Aluminum rod heating furnace monitoring method, device, heating furnace and storage medium

Abstract

The present invention discloses a monitoring method, device, heating furnace and storage medium for an aluminum rod heating furnace, and belongs to the technical field of metal processing. The present invention obtains control data of the heating furnace and material characteristic data of the target aluminum rod, and controls the sensor to obtain the operation data within the time period when the control data is executed; determines a first correlation according to a correlation algorithm, the control data and the operation data, and determines a second correlation according to the correlation algorithm, the material characteristic data and the control data, the first correlation being the correlation between the control data and the operation data, and the second correlation being the correlation between the material characteristic data and the control data; determines the monitoring result of the heating furnace according to the first correlation and the second correlation, thereby achieving the effect of improving the accuracy of detecting abnormalities.

Description

Technical Field

[0001] The invention relates to the field of metal processing, and in particular to a monitoring method and device for an aluminum rod heating furnace, a heating furnace and a storage medium. Background Art

[0002] As the demand for energy conservation gradually increases, in aluminum processing, combustion heating furnaces are gradually replaced by electromagnetic induction aluminum rod heating furnaces. In electromagnetic induction aluminum rod heating furnaces, it is not simply uniform heating, but also necessary to form a temperature gradient in the aluminum during the heating process according to the requirements of aluminum parameters to ensure the quality of the aluminum. At present, multiple different instrument components are generally set to monitor the operating parameters of the heating furnace, such as: monitoring current, voltage, temperature, and pressure, but often only by comparing standard data with the collected operating data, it can only be determined that there is an abnormality, which makes it impossible to determine the component with abnormal heating.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide an aluminum rod heating furnace monitoring method, device, heating furnace and storage medium, aiming to improve the accuracy of abnormality detection. To achieve the above purpose, the present invention provides an aluminum rod heating furnace monitoring method, the aluminum rod heating furnace monitoring method comprising the following steps:

[0005] Acquire control data of the heating furnace and material characteristic data of the target aluminum rod, and control the sensor to acquire operation data within a time period during which the control data is executed;

[0006] Determine a first correlation according to a correlation algorithm, the control data, and the operating data, and determine a second correlation according to the correlation algorithm, the material characteristic data, and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data;

[0007] The monitoring result of the heating furnace is determined according to the first correlation and the second correlation.

[0008] Optionally, the heating furnace is an electromagnetic induction heating furnace, the heating furnace includes a plurality of coil groups, the control data includes: coil position data of the plurality of coil groups and corresponding power control data, the operation data includes: a plurality of material position data of the target aluminum rod and temperature data corresponding to the material position data, and the step of determining the first correlation according to the correlation algorithm, the control data and the operation data includes:

[0009] Determine a first relationship pairing group according to the coil position data and the material position data to obtain a plurality of the first relationship pairing groups, wherein the first relationship pairing groups include the power control data and the temperature data;

[0010] The first correlation relationship is determined according to the first correlation pairing group and the correlation algorithm to obtain a plurality of the first correlation relationships.

[0011] Optionally, the step of determining the first correlation relationship according to the first relationship pairing group and the correlation algorithm includes:

[0012] Mapping the power control data into first data and mapping the temperature data into second data according to a preset conversion relationship;

[0013] Calculate the Pearson correlation coefficient according to the Pearson correlation coefficient calculation formula, the first data and the second data;

[0014] The Pearson correlation coefficient is used as the first correlation.

[0015] Optionally, the step of determining the monitoring result of the heating furnace according to the first correlation and the second correlation includes:

[0016] When the first correlation matches the preset first correlation, and the second correlation does not match the preset second correlation, determining that the monitoring result is that there is an abnormality in the control system of the heating furnace;

[0017] When the first correlation does not match the preset first correlation, and the second correlation matches the preset second correlation, it is determined that the monitoring result is that the sensor of the heating furnace is abnormal;

[0018] When the first correlation matches the preset first correlation, and the second correlation matches the preset second correlation, determining that the monitoring result is that the heating furnace has no abnormality;

[0019] When the first correlation does not match the preset first correlation, and the second correlation does not match the preset second correlation, it is determined that the monitoring result is that the target aluminum rod is abnormal or the heating furnace is abnormal as a whole.

