A method and system for on-line monitoring of the temperature of a smelting furnace

Abstract

The present application is suitable for smelting furnace temperature monitoring technical field, and provides a smelting furnace temperature online monitoring method and system.The present application periodically monitors internal temperature and external environment of the target smelting furnace;matches standard external temperature data;according to different heights, the target smelting furnace is monitored by acoustic temperature measurement technology;it is judged whether there is temperature deviation anomaly, and when there is temperature deviation anomaly, temperature anomaly early warning is triggered.The internal temperature data and external environment data can be matched with the standard external temperature data, and the distributed temperature monitoring of the target smelting furnace can be carried out, the distributed temperature data is obtained, compared with the standard external temperature data, and it is judged whether there is temperature deviation anomaly, so as to provide an effective test method, which can quickly identify and determine the abnormal deviation of the furnace temperature monitoring, not only improve the precision of the smelting furnace temperature monitoring, but also ensure the stability of the smelting process and the product quality.

Description

Technical Field

[0001] This invention belongs to the field of smelting furnace temperature monitoring technology, and particularly relates to an online smelting furnace temperature monitoring method and system. Background Technology

[0002] The smelting furnace plays a central role in metal smelting, material processing, and high-temperature reactions. The stability and precise control of the furnace temperature are directly related to product quality, energy consumption, and equipment safety.

[0003] Furnace temperature monitoring refers to the process of acquiring, processing, and evaluating the temperature changes in a smelting furnace in real time through various sensing and analysis technologies.

[0004] In the existing technology, due to the complex environment inside the smelting furnace, factors such as high temperature, strong radiation and smoke disturbance are intertwined, making it difficult to accurately monitor the furnace temperature. Moreover, there is no effective inspection method, and it is impossible to quickly identify and determine when a large temperature deviation occurs. This not only leads to significant accuracy defects in the smelting furnace temperature monitoring results, but may also adversely affect the stability of subsequent smelting processes and product quality. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for online monitoring of smelting furnace temperature, which aims to solve the technical problems existing in the prior art mentioned in the background.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A method for online monitoring of smelting furnace temperature, the method specifically includes the following steps:

[0008] According to the preset online monitoring cycle, the internal temperature and external environment of the target smelting furnace are periodically monitored to obtain internal temperature data and external environment data.

[0009] Based on the internal temperature data and the external environment data, standard external temperature data is matched from a preset historical standard library;

[0010] Acoustic temperature measurement technology is used to monitor the temperature distribution of the target smelting furnace at different heights and obtain temperature distribution data.

[0011] Based on the standard external temperature data, the distributed temperature data is compared to determine whether there is an abnormal temperature deviation, and if there is an abnormal temperature deviation, a temperature anomaly warning is triggered.

[0012] As a further limitation of the technical solution of this invention, the step of periodically monitoring the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and obtaining internal temperature data and external environment data, specifically includes the following steps:

[0013] According to the preset online monitoring cycle, internal temperature monitoring instructions and environmental monitoring instructions are generated periodically;

[0014] In response to the internal temperature monitoring command, the internal temperature of the target smelting furnace is periodically monitored to obtain internal temperature data;

[0015] In response to the environmental monitoring command, the target smelting furnace is periodically monitored for its external environment to acquire external environmental data.

[0016] As a further limitation of the technical solution of this embodiment of the invention, the step of matching standard external temperature data from a preset historical standard library based on the internal temperature data and the external environment data specifically includes the following steps:

[0017] Based on multiple feature types, multiple monitoring feature values ​​are extracted from the internal temperature data and the external environment data;

[0018] Based on the multiple feature types and multiple monitoring feature values, a similarity matching analysis is performed from a preset historical standard library to calculate the similarity matching value corresponding to multiple historical standard data.

[0019] Based on multiple similarity matching values, the optimal standard data is matched from multiple historical standard data.

[0020] Extract standard external temperature data from the optimal standard data.

