IT service configuration and delivery method and system

Abstract

The invention provides an IT service configuration and delivery method and system, and the method comprises the steps: collecting user data, constructing a user portrait model through a machine learning algorithm, and generating a user portrait; according to the user portrait, automatically configuring a service scheme through a service configuration algorithm and executing the service scheme; and updating the user portrait model and the service configuration algorithm according to the feedback analysis result. A user portrait model is constructed through a machine learning algorithm, and personalized demand characteristics of the user can be intelligently identified; the service scheme is automatically configured based on the user portrait, so that intelligence and automation of service configuration are realized; and continuously updating the model and the algorithm through feedback analysis to form a self-adaptive optimization mechanism, so that the individuation level and the configuration efficiency of IT service configuration are improved.

Description

Technical Field

[0001] The present invention relates to information technology services, and in particular to an IT service configuration and delivery method and system. Background Art

[0002] Currently, IT service configuration and delivery mainly adopt manual configuration or preset template methods. The manual configuration method requires IT personnel to manually configure services according to user requirements, which not only has a large workload, low efficiency, but also is prone to configuration errors; although the preset template method improves the configuration efficiency, due to the fixed template, it cannot flexibly adapt to the personalized needs of different users. At the same time, the existing IT service configuration lacks a mechanism for continuously tracking and optimizing the user usage situation, resulting in the service configuration being unable to be dynamically adjusted as the user requirements change.

[0003] In addition, with the expansion of enterprise scale and the increase of business complexity, the IT service requirements of employees in different departments and positions within the enterprise are significantly different. For example, the design department needs more graphic processing software and storage resources, while the finance department needs more data processing and security control functions. The traditional standardized IT service configuration method is difficult to meet such differentiated requirements, affecting the work efficiency of employees and the user experience. Therefore, there is an urgent need for an IT service configuration and delivery solution that can intelligently identify user requirements, automatically generate personalized configuration solutions and continuously optimize. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problem that the existing IT service configuration cannot intelligently identify the personalized needs of users and automatically adjust the service configuration.

[0005] In the first aspect of the present invention, an IT service configuration and delivery method is provided, including:

[0006] Collect user data, construct a user portrait model through machine learning algorithms, and generate a user portrait;

[0007] According to the user portrait, automatically configure a service plan through a service configuration algorithm and execute the service plan;

[0008] Update the user portrait model and the service configuration algorithm according to the feedback analysis result.

[0009] Furthermore, the user data includes user work habits, usage preferences, and role permission data.

[0010] Furthermore, the construction of the user portrait model includes:

[0011] Collect user behavior data, system log data, and questionnaire data; extract user features; train the user portrait model.

[0012] Furthermore, the user profile includes user role information, frequently used software information, work environment preference information, and security permission information.

[0013] Furthermore, the automatic configuration service plan includes: selecting configuration items from the configuration library; detecting and resolving configuration conflicts; and optimizing the configuration plan.

[0014] Furthermore, performing configuration includes: deploying software applications; executing configuration; managing configuration status; and tracking delivery progress.

[0015] Furthermore, the service plan includes desktop environment configuration, software installation configuration, permission setting configuration, and network connection configuration.

[0016] In a second aspect of the present invention, there is provided an IT service configuration and delivery system, including:

[0017] A user profile module that collects user data, constructs a user profile model through machine learning algorithms, and generates a user profile;

[0018] A service configuration module that automatically configures a service plan through service configuration algorithms;

[0019] A service delivery module that executes the service plan;

[0020] A continuous optimization module that collects user feedback and usage data, and updates the user profile model and service configuration algorithms.

[0021] Furthermore, the user profile module includes:

[0022] A data collection unit that collects user behavior data, system log data, and questionnaire data;

[0023] A feature extraction unit that extracts user features from the collected data;

[0024] A model training unit that trains a user profile model using machine learning algorithms.

[0025] Furthermore, the service configuration module includes:

[0026] A configuration plan generation unit that selects appropriate configuration items from the configuration library according to the user profile;

[0027] A configuration conflict resolution unit that uses a rule engine to detect and resolve configuration conflicts;

[0028] A configuration optimization unit that uses a constraint solver to optimize the configuration plan.

