Method and system for evaluating business transformation

Abstract

The application relates to an enterprise transformation evaluation method and system. The method comprises the following steps: when receiving a transformation evaluation request of an enterprise, evaluating the maturity of the enterprise in five evolution stages, namely, a systematization stage, an automation stage, a digitization stage, a modeling stage and an intelligentization stage, based on IT operation data of the enterprise; determining a current evolution stage of the enterprise according to the maturity of each evolution stage; there is a dependency relationship between two adjacent evolution stages; determining a next evolution stage of the current evolution stage according to the dependency relationship; evaluating the preparation degree of the enterprise in jumping from the current evolution stage to the next evolution stage based on the maturity of the current evolution stage; generating a transformation evaluation report for the enterprise according to the preparation degree; and the transformation evaluation report comprises a transformation adaptation evaluation result and an evolution path suggestion of the enterprise. The scheme provided by the application can solve the problems of lacking systematic guidance and neglecting the dependency relationship in the intelligent transformation of the enterprise in the related art.

Description

Technical Field

[0001] This application relates to the field of IT operations and maintenance management technology, and in particular to a method and system for enterprise transformation assessment. Background Technology

[0002] With the rapid development of cloud computing, big data, and artificial intelligence technologies, enterprise IT (Information Technology) operations and maintenance are transforming from traditional manual operations and maintenance models to intelligent operations and maintenance.

[0003] The relevant technologies mainly include three intelligent transformation methods. The first is the process optimization method based on ITIL (IT Infrastructure Library) / ITSM (IT Service Management). The second is the method of directly purchasing AI operation and maintenance products. The third is the capability maturity assessment method based on CMM (Capability Maturity Model) / CMMI (Capability Maturity Model Integration). However, these methods lack systematic evolution path guidance and ignore the dependencies between stages, resulting in a high risk of enterprise transformation failure. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides a method and system for enterprise transformation assessment, which can solve the problems of lack of systematic guidance and neglect of dependency relationships in the intelligent transformation of enterprises in related technologies.

[0005] The first aspect of this application provides a method for assessing corporate transformation, including: When a transformation assessment request is received from a company, its IT operations and maintenance data is collected. Based on the IT operations and maintenance data, the maturity of the enterprise at each evolution stage is assessed; the evolution stages include, in order, the institutionalization stage, the automation stage, the digitalization stage, the modeling stage, and the intelligentization stage. Based on the maturity of each evolution stage, the current evolution stage of the enterprise is determined; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage; Based on the aforementioned dependencies, determine the next evolution stage of the current evolution stage; Based on the maturity of the current evolution stage, assess the enterprise's readiness to transition from the current evolution stage to the next evolution stage; Based on the preparedness level, a transformation assessment report is generated for the enterprise; the transformation assessment report includes the transformation adaptation assessment results and evolution path recommendations for the enterprise.

[0006] In one embodiment, maturity is represented by a completion score, and the evaluation dimensions for each evolution stage include technical architecture, data infrastructure, personnel capabilities, and organizational processes. The evaluation of the enterprise's maturity at each evolution stage based on the IT operations data includes: Based on the IT operations data, the evaluation metrics belonging to the technical architecture dimension for the enterprise at each evolution stage are calculated, and the enterprise's technical architecture dimension score at each evolution stage is calculated based on the evaluation metrics belonging to the technical architecture dimension; and... Based on the IT operations and maintenance data, the evaluation indicators belonging to the data foundation dimension for the enterprise at each evolution stage are calculated, and the data foundation dimension scores for the enterprise at each evolution stage are calculated based on the evaluation indicators belonging to the data foundation dimension; and, Based on the IT operations and maintenance data, the evaluation indicators belonging to the personnel capability dimension for the enterprise at each evolution stage are calculated, and the personnel capability dimension scores for the enterprise at each evolution stage are calculated based on the evaluation indicators belonging to the personnel capability dimension; and, Based on the IT operations and maintenance data, the evaluation indicators belonging to the organizational process dimension of the enterprise at each evolution stage are calculated, and the organizational process dimension score of the enterprise at each evolution stage is calculated based on the evaluation indicators belonging to the organizational process dimension. Based on the scores for the technical architecture dimension, the data foundation dimension, the personnel capability dimension, and the organizational process dimension, the completion score of the enterprise at each stage of evolution is calculated.

[0007] In one embodiment, determining the current evolution stage of the enterprise based on the maturity of each evolution stage includes: The evolution stage with the highest completion score is determined as the current evolution stage of the enterprise; wherein the highest completion score is greater than or equal to the benchmark maturity threshold.

[0008] In one embodiment, the readiness level is characterized by a readiness score; assessing the enterprise's readiness to transition from the current evolutionary stage to the next evolutionary stage based on the maturity level of the current evolutionary stage includes: Determine whether the enterprise meets the preconditions for transitioning from the current evolution stage to the next evolution stage; Based on the completion score of the current evolution stage and the satisfaction of the preconditions, the readiness score of the enterprise to transition from the current evolution stage to the next evolution stage is calculated.

[0009] In one embodiment, the preconditions include hard conditions and soft conditions; calculating the readiness score for the enterprise to transition from the current evolution stage to the next evolution stage based on the completion score of the current evolution stage and the satisfaction of the preconditions includes: Based on the satisfaction of the aforementioned hard conditions, calculate the enterprise's data foundation score; and, Based on the satisfaction of the aforementioned soft conditions, calculate the personnel capability score and organizational readiness score of the enterprise; Based on the completion score of the current evolution stage, the data foundation score, the personnel capability score, and the organizational readiness score, the readiness score of the enterprise to transition from the current evolution stage to the next evolution stage is calculated.

[0010] In one embodiment, the transformation adaptability assessment result includes a transformation adaptability determination result, and the evolution path suggestion includes a stage transition suggestion. The transformation adaptability determination result is used to characterize whether the enterprise is currently suitable for transformation, and the stage transition suggestion is used to guide the enterprise's implementation plan for entering the next evolution stage. Generating a transformation assessment report for the enterprise based on the readiness level includes: When the readiness score is greater than or equal to the baseline transition threshold, the enterprise is determined to be in a ready state, and the transformation adaptation judgment result and the stage transition suggestion are generated for the enterprise transformation. The transformation assessment request includes the target evolution stage that the enterprise expects to reach. When the level of the next evolution stage is less than the level of the target evolution stage, the stage transition suggestion is also used to guide the enterprise to transition from the current evolution stage to the target evolution stage through an evolution path timeline.

[0011] In another embodiment, the transformation fit assessment result includes a transformation misfit determination result, and the evolution path suggestion includes a phased reinforcement suggestion. The transformation misfit determination result is used to characterize that the enterprise is currently unsuitable for transformation, and the phased reinforcement suggestion is used to guide the enterprise to strengthen its weaknesses in the current evolution phase. Generating a transformation assessment report for the enterprise based on the readiness level includes: When the readiness score is less than the baseline transition threshold, the enterprise is determined to be in an unready state, and the transformation mismatch judgment result and the stage reinforcement suggestion are generated for the enterprise transformation. The transformation assessment request includes the target evolution stage that the enterprise hopes to reach, and the stage reinforcement suggestions include risk warning information, which is obtained through the following methods: Based on the maturity of the current evolution stage and the next evolution stage or the target evolution stage, retrieve historical failure cases that match the enterprise from the failure case knowledge base; Based on the number and matching degree of the historical failure cases, assess the risk level of the enterprise's leap from the current evolution stage to the next evolution stage or the target evolution stage; The historical failure cases and the risk levels are output as risk warning information.

[0012] In one embodiment, the transformation assessment report further includes the company's projected ROI; the projected ROI is obtained through the following method: Based on the enterprise's characteristic information, the similarity between the enterprise and each historical investment benefit case in the ROI benchmark database is calculated; the characteristic information includes enterprise size, industry type, and maturity level. According to the order of similarity from high to low, the top N reference investment benefit cases are selected from the ROI benchmark database; where N is an integer greater than 1, and each of the reference investment benefit cases has a corresponding ROI reference value; Based on the similarity and the ROI reference value, the predicted ROI value of the enterprise is calculated.

[0013] In one embodiment, the method further includes: The ROI forecast is adjusted based on the adjustment factors of the enterprise; the adjustment factors include at least one of team capability factor, infrastructure reuse factor and business growth factor.

[0014] A second aspect of this application provides a corporate transformation assessment system, comprising: The data acquisition module is used to collect the IT operation and maintenance data of an enterprise when it receives a transformation assessment request. The maturity assessment module is used to assess the maturity of the enterprise at each evolution stage based on the IT operation and maintenance data; the evolution stages include, in order, the institutionalization stage, the automation stage, the digitalization stage, the modeling stage, and the intelligentization stage. The current stage determination module is used to determine the current evolution stage of the enterprise based on the maturity of each evolution stage; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage; The next stage determination module is used to determine the next evolution stage of the current evolution stage according to the dependencies. The readiness assessment module is used to assess the readiness of the enterprise to transition from the current evolution stage to the next evolution stage based on the maturity of the current evolution stage. The report generation module is used to generate a transformation assessment report for the enterprise based on the readiness level; the transformation assessment report includes the transformation adaptation assessment results and evolution path suggestions for the enterprise.

[0015] A third aspect of this application provides an electronic device, comprising: Processor; and A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0016] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0017] The technical solution provided in this application may include the following beneficial results: The solution provided in this application, upon receiving a transformation assessment request from an enterprise, collects the enterprise's IT operations and maintenance data; based on this data, it assesses the enterprise's maturity at each evolution stage; the evolution stages sequentially include institutionalization, automation, digitalization, modeling, and intelligentization; based on the maturity of each evolution stage, it determines the enterprise's current evolution stage; there are dependencies between adjacent evolution stages, which characterize the implementation of the latter evolution stage depending on the completion of the former; according to these dependencies, it determines the next evolution stage; based on the maturity of the current evolution stage, it assesses the enterprise's readiness to transition from the current stage to the next; and based on this readiness, it generates a transformation assessment report for the enterprise; the transformation assessment report includes the enterprise's transformation adaptability assessment results and evolution path recommendations. This application systematically defines, for the first time, the complete evolution path of intelligent IT operations and maintenance (institutionalization stage, automation stage, digitalization stage, modeling stage, and intelligentization stage), and establishes strict dependencies between stages. In order to determine the next evolution stage according to these dependencies, the application then assesses the enterprise's readiness to leap from the current stage to the next stage based on the maturity of the current stage. Readiness can effectively reflect the feasibility of enterprise transformation. Therefore, based on readiness, a systematic evolution path guidance can be provided to enterprises. This allows enterprises to clearly understand the complete evolution path from the current state to intelligence, avoid blind investment and detours, and greatly reduce the risk of enterprise transformation failure.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0020] Figure 1 This is a flowchart illustrating the enterprise transformation assessment method shown in the embodiments of this application; Figure 2 This is another schematic diagram of the enterprise transformation assessment method shown in the embodiments of this application; Figure 3 This is a schematic diagram of the hierarchical architecture of the enterprise transformation assessment system shown in the embodiments of this application; Figure 4 This is an overall flowchart of the enterprise transformation assessment method shown in the embodiments of this application; Figure 5 This is a schematic diagram of the module structure of the enterprise transformation assessment system shown in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0021] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0022] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0023] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0024] With the rapid development of cloud computing, big data, and artificial intelligence technologies, enterprise IT operations and maintenance (O&M) are transforming from traditional manual O&M models to intelligent O&M. Currently, the industry mainly employs the following three methods for intelligent transformation: 1. ITIL / ITSM-based process optimization methods: In related technologies, many enterprises adopt the ITIL or ITSM framework to standardize operation and maintenance processes. This method improves operation and maintenance efficiency by establishing standardized processes such as incident management, problem management, and change management. However, this method mainly focuses on process-level improvements, lacks systematic guidance on the technology evolution path, and does not consider the timing and method of introducing artificial intelligence technology.

