Information system data efficiency intelligent management path design method

Abstract

The invention relates to the technical field of information system data management, in particular to an information system data efficiency intelligent management path design method, which comprises the following steps: determining a core index, collecting data and classifying samples, analyzing data and identifying features, and designing an intelligent management path. The advanced artificial intelligence technology is introduced, especially the powerful capacity of a large model is utilized, key challenges in current information system management, namely emergencies with the user response duration exceeding the expectation, are effectively handled, and a set of system with the source end business layer problem event library as the core and the user response duration exceeding the expectation is successfully constructed. An intelligent management implementation path for comprehensively integrating and intelligently processing problems and events at all levels contributes to important theoretical and practical values for the field of intelligent management of an information system, and remarkable improvement of system availability, optimization of a business process and maximization of data efficiency are realized; and a direction is pointed out for deep application and optimization of a future intelligent technology.

Description

Technical Field

[0001] The present invention relates to the technical field of information system data management, and in particular to a method for designing an intelligent management path for information system data efficiency. Background Art

[0002] Data effectiveness refers to the efficiency and effectiveness of users in obtaining data from information systems. It reflects the correctness of the selection of goals for data acquisition activities and the degree of their realization. Data effectiveness research is a study of the degree of match between users' actual ability to obtain data from information systems and their expected ability. The important feature of data effectiveness research is that it focuses on actual user experience, which is of great significance to the high-quality development of information systems.

[0003] As information systems become more and more complex, the components and interactions involved are also increasing, which brings huge challenges to the maintenance and optimization of the system. When institutions strive to optimize business data efficiency, it is difficult to find intelligent paths or methods to effectively improve the two key data efficiency indicators. Although institutions continue to improve the level of intelligence in information systems, this has not translated into an actual reduction in IT positions, and the effect of reducing costs and increasing efficiency is not obvious.

[0004] Therefore, in order to address the above-mentioned problems that it is difficult to find an intelligent path or method to effectively improve the two key data efficiency indicators, and the effect of reducing costs and increasing efficiency is not obvious, a method for designing an intelligent management path for information system data efficiency can be designed. Summary of the invention

[0005] In order to overcome the problem that it is difficult to find an intelligent path or method to effectively improve the two key data efficiency indicators, the effect of cost reduction and efficiency improvement is not obvious.

[0006] The technical solution of the present invention is: a method for designing an intelligent management path for information system data efficiency, comprising the following steps:

[0007] S1: Determine the core indicators: Through a detailed analysis of sample data, starting from the sudden "events", in-depth analysis of the causes and impacts of these sudden events, it is identified that "user response time" is the core indicator for measuring the data effectiveness of the information system;

[0008] S2: Data collection and sample classification: Through real-time monitoring of the organization's operating data, collect user feedback and tool observation data generated by the business information system in actual operation, and classify the sample data to provide a solid empirical basis for subsequent in-depth analysis;

[0009] S3: Data analysis and feature identification: Detailed analysis of the collected data to reveal the frequency, duration, impact range and recurrence characteristics of various events;

[0010] S4: Intelligent management path design: Based on the results of data analysis, clarify the areas and extent to which intelligent means should be applied to improve data efficiency.

[0011] Preferably, the user response time refers to the time interval from when the user initiates a data request to when the system returns a result.

[0012] Preferably, sample collection includes sample sources and sample data classification, and sample data classification includes classification by event data and classification by observation layer.

[0013] Preferably, data analysis and feature recognition include intelligent management objectives, data analysis and feature recognition.

[0014] Preferably, the intelligent management path design includes the intelligent implementation path description, the implementation strategy and core steps of the big model technology in intelligent management.

[0015] As a preferred option, the intelligent implementation path description includes prioritizing the intelligent management of the application level at the source of business, expanding to the intelligent integration of the source of business equipment and the link layer, and full-link intelligent coverage and precise traceability.

[0016] Preferably, feature identification includes application-level issues at the data source end, concentrated data source ends for trend events, and the commonality of trend-type and configuration-type events.

[0017] Preferably, data analysis includes trend events, configuration events, and device functionality events.

