Coke oven coke cake center temperature detection and analysis system based on Internet of Things

Abstract

The invention belongs to the technical field of coke oven supervision, and particularly relates to a coke oven coke cake center temperature detection and analysis system based on Internet of Things, which comprises a high-precision temperature detection module, a data acquisition and processing module, an intelligent identification management module, an Internet of Things communication module and a central control room host, the high-precision optical fiber temperature probes distributed at the upper, middle and lower key points of the side surface of the coke guide grid of the coke barrier car are used for accurately monitoring the center temperature of a coke cake, the data acquisition and processing module is used for periodically acquiring data, and the Internet of Things communication module is used for performing real-time data transmission with the central control room host through the Internet of Things technology. And the central control room host automatically analyzes the received temperature data and calculates various key indexes, so that the accurate, real-time and intelligent monitoring and analysis of the center temperature of the coke cake are realized, the online management of the center temperature of the coke cake is facilitated, the automation and intelligence level of coke oven production is promoted, and the overall production efficiency and product quality are improved.

Description

Technical Field

[0001] The invention relates to the technical field of coke oven monitoring, and in particular to a coke oven coke cake central temperature detection and analysis system based on the Internet of Things. Background Art

[0002] Coke ovens are key equipment in the steel smelting and coal chemical industries. Their production efficiency and product quality directly affect the economic benefits of the enterprise. Coke cakes are the core product of coke oven production. The precise control of their core temperature is crucial to ensure coke quality, reduce energy consumption and extend the life of coke ovens.

[0003] Traditional coke cake center temperature monitoring methods often rely on manual sampling and offline analysis, which is time-delayed and inaccurate. It is difficult to achieve automatic, continuous, and accurate monitoring of the coke cake center temperature and real-time communication with the central control room host. It is also difficult to effectively supervise the central control room host, and the level of intelligence and automation is low.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a coke oven coke cake central temperature detection and analysis system based on the Internet of Things, which solves the problems that the prior art is difficult to realize automatic, continuous and accurate monitoring of the central temperature of the coke cake and real-time communication with the main host in the central control room, and is unable to effectively supervise the main host in the central control room, and has a low level of intelligence and automation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A coke oven coke cake center temperature detection and analysis system based on the Internet of Things, comprising a high-precision temperature detection module, a data acquisition and processing module, an intelligent identification management module, an Internet of Things communication module and a central control room host; the high-precision temperature detection module comprises three high-precision optical fiber temperature probes, which are distributed at the upper, middle and lower key points of the side of the coke guide grid of the coke intercepting vehicle, and each high-precision optical fiber temperature probe captures the coke cake center temperature through non-contact measurement technology;

[0008] The data acquisition and processing module integrates a microprocessor and a large-capacity storage unit, uses a microcontroller as the core and combines flash memory technology to achieve rapid data acquisition once per second. 100 data points are collected at the top, middle and bottom of each coke furnace, and preliminary data cleaning and preprocessing are performed on the collected data.

[0009] The intelligent identification management module automatically identifies the carbonization chamber number through a built-in algorithm, and classifies, stores and manages the collected temperature data; the Internet of Things communication module communicates with the host in the central control room through wired or wireless Internet of Things technology to transmit data in real time; the host in the central control room is deployed with a special data analysis engine and visualization software to automatically analyze the received temperature data, calculate key indicators including instant temperature, peak temperature and average temperature, draw a real-time measurement curve of the center temperature of each coke cake and display it at three points: upper, middle and lower.

[0010] Furthermore, the host in the central control room is also used to automatically identify temperature anomalies and issue immediate warnings based on data analysis results, where temperature anomalies include excessively high, too low or excessive fluctuations;

[0011] In addition, the host computer in the central control room combines expert systems and machine learning algorithms to make intelligent recommendations based on historical data and current production conditions, including suggestions for adjusting the heating system and optimizing operating parameters.

