Method and system for monitoring in real time the operating conditions of a loose conditioning machine

Abstract

The present disclosure provides a method and system for monitoring the running condition of a loose conditioning machine in real time. The method comprises: obtaining one or more real-time running parameters of the loose conditioning machine; applying a trained machine learning model to the one or more real-time running parameters to obtain a predicted output hot air temperature of the loose conditioning machine; obtaining a real-time output hot air temperature of the loose conditioning machine; and comparing the predicted output hot air temperature of the loose conditioning machine with the real-time output hot air temperature and determining whether the loose conditioning machine is running normally based on the result of the comparison. With the method and system of the embodiments of the present disclosure, the running condition of the loose conditioning machine can be monitored in real time using the trained machine learning model, so that the abnormal running condition of the loose conditioning machine can be found in time, the maintenance can be arranged in time, and thus the machine can be prevented from running with faults.

Description

Technical Field

[0001] This disclosure generally relates to methods and systems for real-time monitoring of the operation of loose rehumidifiers. Background Technology

[0002] The loosening and rehumidifying machine, a key piece of equipment in the tobacco processing production line, uses hot and humid air for warm and humidification treatment to adjust the moisture content and temperature of the tobacco leaves, loosening them to achieve the required temperature and humidity. The tobacco leaves treated by the loosening and rehumidifying machine then proceed to subsequent stages in the tobacco processing line, such as leaf moistening, shredding, and drying. Because the tobacco leaves treated by the loosening and rehumidifying machine are used as raw materials for subsequent processes, the operating status (or rather, the normal operation) of the loosening and rehumidifying machine greatly affects the quality and stability of subsequent processes in the tobacco processing production line.

[0003] Therefore, for tobacco processing production lines, ensuring the stable and reliable operation of the loosening and rehumidifying machine to guarantee continuous production is crucial. Currently, to reduce equipment failures, manual inspection is typically used (e.g., checking the operation of valves and other physical components). However, manual inspection suffers from drawbacks such as poor reliability and high workload.

[0004] Due to the lack of effective monitoring methods for the working performance of loosening and rehumidifying machines, deterioration in their operation is often only discovered when there are significant deviations in some process indicators (such as the temperature and / or moisture content of tobacco leaves) or obvious equipment malfunctions. This has a significant impact on production efficiency and product quality.

[0005] Furthermore, in daily maintenance, the overall quality, experience, and sense of responsibility of equipment maintenance personnel can affect the timely detection of abnormal equipment conditions. In particular, some hidden faults are difficult to detect manually when equipment performance deteriorates, as the fault symptoms are not obvious. Only through extensive data comparison and analysis can abnormalities be detected manually, resulting in some equipment abnormalities going undetected for a long time.

[0006] Therefore, there is a need for improved technology for real-time monitoring of the operation of loose rehumidifiers. Summary of the Invention

[0007] One objective of this disclosure is to provide an improved method and system for real-time monitoring of the operation of a loose rehumidifier.

[0008] In one aspect of this disclosure, a method for real-time monitoring of the operation of a loosening and rehumidifying machine is provided, comprising: obtaining one or more real-time operating parameters of the loosening and rehumidifying machine; applying a trained machine learning model to the one or more real-time operating parameters to obtain a predicted output hot air temperature of the loosening and rehumidifying machine; obtaining the real-time output hot air temperature of the loosening and rehumidifying machine; and comparing the predicted output hot air temperature of the loosening and rehumidifying machine with the real-time output hot air temperature and determining whether the loosening and rehumidifying machine is operating normally based on the result of the comparison.

[0009] In another aspect of this disclosure, a system for real-time monitoring of the operation of a loose rehumidifier is provided, comprising: a memory storing one or more programs; and one or more processors coupled to the memory, wherein the one or more programs, when executed by the processors, cause the one or more processors to perform the method described above.

[0010] Using the methods and systems of this disclosure, a trained machine learning model can be used to monitor the operation of a loosening and rehumidifying machine in real time, thereby promptly detecting abnormalities in the machine's operation, arranging timely maintenance, and preventing the machine from operating with faults.

[0011] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided, comprising one or more instruction sequences that, when executed by one or more processors, cause the methods described above to be performed.

[0012] According to another aspect of this disclosure, a computer program product is provided, including program instructions for performing the methods described above. Attached Figure Description

[0013] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments thereof taken in conjunction with the accompanying drawings, wherein like reference numerals generally denote like parts.