[0025] 2. Directly Purchasing AI-Powered Operations and Maintenance Products: Some enterprises attempt to achieve intelligent transformation by directly purchasing AIOPS (Artificial Intelligence for IT Operations) products on the market. These products typically include functions such as intelligent alarms, root cause analysis, and capacity prediction. However, due to the lack of necessary digital infrastructure within the enterprises themselves (such as incomplete monitoring data collection, insufficient historical data accumulation, and lack of business knowledge), the AI ​​products fail to achieve the expected results, resulting in a phenomenon of "affordable but poorly usable." For example, according to a 2023 survey report by XX Company, approximately 65% ​​of enterprises failed to achieve their expected goals after deploying AIOPS.

[0026] 3. Capability Maturity Assessment Method Based on CMM / CMMI: CMM and its subsequent version CMMI are mainly used for maturity assessment of software development processes. They define five levels: Initial, Repeatable, Defined, Managed, and Optimizing. Although this model is widely used in the field of software engineering, its focus is on development process management. It lacks specificity for the assessment of IT operations and maintenance, especially intelligent operations and maintenance, and cannot effectively guide the evolution path of AIOPS for enterprises.

[0027] Therefore, it is evident that enterprises commonly face the following technical challenges during the intelligent transformation of IT operations and maintenance: (1) Lack of systematic evolution path guidance: The relevant technologies fail to clearly define a complete evolution path from traditional operation and maintenance to intelligent operation and maintenance. Enterprises often rely on experience or follow industry trends to make investments, resulting in a "band-aid" approach. Moreover, many enterprises attempt to skip necessary intermediate stages to achieve intelligence directly, such as deploying AI systems directly when their digital infrastructure is weak, ultimately failing due to a lack of high-quality training data. For example, according to survey data from the DBAIOPS (Database Artificial Intelligence for IT Operations) community of more than 1,000 enterprises, about 72% of enterprises have taken detours in their intelligent transformation, wasting a lot of investment.

[0028] (2) Neglecting inter-stage dependencies: The dependencies between the various evolution stages are not clearly defined in the relevant technologies. For example, the intelligentization stage depends on the analytical model established in the modeling stage, while the modeling stage depends on the historical data accumulated in the digitization stage. Therefore, skipping any stage will result in a lack of basic support for subsequent construction. For example, through the analysis of 50+ failed cases, it was found that all companies that tried to "take shortcuts" ultimately failed or achieved poor results due to insufficient basic capabilities.

[0029] To address the aforementioned issues, this application provides a method for assessing enterprise transformation. For the first time, it systematically defines the complete evolution path of intelligent IT operations (institutionalization stage, automation stage, digitalization stage, modeling stage, and intelligentization stage), and establishes strict dependencies between stages. This allows for the determination of the next evolution stage based on these dependencies. Then, based on the maturity of the current evolution stage, the method assesses the enterprise's readiness to leap from the current stage to the next. Readiness effectively reflects the feasibility of enterprise transformation. Therefore, based on readiness, a systematic evolution path guidance can be provided to the enterprise. This allows the enterprise to clearly understand the complete evolution path from its current state to intelligentization, avoiding blind investment and detours, and significantly reducing the risk of transformation failure.

[0030] To facilitate understanding of the technical solutions in the embodiments of this application, the specific meanings of the terms involved in the embodiments of this application are explained below: Intelligent IT Operations and Maintenance: This refers to the application of artificial intelligence technology to the field of IT operations and maintenance. Through data collection, analysis and modeling, intelligent decision-making and other means, it enables the automation, predictability and self-healing of enterprise operations and maintenance work, thereby reducing labor costs, improving service quality and system stability.

[0031] Completion score: Used to quantitatively assess an organization's maturity at a specific stage of development. By defining assessment indicators for different stages of evolution, it helps organizations identify their current status, clarify directions for improvement, and formulate evolution paths.

[0032] DBAIOPS: An intelligent operation and maintenance system specifically designed for database operation and maintenance scenarios. It uses AI technologies such as machine learning, knowledge graphs, and natural language processing to achieve functions such as intelligent database inspection, fault diagnosis, performance optimization, and capacity prediction.

[0033] Digital elements: These refer to data elements that can be quantified, recorded, and analyzed during business operations, including but not limited to performance metrics, log data, configuration information, and change records.

[0034] Ontology model: In the field of artificial intelligence, it is a formal representation method used to describe domain knowledge. It constructs a machine-understandable knowledge structure by defining entities, attributes, and relations, providing a foundation for intelligent reasoning.

[0035] RAG (Retrieval-Augmented Generation): An AI architecture that combines information retrieval and large language models. It improves the accuracy and reliability of answers by first retrieving relevant information from a knowledge base and then generating answers using a language model.

[0036] Readiness score: Used to quantitatively assess an organization's readiness to evolve from the current stage to the next stage, taking into account multiple dimensions such as technological foundation, data accumulation, personnel capabilities, and organizational culture.

[0037] ROI (Return on Investment): A financial indicator used to measure the effectiveness of an investment. The calculation formula is (revenue - cost) / cost × 100%. In this embodiment of the application, it is used to evaluate the economic benefits of each stage of intelligent evolution.

[0038] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0039] Figure 1 This is a flowchart illustrating the enterprise transformation assessment method shown in the embodiments of this application.

[0040] See Figure 1 The enterprise transformation assessment method of this application may include: S110 collects the enterprise's IT operations and maintenance data when it receives a transformation assessment request from the enterprise.

[0041] In the embodiments of this application, it can be applied to an enterprise transformation assessment system (hereinafter referred to as the "assessment system"), such as... Figure 2 As shown, the overall architecture of the evaluation system includes a data source, a data and knowledge layer, a service layer, and a presentation layer. The data source covers automation tools, monitoring systems, CMDB (Configuration Management Database), operation and maintenance documents, inventory lists, data, and materials. The data and knowledge layer covers the five-stage evolution, evaluation indicators, failure cases, model library, knowledge base, benchmark library, and ROI. The service layer covers maturity, readiness, evaluation engine, scoring module, planning module, and prediction module. The presentation layer covers evaluation reports, evolution paths, visualizations, dashboards, planning diagrams, and comparative analysis.

[0042] Users can initiate a transformation assessment request to the assessment system, which will then respond to the request and initiate the data collection phase. The assessment system collects the company's IT operations and maintenance data through methods such as questionnaires, system integration, and document analysis. This IT operations and maintenance data may include, but is not limited to: a list of automation tools, coverage of automation tools, monitoring system configuration, monitoring data integrity, historical operations and maintenance data, historical fault handling records, team personnel information, personnel skill assessments, and operations and maintenance process documents.

[0043] S120 assesses an enterprise's maturity at each stage of its evolution, based on IT operations and maintenance data. The evolution stages include, in order, institutionalization, automation, digitalization, modeling, and intelligence.

[0044] This application clearly defines five evolutionary stages of intelligent IT operations and maintenance: institutionalization stage, automation stage, digitalization stage, modeling stage, and intelligentization stage. Each evolutionary stage has clear core characteristics, capability requirements, and deliverable standards.

[0045] The core objective of the institutionalization stage (evolutionary stage 1) is to establish a standardized operation and maintenance management system to eliminate the arbitrariness and chaos in operation and maintenance work. The core characteristics of the institutionalization stage include: (1) establishing ITIL / ITSM-based operation and maintenance process specifications, including standard processes for incident management, problem management, change management, and configuration management; (2) developing operation and maintenance operation manuals (SOPs (Standard Operating Procedures)) to clarify the standard steps for various operation and maintenance operations; (3) establishing a work order classification system and SLA (Service Level Agreement) standards; and (4) defining resource naming specifications and a tagging system. The evaluation indicators (or capability requirements) of the institutionalization stage may include: process document completeness (requiring coverage of over 90% of common operation and maintenance scenarios), SOP execution rate (requiring over 80%), and work order processing process standardization (requiring 95% of work orders to be processed according to the process). The deliverables of the institutionalization stage may include: operation and maintenance management system documents, various operation and maintenance SOP manuals, work order template libraries, and emergency response plans.

[0046] The core objective of the automation phase (evolutionary phase 2) is to improve operational efficiency by replacing repetitive manual operations with tools and scripts. Key features of the automation phase include: (1) developing or deploying automated operation and maintenance tools to enable scripted execution of enterprise operation and maintenance tasks; (2) establishing a scheduled task system to automate periodic tasks such as inspections and backups; (3) encapsulating cloud platform APIs to enable programmatic calls for resource operations; and (4) developing batch operation tools to support parallel operations on multiple instances. Evaluation metrics for the automation phase include: automation coverage (the percentage of automated processing in enterprise operation and maintenance tasks, requiring a minimum of 60%), automation tool availability (requiring a minimum of 99%), and reduction in manual operations (requiring a reduction of at least 50%). Deliverables of the automation phase include: an automation script library, an operation and maintenance toolset, an API encapsulation library, and scheduled task configurations.

[0047] The core objective of the digitalization phase (Evolution Phase 3) is to establish a data-driven operations and maintenance foundation by integrating various digital elements in the enterprise's operations and maintenance process. Key features of the digitalization phase include: (1) deploying a comprehensive monitoring system to collect digital elements such as performance metrics, logs, configuration information, and change records; (2) establishing a data storage and query platform to support long-term storage and rapid retrieval of historical data; (3) establishing a CMDB to record complete information and relationships of IT resources; and (4) achieving data visualization and building an operations and maintenance dashboard. Evaluation metrics for the digitalization phase include: data collection completeness (key metrics collection coverage must reach 85% or more), data storage duration (retaining at least 30 days of detailed data), data queryability (90% of queries must return results within 5 seconds), and CMDB accuracy (reaching 95% or more). Deliverables for the digitalization phase include: a monitoring system (monitoring system configuration, historical operations and maintenance data, monitoring data integrity, historical fault handling records), a log system, a CMDB system, a data warehouse, and a visualization dashboard.

[0048] The core objective of the modeling phase (evolutionary phase 4) is to establish various analytical models based on digitally accumulated data to achieve intelligent analysis and prediction of enterprise operation and maintenance status. The core features of the modeling phase include: (1) constructing a health assessment model to comprehensively evaluate the system's health status using multi-dimensional indicators; (2) constructing a performance analysis model to automatically identify performance bottlenecks and optimization opportunities; (3) constructing a fault diagnosis model to establish a fault feature library and root cause correlation diagram; (4) constructing a capacity prediction model to predict future resource demands based on historical trends; and (5) constructing a load characteristic model to identify the periodicity and abnormal patterns of business loads. The evaluation indicators for the modeling phase include: model coverage (requiring the establishment of four basic models: health, performance, fault, and capacity), model accuracy (the prediction accuracy of each model must reach over 80%), and model availability (the model can run automatically and output analysis results). The deliverables of the modeling phase include: health assessment models, performance analysis models, fault diagnosis models, capacity prediction models, and load characteristic models.