[0020] Optionally, before the step of the control sensor acquiring the operation data within the time period during which the control data is executed, the step further includes:

[0021] Obtaining a heating requirement of the target aluminum bar, wherein the heating requirement is a temperature that needs to be reached when the target aluminum bar is heated;

[0022] Determining preset control data according to the heating requirement and material characteristic data;

[0023] The heating furnace is controlled according to preset control data and real-time control operations.

[0024] Optionally, after the step of controlling the heating furnace according to preset control data and real-time control operation, the step further includes:

[0025] The control data is determined according to the preset control data and the real-time control operation.

[0026] Optionally, the material characteristic data includes at least one of diameter data, length data, purity and surface quality.

[0027] In addition, in order to achieve the above-mentioned purpose, the present invention also provides an aluminum rod heating furnace monitoring device, the aluminum rod heating furnace monitoring device comprising:

[0028] An acquisition module, used to acquire control data of the heating furnace and material characteristic data of the target aluminum rod, and control the sensor to acquire operation data within a time period during which the control data is executed;

[0029] an analysis module, configured to determine a first correlation according to a correlation algorithm, the control data, and the operating data, and to determine a second correlation according to the correlation algorithm, the material characteristic data, and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data;

[0030] A monitoring module is used to determine the monitoring result of the heating furnace according to the first correlation and the second correlation.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also provides a heating furnace, which includes: a memory, a processor, and an aluminum rod heating furnace monitoring program stored in the memory and runnable on the processor, and the aluminum rod heating furnace monitoring program is configured to implement the steps of the aluminum rod heating furnace monitoring method described in any one of the above items.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which an aluminum rod heating furnace monitoring program is stored. When the aluminum rod heating furnace monitoring program is executed by a processor, the steps of the aluminum rod heating furnace monitoring method described in any one of the above items are implemented.

[0033] The present invention provides a method for monitoring an aluminum rod heating furnace, which obtains control data of the heating furnace, material characteristic data of a target aluminum rod, and operation data, and calculates a first correlation and a second correlation. Since the values ​​of the first correlation and the second correlation are actually due to at least one data being abnormal, the data in the control data, the material characteristic data, and the operation data that are abnormal can be determined according to the changes in the first correlation and the second correlation, so that it can be accurately determined which types of components are abnormal during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a structural schematic diagram of a heating furnace in a hardware operating environment involved in an embodiment of the present invention;

[0035] Figure 2 It is a schematic flow chart of a first embodiment of the monitoring method of an aluminum rod heating furnace of the present invention;

[0036] Figure 3 It is a schematic flow chart of a second embodiment of the monitoring method of an aluminum rod heating furnace of the present invention;

[0037] Figure 4 It is a schematic flow chart of a third embodiment of the aluminum rod heating furnace monitoring method of the present invention;

[0038] Figure 5 It is a schematic flow chart of a fourth embodiment of the monitoring method of an aluminum rod heating furnace of the present invention.

[0039] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0041] Reference Figure 1 , Figure 1 A schematic diagram of the heating furnace structure of the hardware operating environment involved in the embodiment of the present invention.

[0042] like Figure 1As shown, the heating furnace may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The interactive device 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional interactive device 1003 may also be connected to the communication bus through a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0043] In addition, the heating furnace may also be an electromagnetic induction heating furnace, and the heating furnace includes a plurality of coil groups.

[0044] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the heating furnace, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0045] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and an aluminum rod heating furnace monitoring program.

[0046] exist Figure 1 In the heating furnace shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the heating furnace of the present invention can be set in the heating furnace, and the heating furnace calls the aluminum rod heating furnace monitoring program stored in the memory 1005 through the processor 1001, and executes the aluminum rod heating furnace monitoring method provided by the embodiment of the present invention.

[0047] The embodiment of the present invention provides a method for monitoring an aluminum rod heating furnace, referring to Figure 2 , Figure 2 The figure is a flow chart of a first embodiment of a method for monitoring an aluminum rod heating furnace according to the present invention.

