A Method and System for Optimizing Temperature Gradient of Rotary Kiln Based on Big Data

Abstract

The present invention discloses a method and system for optimizing the temperature gradient of a rotary kiln based on big data, specifically relating to the technical field of big data, including: S1: determining a single sub-time monitoring area and a sub-area monitoring area; S2: real-time monitoring of the data inside the rotary kiln and collecting the first key information value of the rotary kiln; S3: constructing a parameter preprocessing model to perform data preprocessing on the process operation parameters and equipment status parameters to obtain the second key information value of the rotary kiln; S4: constructing a data analysis model to comprehensively analyze and process the second key information value of the rotary kiln to obtain the third key information value of the rotary kiln; S5: constructing a temperature gradient optimization model, formulating and optimizing the temperature gradient strategy according to the third key information value of the rotary kiln, and real-time adjusting the set value of the temperature control equipment; S6: real-time displaying various parameter information inside the rotary kiln through a visualization interface and synchronously sending it to the user information terminal. It effectively improves the accuracy and reliability of temperature gradient optimization.

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 method for optimizing the temperature gradient of a rotary kiln based on big data, comprising:

[0007] S1: Determine the single sub-time monitoring area and the sub-area monitoring area. Divide the time for monitoring the rotary kiln into single sub-time monitoring areas according to equal time lengths, and sequentially label them as 1, 2, …, i, …, n. And divide the detection area of the rotary kiln into single sub-area monitoring areas according to equal areas, and sequentially label them as 1, 2, …, j, …, k;

[0008] S2: Real-time monitor the data inside the rotary kiln, collect the first key information values of the rotary kiln, where the first key information values of the rotary kiln include process operation parameters and equipment status parameters, and upload them to the system operation database;

[0009] S3: Construct a parameter preprocessing model, perform data preprocessing on the process operation parameters and equipment status parameters to obtain the second key information values of the rotary kiln. The second key information values of the rotary kiln include the in-kiln temperature evaluation value, the operation control evaluation value, the equipment vibration evaluation value, and the equipment performance evaluation value, and upload them to the system operation database;

[0010] S4: Construct a data analysis model, perform comprehensive analysis and processing on the second key information values of the rotary kiln. The comprehensive analysis and processing is used to obtain the third key information values of the rotary kiln, and upload them to the system operation database;

[0011] S5: Construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the third key information values of the rotary kiln, and real-time adjust the set value of the temperature control equipment;

[0012] S6: Real-time display various parameter information inside the rotary kiln through a visualization interface, and synchronously send it to the user information terminal.

[0013] Preferably, the process operation parameters include the in-kiln temperature parameters and the operation control parameters. The in-kiln temperature parameters specifically include the real-time temperature, the temperature gradient value, and the heat loss, which are respectively labeled as T, ΔT, and Q; the operation control parameters specifically include the coal feeding amount, the kiln speed, the feeding amount, and the air volume, which are respectively labeled as B, v, F, and L.

[0014] Preferably, the equipment status parameters include the equipment vibration parameters and the equipment performance parameters. The equipment vibration parameters specifically include the vibration amplitude of the kiln barrel, the vibration frequency of the kiln barrel, and the vibration amplitude of the supporting wheel, which are respectively labeled as A k 、f, and A r ; the equipment performance parameters specifically include the inner diameter of the cylinder, the length of the cylinder, the slope, and the number of supports, which are respectively labeled as L i 、L c 、η, and N.

[0015] Preferably, the steps of constructing the parameter preprocessing model and the data analysis model are as follows:

[0016] A1: Collect multiple groups of thermal data of the rotary kiln from the rotary kiln database and clean the collected data;

[0017] A2: Extract the features related to the temperature gradient from the collected thermal data of the rotary kiln and perform feature screening on them;

[0018] A3: Perform normalization processing, data dimensionality reduction, and data transformation on the data, and select and train a model according to the data features and optimization objectives.