[0014] Figure 1 A flowchart is shown of a method for real-time monitoring of the operation of a loosening and rehydration machine according to at least one embodiment of the present disclosure.

[0015] Figure 2 A schematic diagram of the structure of a loosening and rehumidifying machine is shown, according to at least one embodiment of the present disclosure, in which a method for real-time monitoring of the operation of the loosening and rehumidifying machine can be applied.

[0016] Figure 3 A schematic diagram of method steps for obtaining a trained machine learning model according to at least one embodiment of the present disclosure is shown.

[0017] Figure 4 A block diagram of a computing device according to at least one embodiment of the present disclosure is shown. Detailed Implementation

[0018] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0020] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0021] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0022] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0023] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0024] The present invention will now be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart illustrations and / or block diagrams.

[0025] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce a manufacture that includes instruction means that implement the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0026] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus can provide a process for implementing the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0027] Although each operation is described in conjunction with a specific device, those skilled in the art will understand that the operations described above can be performed by different devices, and this disclosure does not limit this. Furthermore, although described as different devices, those skilled in the art will understand that the devices described above can be combined together or further divided into several devices, and this disclosure does not limit this.

[0028] As described above, embodiments of this disclosure provide an improved method and system for real-time monitoring of the operating status of a loose rehumidifier.

[0029] Figure 1 This is a flowchart illustrating a method 100 for real-time monitoring of the operation of a loose rehumidifier according to at least one embodiment of the present disclosure. Figure 2 This is a structural schematic diagram illustrating a loose rehumidifier of a method 100 applicable according to at least one embodiment of the present disclosure. The following will be combined with... Figure 1 and Figure 2 Let's describe method 100.

[0030] like Figure 1 As shown, method 100 may include, at step S101, obtaining one or more real-time operating parameters of the loose rehumidifier.

[0031] Figure 2 A schematic diagram of a loose rehumidifier in which method 100 can be applied is shown. (See diagram below.) Figure 2 As shown, the main sources of steam entering the chamber of the loosening and rehumidifying machine 200 include: (1) Steam 1, which is sprayed out through the steam nozzle 5 under the control of the hot air control valve 2 and then enters the chamber of the loosening and rehumidifying machine through the heat exchanger 4 under the action of the circulating fan 7. The flow rate of Steam 1 is measured by the direct injection steam flow meter 3, and (2) Atomized water 11, which enters the chamber through the atomized water nozzle 10. The amount of atomized water 11 is measured by the atomized water flow meter 12.

[0032] In one example, the real-time operating parameters of the loosening and rehumidifying machine include one or more of the following: the ambient temperature of the loosening and rehumidifying area where the loosening and rehumidifying machine is located, the ambient humidity of the loosening and rehumidifying area where the loosening and rehumidifying machine is located, the opening degree of the hot air temperature control valve of the loosening and rehumidifying machine, the actual value of the compensation steam flow of the loosening and rehumidifying machine, the actual value of the water supply flow of the loosening and rehumidifying machine, and the rear chamber temperature of the loosening and rehumidifying machine.

[0033] The loose rehumidification area refers to the area where the loose rehumidification machine is located, such as a factory workshop. Therefore, the ambient temperature and humidity of the loose rehumidification area can be obtained from the thermometer and hygrometer (not shown in the figure) set up in the area where the loose rehumidification machine is located (e.g., the workshop).

[0034] The opening degree of the hot air temperature control valve refers to Figure 3 The degree of opening of hot air control valve 2. The actual value of compensated steam flow refers to the flow rate from... Figure 2 The actual value of the compensated steam flow rate read from the direct injection steam flow meter 3. The actual water flow rate refers to the flow rate from... Figure 2 The actual water flow rate read by the atomizing water flow meter 12. The rear chamber temperature refers to the temperature measured by... Figure 2 The temperature of the rear chamber of the loose rehumidifier is measured by temperature sensor 8.

[0035] Method 100 may further include, at step S103, applying a trained machine learning model to the one or more real-time operating parameters to obtain a predicted output hot air temperature of the loose rehumidifier.

[0036] Applying a trained machine learning model to one or more real-time operating parameters to obtain the predicted output hot air temperature of the loose rehumidifier can be performed at predetermined time intervals, such as once per second, once per minute, etc. Those skilled in the art will understand that the predetermined time intervals can be selected according to actual needs, and this disclosure does not limit this selection.