[0029] Furthermore, the service delivery module includes:

[0030] A deployment tool that automatically installs and configures software;

[0031] Execution tool, execution configuration;

[0032] Configuration management tool, managing and maintaining the configuration status.

[0033] Furthermore, the continuous optimization module includes:

[0034] Feedback collection unit, collecting user feedback through online feedback, questionnaire surveys, and log analysis;

[0035] Feedback analysis unit, analyzing user feedback using natural language processing and sentiment analysis techniques;

[0036] Model update unit, updating the user portrait model and service configuration algorithm according to the feedback analysis results.

[0037] Compared with the prior art, the present invention at least includes the following beneficial effects: By constructing a user portrait model through machine learning algorithms, it can intelligently identify the personalized demand characteristics of users; Automatically configuring service solutions based on the user portrait realizes the intelligence and automation of service configuration; Continuously updating the model and algorithm through feedback analysis forms an adaptive optimization mechanism, thereby improving the personalization level and configuration efficiency of IT service configuration and reducing the manual configuration cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0039] Figure 1 It is a flowchart of the IT service configuration and delivery method in an embodiment of the present invention;

[0040] Figure 2 It is a schematic diagram of the modules of the IT service configuration and delivery method in an embodiment of the present invention;

[0041] Figure 3 It is a specific flowchart of the IT service configuration and delivery method in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The present invention will be described in more detail below with reference to the schematic diagrams, which show the preferred embodiments of the present invention. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as being widely known to those skilled in the art and not as a limitation to the present invention.

[0043] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0044] In the following paragraphs, the present invention will be described more specifically by way of example with reference to the accompanying drawings. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise scales, and are only used to facilitate and clearly assist in explaining the purpose of the embodiments of the present invention.

[0045] Embodiment 1

[0046] This embodiment provides an IT service configuration and delivery method. Please refer to Figure 1 and Figure 3 , including:

[0047] Collect user data, construct a user portrait model through machine learning algorithms, and generate a user portrait;

[0048] According to the user portrait, automatically configure a service plan through a service configuration algorithm and execute the service plan;

[0049] Update the user portrait model and the service configuration algorithm according to the feedback analysis results.

[0050] Specifically, this method includes three core steps: First, various types of user data (such as usage habits, business requirements, operation behaviors, etc.) are collected. These data are input into a machine learning algorithm for processing and learning to construct a portrait model that can accurately describe user characteristics, and a personalized portrait of the user is generated based on this. Second, according to the generated user portrait, the system calls a service configuration algorithm for intelligent decision-making, automatically matches and configures the most suitable IT service solution for this user, and implements this solution. Third, the system collects and analyzes the feedback on the result of service execution, and uses the analysis result to synchronously update the user portrait model and the service configuration algorithm, thereby achieving the continuous optimization of the system. This method realizes the automation and intelligence of IT service delivery by establishing a closed-loop mechanism of data collection, intelligent analysis, solution execution, and feedback optimization. Through user data-driven and machine learning technologies, the intelligence and automation of IT service configuration and delivery are achieved. The system continuously optimizes the model and algorithm by continuously learning user feedback, thereby improving the accuracy of service matching, reducing the cost of manual intervention, enhancing service efficiency, and ensuring the continuous improvement of service quality. This closed-loop optimization mechanism enables the system to adapt to the dynamic changes of user needs and provide more personalized and accurate IT services.

[0051] In this embodiment, the user data includes user work habits, usage preferences, and role permission data.

[0052] Furthermore, the construction of the user portrait model includes:

[0053] Collect user behavior data, system log data, and questionnaire data; extract user characteristics; train the user portrait model.

[0054] Specifically, in this step, the user portrait model is constructed through three levels of data collection and processing. First is the comprehensive data collection, including the actual behavior data of the user in the system (such as operation habits, usage frequency, function preferences, etc.), the log data automatically recorded by the system (such as access records, error messages, performance metrics, etc.), and the subjective feedback data obtained through questionnaires (such as requirement descriptions, satisfaction evaluations, improvement suggestions, etc.); then the multi-source heterogeneous data collected is subjected to feature extraction to identify and screen out the key features that can effectively represent user characteristics; finally, the extracted features are input into a machine learning algorithm for model training, and a portrait model that can accurately depict user characteristics is established in a data-driven manner. The fusion and analysis of this multi-dimensional data ensure the comprehensiveness and accuracy of the user portrait.