[0021] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the plurality of similarity matching values ​​is as follows:

[0022] ;

[0023] in, Representing the A historical standard data point, Representing the There are 10 feature types, with a total of 1000 features. Each feature type For the first Feature weights corresponding to each feature type For the first The monitoring feature values ​​corresponding to each feature type For the first The historical feature values ​​corresponding to each feature type.

[0024] As a further limitation of the technical solution of this invention, the step of monitoring the distributed temperature of the target smelting furnace at different heights using acoustic temperature measurement technology and obtaining distributed temperature data specifically includes the following steps:

[0025] The target smelting furnace is height-distributed and multiple height levels are determined.

[0026] Gas detection is performed on multiple of the aforementioned layer heights to obtain gas detection data;

[0027] Acoustic temperature measurement technology is used to detect and record multiple levels of height from multiple angles, and the detection record data is obtained.

[0028] Based on the gas detection data and the detection record data, calculate the furnace external layer temperature at multiple layer heights;

[0029] The temperatures of multiple furnace external layers are processed to obtain distributed temperature data.

[0030] As a further limitation of the technical solution of this embodiment of the invention, the calculation formulas for the multiple furnace external layer temperatures are as follows:

[0031] ;

[0032] in, Representing the Each level height, For the first The temperature of the outer furnace layer at each level. Representing the For acoustic transmitters and receivers, each level is equipped with [equipment / facilities]. For acoustic transmitters and acoustic receivers, For the first The first level height Regarding the distance between the acoustic transmitter and the acoustic receiver, For the first The first level height The reception duration between the acoustic transmitter and the acoustic receiver, Representing the There are 10 types of gases, totaling 100 types. a kind of gas, For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver Molecular mass of the gas For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver The proportion of each gas This is the preset calculation scaling factor. is the molar gas constant.

[0033] As a further limitation of the technical solution of this embodiment of the invention, the step of comparing the distributed temperature data based on the standard external temperature data to determine whether there is an abnormal temperature deviation, and triggering a temperature anomaly warning when there is an abnormal temperature deviation, specifically includes the following steps:

[0034] Based on the standard external temperature data, the distributed temperature data are compared, and the temperature deviation values ​​corresponding to multiple layer heights are calculated;

[0035] The multiple temperature deviation values ​​are compared with preset abnormal deviation values;

[0036] When at least one temperature deviation value is greater than the abnormal deviation value, it is determined that there is an abnormal temperature deviation.

[0037] Generate an abnormal warning signal and trigger an abnormal temperature warning.

[0038] A smelting furnace temperature online monitoring system, the system comprising a periodic online monitoring module, a historical data matching module, a distributed temperature monitoring module, and a temperature anomaly early warning module, wherein:

[0039] The periodic online monitoring module is used to periodically monitor the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and to acquire internal temperature data and external environment data.

[0040] The historical data matching module is used to match standard external temperature data from a preset historical standard library based on the internal temperature data and the external environment data.

[0041] The distributed temperature monitoring module is used to monitor the distributed temperature of the target smelting furnace at different heights using acoustic temperature measurement technology, and to acquire distributed temperature data.

[0042] The temperature anomaly warning module is used to compare the distributed temperature data based on the standard external temperature data, determine whether there is a temperature deviation anomaly, and trigger a temperature anomaly warning when there is a temperature deviation anomaly.

[0043] As a further limitation of the technical solution of this embodiment of the invention, the historical data matching module specifically includes:

[0044] The feature value extraction unit is used to extract multiple monitoring feature values ​​from the internal temperature data and the external environment data according to multiple feature types;

[0045] The similarity matching value calculation unit is used to perform similarity matching analysis from a preset historical standard library based on multiple feature types and multiple monitoring feature values, and to calculate the similarity matching value corresponding to multiple historical standard data.

[0046] The optimal data matching unit is used to match the optimal standard data from multiple historical standard data based on multiple similar matching values;

[0047] The standard data extraction unit is used to extract standard external temperature data from the optimal standard data.