[0012] Furthermore, the main engine in the central control room is communicated with the intelligent monitoring module. The intelligent monitoring module monitors the central control room through a high-definition camera to determine whether there are management personnel in the central control room, and starts timing when there are no management personnel in the central control room. The unmanned management time is obtained based on this, and the unmanned management time is compared with a preset unmanned management time threshold. If the unmanned management time exceeds the preset unmanned management time threshold, a management warning signal is generated, and the management warning signal is sent to the intelligent terminal of the corresponding responsible personnel via the main engine in the central control room.

[0013] Furthermore, the host in the central control room is connected to the display adaptive control module in communication, and the display adaptive control module monitors the display direction of the host in the central control room and captures the pupil of the human eye. If there is no pupil of the human eye in the display direction of the host in the central control room, it is determined that it is in an unattended state, and the timing starts when it is determined that it is in an unattended state, thereby obtaining the unattended time.

[0014] When the unattended time starts to exceed the first preset unattended time threshold, the display brightness of the host in the central control room is reduced to half of the previous value, and the central control room is in a dark screen state; when the unattended time starts to exceed the second preset unattended time threshold, the host in the central control room is in a screen-off state; wherein the second preset unattended time threshold>the first unattended time threshold>3min;

[0015] Also, when the host computer in the central control room is in a dark screen state or a screen-off state, if the pupil of a person is captured again in the display direction of the host computer in the central control room, the display brightness of the host computer in the central control room is restored.

[0016] Furthermore, the host in the central control room communicates with the host supervision and analysis module, which performs operation evaluation and analysis on the host in the central control room, and determines through analysis whether to generate an operation alarm signal for the host in the central control room. When an operation alarm signal is generated, the host in the central control room is warned, and the warning information is sent to the intelligent terminal of the corresponding responsible personnel.

[0017] Furthermore, the specific analysis process of the host monitoring analysis module is as follows:

[0018] The host operation inspection coefficient is obtained through analysis, and the time ratio of the deviation of the real-time display brightness of the host in the central control room compared with the set standard display brightness in unit time exceeding the corresponding preset deviation threshold is collected and marked as the host display inspection coefficient, and the average value of the adjustment delay time of the display adaptive control module for the host in the central control room in unit time is marked as the adjustment performance coefficient;

[0019] The host supervision coefficient is obtained by numerically calculating the host operation and inspection coefficient, the host display inspection coefficient and the adjustment performance coefficient. The host supervision coefficient is numerically compared with the preset host supervision coefficient threshold. If the host supervision coefficient exceeds the preset host supervision coefficient threshold, an operation alarm signal of the host in the central control room is generated.

[0020] Furthermore, the specific analysis and acquisition method of the host operation and inspection coefficient is as follows:

[0021] The internal temperature of the main engine in the central control room is collected and marked as the main engine temperature coefficient, and the noise decibel value and vibration amplitude data generated by the main engine in the central control room during operation are collected and marked as the main engine noise generation coefficient and the main engine vibration coefficient respectively; the main engine actual coefficient is obtained by weighted summing up the main engine temperature coefficient, the main engine noise generation coefficient and the main engine vibration coefficient, and the main engine operation and inspection coefficient is obtained by averaging all the main engine actual coefficients per unit time.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. In the present invention, the high-precision optical fiber temperature probes distributed at the upper, middle and lower three key points of the side of the coke guide grid of the coke intercepting vehicle are used to accurately monitor the center temperature of the coke cake. The data acquisition and processing module performs periodic data acquisition, and the host in the central control room automatically analyzes the received temperature data and calculates various key indicators, thereby realizing accurate, real-time and intelligent monitoring and analysis of the center temperature of the coke cake, which is conducive to the online management of the center temperature of the coke cake, promoting the automation and intelligence level of coke oven production, and improving the overall production efficiency and product quality.