[0037] In one example, such as Figure 3 As shown, the trained machine learning model is obtained through the following operations: in step S301, the historical operating parameters of the loosening and rehumidifying machine and the corresponding historical output hot air temperature are obtained; and in step S303, the machine learning model is trained using the historical operating parameters and the historical output hot air temperature to obtain the trained machine learning model.

[0038] In one example, the machine learning model includes one of the following: LSTM network model, XGBOOST model, GBDT model.

[0039] In one example, the method 100 further includes: preprocessing the historical operating parameters and the historical output hot air temperature before training the machine learning model using the acquired historical operating parameters and the corresponding historical output hot air temperature.

[0040] In one example, the preprocessing includes one or more of the following: outlier handling, correlation analysis, standardization, and normalization.

[0041] For example, for time point t n In other words, it can be done through t n The first 6 time points (t n-6 , t n-5 , t n-4 , t n-3 , t n-2 , t n-1 The six sets of historical operating parameters are used as input parameters and time point t. n The actual hot air temperature value is used to train the LTSM network model.

[0042] For example, an LSTM network model with one hidden layer can be built in the TensorFlow 2.1.1 environment. The number of iterations (epochs) is set to 50, the optimal number of nodes to 64, and the batch size to 256. The mean squared error (MSE) is chosen as the loss function, and the Adam optimizer is used to optimize the loss function. After training, the loss on the training set reaches 0.000024, and the loss on the validation set reaches 0.000023. The average absolute error between the model's predicted hot air temperature value and the true value on the test set is 0.38 degrees Celsius. Taking a hot air temperature of 60℃ as an example, the prediction accuracy reaches 99.37%. The deviation during actual model operation is generally controlled within 0.5℃, and the cumulative average is controlled within 0.3℃.

[0043] Method 100 may further include, at step S105, obtaining the real-time output hot air temperature of the loosening and rehumidifying machine.

[0044] Real-time output of hot air temperature can be achieved, for example, by reading... Figure 2 The temperature is obtained from the temperature sensor 6 in the middle.

[0045] Method 100 may further include, at step S107, comparing the predicted output hot air temperature of the loosening and rehumidifying machine with the real-time output hot air temperature and determining whether the loosening and rehumidifying machine is operating normally based on the result of the comparison.

[0046] Using the method of this disclosure, a trained machine learning model can be used to monitor the operation of the loosening and rehumidifying machine in real time, thereby promptly detecting abnormalities in the operation of the loosening and rehumidifying machine, arranging timely maintenance, and thus preventing the machine from operating with faults.

[0047] In one example, determining whether the loosening and rehumidifying machine is operating normally based on the result of the comparison may include, for example, determining that the loosening and rehumidifying machine is malfunctioning in response to determining that the predicted output hot air temperature exceeds a predetermined threshold for the real-time output hot air temperature.

[0048] Those skilled in the art will understand that the predetermined threshold can be set according to actual needs, for example, to 0.5 degrees. According to this example, the loosening and rehumidifying machine is determined to be malfunctioning as long as the predicted output hot air temperature exceeds the predetermined threshold (e.g., 0.5 degrees) of the real-time output hot air temperature.

[0049] In one example, determining whether the loosening and rehumidifying machine is operating normally based on the result of the comparison may include, for example, determining that the loosening and rehumidifying machine is malfunctioning in response to determining that the predicted output hot air temperature exceeds a predetermined threshold for a predetermined time in real-time output hot air temperature.

[0050] For example, a predetermined threshold can be set to 0.5 degrees Celsius, and a predetermined duration can be set to 1 minute. According to this example, the loosening and rehumidifying machine is only considered to be malfunctioning if the predicted output hot air temperature exceeds the predetermined threshold (e.g., 0.5 degrees Celsius) for a continuous 1 minute.

[0051] According to this example, since the loose rehumidifier is only considered to be malfunctioning when the predicted output hot air temperature exceeds a predetermined threshold for the real-time output hot air temperature and this exceedance lasts for a predetermined time, misjudgments due to data fluctuations can be reduced.