[0048] As a further limitation of the technical solution of this embodiment of the invention, the distributed temperature monitoring module specifically includes:

[0049] The distribution planning unit is used to plan the height distribution of the target smelting furnace and determine multiple height levels.

[0050] A gas detection unit is used to detect gas at multiple said layer heights and acquire gas detection data;

[0051] The detection and recording unit is used to perform multi-directional detection and recording of multiple layer heights using acoustic temperature measurement technology, and to acquire detection and recording data.

[0052] An external furnace level temperature calculation unit is used to calculate the external furnace level temperature at multiple level heights based on the gas detection data and the detection record data.

[0053] The tiered temperature processing unit is used to process the temperatures of multiple furnace-external tiers to obtain distributed temperature data.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] (1) The present invention can match standard external temperature data with internal temperature data and external environmental data, and perform distributed temperature monitoring on the target smelting furnace to obtain distributed temperature data, compare it with standard external temperature data, and determine whether there is an abnormal temperature deviation, thereby providing an effective inspection method, which can quickly identify and determine the abnormal deviation of the furnace temperature monitoring, not only improving the accuracy of smelting furnace temperature monitoring, but also ensuring the stability of the smelting process and product quality.

[0056] (2) The present invention can extract multiple monitoring feature values ​​from internal temperature data and external environment data according to multiple feature types, and perform similar matching analysis from the preset historical standard library to calculate the similar matching value corresponding to multiple historical standard data, and then compare and match the optimal standard data, and extract standard external temperature data from the optimal standard data, so as to realize the automatic and rapid acquisition of standard external temperature data, and provide a standard data basis for subsequent comparison and judgment of temperature deviation anomalies;

[0057] (3) The present invention can perform height distribution planning for the target smelting furnace, determine multiple layer heights, perform gas detection on multiple layer heights, obtain gas detection data, and perform multi-directional detection recording on multiple layer heights, obtain detection record data, and then calculate the furnace external layer temperature of multiple layer heights, thereby realizing automatic calculation and processing of acoustic temperature measurement. Attached Figure Description

[0058] Figure 1 A flowchart of the online temperature monitoring method for a smelting furnace provided in an embodiment of the present invention is shown;

[0059] Figure 2 The application architecture diagram of the online temperature monitoring system for smelting furnaces provided in an embodiment of the present invention is shown. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0061] Understandably, in the existing technology, due to the complex environment inside the smelting furnace, with factors such as high temperature, strong radiation, and smoke disturbance intertwined, it is difficult to accurately monitor the temperature inside the furnace, and there is no effective verification method. When a large temperature deviation occurs, it is also impossible to quickly identify and determine it. This not only leads to significant accuracy defects in the temperature monitoring results of the smelting furnace, but may also have an adverse impact on the stability of the subsequent smelting process and product quality.

[0062] To address the aforementioned problems, this invention discloses an online temperature monitoring method and system for a smelting furnace. This method periodically monitors the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, acquiring internal temperature data and external environment data. Based on the internal temperature data and external environment data, standard external temperature data is matched from a preset historical standard library. Acoustic temperature measurement technology is used to monitor the distributed temperature of the target smelting furnace at different heights, acquiring distributed temperature data. The distributed temperature data is compared with the standard external temperature data to determine if there are any abnormal temperature deviations, and a temperature anomaly warning is triggered when abnormal temperature deviations are found. This method can match standard external temperature data with internal temperature data and external environment data, monitor the distributed temperature of the target smelting furnace, acquire distributed temperature data, and compare it with the standard external temperature data to determine if there are any abnormal temperature deviations. This provides an effective inspection method that can quickly identify and determine abnormal deviations in furnace temperature monitoring, improving the accuracy of smelting furnace temperature monitoring and ensuring the stability of the smelting process and product quality.

