Calculation method of intelligent operation AI

Abstract

The invention relates to the technical field of intelligent operation, and discloses an intelligent operation AI calculation method comprising the following steps: S1, business demand analysis: understanding business demands, and setting clear and quantifiable project goals; s2, data acquisition and processing: ensuring the accuracy and integrity of a data source, and preprocessing original data at the same time; s3, model selection and training: selecting a learning model based on problem characteristics and data characteristics, and determining a model parameter combination through multiple iterations and cross validation; s4, system integration and development: designing a clear and stable interface for the system to call, and developing a back-end system at the same time; s5, deployment and online; s6, continuous optimization and iteration are carried out; according to the method, the learning model is selected based on the problem characteristics and the data characteristics, so that the AI model can more accurately reflect the actual condition of the service, and has good generalization ability to cope with diversified application scenes.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation technology, and in particular to a calculation method for intelligent operation AI. Background Art

[0002] Smart operation refers to the use of advanced technologies such as artificial intelligence, big data analysis, cloud computing, the Internet of Things, etc. to realize the intelligence, automation and digitization of enterprise operations. It is based on data and provides a scientific basis for enterprise decision-making by collecting, integrating, analyzing and mining large amounts of structured and unstructured data. It uses artificial intelligence technology to realize intelligent scheduling and optimization of processes, resources and manpower, and realize automated and intelligent management. Through refined analysis and mining of data, it realizes refined management of production, sales, customer service, etc., and improves operational efficiency and service quality.

[0003] At present, some existing smart operation AI computing methods have deviations in results when building and training models due to the diversity and complexity of data when facing new data or complex scenarios. At the same time, when business changes or technology upgrades occur, the operation of some models cannot be adjusted in a targeted manner, resulting in reduced accuracy of model operation results. Summary of the invention

[0004] The purpose of the present invention is to provide a calculation method for intelligent operation AI to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a calculation method of intelligent operation AI, comprising the following steps:

[0006] S1. Business requirements analysis: Understand business requirements and set clear and quantifiable project goals;

[0007] S2, data acquisition and processing: ensure the accuracy and integrity of the data source, and pre-process the raw data;

[0008] S3. Model selection and training: Based on the characteristics of the problem and the data, the learning model is selected, and the model parameter combination is determined through multiple iterations and cross-validation;

[0009] S4, System Integration and Development: Design clear and stable interfaces for system calls and develop backend systems;

[0010] S5, Deployment and launch: Conduct a comprehensive test on the system, then select a deployment plan based on business needs and technical requirements, and monitor the launched system;

[0011] S6. Continuous optimization and iteration: Continuously monitor system performance and analyze its operating data, continuously optimize model parameters, and regularly assess system security risks.

[0012] Preferably, in S1, it is necessary to deeply understand the operational needs of the enterprise, clarify the problems that AI needs to solve, and set clear AI project goals.

[0013] Preferably, in S2, high-quality, highly relevant data needs to be collected according to demand, including internal enterprise data and external data sources, and the data needs to be cleaned during data preprocessing.

[0014] Preferably, in S2, the cleaned data is annotated to provide labels for model training to form a training set. In S3, after the deep learning model is selected, the model is trained using the training set and the model parameters are adjusted.

[0015] Preferably, in S3, after the model parameters are adjusted, the model is tuned through a validation set to adjust the accuracy and generalization of the model.

[0016] Preferably, in S4, the trained model needs to be encapsulated into an API service and connected to the system, and the front-end interface of the system needs to be designed and the back-end development needs to be carried out to implement the back-end logic and process data requests and responses.

[0017] Preferably, in said S5, the system testing content includes unit testing, integration testing and performance testing. In said S5, it is necessary to select a cloud deployment solution and monitor the system performance at the same time.

[0018] Preferably, the monitoring system performance indicators include response time, throughput and error rate, and user feedback is collected to evaluate system effectiveness.

[0019] Preferably, in S6, it is necessary to evaluate the system effect by collecting system operation data, and in S6, it is necessary to regularly adjust and optimize model parameters or retrain the model according to business changes and data updates.