[0024] 2. In the present invention, the central control room is monitored by the intelligent monitoring module to determine whether there are management personnel in the central control room and to generate management warning signals in time to avoid production safety hazards. The display adaptive control module automatically and adaptively adjusts the brightness of the main unit in the central control room based on the personnel's gaze status to achieve intelligent management of the brightness of the main unit. The main unit supervision and analysis module is used to evaluate and analyze the operation of the main unit in the central control room. When an operation alarm signal is generated, the operation supervision of the main unit in the central control room is strengthened and corresponding improvement measures are taken for the main unit in the central control room to ensure safe and stable operation of the main unit in the central control room. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0026] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0027] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] Embodiment 1: Figure 1 As shown, the present invention proposes a coke oven coke cake central temperature detection and analysis system based on the Internet of Things, comprising a high-precision temperature detection module, a data acquisition and processing module, an intelligent identification management module, an Internet of Things communication module and a central control room host;

[0030] The high-precision temperature detection module includes three high-precision fiber optic temperature probes, which are distributed at the upper, middle and lower key points on the side of the coke guide grid of the coke interceptor. Each high-precision fiber optic temperature probe captures the center temperature of the coke cake through non-contact measurement technology, realizing continuous and high-precision monitoring of the center temperature of the coke cake and improving data timeliness.

[0031] The data acquisition and processing module integrates a microprocessor and a large-capacity storage unit, uses a microcontroller as the core and combines flash memory technology to be responsible for periodic data acquisition (preferably, to achieve fast data acquisition once per second). Each furnace of coke collects 100 data points at the upper, middle and lower points, and performs preliminary data cleaning and preprocessing on the collected data to ensure the accuracy and integrity of the data;

[0032] The intelligent identification management module automatically identifies the carbonization chamber number through a built-in algorithm, and classifies, stores and manages the collected temperature data; the IoT communication module communicates with the host in the central control room through wired or wireless IoT technology (such as Wi-Fi, 4G / 5G, etc.) to transmit data in real time and realize remote monitoring and analysis;

[0033] The host computer in the central control room is equipped with a special data analysis engine and visualization software, which automatically analyzes the received temperature data, calculates key indicators including instantaneous temperature, peak temperature and average temperature, draws a real-time measurement curve of the center temperature of each coke cake and displays it at three points: upper, middle and lower. Management personnel edit and print out the data as needed, directly guide the production of coke ovens and the formulation of heating systems, and realize online management of the center temperature of coke cakes.

[0034] Furthermore, the main engine in the central control room is also used to automatically identify temperature anomalies and issue immediate warnings based on data analysis results. Temperature anomalies include temperatures that are too high, too low, or fluctuate too much. In addition, the main engine in the central control room combines expert systems with machine learning algorithms to make intelligent recommendations based on historical data and current production conditions, including suggestions for adjusting the heating system and optimizing operating parameters. This is conducive to intelligently adjusting coke oven production and heating systems, optimizing coke quality, and improving production efficiency.

[0035] The technical solution of the present invention realizes accurate, real-time and intelligent monitoring and analysis of the center temperature of the coke cake by integrating advanced fiber optic sensing technology, microcontroller technology, Internet of Things communication technology and data analysis algorithm, which is conducive to the online management of the center temperature of the coke cake, provides strong technical support for the optimization and upgrading of coke oven production, promotes the automation and intelligence level of coke oven production, improves the overall production efficiency and product quality, enhances the traceability of the production process, and facilitates quality control and problem troubleshooting.

[0036] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the host in the central control room is connected to the intelligent monitoring module through communication. The intelligent monitoring module monitors the central control room through a high-definition camera, determines whether there is a manager in the central control room, and starts timing when there is no manager in the central control room, thereby obtaining the unmanned management time; wherein, the greater the value of the unmanned management time, the higher the potential risk to production safety;

[0037] The unmanned management time is compared with the preset unmanned management time threshold. If the unmanned management time exceeds the preset unmanned management time threshold, it indicates that the potential risk to production safety is higher. A management warning signal is generated and sent to the smart terminal of the corresponding responsible personnel via the host in the central control room to remind the responsible personnel to strengthen the personnel supervision in the central control room in time to avoid production safety hazards.