[0055] In this embodiment, the user portrait includes user role information, commonly used software information, work environment preference information, and security permission information.

[0056] Furthermore, the automatic configuration service solution includes: selecting configuration items from a configuration library; detecting and resolving configuration conflicts; and optimizing the configuration solution.

[0057] Furthermore, executing the configuration includes: deploying software applications; executing the configuration; managing the configuration status; and tracking the delivery progress.

[0058] Specifically, in this process, intelligent selection of configuration items is performed from a preset configuration library according to the user profile. The configuration library contains various IT service components and parameters, and the most suitable configuration items are matched based on user characteristics and requirements. Secondly, conflict detection is performed according to the selected configuration items to identify potential conflicts such as possible component compatibility issues, resource competition, and performance bottlenecks, and the conflicts are automatically resolved through preset rules or optimization algorithms. Finally, the overall configuration solution is optimized, considering multiple dimensions such as performance, cost, and reliability, adjusting and balancing the parameters of each configuration item, and generating the optimal configuration solution. This automated configuration process not only ensures the accuracy of service configuration but also improves the configuration efficiency.

[0059] After selecting the configuration solution, the execution operation is carried out. First is the automated deployment of software applications, distributing and installing the required software packages, components, and services according to the configuration solution to ensure the correct construction of the application environment. Secondly is the specific execution of the configuration, performing specific configurations for the service, including parameter settings, environment variable configuration, permission allocation, etc. Then is the real-time management of the configuration status, continuously monitoring the status of configuration execution, recording configuration changes, maintaining version control of the configuration, and intervening or rolling back in case of anomalies. Finally is the full-process tracking of the delivery progress, monitoring the progress and recording the status of the entire service delivery process to ensure that each configuration step is completed as planned and that problems in the delivery process can be discovered and handled in a timely manner. This structured execution mechanism ensures the controllability and reliability of service delivery.

[0060] In this embodiment, the service solution includes desktop environment configuration, software installation configuration, permission setting configuration, and network connection configuration.

[0061] Embodiment 2

[0062] This embodiment provides an IT service configuration and delivery system. Please refer to Figure 2 , including:

[0063] A user profile module that collects user data, constructs a user profile model through machine learning algorithms, and generates a user profile;

[0064] A service configuration module that automatically configures a service solution through service configuration algorithms;

[0065] A service delivery module that executes the service solution;

[0066] Continuous optimization module, which collects user feedback and usage data to update the user portrait model and service configuration algorithm.

[0067] Specifically, the IT service configuration and delivery system provided in this embodiment includes four mutually cooperative functional modules: user portrait module, service configuration module, service delivery module, and continuous optimization module. Through data-driven and algorithm support, this system architecture realizes the automation, personalization, and intelligence of IT service configuration and delivery, significantly improves service configuration efficiency, reduces labor costs, reduces configuration errors, and at the same time ensures the continuous improvement of service quality through a continuous optimization mechanism, can better meet the personalized needs of users, and improves user satisfaction.

[0068] Furthermore, the user portrait module includes:

[0069] Data collection unit, which collects user behavior data, system log data, and questionnaire data;

[0070] Feature extraction unit, which extracts user features from the collected data;

[0071] Model training unit, which trains the user portrait model using machine learning algorithms.

[0072] Specifically, in this module, first, the data collection unit, as the starting point of the entire process, synchronously collects user behavior data (such as clicks, browsing, and dwell time), system log data (such as login frequency, usage duration, etc.), and questionnaire data (such as clear interest preferences, etc.) through multiple channels. The collected raw data is passed to the feature extraction unit after cleaning, deduplication, and structured processing. The feature extraction unit receives the preprocessed data, analyzes it through feature engineering techniques, and extracts multi-dimensional features including user basic attributes, behavior patterns, and interest preferences. This process involves operations such as dimensionality reduction, feature selection, and feature transformation, converting the raw data into feature vectors that can be directly used by machine learning algorithms. Subsequently, the model training unit constructs a user portrait model based on these feature vectors using suitable machine learning algorithms. During the training process, the system continuously feedbacks model performance indicators and optimizes parameter configurations. The trained model will be used to generate user portraits, and these portrait results will in turn feed back to the data collection link, guiding more targeted data collection strategies, forming a complete closed loop of "collection - extraction - training - application - optimization" to ensure the continuous improvement of the accuracy and timeliness of user portraits.