[0019] Preferably, the evaluation value of the temperature inside the kiln and the evaluation value of the operation control are obtained by importing the temperature parameters and operation control parameters inside the kiln into the parameter preprocessing model. The evaluation value of the temperature inside the kiln is specifically expressed as:

[0020]

[0021] y 1 represents the evaluation value of the temperature inside the target monitored rotary kiln, T ij represents the real-time temperature of the i-th sub-time monitoring area and the j-th sub-area monitoring area of the target monitored rotary kiln, ΔT represents the temperature gradient value of the target monitored rotary kiln, Q represents the heat loss of the target monitored rotary kiln, ΔTi represents the temperature gradient value of the i-th sub-time monitoring area of the target monitored rotary kiln, α 1 、α 2 and α 3 respectively represent the influence coefficients of the real-time temperature, temperature gradient value, and heat loss on the evaluation value of the temperature inside the kiln; the evaluation value of the operation control is specifically expressed as:

[0022]

[0023] y 2 represents the evaluation value of the operation control of the target monitored rotary kiln, B represents the coal feeding amount of the target monitored rotary kiln, v represents the kiln speed of the target monitored rotary kiln, F represents the feeding amount of the target monitored rotary kiln, L represents the air volume of the target monitored rotary kiln, β 1 、β 2 、β 3 and β 4 respectively represent the influence coefficients of the coal feeding amount, kiln speed, feeding amount, and air volume on the evaluation value of the operation control, b 1 、b 2 、b 3 and b 4 are known constants.

[0024] Preferably, the acquisition of the equipment vibration evaluation value and the equipment performance evaluation value is 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:

[0025]

[0026] y 3 represents the equipment vibration evaluation value of the target monitored rotary kiln, A k represents the vibration amplitude of the kiln barrel of the target monitored rotary kiln, f represents the vibration frequency of the kiln barrel of the target monitored rotary kiln, A r represents the vibration amplitude of the supporting rollers of the target monitored rotary kiln, ε 1 、ε 2 and ε 3 respectively represent the influence coefficients of the vibration amplitude of the kiln barrel, the vibration frequency of the kiln barrel, and the vibration amplitude of the supporting rollers on the equipment vibration evaluation value; The equipment performance evaluation value is specifically expressed as:

[0027]

[0028] y 4 represents the equipment performance evaluation value of the target monitored rotary kiln, L i represents the inner diameter of the cylinder body of the target monitored rotary kiln, L c represents the cylinder body length of the target monitored rotary kiln, η represents the slope of the target monitored rotary kiln, N represents the number of supports of the target monitored rotary kiln, μ 1 、μ 2 、μ 3 and μ 4 respectively represent the influence coefficients of the inner diameter of the cylinder body, the cylinder body length, the slope, and the number of supports on the equipment performance evaluation value, c 1 、c 2 、c 3 and c 4 are known constants.

[0029] Preferably, the key information value of the third rotary kiln specifically includes the process operation parameter evaluation value, the equipment status parameter evaluation value, and the rotary kiln comprehensive evaluation index. The process operation parameter evaluation value is specifically expressed as:

[0030]

[0031] y c represents the process operation parameter evaluation value of the target monitored rotary kiln, y 1 represents the evaluation value of the temperature inside the kiln of the target monitored rotary kiln, y 2 represents the evaluation value of the operation control of the target monitored rotary kiln, y 1标 represents the standard value of the temperature evaluation inside the kiln of the target monitored rotary kiln, y 2标Denote the operation control evaluation standard value of the target monitored rotary kiln, ρ 1 and ρ 2 respectively represent the influence coefficients of the in-kiln temperature evaluation value and the operation control evaluation value on the process operation parameter evaluation value; The equipment status parameter evaluation value is specifically expressed as:

[0032]

[0033] y e Denote the equipment status parameter evaluation value of the target monitored rotary kiln, y 3 Denote the equipment vibration evaluation value of the target monitored rotary kiln, y 4 Denote the equipment performance evaluation value of the target monitored rotary kiln, y 3标 Denote the equipment vibration evaluation standard value of the target monitored rotary kiln, y 4标 Denote the equipment performance evaluation standard value of the target monitored rotary kiln, ρ 3 and ρ 4 respectively represent the influence coefficients of the equipment vibration evaluation value and the equipment performance evaluation value on the equipment status parameter evaluation value; The rotary kiln comprehensive evaluation index is specifically expressed as:

[0034]

[0035] φ denotes the rotary kiln comprehensive evaluation index of the target monitored rotary kiln, y c Denote the process operation parameter evaluation value of the target monitored rotary kiln, y e Denote the equipment status parameter evaluation value of the target monitored rotary kiln, y c标 Denote the process operation parameter evaluation standard value of the target monitored rotary kiln, y e标 Denote the equipment status parameter evaluation standard value of the target monitored rotary kiln.

[0036] Preferably, the specific steps for constructing the temperature gradient optimization model are as follows:

[0037] B1: Collect multiple groups of the first key information values of the rotary kiln under standard conditions, calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value;

[0038] B2: Compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to judge the operation status of the target monitored rotary kiln;

[0039] B3: According to the comparison result of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value, adjust the temperature gradient strategy in real time.