[0049] The core objective of the intelligent stage (evolution stage 5) is to introduce AI technology to achieve intelligent decision-making and automatic execution based on model-based analytical capabilities. The core characteristics of the intelligent stage include: (1) deploying an NLP (Natural Language Processing) engine to achieve automatic understanding of work orders and inquiries; (2) integrating a RAG architecture to achieve intelligent question answering based on a knowledge base; (3) building an intelligent decision engine to achieve automatic fault classification and disposal solution recommendation; (4) implementing an automatic self-healing mechanism to automatically repair low-risk faults; and (5) establishing a continuous learning mechanism to automatically update the knowledge base and rule base from historical processing records. The evaluation indicators for the intelligent stage include: AI automatic processing rate (the proportion of work orders and inquiries handled automatically by AI, requiring a minimum of 60%), self-healing success rate (the proportion of successful automatic repair attempts, requiring a minimum of 80%), and intelligent recommendation accuracy rate (the proportion of system-recommended disposal solutions adopted, requiring a minimum of 75%). The deliverables in the intelligentization phase may include: NLP engine, RAG question answering system, intelligent decision engine, self-healing execution engine, knowledge graph, etc.

[0050] This application embodiment stores the five evolution stages defined above in a structured manner in the evaluation system, serving as a five-stage evolution framework (or five-stage evolution model) for the evaluation system to subsequently evaluate and plan the enterprise's transformation path. In practical applications, the evaluation system assesses the maturity of the enterprise at each evolution stage based on the enterprise's IT operation and maintenance data, according to the core characteristics, capability requirements, and deliverable standards of each evolution stage under this framework.

[0051] S130, determine the current evolution stage of the enterprise based on the maturity of each evolution stage; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage.

[0052] Maturity effectively reflects a company's infrastructure development status at a given evolutionary stage. For example, a higher maturity level indicates more complete infrastructure development at that stage. Specifically, a maturity level < 40% means the company's infrastructure development is in its initial or unstarted phase; 40% ≤ Maturity < 60% means it's in progress and requires continued investment; 60% ≤ Maturity < 80% means it's largely complete but has shortcomings that need addressing; and 80% ≤ Maturity means it's fully developed and ready to move to the next stage. Based on this, the assessment system can select the stage with a maturity level ≥ 60% (the highest maturity level) as the company's current evolutionary stage.

[0053] It should be noted that, in addition to explicitly defining the above five evolution stages, this application further explicitly defines a strict dependency relationship between adjacent evolution stages. This dependency relationship is used to characterize that the successful implementation of the latter evolution stage depends on the completion of the former evolution stage. Specific dependencies may include the following: (1) Automation stage depends on institutionalization stage: because automation scripts need to be designed according to standardized processes, otherwise the chaotic process will be solidified in the code, making it difficult to optimize later.

[0054] (2) The digitalization stage depends on the automation stage: because the automatic collection of data needs to be achieved through automated tools, manual methods cannot guarantee the integrity and timeliness of data collection.

[0055] (3) The modeling stage depends on the digitization stage: because the establishment of the analysis model requires a large amount of historical data as training samples, the digitization stage needs to accumulate at least 30 days of complete data in order to establish an effective baseline model.

[0056] (4) The intelligent stage depends on the modeling stage: because AI’s intelligent decision-making requires analysis results based on models, without basic models such as health models and fault models, AI cannot make effective reasoning and decisions.

[0057] S140, determine the next evolution stage based on dependencies.

[0058] After determining the current evolutionary stage of the enterprise, the evaluation system can determine the next evolutionary stage according to the dependencies between stages defined above. For example, if the current evolutionary stage is the institutionalization stage, then the next evolutionary stage is determined to be the automation stage based on the dependency relationship between the automation stage and the institutionalization stage; if the current evolutionary stage is the automation stage, then the next evolutionary stage is determined to be the digitalization stage based on the dependency relationship between the digitalization stage and the automation stage; if the current evolutionary stage is the digitalization stage, then the next evolutionary stage is determined to be the modeling stage based on the dependency relationship between the modeling stage and the intelligentization stage; if the current evolutionary stage is the modeling stage, then the next evolutionary stage is determined to be the intelligentization stage based on the dependency relationship between the intelligentization stage and the modeling stage.

[0059] S150 assesses an organization's readiness to move from its current stage of evolution to the next stage, based on its current level of maturity.

[0060] The higher the maturity of the current evolution stage, the better prepared the company is to leap from the current evolution stage to the next evolution stage. Therefore, the assessment system can evaluate the company's preparedness to leap from the current evolution stage to the next evolution stage based on the maturity of the current evolution stage.

[0061] S160 generates a transformation assessment report for the enterprise based on its readiness level; the transformation assessment report includes the enterprise's transformation adaptability assessment results and evolution path recommendations.

[0062] Readiness level effectively reflects the feasibility of enterprise transformation. For example, a higher readiness level indicates a greater ability for the enterprise to leap from the current evolutionary stage to the next, resulting in a higher success rate for the transformation. Therefore, the assessment system can generate a transformation assessment report for the enterprise based on the readiness level. This report includes the enterprise's transformation adaptability assessment results and evolutionary path recommendations. The transformation adaptability assessment results characterize whether the enterprise is currently suitable for transformation (current evolutionary stage → next evolutionary stage), while the evolutionary path recommendations guide the enterprise's implementation plan for the next evolutionary stage or help the enterprise address its weaknesses in the current stage.

[0063] As can be seen, this application's embodiments, by clearly defining five evolutionary stages and their dependencies, can provide enterprises with a clear roadmap for intelligent evolution, avoiding failures caused by blindly skipping stages. Compared with related technologies, this application's embodiments are based on practical data from over 1000 enterprises and 72 historical failure cases (mentioned below), demonstrating strong practicality and reliability. After application verification by over 100 enterprises, companies guided by the five-stage evolutionary framework provided by this application's embodiments have seen their intelligent project success rate increase from 35% to 85%, and investment waste reduced by 70%.

[0064] As this example demonstrates, the solution provided in this application systematically defines, for the first time, the complete evolution path of intelligent IT operations and maintenance (institutionalization stage, automation stage, digitalization stage, modeling stage, and intelligentization stage), and establishes strict dependencies between stages. This allows for the determination of the next evolution stage based on these dependencies. Then, based on the maturity of the current evolution stage, the readiness of the enterprise to leap from the current stage to the next is assessed. Readiness effectively reflects the feasibility of enterprise transformation. Therefore, based on readiness, a systematic evolution path guidance can be provided to enterprises. This allows enterprises to clearly understand the complete evolution path from the current state to intelligentization, avoiding blind investment and detours, and significantly reducing the risk of transformation failure.

[0065] Figure 3 This is another flowchart illustrating the enterprise transformation assessment method shown in this application.

[0066] See Figure 3 The enterprise transformation assessment method of this application may include: S310 collects IT operations and maintenance data from enterprises when it receives a transformation assessment request from an enterprise.

[0067] This step can be found in the description in S110, and will not be repeated here.

[0068] In one implementation, the evaluation dimensions for each evolution stage include the technical architecture dimension, data infrastructure dimension, personnel capability dimension, and organizational process dimension.

[0069] In addition, enterprises still face the following technical challenges in the process of intelligent transformation of IT operations and maintenance: (3) Lack of quantitative stage assessment standards: Qualitative descriptions (such as "basic", "intermediate", "advanced") are commonly used in related technologies, and there is a lack of quantifiable assessment indicator systems. This makes it difficult for enterprises to accurately judge their current stage and assess their readiness to evolve to the next stage. For example, an enterprise may think that it has completed automation construction, but in fact the automation coverage rate is only 30% and the data collection integrity is less than 50%. In this case, blindly entering the digitalization stage will inevitably fail.

[0070] The root cause of the above technical problems lies in the fact that intelligent IT operations and maintenance is a complex system engineering project involving the collaborative evolution of multiple dimensions such as technical architecture, data foundation, personnel capabilities, and organizational processes. However, related technologies often start from a single dimension (such as technical products or management processes) and fail to build a multi-dimensional, phased, and quantifiable maturity assessment system.

[0071] To address this, this application establishes a multi-dimensional quantitative evaluation index system: for each evolution stage, quantitative evaluation indicators are defined from four evaluation dimensions: technical architecture, data foundation, personnel capabilities, and organizational processes, to achieve accurate quantitative evaluation of maturity, enabling enterprises to accurately determine their current stage and the shortcomings of each stage, with an evaluation accuracy rate of 92% (based on validation data from 100+ enterprises).

[0072] In its specific implementation, this application's embodiments evaluate each evolution stage from four dimensions: technical architecture, data infrastructure, personnel capabilities, and organizational processes. These four dimensions collectively determine the maturity level of the enterprise at any given evolution stage.

[0073] The technology architecture dimension is used to assess an enterprise's infrastructure development in terms of tools, platforms, and systems. For example, during the digitalization phase, the technology architecture dimension can assess the completeness of an enterprise's monitoring system deployment and the availability of its data storage platform.

[0074] Among them, the data foundation dimension is used to assess a company's performance in data collection, storage, and quality. For example, during the modeling phase, the data foundation dimension can assess the duration of historical data accumulation (requiring at least 30 days) and data integrity (requiring a key indicator collection rate of over 85%).

[0075] The personnel capability dimension is used to assess the operations and maintenance team's reserves of relevant skills and experience. For example, in the intelligentization phase, the personnel capability dimension can assess whether the enterprise team has AI technicians and whether the DBA (Database Administrator) has the ability to collaborate with AI systems.

[0076] The organizational process dimension is used to assess the maturity of an enterprise in terms of management systems, collaboration mechanisms, and other aspects. For example, in the automation phase, the organizational process dimension can assess whether an enterprise has established usage guidelines for automation tools and whether it has a regular tool optimization mechanism.

[0077] S321, based on IT operations and maintenance data, calculates the evaluation indicators belonging to the technical architecture dimension for enterprises at each evolution stage, and calculates the scores of enterprises in the technical architecture dimension at each evolution stage based on the evaluation indicators belonging to the technical architecture dimension.

[0078] S322, based on IT operations and maintenance data, calculates the evaluation indicators belonging to the data foundation dimension for enterprises at each stage of evolution, and calculates the data foundation dimension scores for enterprises at each stage of evolution based on the evaluation indicators belonging to the data foundation dimension.

[0079] S323, based on IT operations and maintenance data, calculates the assessment indicators belonging to the personnel capability dimension of the enterprise at each stage of evolution, and calculates the personnel capability dimension score of the enterprise at each stage of evolution based on the assessment indicators belonging to the personnel capability dimension.

[0080] S324, based on IT operations and maintenance data, calculates the evaluation indicators belonging to the organizational process dimension for enterprises at each stage of evolution, and calculates the organizational process dimension scores for enterprises at each stage of evolution based on the evaluation indicators belonging to the organizational process dimension.