[0052] In one example, determining whether the loosening and rehumidifying machine is operating normally based on the comparison result may include: calculating the temperature performance health of the loosening and rehumidifying machine; and comparing the calculated temperature performance health with a preset threshold to determine whether the loosening and rehumidifying machine is operating normally; wherein the temperature performance health is calculated using the following formula:

[0053]

[0054] Where T i P represents the predicted output hot air temperature of the loosening and rehumidifying machine. i This represents the real-time output hot air temperature of the loosening and rehumidifying machine, and n represents the number of time points used to calculate the health of temperature performance.

[0055] For example, the predicted output hot air temperature can be calculated every second, and then the temperature performance health H can be calculated for one hour (i.e., n=3600) according to the above formula (1). Then, the equipment's working performance can be judged to be in a stable and normal state based on the calculated temperature performance health H.

[0056] For example, assuming that the predicted temperature differs from the actual temperature by 0.3 degrees per second, and the predicted output temperature is 60 degrees, then according to the above formula (1), the temperature performance health H can be calculated to be 99.5%.

[0057] For example, if the temperature performance health of the loosening and rehumidifying machine is lower than a predetermined threshold (e.g., 95.0%), it can be determined that the loosening and rehumidifying machine is malfunctioning, indicating that the components of the loosening and rehumidifying machine need to be checked for deterioration or failure.

[0058] In one example, method 100 may further include providing an alarm in response to determining that the loose rehumidifier is malfunctioning.

[0059] For example, an alarm can be audible or visual. It can provide an alarm sound or be displayed on a monitor or related device.

[0060] According to one aspect of this disclosure, a system for real-time monitoring of the operation of a loose rehumidifier is also provided, comprising: a memory storing one or more programs; and one or more processors coupled to the memory, wherein the one or more programs, when executed by the processors, cause the methods described above to be performed. For ease of description, specific details regarding method 100 are omitted herein. However, those skilled in the art will understand that all of the above description of method 100 applies equally to the system.

[0061] Using the system, method, and system of this disclosure, a trained machine learning model can be used to monitor the operation of the loosening and rehumidifying machine in real time, thereby promptly detecting abnormalities in the operation of the loosening and rehumidifying machine, arranging timely maintenance, and thus preventing the machine from operating with faults.

[0062] Figure 4 The illustration shows a block diagram of a computing device according to one or more embodiments of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure.

[0063] Now refer to Figure 4The description of computing device 400 is an example of a hardware device that can be applied to various aspects of this disclosure. Computing device 400 can be any machine configured to perform processing and / or computation, and can be, but is not limited to, a workstation, server, desktop computer, laptop computer, tablet computer, personal data assistant, smartphone, in-vehicle computer, or any combination thereof. The various devices / server / client devices mentioned above can be implemented wholly or at least partially by computing device 400 or similar devices or systems.

[0064] The computing device 400 may include elements that may be connected to or communicate with the bus 502 via one or more interfaces. For example, the computing device 400 may include the bus 402, one or more processors 404, one or more input devices 406, and one or more output devices 408. The one or more processors 404 may be any type of processor and may include, but are not limited to, one or more general-purpose processors and / or one or more dedicated processors (such as dedicated processing chips). The input devices 406 may be any type of device capable of inputting information to the computing device and may include, but are not limited to, a mouse, keyboard, touchscreen, microphone, and / or remote control. The output devices 408 may be any type of device capable of presenting information and may include, but are not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. The computing device 400 may also include or be connected to a non-transient storage device 410, which may be any non-transient storage device capable of storing data, and may include, but is not limited to, disk drives, optical storage devices, solid-state storage devices, floppy disks, flexible disks, hard disks, magnetic tapes or any other magnetic media, optical discs or any other optical media, ROM (read-only memory), RAM (random access memory), cache memory and / or any other memory chips or cassettes, and / or any other media from which a computer may read data, instructions and / or code. The non-transient storage device 410 may be detachable from an interface. The non-transient storage device 410 may have data / instructions / code for implementing the methods and steps described above. The computing device 400 may also include a communication device 412. The communication device 412 may be any type of device or system capable of communicating with external devices and / or with networks, and may include, but is not limited to, modems, network interface cards, infrared communication devices, wireless communication devices and / or chipsets, such as Bluetooth™ devices, 1302.11 devices, Wi-Fi devices, WiMAX devices, cellular communication facilities, etc.