[0040] Preferably, a temperature gradient optimization system for a rotary kiln based on big data includes a system operation database, a system central processor, and a user information terminal, and further 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-machine interaction module;

[0041] The system operation database includes all data information of the temperature gradient optimization system and collects in real time the data information output by each module; the system central processor is used to centrally control the information text instructions output by each module; the user information terminal is an information output device that receives the temperature gradient optimization system;

[0042] The monitoring area determination module is used to determine a single sub-time monitoring area and a single sub-area monitoring area;

[0043] The data acquisition module is used to acquire process operation parameters and equipment status parameters, including a process operation parameter acquisition unit and an equipment status parameter acquisition unit, and transmit the acquired data to the data preprocessing module;

[0044] The data preprocessing module is used to construct a parameter preprocessing model, perform data preprocessing on the process operation parameters and equipment status parameters, and transmit the results to the data analysis module;

[0045] 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;

[0046] 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 set value of the temperature control equipment in real time, and transmit it to the human-machine interaction module;

[0047] The human-machine interaction module is used to provide a visual interface, display in real time various parameter information inside the rotary kiln, and synchronously send it to the user information terminal.

[0048] Preferably, the visual interface should include a menu bar, a toolbar, a status bar, and a work area, where the work area should display in real time the parameters and working status of the target monitored rotary kiln and synchronously send the information to the user information terminal.

[0049] The technical effects and advantages of the present invention:

[0050] 1. The present invention collects the in-kiln temperature parameters, operation control parameters, equipment vibration parameters, and equipment performance parameters of the target monitored rotary kiln through the process operation parameter acquisition unit and the equipment status parameter acquisition unit of the data acquisition module, and divides the unit sub-time monitoring area and the unit sub-area monitoring area through the monitoring area determination module, improving the accuracy of data acquisition, comprehensively collecting the parameters affecting the performance of the rotary kiln, and providing effective data support for subsequent analysis and processing;

[0051] 2. The present invention calculates the in-kiln 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 through the data preprocessing module and the data analysis module, accurately reflecting the real-time state inside the rotary kiln, and constructs a temperature gradient optimization model through the temperature gradient optimization setting module, and adjusts the temperature gradient according to the monitored real-time state inside the rotary kiln, improving the accuracy and reliability of the temperature gradient optimization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a method step diagram of the present invention.

[0053] Figure 2 It is a system structure diagram of the present invention.

[0054] Figure 3 It is a method step diagram for constructing the temperature gradient optimization model of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] As shown in the attached Figure 2 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 further 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 man-machine interaction module.

[0057] The output end of the monitoring area determination module is electrically connected to the input end of the data acquisition module. The output end of the data acquisition module is electrically connected to the input end of the data preprocessing module. The output end of the data preprocessing module is electrically connected to the input end of the data analysis module. The output end of the data analysis module is electrically connected to the input end of the temperature gradient optimization setting module. The output end of the temperature gradient optimization setting module is electrically connected to the input end of the human-machine interaction module. The output end of the system central processor is electrically connected to the input ends of each module. The output ends of each module are electrically connected to the input end of the system operation database.

[0058] The system operation database includes all data information of the temperature gradient optimization system and collects the 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 an information output device for receiving the temperature gradient optimization system.

[0059] The monitoring area determination module is used to determine the single sub-time monitoring area. The time for monitoring the rotary kiln is divided into single sub-time monitoring areas according to equal time lengths and is sequentially marked as 1, 2, …, i, …, n. The detection area of the rotary kiln is divided into single sub-area monitoring areas according to equal areas and is sequentially marked as 1, 2, …, j, …, k.

[0060] The data acquisition module is used to acquire process operation parameters and equipment status parameters, including a process operation parameter acquisition unit and an equipment status parameter acquisition unit, and transmits the acquired data to the data preprocessing module.

[0061] Furthermore, the process operation parameters include in-kiln temperature parameters and operation control parameters. The in-kiln temperature parameters specifically include the real-time temperature, temperature gradient value, and heat loss, which are respectively marked as T, ΔT, and Q. The operation control parameters specifically include the coal feeding amount, kiln speed, feeding amount, and air volume, which are respectively marked as B, v, F, and L.

[0062] It should be specifically noted in this embodiment that the real-time temperature in the rotary kiln is monitored by installing temperature sensors in the rotary kiln, and the temperature gradient value in the rotary kiln is calculated using the real-time temperature of each sub-unit monitoring area in real time. 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 air flow to the measuring probe.

