Method for automatically generating and deploying machine learning model based on process simulation platform

Abstract

Disclosed is a method for automatically generating and deploying a machine learning model based on a process simulation platform. Firstly, model training data is acquired, and the acquired model training data is preprocessed. Then, on the basis of a pre-constructed automated machine learning model framework, a machine learning model to be trained is created, and automatic model training is performed on the newly created machine learning model on the basis of the preprocessed model training data, so as to generate a data model upon completion of the model training. Finally, the generated data model is automatically deployed to a process simulation platform, so as to connect to a mechanism model on the process simulation platform for process calculation and prediction.

Description

An Automatic Generation and Deployment Method for Machine Learning Models Based on a Process Simulation Platform Technical Field

[0001] This invention relates to the field of industrial process modeling, and more specifically to a method for automatically generating and deploying machine learning models based on a process simulation platform. Background Technology

[0002] In modern industrial production, process simulation software is widely used for modeling and optimizing complex industrial processes. However, traditional mechanism-based modeling methods rely on expert experience and large amounts of experimental data, making the modeling process cumbersome and time-consuming, especially when processes are complex and involve numerous variables, making it difficult to build high-accuracy models in a short time. Furthermore, as process conditions change, mechanistic models require frequent readjustment and verification, increasing the complexity and cost of model maintenance. Therefore, data-driven industrial process modeling using machine learning techniques is gradually becoming a trend.

[0003] While existing automated machine learning technologies can alleviate the modeling burden to some extent, they still have some limitations. For example, most automated machine learning tools cannot be directly integrated with process simulation platforms and lack sufficient support for complex, multi-input, multi-output industrial processes, making it difficult to promote and apply them on a large scale in real-world production environments. Therefore, there is an urgent need for a highly integrated, complete solution capable of automatically acquiring data, generating models, and deploying them to achieve efficient modeling and optimization of industrial processes. Summary of the Invention

[0004] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of all aspects, nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form to prepare for the more detailed descriptions that follow.

[0005] The purpose of this invention is to solve the above problems and provide a method for automatically generating and deploying machine learning models based on a process simulation platform. The method uses a pre-built automatic machine learning model framework to create a new machine learning model to be trained. Through automated data collection, modeling, training and deployment, it greatly reduces manual intervention, shortens the modeling cycle and improves modeling efficiency and accuracy.

[0006] The technical solution of this invention is as follows:

[0007] This invention provides a method for automatically generating and deploying machine learning models based on a process simulation platform, comprising the following steps:

[0008] Step S1: Collect model training data and preprocess the collected model training data;

[0009] Step S2: Create a new machine learning model to be trained based on the pre-built automatic machine learning model framework;

[0010] Step S3: Automatically train the newly built machine learning model based on the preprocessed model training data to generate a data model that has completed model training;

[0011] Step S4: Automatically deploy the generated data model to the process simulation platform, thereby connecting the mechanism model on the process simulation platform for process calculation and prediction.

[0012] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, in step S1, the automatic generation and deployment method of machine learning model based on process simulation platform collects key process parameters in the process operation as model training data based on process simulation platform or factory real-time database, and then preprocesses the collected model training data; wherein, the preprocessing includes data cleaning, normalization processing, missing value completion and feature selection.

[0013] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, in step S1, after preprocessing the collected model training data, the automatic generation and deployment method of machine learning model based on process simulation platform specifies the input variable feature column and the output variable, i.e., the label column, for the collected model training data, thereby obtaining tabular model training data.

[0014] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, in step S2, the automatic generation and deployment method of machine learning model based on process simulation platform uses AutoGluon to pre-build an automatic machine learning model framework and deploy it in process simulation software, and then builds and trains machine learning models based on the built automatic machine learning model framework; wherein, the automatic machine learning model framework supports the configuration of multiple model inputs and multiple model outputs.

[0015] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, after the machine learning model is newly created, a front-end interactive interface is automatically generated, and the machine learning model is automatically trained through the generated front-end interactive interface; wherein, the front-end interactive interface includes a parameter configuration area, a model selection control area, a training process monitoring area, and a prediction result visualization area.

[0016] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, when the automatic generation and deployment method of machine learning model based on process simulation platform performs automatic model training of machine learning model through front-end interactive interface, the user configures and dynamically adjusts the model input and model output of machine learning model through parameter configuration area, selects the required machine learning model type in model selection control area, monitors the model training process through training process monitoring area, and finally views the model training prediction results through prediction result visualization area.

[0017] According to an embodiment of the automatic generation and deployment method of machine learning models based on a process simulation platform of the present invention, automatic model training includes model selection, feature engineering, and hyperparameter tuning; wherein, when the automatic generation and deployment of the machine learning model performs automatic model training, the model training process is accelerated by parallel computing, and the hyperparameter tuning is performed by Bayesian optimization method.