[0048] In this embodiment, the aluminum rod heating furnace monitoring method includes:

[0049] Step S1, obtaining control data of the heating furnace and material characteristic data of the target aluminum rod, and controlling the sensor to obtain operation data within a time period during which the control data is executed;

[0050] It should be noted that the material characteristic data of the target aluminum rod here may include the material characteristic data of the aluminum rod before heating, or the material characteristic data of the aluminum rod after heating. The material characteristic data here may be the type of aluminum rod, diameter data, length data, purity and surface quality. The material characteristic data here may be obtained by setting a sensor and detecting, such as setting a camera to obtain image data of the target aluminum rod, and determining the diameter data and length data of the aluminum rod through the image data. In addition, the material characteristic data here may also be obtained by acquiring data stored in a storage medium. The control data of the heating furnace here is the control data recorded during the heating process of the target aluminum rod in the heating furnace, and the control data includes heating data for each moment, such as the target heating power. It should be noted that due to the popularization of automation, a preset control data can generally be determined first. The heating furnace is controlled according to the preset control data, and appropriate adjustments are made during the heating process according to the actual situation of the aluminum rod. The collected operation data within the time period for obtaining the execution of the control data, the data type here may be the heating time, the power, voltage, current of each coil group in each time period, and the temperature rise data of the target aluminum rod, etc. It should be noted that, due to energy loss in the heating process, the sum of the powers of the coil groups here cannot actually be equal to the target heating power of the control data.

[0051] Further, optionally, in some heating furnaces, in order to generate a temperature gradient in the aluminum rod during the heating process, the temperature gradient is formed by moving the position of the aluminum rod, so the operation data may also include the moving speed and position data of the target aluminum rod in the heating furnace. Preferably, step S1 here is performed after the heating furnace heats the target aluminum rod.

[0052] Step S2, determining a first correlation according to a correlation algorithm, the control data and the operating data, and determining a second correlation according to the correlation algorithm, the material characteristic data and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data;

[0053] In this embodiment, the type of the correlation algorithm is not limited, but it is necessary to ensure that the algorithm of the first correlation and the algorithm of the second correlation in this embodiment are the same. In addition, it should be noted that since the control data and the operation data often have more than one type of data, the first correlation may include more than one first sub-correlation; there may also be multiple first correlations.

[0054] Step S3, determining the monitoring result of the heating furnace according to the first correlation and the second correlation.

[0055] Optionally, the first correlation and the second correlation are matched with a preset first correlation and a preset second correlation respectively, so as to judge different states in the heating process, so as to determine the monitoring result. Optionally, the monitoring result is judged according to the change of the first correlation and the second correlation.

[0056] In this embodiment, the control data of the heating furnace, the material characteristic data of the target aluminum bar and the operation data are obtained, and the first correlation and the second correlation are calculated. Since the values ​​of the first correlation and the second correlation are actually due to the abnormality of at least one data, the abnormal data in the control data, the material characteristic data and the operation data can be determined according to the changes in the first correlation and the second correlation, so as to accurately determine which types of components are abnormal during operation.

[0057] Further, based on the first embodiment, a second embodiment of the aluminum rod heating furnace monitoring method of the present invention is proposed. In this embodiment, referring to Figure 3 The heating furnace is an electromagnetic induction heating furnace, the heating furnace includes a plurality of coil groups, the control data includes: coil position data of the plurality of coil groups and corresponding power control data, the operation data includes: a plurality of material position data of the target aluminum rod and temperature data corresponding to the material position data, and the step of determining the first correlation according to the correlation algorithm, the control data and the operation data includes:

[0058] Step S21, determining a first relationship pairing group according to the coil position data and the material position data, and obtaining a plurality of the first relationship pairing groups, wherein the first relationship pairing groups include the power control data and the temperature data;

[0059] In this embodiment, the heating furnace is an electromagnetic induction heating furnace, and the heating furnace can be a variable-turn electromagnetic induction heating furnace. It should be noted that the power control data here is not the energy consumption data of the coil group, and the energy consumed by the coil cannot actually be completely converted into the power control data.