[0063] Specifically, Figure 1 A flowchart of the online temperature monitoring method for a smelting furnace provided in an embodiment of the present invention is shown.

[0064] In a preferred embodiment of the present invention, a method for online monitoring of the temperature of a smelting furnace specifically includes the following steps:

[0065] Step S101: According to the preset online monitoring cycle, periodically monitor the internal temperature and external environment of the target smelting furnace to obtain internal temperature data and external environment data.

[0066] In this embodiment of the invention, internal temperature monitoring commands and environmental monitoring commands are periodically generated according to a preset online monitoring cycle. In response to the internal temperature monitoring commands, the temperature of multiple internal locations such as the furnace center, wall surface, and top of the material layer of the target smelting furnace is monitored to obtain internal temperature data. In response to the environmental monitoring commands, the wind speed, wind direction, humidity, air pressure, and other external environmental factors of the target smelting furnace are monitored to obtain external environmental data.

[0067] Specifically, in another preferred embodiment provided by the present invention, the step of periodically monitoring the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and obtaining internal temperature data and external environment data, specifically includes the following steps:

[0068] According to the preset online monitoring cycle, internal temperature monitoring instructions and environmental monitoring instructions are generated periodically;

[0069] In response to the internal temperature monitoring command, the internal temperature of the target smelting furnace is periodically monitored to obtain internal temperature data;

[0070] In response to the environmental monitoring command, the target smelting furnace is periodically monitored for its external environment to acquire external environmental data.

[0071] Furthermore, the online temperature monitoring method for the smelting furnace also includes the following steps:

[0072] Step S102: Based on the internal temperature data and the external environment data, match standard external temperature data from a preset historical standard library.

[0073] In this embodiment of the invention, multiple monitoring feature values ​​are extracted from internal temperature data and external environmental data according to multiple preset feature types. Then, based on the multiple feature types and multiple monitoring feature values, similarity matching analysis is performed on multiple historical standard data in a preset historical standard library to calculate the similarity matching value corresponding to the multiple historical standard data. By comparing the multiple similarity matching values, the largest similarity matching value is marked as the optimal matching value. Then, the historical standard data corresponding to the optimal matching value is selected from the multiple historical standard data and marked as the optimal standard data. Finally, standard external temperature data is extracted from the optimal standard data. Specifically, the calculation formula for the similarity matching value corresponding to the multiple historical standard data is as follows:

[0074] ;

[0075] in, Representing the A historical standard data point, Representing the There are 10 feature types, with a total of 1000 features. Each feature type For the first Feature weights corresponding to each feature type For the first The monitoring feature values ​​corresponding to each feature type For the first The historical feature values ​​corresponding to each feature type.

[0076] Understandably, there are multiple characteristic types, including: furnace center temperature, wall temperature, top temperature of the material bed, external wind speed, external wind direction, external humidity, and external air pressure.

[0077] Specifically, in another preferred embodiment provided by the present invention, the step of matching standard external temperature data from a preset historical standard library based on the internal temperature data and the external environment data specifically includes the following steps:

[0078] Based on multiple feature types, multiple monitoring feature values ​​are extracted from the internal temperature data and the external environment data;

[0079] Based on the multiple feature types and multiple monitoring feature values, a similarity matching analysis is performed from a preset historical standard library to calculate the similarity matching value corresponding to multiple historical standard data.

[0080] Based on multiple similarity matching values, the optimal standard data is matched from multiple historical standard data.

[0081] Extract standard external temperature data from the optimal standard data.

[0082] Furthermore, the online temperature monitoring method for the smelting furnace also includes the following steps:

[0083] Step S103: Using acoustic temperature measurement technology, monitor the distributed temperature of the target smelting furnace at different heights and obtain distributed temperature data.