[0018] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, the automatic generation and deployment method of machine learning model based on process simulation platform performs data transmission and model deployment through API interface; wherein, when the automatic generation and deployment method of machine learning model based on process simulation platform performs automatic model training, it updates the model training data of machine learning model in real time through API interface, and automatically pushes the generated data model to process simulation platform for deployment through API interface, thereby connecting the mechanism model on process simulation platform for process calculation and prediction.

[0019] According to an embodiment of the automatic generation and deployment method for machine learning models based on a process simulation platform of the present invention, after generating a data model, the automatic generation and deployment method automatically monitors the predictive ability of the generated data model based on preset performance indicators to determine whether to retrain the model; wherein,

[0020] If the detected predictability is lower than the preset performance metric, the model will be automatically retrained and the parameters will be adjusted.

[0021] If the monitored predictability is greater than or equal to the preset performance index, the generated data model will be automatically deployed to the process simulation platform.

[0022] According to an embodiment of the automatic generation and deployment method of machine learning model based on process simulation platform of the present invention, the automatic generation and deployment method of machine learning model based on process simulation platform further sets up a running status monitoring strategy, which monitors whether the running status of the deployed data model is abnormal; if so, model maintenance is performed; if not, monitoring continues.

[0023] The present invention also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the method described above.

[0024] The present invention also provides an apparatus for automatically generating and deploying machine learning models based on a process simulation platform, comprising:

[0025] Memory, used to store instructions that can be executed by a processor; and

[0026] A processor for executing the instructions to implement the method described above.

[0027] Compared with existing technologies, this invention offers the following advantages: For the automated generation and deployment of machine learning models on process simulation platforms, this invention utilizes a pre-built automated machine learning model framework to create a new machine learning model to be trained. Through automated data acquisition, modeling, training, and deployment, it significantly shortens the modeling cycle, reduces manual intervention, and improves modeling efficiency and accuracy. Furthermore, this invention supports configuring multiple model inputs and outputs, making it suitable for modeling complex multi-input, multi-output industrial processes and meeting the multi-objective control needs of industries such as chemical, petrochemical, and pharmaceutical. Simultaneously, this invention can seamlessly integrate the generated data model with the process simulation platform, thereby achieving an integrated solution from data acquisition to real-time prediction, effectively improving process control accuracy, reducing energy consumption, and enhancing product quality. Attached Figure Description

[0028] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0029] Figure 1 is a flowchart illustrating an embodiment of the automatic generation and deployment method of machine learning models based on a process simulation platform according to the present invention. Detailed Implementation

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0031] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0032] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0033] This document discloses an embodiment of a method for automatically generating and deploying machine learning models based on a process simulation platform. Figure 1 is a flowchart illustrating an embodiment of this method. Referring to Figure 1, the following is a detailed description of each step in the method for automatically generating and deploying machine learning models based on a process simulation platform.

[0034] Step S1: Collect model training data and preprocess the collected model training data.

[0035] In this embodiment, before training the machine learning model, key process parameters during the process operation are first collected from the process simulation platform or the factory real-time database as model training data. Then, the collected model training data is preprocessed. The preprocessing includes data cleaning, normalization, missing value correction, and feature selection.

[0036] Specifically, in this embodiment, to obtain model training data in a timely manner, a scheduled task or data acquisition trigger condition can be set to collect key process parameters, including but not limited to temperature, pressure, flow rate, and component concentration, from the process simulation platform or the factory's real-time database during process operation as model training data, ensuring the real-time nature and continuity of the data. Simultaneously, to ensure data validity, the collected data is cleaned to remove noise and outliers. Then, normalization is used to convert variables of different dimensions to the same scale, and interpolation and mean imputation methods are employed to complete missing values, ensuring data integrity. Finally, feature selection is performed on the collected data to identify features that have a significant impact on the model.

[0037] Furthermore, in this embodiment, after preprocessing the model training data through the above steps, feature columns and label columns are defined for the preprocessed model training data to obtain tabular model training data. The feature columns represent the model input, and the label columns represent the model output; the input features of the model are specified by constructing tabular model training data.

[0038] Step S2: Create a new machine learning model to be trained based on the pre-built automatic machine learning model framework.

[0039] In this embodiment, AutoGluon is used to pre-build an automated machine learning model framework, and then the machine learning model is built and trained based on the constructed automated machine learning model framework. The automated machine learning model framework supports configuring multiple model inputs and multiple model outputs.

[0040] Specifically, AutoGluon is an open-source tool that automates model selection, feature engineering, and hyperparameter optimization, supporting the modeling of complex industrial processes with multiple inputs and outputs. During machine learning model building, it automates feature selection, choosing features that have a significant impact on the target variable to improve the model's prediction accuracy. AutoGluon supports various machine learning model types, including regression models, classification models, and time series prediction models, with the specific selection depending on the characteristics of the industrial process.