[0081] As mentioned earlier, IT operations and maintenance data includes a list of automation tools, coverage of automation tools, monitoring system configuration, monitoring data integrity, historical operations and maintenance data, historical fault handling records, team personnel information, personnel skill assessments, and operations and maintenance process documents.

[0082] As mentioned earlier, the evaluation indicators for the institutionalization stage include the completeness of process documentation (requiring coverage of over 90% of common operation and maintenance scenarios), SOP execution rate (requiring over 80%), and the standardization of work order processing procedures (requiring 95% of work orders to be processed according to procedures); the evaluation indicators for the automation stage include automation coverage (the proportion of automated processing in enterprise operation and maintenance tasks, requiring over 60%), automation tool availability (requiring over 99%), and the reduction in manual operations (requiring a reduction of over 50%); the evaluation indicators for the digitalization stage include data collection completeness (key indicator collection coverage requires over 85%), data storage duration (requiring the retention of at least 30 days of detailed data), and data queryability (requiring...). The evaluation metrics for the modeling phase include: 90% of queries returning results within 5 seconds; CMDB accuracy (95% or higher); Modeling phase evaluation metrics include: Model coverage (establishing four basic models: health, performance, fault, and capacity); Model accuracy (prediction accuracy of each model must reach 80% or higher); Model availability (the model must be able to run automatically and output analysis results); Intelligent phase evaluation metrics include: AI automatic processing rate (the percentage of work orders and inquiries handled automatically by AI, requiring 60% or higher); Self-healing success rate (the percentage of successful automatic repair attempts, requiring 80% or higher); Intelligent recommendation accuracy (the percentage of system-recommended solutions adopted, requiring 75% or higher).

[0083] Among them, for the various evaluation indicators in the institutionalization stage, the evaluation system can calculate the completeness of the enterprise's process documents in the institutionalization stage based on the operation and maintenance process documents and historical operation and maintenance data; the evaluation system can calculate the SOP execution rate of the enterprise in the institutionalization stage based on historical operation and maintenance data and historical fault handling records; and the evaluation system can calculate the standardization of the enterprise's work order processing process in the institutionalization stage based on the operation and maintenance process documents and historical fault handling records.

[0084] Among them, for the various evaluation indicators in the automation phase, the evaluation system can calculate the automation coverage rate of an enterprise in the automation phase based on the list of automation tools, the coverage of automation tools, and historical operation and maintenance data; the evaluation system can calculate the availability of automation tools in the automation phase based on the list of automation tools, the coverage of automation tools, and historical operation and maintenance data; the evaluation system can calculate the scheduled task configuration of an enterprise in the automation phase based on historical operation and maintenance data and the coverage of automation tools.

[0085] Among them, for the various evaluation indicators in the digitalization phase, the evaluation system can calculate the data collection integrity of the enterprise in the digitalization phase based on the monitoring system configuration, monitoring data integrity, and historical operation and maintenance data; the evaluation system can calculate the data storage duration of the enterprise in the digitalization phase based on historical operation and maintenance data; the evaluation system can calculate the data queryability of the enterprise in the digitalization phase based on historical operation and maintenance data; and the evaluation system can calculate the CMDB accuracy of the enterprise in the digitalization phase based on the list of automation tools, monitoring system configuration, and team personnel information.

[0086] Among them, for the various evaluation indicators in the modeling phase, the evaluation system can calculate the model coverage of the enterprise in the modeling phase based on historical operation and maintenance data and historical fault handling records; the evaluation system can calculate the model accuracy of the enterprise in the modeling phase based on historical operation and maintenance data and historical fault handling records; the evaluation system can calculate the model availability of the enterprise in the modeling phase based on historical operation and maintenance data.

[0087] Among them, for the various evaluation indicators in the intelligentization stage, the evaluation system can calculate the AI ​​automatic processing rate of the enterprise in the intelligentization stage based on historical fault handling records and operation and maintenance process documents; the evaluation system can calculate the self-healing success rate of the enterprise in the intelligentization stage based on historical operation and maintenance data and historical fault handling records; and the evaluation system can calculate the intelligent recommendation accuracy rate of the enterprise in the intelligentization stage based on historical fault handling records.

[0088] The embodiments of this application can assign each evaluation indicator to a corresponding evaluation dimension for each evolution stage, and then calculate the enterprise's score for each evaluation dimension at each evolution stage based on the evaluation indicators of each evaluation dimension.

[0089] Taking the digitalization phase as an example, the evaluation indicators belonging to the technical architecture dimension include monitoring system coverage, data storage platform availability, and data query performance. Specifically, monitoring system coverage is calculated as (number of deployed monitoring instances / total number of instances) × 100%; scoring rules: coverage ≥ 85% receives full marks (30 points), 70% ≤ coverage < 85% receives 20 points, 50% ≤ coverage < 70% receives 10 points, and coverage < 50% receives 0 points. Data storage platform availability is calculated as (platform availability time in the current month / total time in the current month) × 100%; scoring rules: availability ≥ 99.9% receives full marks (20 points), 99% ≤ availability < 99.9% receives 15 points, 95% ≤ availability < 99% receives 10 points, and availability < 95% receives 0 points. Data query performance: The P95 query response time is used as the evaluation indicator; Scoring rules: P95 < 5 seconds gets full marks (20 points), 5 seconds ≤ P95 < 10 seconds gets 15 points, 10 seconds ≤ P95 < 30 seconds gets 10 points, and P95 ≥ 30 seconds gets 0 points.

[0090] Taking the digitization stage as an example, the evaluation indicators belonging to the data foundation dimension include the completeness of key indicator collection, the retention period of historical data, and the data quality score. Specifically, the completeness of key indicator collection is calculated as (number of key indicators collected / total number of key indicators to be collected) × 100%. Key indicators include 30 basic indicators such as CPU, memory, disk, network, database connections, QPS (Queries Per Second), and slow queries. The scoring rules are: completeness ≥ 85% receives full marks (30 points), 70% ≤ completeness < 85% receives 20 points, and completeness < 70% receives 0 points. The retention period of historical data is calculated in days, showing the retention time of detailed monitoring data. The scoring rules are: retention time ≥ 30 days receives full marks (20 points), 14 days ≤ retention time < 30 days receives 15 points, 7 days ≤ retention time < 14 days receives 10 points, and retention time < 7 days receives 0 points. Data quality score: It comprehensively considers three sub-indicators: data accuracy (sampling rate, latency, etc.), consistency (consistency between different data sources), and completeness (missing rate); scoring rules: a quality score ≥ 80 points gets full marks (20 points), 60 points ≤ quality score < 80 points gets 15 points, and a quality score < 60 points gets 0 points.

[0091] Taking the digitalization phase as an example, the assessment indicators for personnel capabilities include data analysis ability assessment and tool proficiency. Specifically, data analysis ability assessment involves evaluating team members' data analysis skills (SQL (Structured Query Language) queries, data visualization, statistical analysis, etc.) through written tests or practical exercises. The scoring rules are: ≥80% of members passing the assessment receive full marks (15 points); 60% ≤ number of members passing the assessment < 80% receive 10 points; and <60% receive 0 points. Tool proficiency assesses the team's proficiency in using monitoring tools, log analysis tools, and data query tools. The scoring rules are: ≥90% proficiency (90% of daily tasks can be completed using the tools) receive full marks (15 points); 70% ≤ proficiency < 90% receive 10 points; and <70% receive 0 points.

[0092] Taking the digitalization phase as an example, evaluation indicators belonging to the organizational process dimension include the proportion of data-driven decisions and the frequency of regular data analysis meetings. Specifically, the proportion of data-driven decisions is calculated based on the percentage of operational decisions made in the past month; the scoring rules are: a proportion ≥ 60% receives full marks (10 points), 40% ≤ proportion < 60% receives 7 points, and proportion < 40% receives 0 points. The frequency of regular data analysis meetings assesses whether a regular data analysis and review mechanism has been established; the scoring rules are: once a week receives full marks (10 points), once a month receives 7 points, and irregular meetings receive 0 points.

[0093] Based on the above calculation methods and scoring rules, the enterprise's scores in the technical architecture dimension, data foundation dimension, personnel capability dimension, and organizational process dimension are calculated respectively in the digitalization stage.

[0094] Similarly, the scores for the enterprise in the institutionalization, automation, modeling, and intelligentization stages are calculated, including scores for the technical architecture, data foundation, personnel capabilities, and organizational processes.

[0095] S330 calculates the completion score of an enterprise at each stage of its evolution based on scores in the dimensions of technical architecture, data foundation, personnel capabilities, and organizational processes.

[0096] Maturity can be represented by a completion score; for example, the higher the completion score, the higher the maturity of the enterprise at the corresponding stage of evolution.

[0097] The evaluation system can calculate the enterprise's completion score for any evolution stage based on the scores of the technology architecture dimension, data foundation dimension, personnel capability dimension, and organizational process dimension. The calculation formula is: Completion score = Technology architecture dimension score + Data foundation dimension score + Personnel capability dimension score + Organizational process dimension score.

[0098] Taking the digitalization stage as an example, the total score is 200 points (70 points for technical architecture, 70 points for data foundation, 30 points for personnel capability, and 30 points for organizational process).

[0099] Similarly, the completion scores of enterprises in the institutionalization, automation, modeling, and intelligentization stages are calculated.

[0100] S340 defines the evolution stage of the highest completion score as the current evolution stage of the enterprise; wherein the highest completion score is greater than or equal to the baseline maturity threshold.

[0101] Completion scores effectively reflect a company's infrastructure development status at a given evolution stage. A higher completion score indicates more complete infrastructure development at that stage. For example, a completion score < 80 (corresponding to 40% maturity) means the company's infrastructure development is in its initial or unstarted phase; 80 ≤ completion score < 120 (corresponding to 40%~60% maturity) means the infrastructure development is in progress and requires continued investment; 120 ≤ completion score < 160 (corresponding to 60%~80% maturity) means the infrastructure development is basically complete, but has shortcomings that need improvement; and 160 ≤ completion score (corresponding to 80% maturity) means the infrastructure development is complete and ready to move to the next evolution stage. Based on this, the evaluation system can select the evolution stage with the highest completion score (≥ the baseline maturity threshold, such as 120 points) from the five evolution stages as the current evolution stage of the enterprise.

[0102] As can be seen, this application embodiment achieves accurate quantitative assessment of enterprise IT operation and maintenance maturity by establishing a multi-dimensional quantitative evaluation index system. Compared with qualitative evaluation methods (such as "basic," "intermediate," and "advanced") used in related technologies, the maturity assessment results of this application embodiment are objective, repeatable, and comparable. Validated by over 100 enterprises, the assessment accuracy rate of this application embodiment reaches 92%, effectively identifying the shortcomings of enterprises at each stage of evolution and providing a basis for targeted improvements.

[0103] S350, based on dependencies, determines the next evolution stage of the current evolution stage.

[0104] This step can be found in the description in S140, and will not be repeated here.

[0105] S360 assesses an organization's readiness to transition from its current stage of evolution to the next, based on its current level of maturity.

[0106] This step can be found in the description in S150, and will not be repeated here.

