, y4 represents the equipment performance evaluation value of the target monitoring rotary kiln, and y 3标 Indicates the equipment vibration evaluation standard value of the target monitoring rotary kiln, y 4标 It represents the equipment performance evaluation standard value of the target monitoring rotary kiln, ρ3 and ρ4 represent the influence coefficients of the equipment vibration evaluation value and the equipment performance evaluation value on the equipment status parameter evaluation value respectively; the comprehensive evaluation index of the rotary kiln is specifically expressed as: , φ represents the comprehensive evaluation index of the rotary kiln of the target monitoring rotary kiln, y c represents the process operation parameter evaluation value of the target monitoring rotary kiln, y e represents the equipment status parameter evaluation value of the target monitoring rotary kiln, y c标 represents the standard value of the process operation parameter evaluation of the target monitoring rotary kiln, y e标 It indicates the standard value of equipment status parameter evaluation of the target monitored rotary kiln.

8. The method for optimizing the temperature gradient of a rotary kiln based on big data according to claim 1, characterized in that: The specific steps of constructing the temperature gradient optimization model are: B1: Collect the key information value of the first rotary kiln of multiple groups of rotary kilns under standard conditions, and calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value; B2: Compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to determine the operating status of the target monitored rotary kiln; B3: According to the comparison results of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value, the temperature gradient strategy is adjusted in real time.

9. A rotary kiln temperature gradient optimization system based on big data, according to any one of claims 1-8, a rotary kiln temperature gradient optimization method based on big data, characterized in that: include: System operation database, system central processor, user information terminal, monitoring area determination module, data acquisition module, data preprocessing module, data analysis module, temperature gradient optimization setting module and human-computer interaction module; The system operation database includes all data information of the temperature gradient optimization system, and collects data information output by each module in real time; The system central processor is used to control the information text instructions output by each module; the user information terminal is a device for receiving information output by the temperature gradient optimization system; The monitoring area determination module is used to determine a single sub-time monitoring area and a single sub-area monitoring area; The data acquisition module is used to collect process operation parameters and equipment status parameters, including a process operation parameter acquisition unit and an equipment status parameter acquisition unit, and transmits the collected data to the data preprocessing module; The data preprocessing module is used to construct a parameter preprocessing model, perform data preprocessing on process operation parameters and equipment status parameters, and transmit the results to the data analysis module; The data analysis module is used to construct a data analysis model, analyze the data obtained by the data preprocessing module, and transmit the results to the temperature gradient optimization setting module; The temperature gradient optimization setting module is used to construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the results of data analysis, adjust the setting value of the temperature control device in real time, and transmit it to the human-computer interaction module; The human-computer interaction module is used to provide a visual interface, display various parameter information inside the rotary kiln in real time, and send it to the user information terminal synchronously.

10. The rotary kiln temperature gradient optimization system based on big data according to claim 9, characterized in that: The visualization interface should include a menu bar, a tool bar, a status bar and a work area, wherein the work area should display the parameters and working status of the target monitored rotary kiln in real time, and send the information synchronously to the user information terminal.

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