[0038] Furthermore, the control room host is connected to the display adaptive control module through communication. The display adaptive control module monitors the display direction of the control room host and captures the pupil of the human eye. If there is no pupil of the human eye in the display direction of the control room host, it is determined that the display is in an unattended state, and the timing starts when it is determined that the display is in an unattended state, thereby obtaining the unattended time.

[0039] When the unattended time begins to exceed the first preset unattended time threshold, the display brightness of the host in the central control room is reduced to half of the previous value, and the central control room is in a dark screen state; when the unattended time begins to exceed the second preset unattended time threshold, the host in the central control room is in a screen-off state; preferably, the second preset unattended time threshold>the first unattended time threshold>3min;

[0040] Also, when the control room host is in a dark screen state or a screen-off state, if the pupil of a person is recaptured in the display direction of the control room host, the display brightness of the control room host will be restored. The brightness of the control room host can be automatically and adaptively adjusted based on the person's gaze status, thereby realizing intelligent management of the brightness of the control room host and significantly reducing energy waste.

[0041] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the host in the central control room is connected to the host supervision and analysis module in communication. The host supervision and analysis module performs operation evaluation and analysis on the host in the central control room, and determines whether to generate an operation alarm signal of the host in the central control room through analysis. When the operation alarm signal is generated, the host in the central control room is warned, and the warning information is sent to the smart terminal of the corresponding responsible personnel to remind the corresponding personnel to strengthen the operation supervision of the host in the central control room, and take corresponding improvement measures for the host in the central control room to ensure the safe and stable operation of the host in the central control room; the specific analysis process of the host supervision and analysis module is as follows:

[0042] The internal temperature of the host in the central control room is collected and marked as the host temperature coefficient, and the noise decibel value and vibration amplitude data generated by the host in the central control room during operation are collected and marked as the host noise coefficient and the host vibration coefficient respectively;

[0043] The host temperature coefficient W, the host noise coefficient Y and the host vibration coefficient M are weighted and calculated by the formula Z = q × W + t × Y + n × M to obtain the host actual coefficient Z; wherein q, t, and n are preset weight coefficients with values ​​greater than zero, and the larger the value of the host actual coefficient Z, the worse the real-time operation status of the host in the central control room; the host operation and inspection coefficient is obtained by averaging all the host actual coefficients within a unit time;

[0044] The percentage of the time during which the deviation of the real-time display brightness of the host in the control room from the set standard display brightness exceeds the corresponding preset deviation threshold within a unit time is collected and marked as the host display detection coefficient, and the average value of the adjustment delay time (i.e., the interval between the time when the brightness adjustment is required and the time when the brightness adjustment is completed) of the display adaptive control module for the host in the control room within a unit time is marked as the adjustment performance coefficient;

[0045] The host operation and inspection coefficient T, the host display inspection coefficient X and the adjustment performance coefficient G are numerically calculated by the formula H=k×T+s×X+e×G to obtain the host supervision coefficient H, wherein k, s, and e are preset weight coefficients whose values ​​are greater than zero, and the larger the value of the host supervision coefficient H is, the more abnormal the operation performance of the host in the central control room is overall; the host supervision coefficient H is numerically compared with the preset host supervision coefficient threshold. If the host supervision coefficient H exceeds the preset host supervision coefficient threshold, it indicates that the operation performance of the host in the central control room is generally abnormal, and an operation alarm signal of the host in the central control room is generated.

[0046] The working principle of the present invention is as follows: when in use, the center temperature of the coke cake is accurately monitored by high-precision fiber optic temperature probes distributed at the upper, middle and lower key points on the side of the coke guide grid of the coke intercepting vehicle. The data acquisition and processing module uses a microcontroller as the core and combines flash memory technology to perform periodic data acquisition. The intelligent identification management module classifies, stores and manages the collected temperature data. The Internet of Things communication module transmits real-time data with the host in the central control room through the Internet of Things technology. The host in the central control room automatically analyzes the received temperature data and calculates various key indicators, thereby realizing accurate, real-time and intelligent monitoring and analysis of the center temperature of the coke cake, which is conducive to the online management of the center temperature of the coke cake, promotes the automation and intelligence level of coke oven production, and improves the overall production efficiency and product quality.