[0041] Furthermore, in this embodiment, the model configuration supports multiple types of model inputs and outputs, including numerical data, categorical data, and time series data. By supporting multiple inputs and multiple outputs, this invention can effectively address the multi-variable and multi-objective modeling needs present in industrial processes. For example, in chemical production, when multiple input variables (such as temperature and pressure) affect multiple output objectives (such as yield and quality), the machine learning model built using AutoGluon can automatically adapt to this complex input-output relationship.

[0042] In this embodiment, after creating a new machine learning model to be trained based on the automatic machine learning model framework, a front-end interactive interface is automatically generated. This interface is used to automatically train the machine learning model. The front-end interactive interface includes a parameter configuration area, a model selection control area, a training process monitoring area, and a prediction result visualization area. Therefore, when automatically training the machine learning model through the front-end interactive interface, the user can configure and dynamically adjust the model input and output through the parameter configuration area, select the desired machine learning model type in the model selection control area, monitor the model training process through the training process monitoring area, and finally view the model training prediction results through the prediction result visualization area.

[0043] Furthermore, in this embodiment, the front-end interactive interface also supports graphical displays, such as time series graphs, pie charts, and bar charts, making the prediction results more intuitive and easier to understand. Users can fine-tune the model's input parameters through the interface and view the model's prediction results in real time, thereby better supporting decision-making in the production process.

[0044] Step S3: Automatically train the newly created machine learning model based on the preprocessed model training data to generate a data model that has completed model training.

[0045] In this embodiment, automatic model training includes model selection, feature engineering, and hyperparameter tuning. Specifically, during the automatic generation and deployment of the machine learning model, parallel computing is used to accelerate the model training process, and Bayesian optimization methods are employed for hyperparameter tuning.

[0046] Specifically, in this embodiment, during the training of the machine learning model, a suitable initial model framework is selected through model selection strategies such as stacking and weighted averaging. Then, feature engineering, including feature extraction and feature selection, is used to extract the most representative features from the original data to reduce the interference of redundant data on the model. Simultaneously, parallel computing is used to accelerate the training process, thereby quickly completing model training and validation and saving time. For model parameter tuning, Bayesian optimization is used to search the model's parameter space and automatically find the optimal parameter combination, thereby optimizing the hyperparameters of the machine learning model. Furthermore, this embodiment supports distributed computing for large-scale datasets to ensure the efficiency of model training. Additionally, this embodiment allows for the setup of multiple models for separate training according to different needs, followed by model ensemble to integrate the prediction results of multiple models to improve overall prediction accuracy and robustness.

[0047] Step S4: Automatically deploy the generated data model to the process simulation platform, thereby connecting the mechanism model on the process simulation platform for process calculation and prediction.

[0048] In this embodiment, after generating the required data model through the above steps, data transmission and model deployment are performed via an API interface. During automatic model training, the API interface can also be used to update the machine learning model's training data in real time, and the generated data model can be automatically pushed to the process simulation platform for deployment, seamlessly integrating with the existing mechanistic model. This allows the connection to the mechanistic model on the process simulation platform to receive process parameters in real time and perform online predictions. By combining the real-time performance and flexibility of the data model with the process physical constraints and interpretability of the mechanistic model, prediction accuracy is effectively improved, and system reliability is enhanced.

[0049] In this embodiment, in addition to the real-time mode described above, the deployment and prediction of the data model can also adopt an offline mode. In the offline mode, prediction and analysis are performed based on historical data, thereby providing a basis for process optimization and improvement. Furthermore, this embodiment can also utilize historically stored molecular sieve data to synthesize experimental data, establishing a predictive model from operating conditions to molecular sieve types. The established predictive model can then predict the types of molecular sieves produced in the experiment, providing guidance for actual molecular sieve synthesis experiments. This allows for targeted adjustments to experimental operating conditions, reducing R&D costs.

[0050] Furthermore, in this embodiment, anomaly detection and handling can be performed during the model's calculation and prediction process. When anomalies are detected during prediction (such as a significant deviation between the model's prediction results and actual observations), corresponding measures can be taken, such as notifying relevant maintenance personnel or rolling back to a previous stable model version, to ensure the safety and reliability of the production process.

[0051] Specifically, in this embodiment, after generating the data model through the above steps, the predictive power of the generated data model is automatically monitored according to preset performance indicators to determine whether model retraining is necessary. If the monitored predictive power is lower than the preset performance indicators, model retraining and parameter adjustment are automatically triggered. If the monitored predictive power is greater than or equal to the preset performance indicators, the generated data model is automatically deployed to the process simulation platform. Furthermore, this embodiment also sets up a runtime status monitoring strategy to monitor whether the runtime status of the deployed data model exhibits any abnormalities. If so, model maintenance is performed. If not, monitoring continues.