[0073] Furthermore, the service configuration module includes:

[0074] Configuration plan generation unit, which selects appropriate configuration items from the configuration library according to the user portrait;

[0075] A configuration conflict resolution unit that uses a rule engine to detect and resolve configuration conflicts;

[0076] A configuration optimization unit that uses a constraint solver to optimize the configuration plan.

[0077] For the service configuration module, through the coordinated operation of three functional units, the transformation process from user portraits to the final personalized configuration plan is realized. The workflow starts with the configuration plan generation unit, which receives detailed user feature data from the user portrait module. Through a matching algorithm, it screens for a combination of configuration items in the preset configuration library that highly matches the user portrait. This process comprehensively considers the user's usage habits, preference characteristics, and scenario requirements, and extracts a set of initial configuration plans that may be suitable for the user from the configuration library.

[0078] Subsequently, the configuration conflict resolution unit receives this initial configuration plan and uses the built-in rule engine for conflict detection. This unit maintains a complete set of configuration item dependency relationships and mutual exclusion rules, and can identify configuration combinations that may cause system instability or a decline in user experience. When a conflict is detected, the rule engine will automatically adjust the configuration parameters or replace the conflicting items according to the preset priority strategy and solution library to ensure the logical consistency and technical feasibility of the generated configuration plan.

[0079] Finally, the configuration optimization unit receives the conflict-free configuration plans, but these plans may still have room for optimization in terms of overall performance, resource consumption, etc. At this time, the constraint solver will, on the premise of meeting all necessary configuration requirements, use mathematical optimization algorithms (such as linear programming, genetic algorithms, etc.) to find the configuration parameter combination with the least resource occupancy and the best performance. The optimization process takes into account multiple constraint conditions such as system resource limitations, performance goals, and user experience, and finally outputs an optimal configuration plan that not only meets the user's personalized needs but also meets the system operation efficiency requirements.

[0080] Furthermore, the service delivery module includes:

[0081] A deployment tool that automatically installs and configures software;

[0082] An execution tool that executes the configuration;

[0083] A configuration management tool that manages and maintains the configuration status.

[0084] First, the deployment tool receives the configuration plan generated by the service configuration module and is responsible for the automatic software installation process. It detects the target system environment, installs the required software in the order of dependencies, and performs preliminary configuration according to the parameters recommended by the user profile. The deployment tool ensures that all necessary components are correctly installed, laying the foundation for subsequent fine-tuning configuration. The execution tool takes over the subsequent configuration work, converting the abstract configuration items in the configuration plan into specific settings. It is responsible for operations such as adjusting system parameters, modifying application configurations, and setting permissions. The execution tool verifies the validity of each configuration change, ensures that the configuration takes effect as expected, and can roll back changes in case of exceptions to ensure system stability. The configuration management tool is responsible for maintaining the long-term stability of the configuration. It records the configuration status, tracks the configuration change history, and regularly checks whether the actual configuration is consistent with the expected configuration to prevent configuration deviations. When problems are found, the management tool triggers a correction process or issues a reminder. In addition, it also collects the system performance data after configuration to provide feedback for continuous optimization.

[0085] Furthermore, the continuous optimization module includes:

[0086] A feedback collection unit that collects user feedback through online feedback, questionnaires, and log analysis;

[0087] A feedback analysis unit that analyzes user feedback using natural language processing and sentiment analysis techniques;

[0088] A model update unit that updates the user profile model and service configuration algorithm based on the feedback analysis results.