[0063] 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 barrel, the vibration frequency of the kiln barrel, and the vibration amplitude of the supporting roller, which are respectively marked as A k 、f, and A r ; the equipment performance parameters specifically include the inner diameter of the cylinder body, the length of the cylinder body, the slope, and the number of supports, which are respectively marked as L i, L c , η, and N.

[0064] The data preprocessing module is used to build a parameter preprocessing model, perform data preprocessing on the process operation parameters and equipment status parameters, obtain the in-kiln temperature evaluation value, operation control evaluation value, equipment vibration evaluation value, and equipment performance evaluation value, and transfer the results to the data analysis module.

[0065] Furthermore, the in-kiln temperature evaluation value and the operation control evaluation value are obtained by importing the in-kiln temperature parameters and operation control parameters into the parameter preprocessing model. The in-kiln temperature evaluation value is specifically expressed as:

[0066]

[0067] y 1 represents the in-kiln temperature evaluation value of the target monitoring rotary kiln, T ij represents the real-time temperature of the i-th sub-time monitoring area and the j-th sub-area 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, α 1 , α 2 , and α 3 respectively represent the influence coefficients of the real-time temperature, temperature gradient value, and heat loss on the in-kiln temperature evaluation value. The operation control evaluation value is specifically expressed as:

[0068]

[0069] y 2 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, β 1 , β 2 , β 3 , and β 4 respectively represent the influence coefficients of the coal feeding amount, kiln speed, feeding amount, and air volume on the operation control evaluation value, b 1 , b 2 , b 3 , and b 4 are known constants.

[0070] Furthermore, the equipment vibration evaluation value and the equipment performance evaluation value are obtained by importing the equipment vibration parameters and equipment performance parameters into the parameter preprocessing model. The equipment vibration evaluation value is specifically expressed as:

[0071]

[0072] y3 Represents the equipment vibration evaluation value of the target monitored rotary kiln, A k Represents the vibration amplitude of the kiln barrel of the target monitored rotary kiln, f represents the vibration frequency of the kiln barrel of the target monitored rotary kiln, A r Represents the vibration amplitude of the supporting roller of the target monitored rotary kiln, ε 1 、ε 2 And ε 3 Respectively represent the influence coefficients of the vibration amplitude of the kiln barrel, the vibration frequency of the kiln barrel, and the vibration amplitude of the supporting roller on the equipment vibration evaluation value; the equipment performance evaluation value is specifically expressed as:

[0073]

[0074] y 4 Represents the equipment performance evaluation value of the target monitored rotary kiln, L i Represents the inner diameter of the kiln barrel of the target monitored rotary kiln, L c Represents the length of the kiln barrel of the target monitored rotary kiln, η represents the slope of the target monitored rotary kiln, N represents the number of supports of the target monitored rotary kiln, μ 1 、μ 2 、μ 3 And μ 4 Respectively represent the influence coefficients of the inner diameter of the kiln barrel, the length of the kiln barrel, the slope, and the number of supports on the equipment performance evaluation value, c 1 、c 2 、c 3 And c 4 Are known constants.

[0075] 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, the equipment state parameter evaluation value, and the rotary kiln comprehensive evaluation index, and transfer the results to the temperature gradient optimization setting module.

[0076] Furthermore, the process operation parameter evaluation value is specifically expressed as:

[0077]

[0078] y c Represents the process operation parameter evaluation value of the target monitored rotary kiln, y 1 Represents the evaluation value of the temperature inside the kiln of the target monitored rotary kiln, y 2 Represents the operation control evaluation value of the target monitored rotary kiln, y 1标 Represents the standard value of the temperature evaluation inside the kiln of the target monitored rotary kiln, y 2标 Represents the standard value of the operation control evaluation of the target monitored rotary kiln, ρ 1 And ρ 2respectively represent the influence coefficients of the in-kiln temperature evaluation value and the operation control evaluation value on the process operation parameter evaluation value; the equipment status parameter evaluation value is specifically expressed as:

[0079]

[0080] y e represents the equipment status parameter evaluation value of the target monitored rotary kiln, y 3 represents the equipment vibration evaluation value of the target monitored rotary kiln, y 4 represents the equipment performance evaluation value of the target monitored rotary kiln, y 3标 represents the equipment vibration evaluation standard value of the target monitored rotary kiln, y 4标 represents the equipment performance evaluation standard value of the target monitored rotary kiln, ρ 3 and ρ 4 respectively represent the influence coefficients of the equipment vibration evaluation value and the equipment performance evaluation value on the equipment status parameter evaluation value; the rotary kiln comprehensive evaluation index is specifically expressed as:

[0081]

[0082] φ represents the rotary kiln comprehensive evaluation index of the target monitored rotary kiln, y c represents the process operation parameter evaluation value of the target monitored rotary kiln, y e represents the equipment status parameter evaluation value of the target monitored rotary kiln, y c标 represents the process operation parameter evaluation standard value of the target monitored rotary kiln, y e标 represents the equipment status parameter evaluation standard value of the target monitored rotary kiln.