[0047] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A coke oven coke cake center temperature detection and analysis system based on the Internet of Things, characterized in that: It includes a high-precision temperature detection module, a data acquisition and processing module, an intelligent identification management module, an Internet of Things communication module and a central control room host; the high-precision temperature detection module includes three high-precision fiber optic temperature probes, which are distributed at the upper, middle and lower key points on the side of the coke guide grid of the coke interceptor. Each high-precision fiber optic temperature probe captures the center temperature of the coke cake through non-contact measurement technology; The data acquisition and processing module integrates a microprocessor and a large-capacity storage unit, uses a microcontroller as the core and combines flash memory technology to achieve rapid data acquisition once per second. 100 data points are collected at the top, middle and bottom of each coke furnace, and preliminary data cleaning and preprocessing are performed on the collected data. The intelligent identification management module automatically identifies the carbonization chamber number through the built-in algorithm, and classifies, stores and manages the collected temperature data. The Internet of Things communication module communicates with the host in the central control room through wired or wireless Internet of Things technology; the host in the central control room automatically analyzes the received temperature data, calculates key indicators including instantaneous temperature, peak temperature and average temperature, draws a real-time measurement curve of the center temperature of each coke cake and displays it at three points: upper, middle and lower.

2. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 1 is characterized in that: The host in the central control room is also used to automatically identify temperature anomalies and issue immediate warnings based on data analysis results. Temperature anomalies include excessively high, too low, or excessive fluctuations. In addition, the host computer in the central control room combines expert systems and machine learning algorithms to make intelligent recommendations based on historical data and current production conditions, including suggestions for adjusting the heating system and optimizing operating parameters.

3. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 2 is characterized in that: The main control room host is connected to the intelligent monitoring module, which monitors the control room through a high-definition camera. If the unmanned management time exceeds the preset unmanned management time threshold, a management warning signal is generated and sent to the intelligent terminal of the corresponding responsible person via the main control room host.

4. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 3 is characterized in that: The host in the central control room is connected to the display adaptive control module in communication. If there is no pupil of the human eye in the display direction of the host in the central control room, it is judged that it is in an unattended state. When the unattended time length begins to exceed a first preset unattended time length threshold, the display brightness of the host in the central control room is reduced to half of the previous brightness, and the central control room is in a dark screen state. When the unattended time length begins to exceed a second preset unattended time length threshold, the host computer in the central control room is put into a screen-off state; Also, when the host computer in the central control room is in a dark screen state or a screen-off state, if the pupil of a person is captured again in the display direction of the host computer in the central control room, the display brightness of the host computer in the central control room is restored.

5. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 4 is characterized in that: The host in the central control room is connected to the host supervision and analysis module through communication. The host supervision and analysis module performs operation evaluation and analysis on the host in the central control room. Through analysis, it is determined whether to generate an operation alarm signal for the host in the central control room. When an operation alarm signal is generated, the host in the central control room issues an early warning.

6. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 5 is characterized in that: The specific analysis process of the host monitoring analysis module is as follows: The host operation and inspection coefficient is obtained through analysis, and the host operation and inspection coefficient, host display inspection coefficient and adjustment performance coefficient are numerically calculated to obtain the host supervision coefficient. If the host supervision coefficient exceeds the preset host supervision coefficient threshold, an operation alarm signal of the host in the central control room is generated.

7. The coke oven coke cake center temperature detection and analysis system based on the Internet of Things according to claim 6 is characterized in that: The specific analysis and acquisition method of the host operation and inspection coefficient is as follows: The host actual coefficient is obtained by weighted summing up the host temperature coefficient, host noise coefficient and host vibration coefficient, and the host operation and inspection coefficient is obtained by averaging all host actual coefficients within unit time.

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