[0060] Generally, the coil position data and the material position data can be mapped to the same space for comparison, and a plurality of power control data and the temperature data can be paired according to the coil position data and the material position data, so as to obtain the first relationship pairing group. Specifically, the corresponding coil position value of the coil position data under the material position data standard is calculated according to the coil position data and a preset conversion formula, the coil position value and the material position data are compared, and the power control data and the temperature data corresponding to the same coil position value and the material position data generate a first relationship pairing group. The target heating power in the first embodiment can be the power control data of each coil group at the same time. In other embodiments, the operating data can also be pressure data, but compared with the temperature data, the pressure is generally the same everywhere in the heating furnace.

[0061] Step S22: determining the first correlation relationship according to the first relationship pairing group and the correlation algorithm to obtain a plurality of the first correlation relationships.

[0062] Preferably, the power control data and the temperature data in the first relationship pairing group are data recorded at each moment in the heating process, rather than data detected at a single moment. The target number of power control data and temperature data can be obtained according to the detection frequency and detection time of the sensor. Thus, the first correlation is determined according to the first relationship pairing group and the correlation algorithm, and since there are multiple first relationship pairing groups, multiple first correlations can be obtained.

[0063] In this embodiment, a first relationship pairing group is determined according to the coil position data and the material position data to obtain a plurality of the first relationship pairing groups. Since the first correlation relationship subsequently calculated can be used to determine whether there is an abnormality, it is possible to more accurately determine whether each coil group is operating normally, thereby improving the accuracy of detection and being able to pinpoint abnormal coil groups.

[0064] Further, the step of determining the first correlation relationship according to the first relationship pairing group and the correlation algorithm includes:

[0065] Mapping the power control data into first data and mapping the temperature data into second data according to a preset conversion relationship;

[0066] Calculate the Pearson correlation coefficient according to the Pearson correlation coefficient calculation formula, the first data and the second data;

[0067] The Pearson correlation coefficient is used as the first correlation.

[0068] In some embodiments, the first correlation is determined based on the fluctuation relationship between the power control data and the temperature data at different times. In other embodiments, other types of calculation methods may be used to determine the first correlation, such as cosine similarity.

[0069] In this embodiment, the Pearson correlation coefficient is calculated by using the Pearson correlation coefficient calculation formula, the first data, and the second data, and the Pearson correlation coefficient is used as the first correlation relationship, thereby improving the accuracy of the first correlation relationship.

[0070] Further, based on the first embodiment or the second embodiment, a third embodiment of the aluminum rod heating furnace monitoring method of the present invention is proposed. In this embodiment, referring to Figure 4 The step of determining the monitoring result of the heating furnace according to the first correlation and the second correlation comprises:

[0071] Step S31, when the first correlation matches the preset first correlation, and the second correlation does not match the preset second correlation, determining that the monitoring result is that there is an abnormality in the control system of the heating furnace;

[0072] Step S32, when the first correlation does not match the preset first correlation, and the second correlation matches the preset second correlation, determining that the monitoring result is that the sensor of the heating furnace is abnormal;

[0073] Step S33, when the first correlation matches the preset first correlation, and the second correlation matches the preset second correlation, determining that the monitoring result is that the heating furnace has no abnormality;

[0074] Step S34: when the first correlation does not match the preset first correlation, and the second correlation does not match the preset second correlation, it is determined that the monitoring result is that the target aluminum rod is abnormal or the heating furnace is abnormal as a whole.

[0075] The preset first correlation here is obtained based on the normal operation data before the current moment and can be adjusted by the equipment control personnel. By comparing the correlation coefficient corresponding to the first correlation with the correlation coefficient of the preset first correlation, it is determined whether the first correlation matches the preset first correlation.

[0076] When the first correlation matches the preset first correlation, it means that at least one abnormality occurs in the control data and the operating data, and the second correlation needs to be further judged based on the matching result of the preset second correlation. On this basis, when the second correlation does not match the preset second correlation, it can be determined that there is no abnormality in the operating data. At the same time, it can be determined that there is an abnormality in the control data and the monitoring result is that there is an abnormality in the control system of the heating furnace.