[0084] In this embodiment of the invention, the target smelting furnace is height-distributed according to a preset height difference, dividing the exterior of the furnace into multiple height levels. Gas detection data is obtained by monitoring these multiple height levels. Furthermore, using acoustic thermography, multiple pairs of acoustic transmitters and receivers are deployed at each height level to perform multi-directional detection of the furnace exterior. Detection data is obtained by recording these multi-directional measurements across the various height levels. Based on the gas detection data and the recorded data, the furnace exterior temperature at each of the multiple height levels is calculated. Finally, the temperatures at each of the multiple furnace exterior levels are processed to obtain distributed temperature data. Specifically, the calculation formula for the furnace exterior temperature corresponding to each of the multiple height levels is as follows:

[0085] ;

[0086] in, Representing the Each level height, For the first The temperature of the outer furnace layer at each level. Representing the For acoustic transmitters and receivers, each level is equipped with [equipment / facilities]. For acoustic transmitters and acoustic receivers, For the first The first level height Regarding the distance between the acoustic transmitter and the acoustic receiver, For the first The first level height The reception duration between the acoustic transmitter and the acoustic receiver, Representing the There are 10 types of gases, totaling 100 types. a kind of gas, For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver Molecular mass of the gas For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver The proportion of each gas This is the preset calculation scaling factor. is the molar gas constant.

[0087] It is understandable that there is a height difference between two adjacent levels.

[0088] Specifically, in another preferred embodiment provided by the present invention, the step of monitoring the distributed temperature of the target smelting furnace at different heights using acoustic temperature measurement technology and obtaining distributed temperature data specifically includes the following steps:

[0089] The target smelting furnace is height-distributed and multiple height levels are determined.

[0090] Gas detection is performed on multiple of the aforementioned layer heights to obtain gas detection data;

[0091] Acoustic temperature measurement technology is used to detect and record multiple levels of height from multiple angles, and the detection record data is obtained.

[0092] Based on the gas detection data and the detection record data, calculate the furnace external layer temperature at multiple layer heights;

[0093] The temperatures of multiple furnace external layers are processed to obtain distributed temperature data.

[0094] Furthermore, the online temperature monitoring method for the smelting furnace also includes the following steps:

[0095] Step S104: Based on the standard external temperature data, compare the distributed temperature data to determine whether there is an abnormal temperature deviation, and trigger a temperature abnormality warning when there is an abnormal temperature deviation.

[0096] In this embodiment of the invention, based on standard external temperature data, the distributed temperature data is compared to calculate the temperature deviation values ​​corresponding to multiple layer heights. Then, the multiple temperature deviation values ​​are compared with preset abnormal deviation values ​​to determine whether there is a temperature deviation anomaly. Specifically, when at least one temperature deviation value is greater than the abnormal deviation value, it is determined that there is a temperature deviation anomaly. At this time, an anomaly warning signal is generated to trigger a temperature anomaly warning, thereby achieving effective detection and timely warning of temperature deviation anomalies.

[0097] Specifically, in another preferred embodiment provided by the present invention, the step of comparing the distributed temperature data based on the standard external temperature data to determine whether there is an abnormal temperature deviation, and triggering a temperature anomaly warning when there is an abnormal temperature deviation, specifically includes the following steps:

[0098] Based on the standard external temperature data, the distributed temperature data are compared, and the temperature deviation values ​​corresponding to multiple layer heights are calculated;

[0099] The multiple temperature deviation values ​​are compared with preset abnormal deviation values;

[0100] When at least one temperature deviation value is greater than the abnormal deviation value, it is determined that there is an abnormal temperature deviation.

[0101] Generate an abnormal warning signal and trigger an abnormal temperature warning.

[0102] Furthermore, Figure 2 The application architecture diagram of the online temperature monitoring system for smelting furnaces provided in an embodiment of the present invention is shown.

[0103] Specifically, in another preferred embodiment of the present invention, a smelting furnace temperature online monitoring system includes:

[0104] The periodic online monitoring module 101 is used to periodically monitor the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and to acquire internal temperature data and external environment data.