[0065] Furthermore, the non-transient storage device 410 may contain map information and software elements, enabling the processor 404 to perform route guidance processing. Additionally, the output device 406 may include a display for showing maps, vehicle location markers, and images indicating vehicle movement. The output device 406 may also include a speaker or headphone interface for audio guidance.

[0066] Bus 402 may include, but is not limited to, Industry Standard Architecture (ISA) bus, Microchannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. In particular, for automotive devices, bus 402 may also include Controller Area Network (CAN) bus or other architectures designed for automotive applications.

[0067] The computing device 400 may also include working memory 414, which may be any kind of working memory that can store instructions and / or data useful for the operation of the processor 404, and may include, but is not limited to, random access memory and / or read-only memory devices.

[0068] Software elements may reside in working memory 414, including but not limited to operating system 416, one or more application programs 418, drivers, and / or other data and code. Instructions for performing the methods and steps described above may be included in one or more application programs 418, and components / units / elements of the various devices / servers / clients mentioned above may be implemented by processor 404 reading and executing the instructions of one or more application programs 418.

[0069] It should also be recognized that variations are possible depending on specific requirements. For example, custom hardware may be used, and / or specific components may be implemented in hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. Additionally, connections to other computing devices (such as network input / output devices) may be employed. For instance, some or all of the disclosed methods and systems can be implemented by programming hardware (e.g., programmable logic circuit systems including field-programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) using the logic and algorithms according to this disclosure in assembly language or hardware programming languages ​​(such as Verilog, VHDL, C++).

[0070] This disclosure also provides a non-transitory computer-readable storage medium including one or more instruction sequences that, when executed by one or more processors, cause the methods described above to be performed.

[0071] This disclosure also provides a computer program product including program instructions for performing the methods described above.

[0072] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0073] Various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring a loose conditioning machine in real time, comprising: obtaining one or more real-time operating parameters of a loose conditioning machine, wherein the real-time operating parameters of the loose conditioning machine comprise one or more of: an ambient temperature of a loose conditioning area in which the loose conditioning machine is located, an ambient humidity of the loose conditioning area, an opening degree of a hot air temperature control valve of the loose conditioning machine, a compensation steam flow actual value of the loose conditioning machine, a water addition flow actual value of the loose conditioning machine, and a back room temperature of the loose conditioning machine; applying a trained machine learning model to the one or more real-time operating parameters to obtain a predicted output hot air temperature of the loose conditioning machine; obtaining a real-time output hot air temperature of the loose conditioning machine; and comparing the predicted output hot air temperature of the loose conditioning machine with the real-time output hot air temperature and determining whether the loose conditioning machine is operating normally based on a result of the comparison, comprising: calculating a temperature performance health degree of the loose conditioning machine; comparing the calculated temperature performance health degree with a pre-set threshold to determine whether the loose conditioning machine is operating normally; wherein the temperature performance health degree H is determined using the following formula:

2. The method of claim 1, wherein the trained machine learning model is obtained by: obtaining historical operating parameters of the loose conditioning machine and corresponding historical output hot air temperatures; and where T i represents the predicted output hot air temperature of the loose conditioning machine, P i represents the real-time output hot air temperature of the loose conditioning machine, and n represents the number of time points for calculating the temperature performance health. training a machine learning model using the obtained historical operating parameters and corresponding historical output hot air temperatures to obtain the trained machine learning model.

3. The method of claim 2, further comprising: preprocessing the obtained historical operating parameters and the historical output hot air temperatures before training the machine learning model using the obtained historical operating parameters and corresponding historical output hot air temperatures.

4. The method of claim 3, wherein the preprocessing comprises one or more of: outlier processing, correlation analysis, standardization, and normalization processing.

5. The method of claim 3, wherein the machine learning model comprises one of: an LSTM network model, an XGBOOST model, a GBDT model. providing an alarm in response to determining that the loose conditioning machine is operating abnormally.

7. A system for monitoring a loose conditioning machine in real time, comprising:

6. The method of claim 1, further comprising: a memory storing one or more programs; and one or more processors coupled to the memory, the one or more programs, when executed by the processors, cause the one or more processors to perform the method of any one of claims 1-6.

8. A non-transitory computer-readable storage medium comprising one or more sequences of instructions which, when executed by one or more processors, cause a method of any one of claims 1-6 to be performed.

9. A computer program product comprising program instructions for performing the method of any one of claims 1-6. ​ ​ ​

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