[0018] The beneficial effects of the present invention are as follows: by introducing advanced artificial intelligence technology, especially utilizing the powerful capabilities of large models, it not only effectively copes with the key challenges in current information system management, namely, emergencies in which user response time exceeds expectations, but also successfully constructs a set of intelligent management implementation paths with the source-side business layer problem event library as the core, which comprehensively integrates and intelligently handles problems and events at all levels. It not only contributes important theoretical and practical values ​​to the field of intelligent management of information systems, achieves significant improvement in system availability, optimization of business processes, and maximization of data efficiency, but also points out the direction for the in-depth application and optimization of future intelligent technologies. With the continuous evolution of AI technology and the continuous deepening of the application of the results of this research, we have reason to believe that more efficient, autonomous, and intelligent information system management models will continue to emerge, injecting strong impetus into the digital transformation and upgrading of various fields of society, and opening a new chapter in management in the information age. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shown is a schematic diagram of the service source end, link layer, and client end of the information system data efficiency intelligent management path design method of the present invention;

[0020] Figure 2 Shown is an illustration of the full-process training of the information system data efficiency intelligent management path design method and information system efficiency AI implementation of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the embodiments.

[0022] The present invention provides an embodiment: an information system data efficiency intelligent management path design method, through a detailed analysis of sample data, starting from the sudden "event", in-depth analysis of the causes and impacts of these sudden events, identifying "user response time" as a core indicator for measuring information system data efficiency; through real-time monitoring of the operation data of the organization, collecting user feedback and tool observation data generated by the business information system in actual operation, and classifying the sample data according to event characteristics and observation layers, the classification according to event characteristics is shown in Table 1, and the classification according to observation layers is shown in Table 2, providing a solid empirical basis for subsequent in-depth analysis;

[0023] Table 1 shows the classification by event characteristics, and Table 2 shows the classification by observation layer.

[0024] Table 1

[0025]

[0026] The time samples collected in Table 1 mainly include events that cause observable decline in actual business data service performance or increase in availability risk. The disposal plans for these events usually fall within the scope of daily operation and maintenance management. Therefore, if the information system has hardware redundancy, events in which some equipment modules or parts fail but do not have an adverse impact on business services are not included in the event samples of this study. This is because the planning of hardware redundancy is usually determined during the system construction phase and is not suitable for change management in daily operation and maintenance.

[0027] Table 2

[0028]

[0029]

[0030] A detailed analysis of the collected data shows that the current intelligent management methods of information systems have two main prerequisites for intelligent management of information systems. One is whether the management and disposal rules and conditions are clear and explicit; the other is whether the service resources required for management and disposal are well prepared. Some configuration issues must be addressed manually before they can be moved forward. The current goals of intelligence in dealing with three types of performance anomalies are shown in the following table:

[0031] Table 3: Intelligent judgment table

[0032] feature type Discover diagnosis Disposal Trend Type software √ √ √ Configuration software √ √ √◇ Equipment function hardware √ ◇ ◇

[0033] Due to the different causes and development processes of different performance events, not all abnormal events can be discovered, diagnosed and handled before the incident. According to the classification of events in Table 3, the best time point targets for response are shown in the following table:

[0034] Table 4: Optimal response time target table

[0035]

[0036]

[0037] After completing the classification of event types, a large amount of sample data was comprehensively collected and summarized; through in-depth analysis of these data, the occurrence ratios of different types of events and their recurrence characteristics were revealed; this process not only helps to understand the distribution and performance of events in different information systems, but also provides an important basis for further intelligent management strategies; data analysis first focuses on the basic types of events and their proportion in the overall sample; mainly including trend events, configuration events and equipment functional events; the distribution of each type of event at different system levels is considered in detail, so as to form a comprehensive view of the occurrence of events at each major technical architecture level;

[0038] Table 5 shows the event types and proportions (1), and Table 6 shows the event types and proportions (2)

[0039]

[0040] Table 6

[0041]

[0042]

[0043] Based on the recurrence characteristics of the problem, the six observation levels are briefly described in the following table:

[0044] Table 7: Recurrence characteristics table (2)

[0045] System Level Recurring features 1 Source Application Most of the questions are general 2 Source device Most of the problems are individual 3 Link layer applications Most of the questions are general 4 Link layer devices Most of the problems are individual 5 Client Application Most of the problems are individual 6 Client Devices Most of the problems are individual