[0020] Preferably, in S6, when regularly evaluating system security risks, corresponding measures need to be taken to manage and prevent them.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] First, the present invention, through business demand analysis to continuous system optimization and iteration, each step is closely linked to form a closed-loop, efficient system, which can ensure that the project can be carried out in an orderly manner during the implementation process, reduce resource waste and time delays, and improve the overall efficiency and success rate of the project. At the same time, in S3, through model selection and training, the actual situation and specific needs of the project can be selected. Appropriate machine learning algorithms, deep learning models and deployment schemes can be selected. At the same time, during the operation of the system, continuous optimization and iteration can be performed according to business changes and data updates to adapt to the ever-changing market environment and customer needs, so that the system has strong adaptability and scalability, and can cope with various complex and changeable application scenarios. By setting data acquisition and processing, strict data preprocessing, efficient model training and comprehensive system testing, etc., the system's rapid response and accurate prediction are ensured. At the same time, the application of modern deployment schemes such as cloud deployment has further improved the system's operating efficiency and scalability. Through the model selection and training process based on problem characteristics and data characteristics, as well as multiple iterations and cross-validation tuning methods, the model's prediction ability and generalization ability are ensured, and the system's decision support capability can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of the structure of a preferred embodiment of the calculation method of the intelligent operation AI provided by the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 As shown, a calculation method of intelligent operation AI includes the following steps:

[0026] S1. Business needs analysis: Understand business needs and set clear and quantifiable project goals; clarify the basis for project launch, set clear and quantifiable goals, ensure the project is in the right direction, avoid waste of resources, and improve the project success rate.

[0027] S2. Data acquisition and processing: Ensure the accuracy and completeness of the data source and pre-process the raw data; emphasize the importance of data quality, including data accuracy and completeness. Data pre-processing steps help improve the accuracy and reliability of the model.

[0028] S3. Model selection and training: Select the learning model based on the problem characteristics and data characteristics, and determine the model parameter combination through multiple iterations and cross-validation; Select the model based on the problem characteristics and data characteristics, and determine the best parameter combination through iteration and cross-validation, which can improve the model's predictive ability and generalization ability

[0029] S4. System integration and development: Design clear and stable interfaces for system calls, and develop the back-end system at the same time; encapsulate the trained model into an API service for easy system calls, and design a clear front-end interface and stable back-end logic to help improve user experience and system stability.

[0030] S5. Deployment and launch: Conduct comprehensive testing on the system, then select a deployment plan based on business needs and technical requirements, and monitor the launched system; ensure system quality through comprehensive testing, select a suitable deployment plan to support the scalability and flexibility of the system, and monitor system performance to quickly respond to potential problems.

[0031] S6. Continuous optimization and iteration: Continuously monitor system performance and analyze its operating data, continuously optimize model parameters, and regularly evaluate system security risks. Emphasis on continuous monitoring of system performance and continuous optimization of model parameters will help maintain the competitiveness and adaptability of the system, while regularly evaluating system security risks to ensure system security.

[0032] In S1, it is necessary to have a deep understanding of the operational needs of the enterprise, identify the problems that AI needs to solve, and set clear AI project goals.

[0033] In S2, high-quality and highly relevant data needs to be collected according to needs, including internal enterprise data and external data sources. When performing data preprocessing, the data needs to be cleaned.

[0034] In S2, the cleaned data is labeled to provide labels for model training and form a training set. In S3, after the deep learning model is selected, the model is trained with the training set and the model parameters are adjusted. Data labeling provides necessary labels for model training, and forming a training set helps to train and optimize the model on specific tasks.

[0035] In S3, after the model parameters are adjusted, the model is tuned through the validation set to adjust the model's accuracy and generalization. Tuning the model through the validation set can further improve the model's accuracy and generalization ability and ensure the model's performance in actual applications.

[0036] In S4, the trained model needs to be encapsulated into an API service and connected to the system. At the same time, the front-end interface of the system needs to be designed, and the back-end development needs to be carried out to implement the back-end logic and process data requests and responses.

[0037] In S5, system testing includes unit testing, integration testing, and performance testing. In S5, you need to select a cloud deployment solution and monitor system performance. Comprehensive testing helps to identify potential problems and solve them in advance, and the cloud deployment solution provides flexibility and scalability.