[0105] In this embodiment of the invention, the periodic online monitoring module 101 periodically generates internal temperature monitoring commands and environmental monitoring commands according to a preset online monitoring cycle. In response to the internal temperature monitoring commands, it monitors the temperature at multiple internal locations of the target smelting furnace, such as the furnace center, wall surface, and top of the material layer, and obtains internal temperature data. In response to the environmental monitoring commands, it monitors the wind speed, wind direction, humidity, air pressure, and other external environmental factors of the target smelting furnace and obtains external environmental data.

[0106] The historical data matching module 102 is used to match standard external temperature data from a preset historical standard library based on the internal temperature data and the external environment data.

[0107] In this embodiment of the invention, the historical data matching module 102 extracts multiple monitoring feature values ​​from internal temperature data and external environment data according to multiple preset feature types. Then, based on the multiple feature types and multiple monitoring feature values, it performs similarity matching analysis on multiple historical standard data in a preset historical standard library, calculates the similarity matching value corresponding to the multiple historical standard data, compares the multiple similarity matching values, marks the largest similarity matching value as the optimal matching value, selects the historical standard data corresponding to the optimal matching value from the multiple historical standard data, marks it as the optimal standard data, and then extracts standard external temperature data from the optimal standard data. Specifically, the calculation formula for the similarity matching value corresponding to the multiple historical standard data is as follows:

[0108] ;

[0109] in, Representing the A historical standard data point, Representing the There are 10 feature types, with a total of 1000 features. Each feature type For the first Feature weights corresponding to each feature type For the first The monitoring feature values ​​corresponding to each feature type For the first The historical feature values ​​corresponding to each feature type.

[0110] Specifically, in another preferred embodiment provided by the present invention, the historical data matching module 102 specifically includes:

[0111] The feature value extraction unit is used to extract multiple monitoring feature values ​​from the internal temperature data and the external environment data according to multiple feature types;

[0112] The similarity matching value calculation unit is used to perform similarity matching analysis from a preset historical standard library based on multiple feature types and multiple monitoring feature values, and to calculate the similarity matching value corresponding to multiple historical standard data.

[0113] The optimal data matching unit is used to match the optimal standard data from multiple historical standard data based on multiple similar matching values;

[0114] The standard data extraction unit is used to extract standard external temperature data from the optimal standard data.

[0115] Furthermore, the online temperature monitoring system for the smelting furnace also includes:

[0116] The distributed temperature monitoring module 103 is used to monitor the distributed temperature of the target smelting furnace at different heights using acoustic temperature measurement technology, and to obtain distributed temperature data.

[0117] In this embodiment of the invention, the distributed temperature monitoring module 103 performs height distribution planning for the target smelting furnace. According to a preset height difference, the exterior of the target smelting furnace is divided into multiple height levels. Gas detection data is obtained by monitoring these multiple height levels. Furthermore, using acoustic temperature measurement technology, multiple pairs of acoustic transmitters and receivers are arranged at each height level to perform multi-directional detection of the exterior of the target smelting furnace. Detection record data is obtained by recording the multi-directional detection data across multiple height levels. Then, based on the gas detection data and the detection record data, the external temperature of each of the multiple height levels is calculated. Finally, the multiple external temperature levels are processed to obtain distributed temperature data. Specifically, the calculation formula for the external temperature of each of the multiple height levels is as follows:

[0118] ;

[0119] in, Representing the Each level height, For the first The temperature of the outer furnace layer at each level. Representing the For acoustic transmitters and receivers, each level is equipped with [equipment / facilities]. For acoustic transmitters and acoustic receivers, For the first The first level height Regarding the distance between the acoustic transmitter and the acoustic receiver, For the first The first level height The reception duration between the acoustic transmitter and the acoustic receiver, Representing the There are 10 types of gases, totaling 100 types. a kind of gas, For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver Molecular mass of the gas For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver The proportion of each gas This is the preset calculation scaling factor. is the molar gas constant.