[0107] In one implementation, readiness is represented by a readiness score; assessing an enterprise's readiness to transition from the current evolutionary stage to the next evolutionary stage based on the maturity of the current evolutionary stage may include: Determine whether the enterprise meets the prerequisites for transitioning from the current evolution stage to the next evolution stage; based on the completion score of the current evolution stage and the fulfillment of the prerequisites, calculate the readiness score for the enterprise to transition from the current evolution stage to the next evolution stage.

[0108] In addition, the relevant technologies not only ignore the dependencies between stages, but also ignore the prerequisites for cross-stage construction, resulting in a lack of basic support for subsequent construction.

[0109] In this application embodiment, for the transition from the current evolution stage to the next evolution stage, a set of prerequisite checks must be defined. These prerequisites may include hard conditions and soft conditions. Taking the transition from the digitization stage to the modeling stage as an example, the hard conditions (which must be met, otherwise the transition is not allowed) include: (1) a digitization stage completion score ≥ 120 points (60%); (2) a historical data retention period ≥ 30 days; (3) a key indicator collection integrity ≥ 70%; and (4) a data quality score ≥ 60 points. The soft conditions (which are recommended to be met, otherwise they will affect the effect of the next stage) include: (1) at least one data analyst in the team; (2) an organizational process for data analysis has been established; and (3) there have been successful cases of data-driven decision-making.

[0110] Readiness can be represented by a readiness score. For example, the higher the readiness score, the better the company is prepared to move from the current stage of evolution to the next stage.

[0111] The evaluation system can determine whether a company meets the prerequisites (hard conditions + soft conditions) for transitioning from the current evolution stage to the next evolution stage. Based on the completion score of the current evolution stage and the fulfillment of the prerequisites (hard conditions + soft conditions), the system can calculate the company's readiness score for transitioning from the current evolution stage to the next evolution stage.

[0112] In one implementation, calculating the enterprise's readiness score for transitioning from the current evolution stage to the next evolution stage, based on the completion score of the current evolution stage and the fulfillment of preconditions, may include: Based on the fulfillment of hard conditions, calculate the enterprise's data foundation score; and based on the fulfillment of soft conditions, calculate the enterprise's personnel capability score and organizational readiness score; based on the completion score of the current evolution stage, as well as the data foundation score, personnel capability score, and organizational readiness score, calculate the enterprise's readiness score for leaping from the current evolution stage to the next evolution stage.

[0113] The evaluation system can calculate a company's data foundation score based on the fulfillment of hard requirements. Specifically, the evaluation system calculates the score based on three evaluation indicators: the duration of historical data accumulation, the completeness of key indicator collection, and the data quality score. A company receives full marks (100 points) for meeting all hard requirements, and deducts 30 points for each missing requirement.

[0114] The assessment system can calculate a company's personnel capability score based on the fulfillment of soft requirements. Specifically, the assessment system calculates scores based on evaluation indicators such as team skill reserves and training completion. For example, the modeling phase requires at least one data analyst; fulfilling this requirement earns full marks (100 points), while failing to do so earns 0 points.

[0115] The assessment system can calculate an organization's readiness score based on the fulfillment of soft conditions. Specifically, the system calculates scores based on indicators such as whether corresponding processes, systems, and cultures have been established, including whether a data-driven decision-making culture has been established and whether there is a regular data review mechanism.

[0116] In this application embodiment, corresponding weight values ​​can be pre-assigned to the completion score, data foundation score, personnel capability score, and organizational readiness score of the current evolution stage. For example, a first weight value (e.g., 0.4) can be assigned to the completion score of the current evolution stage, a second weight value (e.g., 0.3) can be assigned to the data foundation score, a third weight value (e.g., 0.2) can be assigned to the personnel capability score, and a fourth weight value (e.g., 0.1) can be assigned to the organizational readiness score. The first weight value + the second weight value + the third weight value + the fourth weight value = 1.0.

[0117] The evaluation system can calculate the readiness score of an enterprise to leap from the current evolution stage to the next evolution stage based on the completion score of the current evolution stage (normalized to 0-100 points) and the first weight value, the data basis score and the second weight value, the personnel capability score and the third weight value, and the organizational readiness score and the fourth weight value. The calculation formula is: Readiness score = Completion score of the current evolution stage × First weight value + Data basis score × Second weight value + Personnel capability score × Third weight value + Organizational readiness score × Fourth weight value.

[0118] S370 generates a transformation assessment report for enterprises based on their readiness level; the transformation assessment report includes the enterprise's transformation adaptability assessment results and evolution path recommendations.

[0119] This step can be found in the description in S160, and will not be repeated here.

[0120] In one implementation, the transformation adaptability assessment results include transformation adaptability determination results, and the evolution path recommendations include stage transition recommendations. The transformation adaptability determination results are used to characterize the enterprise's current suitability for transformation, and the stage transition recommendations are used to guide the enterprise's implementation plan for the next evolution stage. Based on the readiness level, a transformation assessment report is generated for the enterprise, which may include: When the readiness score is greater than or equal to the baseline transition threshold, the enterprise is determined to be in a ready state, and a transformation adaptation judgment result and stage transition suggestions are generated for the enterprise transformation.

[0121] The assessment system can compare readiness scores with a baseline transition threshold (such as 60 points) to generate corresponding transition adaptation assessment results and personalized evolution path recommendations based on the comparison results.

[0122] Specifically, if the readiness score is greater than or equal to the baseline transition threshold (e.g., 60 points), it indicates that the enterprise is in a ready state. The assessment system can then generate a transformation adaptability judgment result and a stage transition suggestion. The transformation adaptability judgment result is used to characterize the enterprise's current suitability for transformation (i.e., it is recommended that the enterprise enter the next evolution stage), and the stage transition suggestion is used to guide the enterprise's implementation plan for entering the next evolution stage.

[0123] Furthermore, the transformation adaptability assessment result can include a first adaptability assessment result and a second adaptability assessment result; the stage transition suggestions can include a first transition suggestion and a second transition suggestion; the ready state can include a baseline ready state and a highly ready state; and the baseline transition threshold can include a first transition threshold (e.g., 60 points) and a second transition threshold (e.g., 80 points). If the first transition threshold ≤ readiness score < second transition threshold, it indicates that the enterprise is in a basically ready state. In this case, the assessment system can generate a first adaptability assessment result and a first transition suggestion. The first adaptability assessment result indicates that the enterprise can basically enter the next evolution stage, and the first transition suggestion includes a specific implementation plan for the next evolution stage and a reminder of the shortcomings to be addressed in the current evolution stage. If the readiness score ≥ the second transition threshold, it indicates that the enterprise is in a highly ready state. In this case, the assessment system can generate a second adaptability assessment result and a second transition suggestion. The second adaptability assessment result indicates that the enterprise can fully enter the next evolution stage, and the second transition suggestion includes a specific implementation plan for the next evolution stage.

[0124] In one implementation, the transformation assessment request includes the target evolution stage that the enterprise expects to reach. When the level of the next evolution stage is less than the level of the target evolution stage, the stage transition suggestion is also used to guide the enterprise to transition from the current evolution stage to the target evolution stage in a timeline.

[0125] When submitting a transformation assessment request, users can input information such as the company's desired evolution stage, time requirements, and budget constraints. The level of the desired evolution stage (e.g., the intelligentization stage) is greater than the level of the current evolution stage (e.g., the digitalization stage). The next evolution stage may be the same as or different from the next evolution stage. If the desired evolution stage is not equal to the next evolution stage, it means the level of the next evolution stage (e.g., the modeling stage) is less than the level of the desired evolution stage (e.g., the intelligentization stage). In this case, the stage transition suggestions generated by the assessment system (first transition suggestion / second transition suggestion) can also be used to guide the company's evolution path timeline from the current evolution stage to the desired evolution stage (current evolution stage → next evolution stage → desired evolution stage).

[0126] In another implementation, the transformation fit assessment results include a transformation incompatibility determination result, and the evolution path recommendations include phased reinforcement recommendations. The transformation incompatibility determination result is used to characterize that the enterprise is currently unsuitable for transformation, and the phased reinforcement recommendations are used to guide the enterprise to strengthen its shortcomings at the current evolution stage. Based on the readiness level, a transformation assessment report is generated for the enterprise, which may include: When the readiness score is less than the baseline transition threshold, the enterprise is determined to be in an unready state, and a transformation mismatch judgment result and stage reinforcement suggestions are generated for the enterprise transformation.

[0127] As mentioned earlier, the assessment system can compare the readiness score with a baseline transition threshold (such as 60 points) to generate corresponding transition adaptation assessment results and personalized evolution path recommendations based on the comparison results.

[0128] Specifically, if the readiness score is less than the baseline transition threshold (e.g., 60 points), it indicates that the company is not ready. The assessment system can then generate a transformation mismatch judgment result and a phase reinforcement suggestion. The transformation mismatch judgment result is used to characterize that the company is not suitable for transformation at present (i.e., it is not recommended that the company enter the next evolution stage), and the phase reinforcement suggestion is used to guide the company to strengthen its shortcomings in the current evolution stage.

[0129] Furthermore, the transformation mismatch assessment result can include a first mismatch assessment result and a second mismatch assessment result. The phased reinforcement recommendations can include a first reinforcement recommendation and a second reinforcement recommendation. The unreadiness status can include a partial unreadiness status and an overall unreadiness status. The baseline transition threshold can also include a third transition threshold (e.g., 40 points). If the third transition threshold ≤ readiness score < second transition threshold, it indicates that the enterprise is in a partial unreadiness status. The assessment system can then generate a first mismatch assessment result and a first reinforcement recommendation. The first mismatch assessment result instructs the enterprise to first reinforce the shortcomings of the current evolution stage before transitioning to the next evolution stage. The first reinforcement recommendation includes reinforcement measures for the shortcomings of the current evolution stage (addressing unmet conditions) and estimated reinforcement costs (e.g., time, expenses). If the readiness score < third transition threshold, it indicates that the enterprise is in an overall unreadiness status. The assessment system can then generate a second mismatch assessment result and a second reinforcement recommendation. The second mismatch assessment result discourages the enterprise from transitioning to the next evolution stage (otherwise, the risk of failure is extremely high). The second reinforcement recommendation includes the facility construction plan for the current evolution stage.

[0130] In one implementation, the phased reinforcement recommendations include risk warning information, which can be obtained through the following methods: Based on the maturity of the current evolution stage and the next or target evolution stage, historical failure cases that match the enterprise are obtained from the failure case knowledge base; based on the number and matching degree of historical failure cases, the risk level of the enterprise's leap from the current evolution stage to the next or target evolution stage is assessed; and historical failure cases and risk levels are output as risk warning information.

[0131] Furthermore, the lack of systematic summaries of numerous practical cases (including both successes and failures) in related technologies leads to companies repeatedly making the same mistakes and wasting resources.

[0132] In this embodiment of the application, a failure case knowledge base is integrated: real-world failure cases from the industry (especially failure cases that span multiple stages) are stored in a structured manner for risk warning and path optimization.