[0077] In another case, when the second correlation matches the preset second correlation, it reflects that the operation of the heating furnace can obtain the required aluminum rod. Therefore, if the first correlation does not match the preset first correlation at this time, it means that the monitoring result is that there is an abnormality in the sensor of the heating furnace. It needs to be explained that the abnormality here can include faults and changes in measurement accuracy. For example: there is a deviation in the measured temperature. In this embodiment based on the second embodiment, since the first correlation actually reflects the power control data of the coil group and the temperature data of the operation data. Therefore, it can be judged whether there is an abnormality in each coil group.

[0078] In this embodiment, by respectively judging whether the first correlation and the second correlation match the preset data, the monitoring of the heating furnace can be completed, thereby improving the accuracy of the monitoring result.

[0079] Further, based on any of the above embodiments, a fourth embodiment of the aluminum rod heating furnace monitoring method of the present invention is proposed. In this embodiment, referring to Figure 5 , before the step of the control sensor acquiring the operation data within the time period during which the control data is executed, the step further includes:

[0080] Step S01, obtaining the heating requirement of the target aluminum bar, where the heating requirement is the temperature that the target aluminum bar needs to reach when heated;

[0081] Step S02, determining preset control data according to the heating requirement and material characteristic data;

[0082] Step S03, controlling the heating furnace according to preset control data and real-time control operations.

[0083] In this embodiment, it is ensured that the aluminum rod is heated to an appropriate temperature to meet the requirements of subsequent processing or treatment, for example, the melting point of aluminum is 660.3°C. In other embodiments, the heating requirement may be a heating speed, and the heating speed is adjusted according to different requirements, in order to avoid thermal stress caused by overheating. The preset control data is determined according to the heating requirement and the material characteristic data; the preset control data here often differs from the actual control data, because during the heating process, appropriate adjustments are made by detecting the heating situation.

[0084] Furthermore, after the step of controlling the heating furnace according to the preset control data and the real-time control operation, the step further includes:

[0085] The control data is determined according to the preset control data and the real-time control operation.

[0086] In this embodiment, the control data is not preset but data recorded during actual operation, thereby avoiding the problem that the control data is different from the actual data.

[0087] In addition, an embodiment of the present invention further provides a monitoring device for an aluminum rod heating furnace, the monitoring device for an aluminum rod heating furnace comprising:

[0088] An acquisition module, used to acquire control data of the heating furnace and material characteristic data of the target aluminum rod, and control the sensor to acquire operation data within a time period during which the control data is executed;

[0089] an analysis module, configured to determine a first correlation according to a correlation algorithm, the control data, and the operating data, and to determine a second correlation according to the correlation algorithm, the material characteristic data, and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data;

[0090] A monitoring module is used to determine the monitoring result of the heating furnace according to the first correlation and the second correlation.

[0091] In addition, an embodiment of the present invention also proposes a heating furnace, which includes: a memory, a processor, and an aluminum rod heating furnace monitoring program stored in the memory and runnable on the processor, and the aluminum rod heating furnace monitoring program is configured to implement the steps of the embodiment of the aluminum rod heating furnace monitoring method described in any one of the above items.

[0092] In addition, an embodiment of the present invention further proposes a storage medium, on which an aluminum rod heating furnace monitoring program is stored. When the aluminum rod heating furnace monitoring program is executed by a processor, the steps of the embodiment of the aluminum rod heating furnace monitoring method described in any one of the above items are implemented.

[0093] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0094] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0096] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring an aluminum rod heating furnace, characterized in that: The aluminum rod heating furnace monitoring method comprises the following steps: Acquire control data of the heating furnace and material characteristic data of the target aluminum rod, and control the sensor to acquire operation data within a time period during which the control data is executed; Determine a first correlation according to a correlation algorithm, the control data, and the operating data, and determine a second correlation according to the correlation algorithm, the material characteristic data, and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data; Determining a monitoring result of the heating furnace according to the first correlation and the second correlation; The heating furnace is an electromagnetic induction heating furnace, and the heating furnace includes multiple coil groups. The control data includes: coil position data of the multiple coil groups and corresponding power control data. The operation data includes: multiple material position data of the target aluminum rod and temperature data corresponding to the material position data. The material characteristic data includes: at least one of diameter data, length data, purity and surface quality.