[0046] Based on the above analysis, the following characteristics are identified: For business information systems, the vast majority of data source-side application-level problems on the relational database side account for 60% of the impact on information system performance; trend problems mostly exist on the data source side, which indicates that these performance problems can be predicted and handled in advance from an intelligent perspective; the problems reflected by trend-type and configuration-type events are universal across vendors and applications, that is, the causes of performance problems that occur at the business source side in different organizations and different business applications and the corresponding treatment solutions are largely universal, which is suitable for generating management rules and forming universal algorithms for intelligent handling;

[0047] Based on the above, a summary table 8 is formed:

[0048] Table 8

[0049]

[0050]

[0051] Given that 60% of the performance problems of information systems are concentrated at the source application level, optimizing this link can significantly improve user response speed and fault handling efficiency. Usually, only the source application layer is optimized, and user response time and fault convergence time are significantly improved; secondly, due to the broad spectrum and cross-vendor reproducibility of source application layer problems, the discovery, diagnosis and disposal knowledge graph formed can be widely reused in various systems, making the fault (discovery, diagnosis and disposal) algorithm library at the source application level the knowledge fulcrum of the intelligent performance management of the entire information system. Only with this as the fulcrum can the accurate traceability of performance problems in the entire link be gradually realized; starting from the business source application layer , integrate the monitoring data of source devices and link layer applications and equipment to realize the intelligent tracing and disposal of efficiency problems in a wider range; further integrate client application and equipment observation data to realize the full-link accurate tracing and intelligent disposal of efficiency problems; through the two key links of pre-training and fine-tuning, the industry's general corpus, general problem knowledge graphs and other knowledge are trained and learned to generate the initial version of the organization's data efficiency knowledge feature library. Subsequently, new problems will be continuously collected, analyzed and introduced, and the training content of the self-built knowledge base will be continuously enriched to form a feature library based on large models that is continuously optimized, providing continuous knowledge support for intelligent management.

[0052] The above is a detailed description of the embodiments of the present invention, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the purpose of the present invention.

Claims

1. The method for designing an intelligent management path for information system data efficiency includes the following steps: S1: Determine the core indicators: Through a detailed analysis of sample data, starting from the sudden "events", in-depth analysis of the causes and impacts of these sudden events, it is identified that "user response time" is the core indicator for measuring the data effectiveness of the information system; S2: Data collection and sample classification: Through real-time monitoring of the organization's operating data, collect user feedback and tool observation data generated by the business information system in actual operation, and classify the sample data to provide a solid empirical basis for subsequent in-depth analysis; S3: Data analysis and feature identification: Detailed analysis of the collected data to reveal the frequency, duration, impact range and recurrence characteristics of various events; S4: Intelligent management path design: Based on the results of data analysis, clarify the areas and extent to which intelligent means should be applied to improve data efficiency.

2. The method for designing an intelligent management path for information system data efficiency according to claim 1, characterized in that: User response time refers to the time interval from the user initiating a data request to the system returning the result.

3. The method for designing an intelligent management path for information system data efficiency according to claim 1, characterized in that: Sample collection includes sample sources and sample data classification. Sample data classification includes classification by event data and classification by observation layer.

4. The method for designing an intelligent management path for information system data efficiency according to claim 1, characterized in that: Data analysis and feature recognition include intelligent management objectives, data analysis and feature recognition.

5. The method for designing an intelligent management path for information system data efficiency according to claim 1, characterized in that: The intelligent management path design includes the intelligent implementation path description, the implementation strategy and core steps of big model technology in intelligent management.

6. The method for designing an intelligent management path for information system data efficiency according to claim 5, characterized in that: The description of the intelligent implementation path includes prioritizing the intelligent management of the application level at the source of business, expanding to the intelligent integration of the devices and link layers at the source of business, and full-link intelligent coverage and precise traceability.

7. The method for designing an intelligent management path for information system data efficiency according to claim 4, characterized in that: Feature Recognition It includes issues at the application level of the data source, the centralized data source of trend events, and the universality of trend-type and configuration-type events.

8. The method for designing an intelligent management path for information system data efficiency according to claim 4, characterized in that: Data analysis includes trend events, configuration events, and device functional events.

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