[0089] Specifically, the feedback collection unit constructs a multi-channel user feedback collection network. It obtains instant evaluations and suggestions from users through the feedback function in the application interface; regularly sends out questionnaires to collect user satisfaction and requirement data; at the same time, analyzes the system usage logs to capture implicit feedback signals such as user interaction behaviors, usage frequencies, and potential problems. These feedback data from different sources are sorted out and passed to the analysis unit. The feedback analysis unit processes the multi-source feedback data collected, uses natural language processing techniques to understand the text feedback content, and extracts key topics and intentions. At the same time, sentiment analysis techniques help evaluate the emotional tendency of the feedback, distinguishing positive, neutral, and negative feedback. In addition, the analysis unit also correlates user profile features with the feedback content to discover common problems and personalized needs of specific user groups. The analysis results are classified into categories such as functional improvement suggestions, configuration adjustment requirements, and user experience problems, and the priorities are marked. The model update unit triggers system updates based on these analysis results: on the one hand, integrates new user preferences and behavior characteristics into the user profile model so that the profile can reflect the latest needs of users; on the other hand, adjusts the parameters and rules in the service configuration algorithm according to the feedback on the configuration effect, optimizing the selection logic of configuration items and the conflict resolution strategy. The updated model and algorithm are applied to the system after verification.

[0090] The above uses specific examples to illustrate the present invention, which is only for helping to understand the present invention and not for limiting the present invention. For those skilled in the technical field to which the present invention pertains, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made.

Claims

1. An IT service configuration and delivery method, characterized in that It includes: Collect user data, construct a user portrait model through machine learning algorithms, and generate a user portrait. According to the user portrait, automatically configure a service plan through a service configuration algorithm and execute the service plan. Update the user portrait model and service configuration algorithm according to the feedback analysis results.

2. The IT service configuration and delivery method according to claim 1, wherein The user data includes user work habits, usage preferences, and role permission data.

3. The IT service configuration and delivery method according to claim 1, characterized in that, The construction of the user portrait model includes: Collect user behavior data, system log data, and questionnaire data; extract user features; train the user portrait model.

4. The IT service configuration and delivery method according to claim 1, characterized in that The user portrait includes user role information, commonly used software information, work environment preference information, and security permission information.

5. The IT service configuration and delivery method according to claim 1, wherein, The automatic configuration of the service plan includes: select configuration items from the configuration library; detect and resolve configuration conflicts; optimize the configuration plan.

6. The IT service configuration and delivery method according to claim 1, characterized in that, The execution of the configuration includes: deploy software applications; execute the configuration; manage the configuration status; track the delivery progress.

7. The IT service configuration and delivery method according to claim 1, wherein The service plan includes desktop environment configuration, software installation configuration, permission setting configuration, and network connection configuration.

8. An IT service configuration and delivery system, characterized in that, It includes: A user portrait module that collects user data, constructs a user portrait model through machine learning algorithms, and generates a user portrait. A service configuration module that automatically configures a service plan through a service configuration algorithm. A service delivery module that executes the service plan. A continuous optimization module that collects user feedback and usage data, and updates the user portrait model and service configuration algorithm.

9. The IT service configuration and delivery system according to claim 8, wherein, The user portrait module includes: A data collection unit that collects user behavior data, system log data, and questionnaire data. A feature extraction unit that extracts user features from the collected data. A model training unit that trains the user portrait model using machine learning algorithms.

10. The IT service configuration and delivery system according to claim 8, wherein The service configuration module includes: A configuration plan generation unit that selects appropriate configuration items from the configuration library according to the user portrait. A configuration conflict resolution unit that uses a rule engine to detect and resolve configuration conflicts. A configuration optimization unit that uses a constraint solver to optimize the configuration plan.

11. The IT service configuration and delivery system according to claim 8, characterized in that, The service delivery module includes: A deployment tool that automatically installs and configures software. An execution tool that executes the configuration. A configuration management tool that manages and maintains the configuration status.

12. The IT service configuration and delivery system according to claim 8, characterized in that, The continuous optimization module includes: A feedback collection unit that collects user feedback through online feedback, questionnaires, and log analysis. A feedback analysis unit that analyzes user feedback using natural language processing and sentiment analysis techniques. A model update unit that updates the user portrait model and service configuration algorithm according to the feedback analysis results.

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