2. The aluminum rod heating furnace monitoring method according to claim 1, characterized in that: The step of determining the first correlation according to the correlation algorithm, the control data and the operation data comprises: Determine a first relationship pairing group according to the coil position data and the material position data to obtain a plurality of the first relationship pairing groups, wherein the first relationship pairing groups include the power control data and the temperature data; The first correlation relationship is determined according to the first correlation pairing group and the correlation algorithm to obtain a plurality of the first correlation relationships.

3. The aluminum rod heating furnace monitoring method according to claim 2, characterized in that: The step of determining the first correlation relationship according to the first correlation pairing group and the correlation algorithm comprises: Mapping the power control data into first data and mapping the temperature data into second data according to a preset conversion relationship; Calculate the Pearson correlation coefficient according to the Pearson correlation coefficient calculation formula, the first data and the second data; The Pearson correlation coefficient is used as the first correlation.

4. The aluminum rod heating furnace monitoring method according to claim 1, characterized in that: The step of determining the monitoring result of the heating furnace according to the first correlation and the second correlation comprises: When the first correlation matches the preset first correlation, and the second correlation does not match the preset second correlation, determining that the monitoring result is that there is an abnormality in the control system of the heating furnace; When the first correlation does not match the preset first correlation, and the second correlation matches the preset second correlation, it is determined that the monitoring result is that the sensor of the heating furnace is abnormal; When the first correlation matches the preset first correlation, and the second correlation matches the preset second correlation, determining that the monitoring result is that the heating furnace has no abnormality; When the first correlation does not match the preset first correlation, and the second correlation does not match the preset second correlation, it is determined that the monitoring result is that the target aluminum rod is abnormal or the heating furnace is abnormal as a whole.

5. The aluminum rod heating furnace monitoring method according to claim 1, characterized in that: Before the step of the control sensor acquiring the operation data within the time period in which the control data is executed, the method further includes: Obtaining a heating requirement of the target aluminum bar, wherein the heating requirement is a temperature that needs to be reached when the target aluminum bar is heated; Determining preset control data according to the heating requirement and material characteristic data; The heating furnace is controlled according to preset control data and real-time control operations.

6. The aluminum rod heating furnace monitoring method according to claim 5, characterized in that: After the step of controlling the heating furnace according to the preset control data and the real-time control operation, the following steps are further included: The control data is determined according to the preset control data and the real-time control operation.

7. A monitoring device for an aluminum rod heating furnace, characterized in that: The aluminum rod heating furnace monitoring device comprises: An acquisition module, used to acquire control data of the heating furnace and material characteristic data of the target aluminum rod, and control the sensor to acquire operation data within a time period during which the control data is executed; an analysis module, configured to determine a first correlation according to a correlation algorithm, the control data, and the operating data, and to determine a second correlation according to the correlation algorithm, the material characteristic data, and the control data, wherein the first correlation is a correlation between the control data and the operating data, and the second correlation is a correlation between the material characteristic data and the control data; A monitoring module, configured to determine a monitoring result of the heating furnace according to the first correlation and the second correlation; The heating furnace is an electromagnetic induction heating furnace, and the heating furnace includes multiple coil groups. The control data includes: coil position data of the multiple coil groups and corresponding power control data. The operation data includes: multiple material position data of the target aluminum rod and temperature data corresponding to the material position data. The material characteristic data includes: at least one of diameter data, length data, purity and surface quality.

8. A heating furnace, characterized in that: The heating furnace comprises: a memory, a processor and an aluminum rod heating furnace monitoring program stored in the memory and executable on the processor, wherein the aluminum rod heating furnace monitoring program is configured to implement the steps of the aluminum rod heating furnace monitoring method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores an aluminum rod heating furnace monitoring program, and when the aluminum rod heating furnace monitoring program is executed by the processor, the steps of the aluminum rod heating furnace monitoring method according to any one of claims 1 to 6 are implemented.

Images