[0133] In its specific implementation, this application's embodiments analyze numerous historical failure cases to identify common stage-jumping failure patterns. As an example, failure pattern 1 involves jumping directly from the institutionalization stage to the intelligentization stage. The failure is due to a lack of digital data foundation; the AI ​​model lacks usable training data. Typically, after deployment, the AI ​​product suffers from a false alarm rate as high as 80% due to poor data quality, ultimately leading to its abandonment. This failure pattern 1 has 23 historical failure cases. As another example, failure pattern 2 involves jumping directly from the automation stage to the intelligentization stage. The failure is due to a lack of model-based analytical capabilities; the AI ​​cannot understand business logic and fault modes. Typically, the AI's recommended solutions have an accuracy rate below 30%, and maintenance personnel distrust the AI's judgment. This failure pattern 2 has 31 historical failure cases. As yet another example, failure pattern 3 involves jumping directly from the digitization stage to the intelligentization stage. The failure is due to the existence of data but a lack of model-based knowledge accumulation. The AI ​​struggles to learn directly from raw data. Typically, the AI ​​requires extensive manual annotation of training data, but enterprises lack experience and knowledge, resulting in poor annotation quality and ineffective model performance. This failure pattern 3 has 18 historical failure cases.

[0134] This application embodiment stores the various failure modes and their historical failure cases in a structured manner to form a failure case knowledge base. In practical applications, when the readiness score is less than the baseline transition threshold (e.g., 60 points), the assessment system triggers a risk warning and lists possible failure modes. For example, if an enterprise attempts to enter the modeling stage (next evolution stage / target evolution stage) when its maturity level in the digitalization stage (current evolution stage) is only 40%, the risk warning information generated by the assessment system could be: "Risk level: High. The matching degree with 18 historical failure cases in the failure case knowledge base is 85%. These historical failure cases share the common characteristics of insufficient data accumulation leading to too few model training samples, resulting in a false alarm rate of up to 70% for the established baseline model, ultimately leading to project failure. Recommendation: Enterprises should first complete digitalization construction, ensuring data collection completeness reaches 85% and historical data accumulation time reaches 60 days before starting modeling construction."

[0135] As can be seen, this application's embodiments, through readiness scores and a failure case knowledge base, can effectively identify cross-stage risks for enterprises, preventing them from blindly advancing without adequate resources. Verification has shown that enterprises assessed through this application's embodiments have reduced their cross-stage failure rate from 65% to 15%, thereby avoiding significant ineffective investment (e.g., reducing ineffective investment by 70%).

[0136] In one implementation, the transformation assessment report also includes the company's projected ROI; the projected ROI can be obtained through the following methods: Based on the company's characteristic information, the similarity between the company and each historical investment benefit case in the ROI benchmark database is calculated. The characteristic information includes company size, industry type, and maturity. The top N reference investment benefit cases are selected from the ROI benchmark database in descending order of similarity. N is an integer greater than 1, and each reference investment benefit case has a corresponding ROI reference value. Based on the similarity and ROI reference value, the company's predicted ROI value is calculated.

[0137] In addition, enterprises still face the following technical challenges in the process of intelligent transformation of IT operations and maintenance: (4) Lack of economic benefit assessment mechanism: When guiding enterprises in intelligent transformation, related technologies lack quantitative assessment of the input-output ratio at each stage. When deciding whether to proceed to the next stage, enterprises often rely on subjective judgment and find it difficult to obtain support and resource investment from management. In fact, the ROI varies significantly between different stages. For example, the ROI of the automation stage can reach 300%, while forcibly launching intelligent projects without completing digital construction may result in a negative ROI.

[0138] In this regard, the embodiments of this application can provide support for optimizing investment decisions: by predicting the ROI (investment cost and economic return) of each evolution stage, it helps enterprises to allocate resources rationally, prioritize investment in high-return stages, and improve the overall ROI by more than 40%.

[0139] In a specific implementation, this application embodiment can collect actual input and output data from multiple companies in the industry at various stages of evolution to establish an ROI benchmark database. The ROI benchmark database includes basic company information, project input data, project output data, and project ROI. The basic company information may include company size (number of instances), industry type, IT maturity, etc. The project input data may include human resource input (person / month), software procurement costs, hardware investment, consulting fees, etc. The project output data may include labor cost savings, efficiency improvements, failure rate reductions, and customer satisfaction improvements, etc. The project ROI = (annualized revenue - annualized cost) / annualized cost × 100%.

[0140] This application's embodiments establish ROI reference values ​​for different scales and industries by collecting input and output data from over 100 enterprises. For example, for public cloud enterprises managing 1,000-5,000 instances, the investment in the digitalization phase is 1.5 million to 2.5 million, with an annualized return of 4 million to 6 million, and an ROI of approximately 150% to 200%.

[0141] The evaluation system can obtain characteristic information of enterprises initiating transformation evaluation requests. This characteristic information may include enterprise size, industry type, and maturity (i.e., the maturity of each evolution stage obtained in step S120 above). Based on this characteristic information (enterprise size, industry type, maturity), the system calculates the similarity between the enterprise and various historical investment benefit cases in the ROI benchmark database. The calculation formula is: Similarity = w1 × Size Similarity + w2 × Industry Similarity + w3 × Maturity Similarity. Here, size similarity is determined based on the matching degree between the enterprise's size and the size of a historical investment benefit case; industry similarity is determined based on the matching degree between the enterprise's industry type and the industry type of a historical investment benefit case; and maturity similarity is determined based on the matching degree between the enterprise's maturity and the IT maturity of a historical investment benefit case. w1, w2, and w3 are weighting coefficients, obtained through machine learning training.

[0142] The evaluation system can select the top N (N is an integer greater than 1, e.g., N=10) historical investment benefit cases from the ROI benchmark database, ranked from highest to lowest similarity. For ease of differentiation, these top N historical investment benefit cases are defined as reference investment benefit cases. Each reference investment benefit case has a corresponding ROI reference value (i.e., the project ROI mentioned above).

[0143] The evaluation system can calculate the company's ROI forecast based on similarity and ROI reference value: ROI forecast value = Σ(ROI reference value of reference investment benefit case i × similarity i) / Σ similarity i.

[0144] It should be noted that the formulas for calculating similarity and ROI predictions are consistent across different stages of evolution. Simply use the corresponding maturity level to match and obtain the corresponding reference investment benefit case.

[0145] In one embodiment, the method may further include: The ROI forecast is adjusted based on the company's adjustment factors, which include at least one of the following: team capability factor, infrastructure reuse factor, and business growth factor.

[0146] The embodiments of this application can adjust the ROI forecast value by taking into account enterprise-specific factors (such as strong team capabilities, reusable existing infrastructure, etc.).

[0147] In its implementation, the evaluation system can determine adjustment factors for a company (such as team capability factors, infrastructure reuse factors, and business growth factors) to adjust the company's ROI forecast based on these factors. Specifically, the team capability factor characterizes the company's team capability score; if the score is greater than 80, the system can increase the ROI forecast by 10%. The infrastructure reuse factor indicates whether the company possesses reusable infrastructure; if so, construction costs can be reduced, allowing the system to increase the ROI forecast by 15%. The business growth factor indicates whether the company's business is in a rapid growth phase; such a phase suggests greater benefits from intelligent technology, leading the system to increase the ROI forecast by 20%.

[0148] As can be seen, the embodiments of this application, through ROI prediction based on historical investment benefit cases, can provide enterprises with quantitative investment decision-making basis. Verification has shown that the ROI prediction error of the embodiments of this application is within ±20%, with an accuracy rate exceeding 75%, thereby helping enterprises to rationally formulate budgets, obtain management support, and improve the success rate of intelligent projects.

[0149] It should be noted that the transformation assessment report includes, but is not limited to: completion scores and visualization radar charts for each evolution stage, detailed scores and weakness identification for each assessment dimension, comparison with industry averages (based on ROI benchmark database), specific improvement suggestions (for assessment indicators with low scores), readiness scores and scores for each dimension, fulfillment status of hard conditions (yes / no), fulfillment status of soft conditions and impact analysis, risk level and failure case matching results, suggested reinforcement measures (for unmet conditions), estimated reinforcement costs, estimated input costs for each evolution stage (sub-items such as human resources, software, hardware, consulting, etc.), estimated benefits for each evolution stage (sub-items such as labor cost savings, efficiency improvement, and failure reduction), estimated ROI and confidence intervals for each evolution stage, investment payback period forecast, and sensitivity analysis (the impact on ROI if key assumptions change).

[0150] It should be noted that the embodiments of this application are applicable to the evolution assessment and path planning of database operation and maintenance and application operation and maintenance scenarios (such as the intelligent transformation of IT operation and maintenance in various enterprises) in a cloud computing environment. Three typical application scenarios are listed here as follows: Scenario 1: Intelligent Database Operations and Maintenance for Public Cloud Service Providers: Public cloud service providers typically manage tens of thousands of database instances, involving various database types such as MySQL (an open-source lightweight relational database), PostgreSQL (an open-source advanced relational database), and Redis (an in-memory key-value NoSQL database). Operations and maintenance teams face challenges such as heavy workloads during inspections, slow fault response, and low efficiency in work order processing. The evaluation system provided in this application embodiment can systematically assess the current evolution stage of an enterprise's operations and maintenance capabilities (e.g., in the automation stage with 65% automation coverage), identify shortcomings (e.g., data collection completeness is only 40%), and provide clear evolution path suggestions (e.g., suggesting that the enterprise first improve data collection to 85% before entering the digitization stage). It can also predict the investment (e.g., the digitization stage requires an investment of 2 million RMB over a 6-month period) and return (estimated annual savings in labor costs of 5 million RMB, with an ROI of 150%) for each evolution stage.

[0151] Scenario 2: Intelligent Transformation of Application Operations and Maintenance in Large Enterprises: Large enterprises typically have hundreds of business systems and thousands of servers. Traditional manual operation and maintenance models can no longer meet the needs of rapid business development. Enterprises hope to achieve intelligent alarms, root cause analysis, and automatic repair capabilities through AIOPS. The evaluation system provided in this application embodiment can help enterprises identify the current level of standardization of operation and maintenance processes (e.g., institutionalization stage maturity reaches 80%), the coverage of automated scripts (e.g., automation stage maturity is only 45%), and the status of monitoring data collection (e.g., digitalization stage maturity is only 35%), and provide clear evolution path suggestions: Directly deploying AIOPS products in the current state will fail (readiness score is only 35 points, below the passing score of 60 points), and it is recommended to complete the automation construction and data collection infrastructure construction first.

[0152] Scenario 3: Gradual Intelligent Construction for SMEs: SMEs have limited resources and need to gradually achieve intelligentization within a limited budget. The evaluation system provided in this application's embodiments can help companies develop phased investment plans. For example, investing 300,000 in the first year to complete institutionalization and basic automation (ROI of 200%), 500,000 in the second year to complete digital construction (ROI of 150%), and 800,000 in the third year to complete modeling and initial intelligentization (ROI of 120%). This gradual approach ensures that each stage of evolution generates practical benefits while avoiding the risk of a large one-time investment.

[0153] The embodiments of this application have been applied and verified in over 100 enterprises, achieving significant technical results: (1) Improve the success rate of intelligent transformation: Enterprises guided by the embodiments of this application have increased the success rate of their intelligent transformation projects from 35% (the industry average) to 85%. This is because the embodiments of this application can accurately identify the current evolution stage of the enterprise and avoid failure caused by blindly skipping stages.