[0052] In this embodiment, to facilitate model maintenance and upgrades, model version management is also supported. After each model update and retraining, a new model version is generated and stored, allowing users to switch between different versions to ensure the stability of the production process.

[0053] This invention utilizes historically stored molecular sieve synthesis experimental data to establish a predictive model from operating conditions to molecular sieve types, predicting the types of molecular sieves produced in the experiment, providing guidance for actual molecular sieve synthesis experiments, and allowing for targeted adjustments to experimental operating conditions, thereby reducing R&D experimental costs.

[0054] This specification also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the method for automatically generating and deploying machine learning models based on a process simulation platform as described above.

[0055] This specification also provides a method for automatically generating and deploying machine learning models based on a process simulation platform, including storing an instruction memory executable by a processor, and a processor for executing instructions in the instruction memory to implement the method for automatically generating and deploying machine learning models based on a process simulation platform as described above.

[0056] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0057] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0058] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0059] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0060] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

Claims

1. A method for automatically generating and deploying machine learning models based on a process simulation platform, characterized in that, Includes the following steps: Step S1: Collect model training data and preprocess the collected model training data; Step S2: Create a new machine learning model to be trained based on the pre-built automatic machine learning model framework; Step S3: Automatically train the newly built machine learning model based on the preprocessed model training data to generate a data model that has completed model training; Step S4: Automatically deploy the generated data model to the process simulation platform, thereby connecting the mechanism model on the process simulation platform for process calculation and prediction.

2. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 1, characterized in that, In step S1, the automatic generation and deployment method of machine learning model based on process simulation platform collects key process parameters during process operation as model training data based on process simulation platform or factory real-time database, and then preprocesses the collected model training data; wherein, the preprocessing includes data cleaning, normalization, missing value completion and feature selection.

3. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 1, characterized in that, In step S1, the automatic generation and deployment method of machine learning model based on process simulation platform preprocesses the collected model training data and specifies the input variable feature column and the output variable, i.e., the label column, for the collected model training data to obtain tabular model training data.

4. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 1, characterized in that, In step S2, the automatic generation and deployment method of machine learning model based on process simulation platform adopts AutoGluon to pre-build an automatic machine learning model framework and deploy it in process simulation software. Then, the machine learning model is built and trained based on the built automatic machine learning model framework. The automatic machine learning model framework supports the configuration of multiple model inputs and multiple model outputs.

5. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 4, characterized in that, The automatic generation and deployment method for machine learning models based on the process simulation platform automatically generates a front-end interactive interface after creating a new machine learning model. The generated front-end interactive interface is used to automatically train the machine learning model. The front-end interactive interface includes a parameter configuration area, a model selection control area, a training process monitoring area, and a prediction result visualization area.

6. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 5, characterized in that, The automatic generation and deployment method of machine learning models based on the process simulation platform allows users to configure and dynamically adjust the model input and output through the parameter configuration area, select the desired machine learning model type in the model selection control area, monitor the model training process through the training process monitoring area, and finally view the model training prediction results through the prediction result visualization area.

7. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 1, characterized in that, Automatic model training includes model selection, feature engineering, and hyperparameter tuning; wherein, the automatic generation and deployment of the machine learning model accelerates the model training process through parallel computing and uses Bayesian optimization methods for hyperparameter tuning during automatic model training.

8. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 1, characterized in that, The automatic generation and deployment method of machine learning models based on the process simulation platform uses an API interface for data transmission and model deployment. When automatically training the machine learning model, the method updates the model training data in real time through the API interface and automatically pushes the generated data model to the process simulation platform for deployment, thereby connecting the mechanism model on the process simulation platform for process calculation and prediction.

9. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 4, characterized in that, After generating a data model, the automatic generation and deployment method for machine learning models based on the process simulation platform automatically monitors the predictive ability of the generated data model based on preset performance indicators, thereby determining whether to retrain the model; wherein, If the detected predictability is lower than the preset performance metric, the model will be automatically retrained and the parameters will be adjusted. If the monitored predictability is greater than or equal to the preset performance index, the generated data model will be automatically deployed to the process simulation platform.

10. The method for automatic generation and deployment of machine learning models based on a process simulation platform according to claim 4, characterized in that, The automatic generation and deployment method of machine learning models based on the process simulation platform also sets up a running status monitoring strategy. The running status monitoring strategy monitors whether the running status of the deployed data model is abnormal; if so, model maintenance is performed; if not, monitoring continues.

11. A computer-readable medium storing computer program code, characterized in that, The computer program code, when executed by a processor, implements the method as described in any one of claims 1-10.

12. A machine learning model automatic generation and deployment device, characterized in that, include: Memory is used to store instructions that can be executed by the processor; as well as A processor for executing the instructions to implement the method as described in any one of claims 1-10.

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