[0038] Monitor system performance indicators including response time, throughput, and error rate, and collect user feedback to evaluate system effectiveness.

[0039] In S6, it is necessary to evaluate the system effect by collecting system operation data. In S6, it is necessary to regularly adjust and optimize model parameters or retrain the model according to business changes and data updates.

[0040] In S6, when regularly evaluating system security risks, corresponding measures need to be taken to manage and prevent them. Collecting system operation data to evaluate system effects helps to understand the actual performance of the system and make corresponding adjustments. At the same time, regularly evaluating system security risks and taking corresponding measures helps to ensure safe and stable operation of the system.

[0041] Working principle: When calculating the intelligent operation AI, the staff first deeply understands the operational needs of the enterprise, clarifies the problems that AI needs to solve, and sets clear and quantifiable project goals. This is the starting point and foundation of the entire project. Then, according to business needs, high-quality and highly relevant data is collected, including internal enterprise data and external data sources. Then, the data is cleaned, denoised, labeled and other pre-processing steps are performed to form a training set suitable for model training. Based on the characteristics of the problem and the data, a suitable machine learning algorithm or deep learning model is selected. Then, the model is trained using the training set, and the best model parameter combination is determined through multiple iterations and cross-validation. The trained model is encapsulated into an API service and connected to other parts of the system. At the same time, a clear and stable front-end interface and back-end logic are designed to realize user interaction and data processing functions. On the basis of comprehensive testing of system functions and performance, cloud deployment is selected to launch the system. At the same time, the system performance is monitored to quickly respond to potential problems. During the operation of the system, the system performance and operation data are continuously monitored, and the model parameters are optimized to improve the prediction accuracy. At the same time, the model is regularly adjusted and optimized or retrained according to business changes and data updates. In addition, it is necessary to regularly assess system security risks and take appropriate measures to manage and prevent them.

[0042] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device 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 device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A calculation method for intelligent operation AI, characterized in that: The following steps are involved: S1. Business requirements analysis: Understand business requirements and set clear and quantifiable project goals; S2, data acquisition and processing: ensure the accuracy and completeness of the data source, and pre-process the raw data; S3. Model selection and training: Based on the characteristics of the problem and the data, the learning model is selected, and the model parameter combination is determined through multiple iterations and cross-validation; S4, System Integration and Development: Design clear and stable interfaces for system calls and develop backend systems; S5, Deployment and launch: Conduct comprehensive testing on the system, then select a deployment plan based on business needs and technical requirements, and monitor the launched system; S6. Continuous optimization and iteration: Continuously monitor system performance and analyze its operating data, continuously optimize model parameters, and regularly assess system security risks.

2. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S1, it is necessary to have an in-depth understanding of the enterprise's operational needs, identify the problems that AI needs to solve, and set clear AI project goals.

3. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S2, high-quality, highly relevant data needs to be collected according to needs, including internal enterprise data and external data sources. When performing data preprocessing, the data needs to be cleaned.

4. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S2, the cleaned data is annotated to provide labels for model training to form a training set. In S3, after the deep learning model is selected, the model is trained using the training set and the model parameters are adjusted.

5. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S3, after the model parameters are adjusted, the model is tuned through the validation set to adjust the accuracy and generalization of the model.

6. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S4, the trained model needs to be encapsulated into an API service and connected to the system. At the same time, the front-end interface of the system needs to be designed, and the back-end development needs to be carried out to implement the back-end logic and process data requests and responses.

7. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S5, the system testing content includes unit testing, integration testing and performance testing. In S5, it is necessary to select a cloud deployment solution and monitor the system performance at the same time.

8. The calculation method of intelligent operation AI according to claim 7 is characterized by: The monitoring system performance indicators include response time, throughput and error rate, and user feedback is collected to evaluate the system effect.

9. The calculation method of intelligent operation AI according to claim 1, characterized in that: In S6, it is necessary to evaluate the system effect by collecting system operation data. In S6, it is necessary to regularly adjust and optimize model parameters or retrain the model according to business changes and data updates.

10. The method for calculating intelligent operation AI according to claim 1, characterized in that: In S6, when regularly evaluating system security risks, corresponding measures need to be taken to manage and prevent them.

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