[0120] Specifically, in another preferred embodiment provided by the present invention, the distributed temperature monitoring module 103 specifically includes:

[0121] The distribution planning unit is used to plan the height distribution of the target smelting furnace and determine multiple height levels.

[0122] A gas detection unit is used to detect gas at multiple said layer heights and acquire gas detection data;

[0123] The detection and recording unit is used to perform multi-directional detection and recording of multiple layer heights using acoustic temperature measurement technology, and to acquire detection and recording data.

[0124] An external furnace level temperature calculation unit is used to calculate the external furnace level temperature at multiple level heights based on the gas detection data and the detection record data.

[0125] The tiered temperature processing unit is used to process the temperatures of multiple furnace-external tiers to obtain distributed temperature data.

[0126] Furthermore, the online temperature monitoring system for the smelting furnace also includes:

[0127] The temperature anomaly warning module 104 is used to compare the distributed temperature data based on the standard external temperature data, determine whether there is a temperature deviation anomaly, and trigger a temperature anomaly warning when there is a temperature deviation anomaly.

[0128] In this embodiment of the invention, the temperature anomaly warning module 104 compares the distributed temperature data based on standard external temperature data, calculates the temperature deviation values ​​corresponding to multiple layer heights, and then compares the multiple temperature deviation values ​​with preset abnormal deviation values ​​to determine whether there is a temperature deviation anomaly. Specifically, when at least one temperature deviation value is greater than the abnormal deviation value, it is determined that there is a temperature deviation anomaly. At this time, an anomaly warning signal is generated to trigger the temperature anomaly warning, thereby realizing the effective detection and timely warning of temperature deviation anomalies.

[0129] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for online monitoring of smelting furnace temperature, characterized in that, The method specifically includes the following steps: According to the preset online monitoring cycle, the internal temperature and external environment of the target smelting furnace are periodically monitored to obtain internal temperature data and external environment data. Based on the internal temperature data and the external environment data, standard external temperature data is matched from a preset historical standard library; Based on multiple preset feature types, several monitoring feature values ​​are extracted from internal temperature data and external environmental data. Then, according to the multiple feature types and monitoring feature values, similarity matching analysis is performed on multiple historical standard data in a preset historical standard library to calculate the similarity matching value corresponding to the multiple historical standard data. By comparing the multiple similarity matching values, the largest similarity matching value is marked as the optimal matching value. From the multiple historical standard data, the historical standard data corresponding to the optimal matching value is selected and marked as the optimal standard data. Then, standard external temperature data is extracted from the optimal standard data. Specifically, the calculation formula for the similarity matching value corresponding to multiple historical standard data is as follows: ； in, Representing the A historical standard data point, Representing the There are 10 feature types, with a total of 1000 features. Each feature type For the first Feature weights corresponding to each feature type For the first The monitoring feature values ​​corresponding to each feature type For the first Historical feature values ​​corresponding to each feature type; Acoustic temperature measurement technology is used to monitor the temperature distribution of the target smelting furnace at different heights and obtain temperature distribution data. The target smelting furnace is height-distributed according to a pre-defined height difference, dividing its exterior into multiple height levels. Gas detection is performed at these levels to acquire data. Acoustic thermography is used, with multiple pairs of acoustic transmitters and receivers positioned at each level to perform multi-directional detection of the furnace's exterior. This multi-directional detection recording data is then used to calculate the external temperature at each of the multiple height levels. Finally, the external temperature data is compiled to obtain the distributed temperature data. Specifically, the calculation formula for the external temperature at each of the multiple height levels is as follows: ； in, Representing the Each level height, For the first The temperature of the outer furnace layer at each level. Representing the For acoustic transmitters and receivers, each level is equipped with [equipment / facilities]. For acoustic transmitters and acoustic receivers, For the first The first level height Regarding the distance between the acoustic transmitter and the acoustic receiver, For the first The first level height The reception duration between the acoustic transmitter and the acoustic receiver, Representing the There are 10 types of gases, totaling 100 types. a kind of gas, For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver Molecular mass of the gas For the first The first level height The first [unclear] between the acoustic transmitter and the acoustic receiver The proportion of each gas This is the preset calculation scaling factor. It is the molar gas constant; Based on the standard external temperature data, the distributed temperature data is compared to determine whether there is an abnormal temperature deviation, and if there is an abnormal temperature deviation, a temperature anomaly warning is triggered.