[0083] 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 equipment in real time, and transmit it to the human-computer interaction module.

[0084] Furthermore, collect multiple groups of the first key information values of the rotary kiln under standard conditions, calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value; compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to judge the operating state of the target monitored rotary kiln; according to the comparison result of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value, adjust the temperature gradient strategy in real time.

[0085] It should be specifically explained in this embodiment that the rotary kiln comprehensive evaluation warning value is marked as φ 标 , when φ < φ 标 , it means that the rotary kiln comprehensive evaluation index is less than the rotary kiln comprehensive evaluation warning value, indicating that the operating state of the target monitored rotary kiln is in a normal state and there is no need to adjust the temperature gradient strategy; when φ > φ标 When it is the case, it means that the comprehensive evaluation index of the rotary kiln is greater than the warning value of the comprehensive evaluation of the rotary kiln, indicating that the operating state of the target monitored rotary kiln is in an abnormal state, and the temperature gradient strategy needs to be adjusted according to the first key information value of the rotary kiln under the standard state.

[0086] The human-computer interaction module is used to provide a visual interface, display various parameter information inside the rotary kiln in real time, and synchronously send it to the user information terminal.

[0087] Furthermore, the visual interface should include a menu bar, a toolbar, a status bar, and a work area. The work area should display the parameters and working status of the target monitored rotary kiln in real time, and synchronize the information to the user information terminal.

[0088] It should be specifically noted in this embodiment that the menu bar of the visual interface provides user navigation and setting options, including file operations, view control, and import and export of files. The toolbar includes common tool buttons, which facilitate users to interact with the interface and display the parameters and working status of the target monitored rotary kiln in real time, including in-kiln temperature parameters, operation control parameters, equipment vibration parameters, equipment performance parameters, in-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 the comprehensive evaluation index of the rotary kiln.

[0089] As shown in the appendix Figure 1 This embodiment provides a method for optimizing the temperature gradient of a rotary kiln based on big data, including the following steps:

[0090] S1: Determine the single sub-time monitoring area and the 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. 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;

[0091] S2: Monitor the data inside the rotary kiln in real time, collect the first key information value of the rotary kiln. The first key information value of the rotary kiln includes process operation parameters and equipment status parameters, and upload them to the system operation database;

[0092] S3: Build a parameter preprocessing model, perform data preprocessing on the process operation parameters and equipment status parameters to obtain the second key information value of the rotary kiln. The second key information value of the rotary kiln includes in-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;

[0093] S4: Construct a data analysis model to comprehensively analyze and process the key information values of the second rotary kiln. The comprehensive analysis and processing are used to obtain the key information values of the third rotary kiln and upload them to the system operation database;

[0094] S5: Construct a temperature gradient optimization model, formulate and optimize the temperature gradient strategy according to the key information values of the third rotary kiln, and adjust the set value of the temperature control equipment in real time;

[0095] S6: Real-time display various parameter information inside the rotary kiln through a visualization interface and synchronously send it to the user information terminal.

[0096] As shown Figure 3 in the attached figure, this embodiment provides a method for constructing a temperature gradient optimization model, including the following steps:

[0097] B1: Collect multiple groups of key information values of the rotary kiln under standard conditions, calculate the rotary kiln comprehensive evaluation index corresponding to the rotary kiln under standard conditions, which is the rotary kiln comprehensive evaluation warning value;

[0098] B2: Compare the rotary kiln comprehensive evaluation index with the rotary kiln comprehensive evaluation warning value to judge the operating state of the target monitored rotary kiln;

[0099] B3: Adjust the temperature gradient strategy in real time according to the comparison result of the rotary kiln comprehensive evaluation index and the rotary kiln comprehensive evaluation warning value.

[0100] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0101] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall 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, wherein the key information value of the third rotary kiln specifically includes the process operation parameter evaluation value, the equipment status parameter evaluation value and the rotary kiln comprehensive evaluation index, 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. 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.

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 kiln temperature parameters and the operation control 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 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. A rotary kiln temperature gradient optimization system based on big data, according to any of claims 1-7, 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.

9. The rotary kiln temperature gradient optimization system based on big data according to claim 8, 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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