[0154] (2) Reduce ineffective investment: Through readiness assessment and risk warning, the embodiments of this application enable enterprises to be promptly stopped from skipping stages when they do not meet the conditions, thus avoiding a large amount of ineffective investment. Statistical data shows that, under the guidance of the embodiments of this application, the waste rate of intelligent related investments of enterprises has been reduced from 45% to 15%, and each enterprise saves an average of RMB 1 million to RMB 3 million in investment.

[0155] (3) Shortening the transformation cycle: The embodiments of this application provide clear evolution path suggestions (such as specific implementation plans for entering the next evolution stage / shortcoming-repairing measures for the current evolution stage), enabling enterprises to efficiently advance the construction of facilities at each evolution stage and avoid detours. Statistical data shows that enterprises guided by the embodiments of this application have seen their average transformation cycle shortened from 36 months to 24 months, a reduction of 33%.

[0156] (4) Improve ROI: The embodiments of this application enable enterprises to prioritize investment in high-return stages by predicting and prioritizing the ROI of each evolution stage, thereby increasing the overall ROI from 80% to 120%, an increase of 50%.

[0157] As can be seen from this example, the solution provided in this application has at least the following technical advantages: (1) Definition of the five-stage evolution framework and its dependencies: This application clearly defines five evolution stages: institutionalization, automation, digitization, modeling, and intelligence. Each evolution stage has clear core characteristics, capability requirements, and deliverable standards. Strict dependencies are established between stages, with the implementation of the next evolution stage depending on the completion of the previous one. Various failure modes across stages are identified through the analysis of 72 historical failure cases. It is evident that, compared to related technologies, this application systematically defines the complete evolution path of intelligent IT operations and maintenance for the first time, and verifies the principle that stages cannot be skipped through through numerous historical failure cases. This solves the problem of enterprises lacking systematic evolution guidance in related technologies, increasing the success rate of intelligent transformation from 35% to 85% and avoiding 70% of ineffective investment.

[0158] (2) Multi-dimensional quantitative evaluation index system: This application embodiment defines quantitative evaluation indicators for each evolution stage from four evaluation dimensions: technical architecture, data foundation, personnel capability, and organizational process. Each evaluation indicator has a clear calculation formula and scoring rules. The completion score of each evolution stage is calculated by weighted summation. Based on the completion score, the current evolution stage of the enterprise and the shortcomings in each dimension are determined. It can be seen that compared with the qualitative evaluation methods (such as "basic", "intermediate", "advanced") used in related technologies, this application embodiment achieves accurate quantitative evaluation of maturity, and the evaluation results are objective, repeatable, and comparable. Validated by 100+ enterprises, the evaluation accuracy rate reaches 92%, which can effectively identify shortcomings and provide targeted improvement suggestions.

[0159] (3) Readiness Score and Risk Warning Mechanism: This application embodiment defines the preconditions (hard and soft conditions) for transitioning from the current evolution stage to the next evolution stage; calculates the readiness score based on the satisfaction of the preconditions; identifies transition risks and generates risk warning information based on the readiness score and a failure case knowledge base; and provides specific measures to address shortcomings for enterprises. It is evident that, compared to the lack of stage transition assessment in related technologies, this application embodiment can effectively prevent enterprises from blindly skipping stages without the necessary conditions. Verification shows that the cross-stage failure rate has decreased from 65% to 15%, because this application embodiment is the first to systematically apply historical failure cases to risk warning.

[0160] (4) ROI Prediction Based on Historical Investment Benefit Cases: This application embodiment establishes an ROI benchmark database containing data from 100+ enterprises; based on similarity matching of enterprise characteristics (enterprise size, industry type, maturity), similar reference investment benefit cases are retrieved from the benchmark database; a weighted average ROI is calculated as the ROI prediction value for the enterprise; the ROI prediction value is adjusted according to specific enterprise factors (such as team capabilities, infrastructure reuse, business growth, etc.). It is evident that, compared to the lack of economic benefit assessment in related technologies, this application embodiment provides enterprises with a quantitative basis for investment decisions, with an ROI prediction error within ±20% and an accuracy rate exceeding 75%, thereby helping enterprises to rationally formulate budgets, obtain management support, and improve the success rate of intelligent project initiation.

[0161] (5) Personalized Evolution Path Planning Method: Based on the current assessment results (completion score, readiness score) and corporate goals (expected evolution stages, time requirements, budget constraints), this application generates personalized evolution path suggestions. If readiness is insufficient, priority is given to addressing the shortcomings of the current evolution stage. If readiness is sufficient, a specific implementation plan for the next evolution stage is provided. Multiple evolution strategies (such as robust, aggressive, and economical) are supported. It is evident that, compared to the "one-size-fits-all" approach in related technologies, this application provides personalized suggestions based on the specific circumstances of the enterprise, improving the operability and effectiveness of the solution. Verification shows that enterprises using the personalized evolution path suggestions planned in this application experience a 33% reduction in average transformation cycle and a 50% increase in overall ROI.

[0162] To facilitate understanding of the complete solution of the embodiments of this application, such as Figure 4 As shown, the overall workflow of the evaluation system is as follows: Step S1, Data Collection: The evaluation system collects enterprise IT operation and maintenance data through questionnaires, system integration, document analysis, etc., including a list of automation tools, coverage of automation tools, monitoring system configuration, monitoring data integrity, historical operation and maintenance data, historical fault handling records, team personnel information, personnel skill assessment, operation and maintenance process documents, etc.

[0163] Step S2, Maturity Assessment: Based on the five-stage evolution framework and multi-dimensional assessment indicator system, the assessment system calculates the enterprise's completion score at each evolution stage. It outputs the completion score for each evolution stage, detailed scores for each assessment dimension, and the results of weakness identification.

[0164] Step S3, Current Stage Determination: The evaluation system determines the current evolution stage of the enterprise based on the completion scores of each evolution stage. Determination rule: The evolution stage with a completion score ≥ 120 points, which is the highest completion score, is identified as the current evolution stage of the enterprise.

[0165] Step S4, Readiness Assessment: The assessment system calculates the enterprise's readiness score for transitioning to the next evolution stage, checks whether the preconditions are met, and identifies transition risks. It outputs the readiness score, risk level, and failure case matching results.

[0166] Step S5, Evolution Path Planning: Based on the current assessment results and the company's goals, the evaluation system generates personalized evolution path suggestions. For example, if the readiness score is ≥60 points, the system recommends that the company proceed to the next evolution stage and provides a specific implementation plan; if the readiness score is <60 points, the system recommends that the company first address the shortcomings of the current evolution stage and provides specific reinforcement measures and estimated time costs.

[0167] Step S6, ROI Prediction: The evaluation system, based on the ROI benchmark database and enterprise characteristics (enterprise size, industry type, maturity), predicts the enterprise's input costs and investment returns at each stage of evolution, and generates ROI prediction values.

[0168] Step S7, Results Display: The evaluation system displays the evaluation results (completion score, readiness score), evolution path, ROI prediction value and other information in a visual manner, and generates a complete transformation evaluation report, which can be exported to PDF format.

[0169] Step S8, Continuous Tracking: The assessment system supports regular (e.g., quarterly) reassessments to track the company's evolution and dynamically update assessment results and recommendations.

[0170] Corresponding to the aforementioned application function implementation method embodiments, this application also provides an enterprise transformation assessment system, electronic device, and corresponding embodiments.

[0171] Figure 5 This is a schematic diagram of the module structure of the enterprise transformation assessment system shown in the embodiments of this application.

[0172] See Figure 5 The enterprise transformation assessment system of this application may include: Data acquisition module 510 is used to collect IT operation and maintenance data of an enterprise when it receives a transformation assessment request from the enterprise. The maturity assessment module 520 is used to assess the maturity of an enterprise at each stage of its evolution based on IT operations and maintenance data. The evolution stages include, in order, the institutionalization stage, the automation stage, the digitalization stage, the modeling stage, and the intelligentization stage. At this stage, module 530 is identified, which is used to determine the current evolution stage of the enterprise based on the maturity of each evolution stage; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage. The next stage is determined by module 540, which is used to determine the next evolution stage of the current evolution stage according to the dependencies. The readiness assessment module 550 is used to assess an enterprise's readiness to transition from the current evolution stage to the next evolution stage based on the maturity level of the current evolution stage. The report generation module 560 is used to generate a transformation assessment report for enterprises based on their readiness level. The transformation assessment report includes the enterprise's transformation adaptation assessment results and evolution path recommendations.

[0173] In one implementation, maturity is represented by a completion score, and the evaluation dimensions for each evolution stage include technical architecture, data infrastructure, personnel capabilities, and organizational processes; the maturity assessment module 520 may include: The technical architecture assessment submodule is used to calculate assessment indicators belonging to the technical architecture dimension for enterprises at each evolution stage based on IT operations and maintenance data, and to calculate the enterprise's technical architecture dimension score at each evolution stage based on the assessment indicators belonging to the technical architecture dimension; and, The data infrastructure assessment submodule is used to calculate assessment indicators belonging to the data infrastructure dimension for enterprises at each stage of evolution, based on IT operations and maintenance data; and to calculate the data infrastructure dimension scores for enterprises at each stage of evolution based on these assessment indicators; and... The personnel capability assessment submodule is used to calculate assessment indicators belonging to the personnel capability dimension for an enterprise at each stage of evolution, based on IT operations and maintenance data; and to calculate the personnel capability dimension scores for the enterprise at each stage of evolution based on these assessment indicators; and... The Organizational Process Assessment Submodule is used to calculate the assessment indicators belonging to the organizational process dimension of an enterprise at each stage of its evolution based on IT operations and maintenance data, and to calculate the organizational process dimension score of the enterprise at each stage of its evolution based on the assessment indicators belonging to the organizational process dimension. The completion score calculation submodule is used to calculate the completion score of an enterprise at each stage of its evolution, based on scores in the dimensions of technical architecture, data foundation, personnel capabilities, and organizational processes.

[0174] In one embodiment, the current determining module 530 may include: The current sub-module is used to determine the evolution stage with the highest completion score as the current evolution stage of the enterprise; wherein, the highest completion score is greater than or equal to the benchmark maturity threshold.

[0175] In one embodiment, readiness is characterized by a readiness score; the readiness assessment module 550 may include: The condition judgment submodule is used to determine whether an enterprise meets the prerequisites for transitioning from the current evolution stage to the next evolution stage; The readiness score calculation submodule is used to calculate the readiness score of an enterprise to leap from the current evolution stage to the next evolution stage, based on the completion score of the current evolution stage and the fulfillment of the preconditions.

[0176] In one embodiment, the preconditions include hard conditions and soft conditions; the readiness score calculation submodule may include: The hardware metrics calculation unit is used to calculate the enterprise's data foundation score based on the fulfillment of hard requirements; and, The software indicator calculation unit is used to calculate the enterprise's personnel capability score and organizational readiness score based on the satisfaction of soft conditions. The readiness score calculation unit is used to calculate the readiness score of an enterprise to leap from the current evolution stage to the next evolution stage, based on the completion score of the current evolution stage, as well as the data foundation score, personnel capability score, and organizational readiness score.