2. The online temperature monitoring method for a smelting furnace according to claim 1, characterized in that, The process of periodically monitoring the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and obtaining internal temperature data and external environment data, specifically includes the following steps: According to the preset online monitoring cycle, internal temperature monitoring instructions and environmental monitoring instructions are generated periodically; In response to the internal temperature monitoring command, the internal temperature of the target smelting furnace is periodically monitored to obtain internal temperature data; In response to the environmental monitoring command, the target smelting furnace is periodically monitored for its external environment to acquire external environmental data.

3. The online temperature monitoring method for a smelting furnace according to claim 1, characterized in that, The process of comparing the distributed temperature data based on the standard external temperature data to determine whether there is an abnormal temperature deviation, and triggering a temperature anomaly warning when an abnormal temperature deviation is found, specifically includes the following steps: Based on the standard external temperature data, the distributed temperature data are compared, and the temperature deviation values ​​corresponding to multiple layer heights are calculated; The multiple temperature deviation values ​​are compared with preset abnormal deviation values; When at least one temperature deviation value is greater than the abnormal deviation value, it is determined that there is an abnormal temperature deviation. Generate an abnormal warning signal and trigger an abnormal temperature warning.

4. A furnace temperature online monitoring system for performing the furnace temperature online monitoring method as described in any one of claims 1-3, characterized in that, The system includes a periodic online monitoring module, a historical data matching module, a distributed temperature monitoring module, and a temperature anomaly early warning module, wherein: The periodic online monitoring module is used to periodically monitor the internal temperature and external environment of the target smelting furnace according to a preset online monitoring cycle, and to acquire internal temperature data and external environment data. The historical data matching module is used to match standard external temperature data from a preset historical standard library based on the internal temperature data and the external environment data. The distributed temperature monitoring module is used to monitor the distributed temperature of the target smelting furnace at different heights using acoustic temperature measurement technology, and to acquire distributed temperature data. The temperature anomaly warning module is used to compare the distributed temperature data based on the standard external temperature data, determine whether there is a temperature deviation anomaly, and trigger a temperature anomaly warning when there is a temperature deviation anomaly.

5. The online temperature monitoring system for a smelting furnace according to claim 4, characterized in that, The historical data matching module specifically includes: The feature value extraction unit is used to extract multiple monitoring feature values ​​from the internal temperature data and the external environment data according to multiple feature types; The similarity matching value calculation unit is used to perform similarity matching analysis from a preset historical standard library based on multiple feature types and multiple monitoring feature values, and to calculate the similarity matching value corresponding to multiple historical standard data. The optimal data matching unit is used to match the optimal standard data from multiple historical standard data based on multiple similar matching values; The standard data extraction unit is used to extract standard external temperature data from the optimal standard data.

6. The online temperature monitoring system for a smelting furnace according to claim 4, characterized in that, The distributed temperature monitoring module specifically includes: The distribution planning unit is used to plan the height distribution of the target smelting furnace and determine multiple height levels. A gas detection unit is used to detect gas at multiple said layer heights and acquire gas detection data; The detection and recording unit is used to perform multi-directional detection and recording of multiple layer heights using acoustic temperature measurement technology, and to acquire detection and recording data. An external furnace level temperature calculation unit is used to calculate the external furnace level temperature at multiple level heights based on the gas detection data and the detection record data. The tiered temperature processing unit is used to process the temperatures of multiple furnace-external tiers to obtain distributed temperature data.

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