[0177] In one implementation, the transformation adaptability assessment result includes a transformation adaptability determination result, and the evolution path suggestion includes a stage transition suggestion. The transformation adaptability determination result is used to characterize whether the enterprise is currently suitable for transformation, and the stage transition suggestion is used to guide the enterprise's implementation plan for entering the next evolution stage; the report generation module 550 may include: The transformation adaptation suggestion generation submodule is used to determine that the enterprise is in a ready state when the readiness score is greater than or equal to the baseline transition threshold, and then generate transformation adaptation judgment results and stage transition suggestions for enterprise transformation. The transformation assessment request includes the target evolution stage that the company hopes to reach. When the level of the next evolution stage is less than the level of the target evolution stage, the stage jump suggestion is also used to guide the company to jump from the current evolution stage to the target evolution stage in a timeline.

[0178] In another embodiment, the transformation fit assessment results include a transformation fit determination result, and the evolution path suggestions include phased reinforcement suggestions. The transformation fit determination result is used to characterize that the enterprise is currently unsuitable for transformation, and the phased reinforcement suggestions are used to guide the enterprise to strengthen its shortcomings in the current evolution phase; the report generation module 550 may include: The Transformation Incompatibility Recommendation Generation Submodule is used to determine that the enterprise is in an unready state when the readiness score is less than the baseline transition threshold, and then generate transformation incompatibility judgment results and phase reinforcement suggestions for the enterprise's transformation. The transformation assessment request includes the target evolution stage that the enterprise hopes to reach, and the stage reinforcement suggestions include risk warning information, which can be obtained through the following sub-modules: The failure case acquisition submodule is used to retrieve historical failure cases that match the enterprise from the failure case knowledge base based on the maturity of the current evolution stage and the next evolution stage or the target evolution stage. The risk level assessment submodule is used to assess the risk level of an enterprise as it moves from the current stage of evolution to the next stage or the target stage of evolution, based on the number and matching degree of historical failure cases. The risk warning information generation submodule is used to output historical failure cases and risk levels as risk warning information.

[0179] In one implementation, the transformation assessment report also includes the company's projected ROI; the projected ROI can be obtained through the following modules: The similarity calculation module is used to calculate the similarity between a company and various historical investment benefit cases in the ROI benchmark database, based on the company's characteristic information. The characteristic information includes company size, industry type, and maturity. The reference case selection module is used to select the top N reference investment benefit cases from the ROI benchmark database in descending order of similarity; N is an integer greater than 1, and each reference investment benefit case has a corresponding ROI reference value; The ROI prediction module is used to calculate the predicted ROI value for a company based on similarity and ROI reference value.

[0180] In one embodiment, the system may further include: The ROI adjustment module is used to adjust the ROI forecast based on the company's adjustment factors; the adjustment factors include at least one of the following: team capability factor, infrastructure reuse factor, and business growth factor.

[0181] As this example demonstrates, the solution provided in this application systematically defines, for the first time, the complete evolution path of intelligent IT operations and maintenance (institutionalization stage, automation stage, digitalization stage, modeling stage, and intelligentization stage), and establishes strict dependencies between stages. This allows for the determination of the next evolution stage based on these dependencies. Then, based on the maturity of the current evolution stage, the readiness of the enterprise to leap from the current stage to the next is assessed. Readiness effectively reflects the feasibility of enterprise transformation. Therefore, based on readiness, a systematic evolution path guidance can be provided to enterprises. This allows enterprises to clearly understand the complete evolution path from the current state to intelligentization, avoiding blind investment and detours, and significantly reducing the risk of transformation failure.

[0182] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0183] Figure 6 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application.

[0184] See Figure 6 The electronic device 600 includes a memory 610 and a processor 620.

[0185] The processor 620 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. Memory 610 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor 620 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 610 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, memory 610 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0186] The memory 610 stores executable code, which, when processed by the processor 620, can cause the processor 620 to execute part or all of the methods described above.

[0187] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0188] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) that, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0189] This application also provides a computer program product, which includes computer instructions that, when executed by a processor, implement the method described above.

[0190] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating enterprise transformation, characterized in that, include: When a transformation assessment request is received from a company, its IT operations and maintenance data is collected. Based on the IT operations and maintenance data, the maturity of the enterprise at each evolution stage is assessed; the evolution stages include, in order, the institutionalization stage, the automation stage, the digitalization stage, the modeling stage, and the intelligentization stage. Based on the maturity of each evolution stage, the current evolution stage of the enterprise is determined; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage; Based on the aforementioned dependencies, determine the next evolution stage of the current evolution stage; Based on the maturity of the current evolution stage, assess the enterprise's readiness to transition from the current evolution stage to the next evolution stage; Based on the preparedness level, a transformation assessment report is generated for the enterprise; the transformation assessment report includes the transformation adaptation assessment results and evolution path recommendations for the enterprise.

2. The method according to claim 1, characterized in that, The maturity level is represented by a completion score, and the evaluation dimensions for each evolution stage include technical architecture, data infrastructure, personnel capabilities, and organizational processes. The assessment of the enterprise's maturity at each stage of evolution based on the IT operations data includes: Based on the IT operations data, the evaluation metrics belonging to the technical architecture dimension for the enterprise at each evolution stage are calculated, and the enterprise's technical architecture dimension score at each evolution stage is calculated based on the evaluation metrics belonging to the technical architecture dimension; and... Based on the IT operations and maintenance data, the evaluation indicators belonging to the data foundation dimension for the enterprise at each evolution stage are calculated, and the data foundation dimension scores for the enterprise at each evolution stage are calculated based on the evaluation indicators belonging to the data foundation dimension; and, Based on the IT operations and maintenance data, the evaluation indicators belonging to the personnel capability dimension for the enterprise at each evolution stage are calculated, and the personnel capability dimension scores for the enterprise at each evolution stage are calculated based on the evaluation indicators belonging to the personnel capability dimension; and, Based on the IT operations and maintenance data, the evaluation indicators belonging to the organizational process dimension of the enterprise at each evolution stage are calculated, and the organizational process dimension score of the enterprise at each evolution stage is calculated based on the evaluation indicators belonging to the organizational process dimension. Based on the scores for the technical architecture dimension, the data foundation dimension, the personnel capability dimension, and the organizational process dimension, the completion score of the enterprise at each stage of evolution is calculated.

3. The method according to claim 2, characterized in that, Determining the current evolution stage of the enterprise based on the maturity of each evolution stage includes: The evolution stage with the highest completion score is determined as the current evolution stage of the enterprise; wherein the highest completion score is greater than or equal to the benchmark maturity threshold.

4. The method according to claim 2, characterized in that, The readiness level is characterized by a readiness score; the assessment of the enterprise's readiness to transition from the current evolutionary stage to the next evolutionary stage based on the maturity level of the current evolutionary stage includes: Determine whether the enterprise meets the preconditions for transitioning from the current evolution stage to the next evolution stage; Based on the completion score of the current evolution stage and the satisfaction of the preconditions, the readiness score of the enterprise to transition from the current evolution stage to the next evolution stage is calculated.

5. The method according to claim 4, characterized in that, The prerequisites include hard and soft conditions; the calculation of the enterprise's readiness score for transitioning from the current evolution stage to the next evolution stage, based on the completion score of the current evolution stage and the satisfaction of the prerequisites, includes: Based on the satisfaction of the aforementioned hard conditions, calculate the enterprise's data foundation score; and, Based on the satisfaction of the aforementioned soft conditions, calculate the personnel capability score and organizational readiness score of the enterprise; Based on the completion score of the current evolution stage, the data foundation score, the personnel capability score, and the organizational readiness score, the readiness score of the enterprise to transition from the current evolution stage to the next evolution stage is calculated.

6. The method according to claim 4, characterized in that, The transformation adaptation assessment results include transformation adaptation determination results, and the evolution path suggestions include stage transition suggestions. The transformation adaptation determination results are used to characterize whether the enterprise is currently suitable for transformation, and the stage transition suggestions are used to guide the enterprise's implementation plan for entering the next evolution stage. The step of generating a transformation assessment report for the enterprise based on the preparedness level includes: When the readiness score is greater than or equal to the baseline transition threshold, the enterprise is determined to be in a ready state, and the transformation adaptation judgment result and the stage transition suggestion are generated for the enterprise transformation. The transformation assessment request includes the target evolution stage that the enterprise expects to reach. When the level of the next evolution stage is less than the level of the target evolution stage, the stage transition suggestion is also used to guide the enterprise to transition from the current evolution stage to the target evolution stage through an evolution path timeline.

7. The method according to claim 4, characterized in that, The transformation fit assessment results include transformation fit determination results, and the evolution path suggestions include phase reinforcement suggestions. The transformation fit determination results are used to characterize that the enterprise is not currently suitable for transformation, and the phase reinforcement suggestions are used to guide the enterprise to strengthen the shortcomings in the current evolution stage. The step of generating a transformation assessment report for the enterprise based on the preparedness level includes: When the readiness score is less than the baseline transition threshold, the enterprise is determined to be in an unready state, and the transformation mismatch judgment result and the stage reinforcement suggestion are generated for the enterprise transformation. The transformation assessment request includes the target evolution stage that the enterprise hopes to reach, and the stage reinforcement suggestions include risk warning information, which is obtained through the following methods: Based on the maturity of the current evolution stage and the next evolution stage or the target evolution stage, retrieve historical failure cases that match the enterprise from the failure case knowledge base; Based on the number and matching degree of the historical failure cases, assess the risk level of the enterprise's leap from the current evolution stage to the next evolution stage or the target evolution stage; The historical failure cases and the risk levels are output as risk warning information.

8. The method according to claim 1, characterized in that, The transformation assessment report also includes the company's projected ROI; the projected ROI is obtained through the following methods: Based on the enterprise's characteristic information, the similarity between the enterprise and each historical investment benefit case in the ROI benchmark database is calculated; the characteristic information includes enterprise size, industry type, and maturity level. According to the order of similarity from high to low, the top N reference investment benefit cases are selected from the ROI benchmark database; where N is an integer greater than 1, and each of the reference investment benefit cases has a corresponding ROI reference value; Based on the similarity and the ROI reference value, the predicted ROI value of the enterprise is calculated.

9. The method according to claim 8, characterized in that, The method further includes: The ROI forecast is adjusted based on the adjustment factors of the enterprise; the adjustment factors include at least one of team capability factor, infrastructure reuse factor and business growth factor.

10. A corporate transformation assessment system, characterized in that, include: The data acquisition module is used to collect the IT operation and maintenance data of an enterprise when it receives a transformation assessment request. The maturity assessment module is used to assess the maturity of the enterprise at each evolution stage based on the IT operation and maintenance data; the evolution stages include, in order, the institutionalization stage, the automation stage, the digitalization stage, the modeling stage, and the intelligentization stage. The current stage determination module is used to determine the current evolution stage of the enterprise based on the maturity of each evolution stage; there is a dependency relationship between two adjacent evolution stages, which is used to characterize that the implementation of the latter evolution stage depends on the completion of the former evolution stage; The next stage determination module is used to determine the next evolution stage of the current evolution stage according to the dependencies. The readiness assessment module is used to assess the readiness of the enterprise to transition from the current evolution stage to the next evolution stage based on the maturity of the current evolution stage. The report generation module is used to generate a transformation assessment report for the enterprise based on the readiness level; the transformation assessment report includes the transformation adaptation assessment results and evolution path suggestions for the enterprise.

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