Business system changing method and device

Abstract

The invention provides a business system change method and device, and relates to the technical field of big data, and the method comprises the steps: obtaining a business system change application and business system monitoring data; determining the type of the business system change application; determining a change element and a weight corresponding to the change element; predicting the change duration of the service system change application according to the service system monitoring data and the historical service system change database by using a six-sigma criterion; determining a recommended change time period which does not affect the change application by using a total probability formula according to the predicted change duration and the service system monitoring data; determining a recommended change time period influencing the change application by using a Bayesian rule; the service system is changed according to the non-influence change application and the influence change application, the invalid data node can be accurately identified and timely and automatically offline, and invalid data processing and resource waste are avoided.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method and device for changing a business system. Background Art

[0002] This section is intended to provide a background or context for embodiments of the present invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] With the development of information technology, the current level of informatization is relatively high, which brings with it a large amount of data redundancy. Effective data analysis can not only save a lot of manpower and material costs, but also improve production efficiency. Correspondingly, the complexity of business overlays is also constantly increasing, the workload of information system maintenance is gradually increasing, and the number of production change operations required in daily operations is also increasing. However, implementing production change operations may affect the stable operation of the system and may even cause information system anomalies or affect external services. Therefore, before implementing production change operations, it is often necessary to control the change risks of executing production change operations to ensure the stable operation of the production system.

[0004] Currently, effective change methods often rely on technical experts in various specialized fields to assess the change plan and its impact. However, manual evaluation of production change operations carries significant implementation risks and cannot accurately identify the timeframe for implementing changes that minimize impact on business systems. This leads to low production efficiency, frequent production incidents caused by subjective judgment, and high labor costs, making it difficult to meet the needs of large-scale data center operations and refined management. Summary of the Invention

[0005] To address the challenges of existing technologies, this paper proposes a business system modification method and device. This method can accurately locate and minimize the time period during which changes are executed that impact business systems, improving production efficiency, avoiding production incidents caused by subjective assumptions, and reducing labor costs, thus meeting the needs of large-scale data center operations and refined management.

[0006] An embodiment of the present invention provides a method for changing a business system, including:

[0007] Obtain business system change applications and business system monitoring data for changes to business systems;

[0008] Determining the type of the business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; a no-impact change application has no impact on the business in the business system during the change execution process; an impact change application has an impact on the business in the business system during the change execution process;

[0009] Analyze the impact scope of the impact change application and determine the change elements and their corresponding weights;

[0010] Using Six Sigma principles, based on business system monitoring data and historical business system change databases, we can predict the duration of business system change applications.

[0011] Using the total probability formula, based on the predicted change duration and business system monitoring data, determine the recommended change time period that will not affect the change application;

[0012] Using Bayesian theorem, based on the predicted change duration, business system monitoring data, change elements, and their corresponding weights, we determine the recommended change timeframe that will impact the change application.

[0013] During the recommended change time period for the non-impact change application, make changes to the business system according to the non-impact change application;

[0014] During the recommended change time period for the impact change application, the business system is changed according to the impact change application.

[0015] An embodiment of the present invention further provides a business system modification device, comprising:

[0016] The acquisition module is used to obtain business system change applications and business system monitoring data for changes to the business system;

[0017] A type determination module is configured to determine the type of a business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; a no-impact change application has no impact on the business in the business system during the change execution process; an impact change application has an impact on the business in the business system during the change execution process;

[0018] Impact scope analysis module, used to analyze the impact scope of the impact change application and determine the change elements and their corresponding weights;

[0019] The change duration prediction module is used to predict the change duration of business system change applications based on business system monitoring data and historical business system change databases using Six Sigma principles;

[0020] The first change time period recommendation module is used to determine a recommended change time period that does not affect the change application based on the predicted change duration and business system monitoring data using a full probability formula;

[0021] The second change time period recommendation module is used to determine the recommended change time period that will affect the change application based on the predicted change duration, business system monitoring data, change factors, and their corresponding weights using the Bayesian theorem;

[0022] A non-impact change application change module, configured to modify the business system according to the non-impact change application within the recommended change time period of the non-impact change application;

[0023] The impact change application change module is used to change the business system according to the impact change application within the recommended change time period of the impact change application.

[0024] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a business system change method when executing the computer program.

[0025] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the business system change method is implemented.

[0026] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the business system change method is implemented.

[0027] Compared with the technical solutions in the prior art that rely on personnel to proactively request offline business functions or invalid data nodes, the embodiments of the present invention obtain business system change applications and business system monitoring data for changes to the business system; determine the type of business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; no-impact change applications have no impact on the business in the business system during the change execution process; impact change applications have an impact on the business in the business system during the change execution process; analyze the impact scope of the impact change application, determine the change elements and the corresponding weights of the change elements; use Six Sigma principles, based on the business system monitoring data and the historical business system change database, to evaluate the business system change application. Predict the change duration; use the total probability formula to determine the recommended change time period for non-impact change applications based on the predicted change duration and business system monitoring data; use Bayesian law to determine the recommended change time period for impact change applications based on the predicted change duration, business system monitoring data, change elements and the weights corresponding to the change elements; in the recommended change time period for non-impact change applications, make changes to the business system based on non-impact change applications; in the recommended change time period for impact change applications, make changes to the business system based on impact change applications. This can accurately locate the change execution time period that minimizes the impact on the business system, improve production efficiency, avoid production incidents caused by human subjective assumptions, reduce labor costs, and meet the needs of large-scale operation and refined management of data centers. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0029] Figure 1 is a flow chart of a method for changing a business system according to an embodiment of the present invention;

[0030] Figure 2 is a flowchart of a specific example of a method for changing a business system according to an embodiment of the present invention;

[0031] Figure 3 is a flowchart of a specific example of a method for changing a business system according to an embodiment of the present invention;

[0032] Figure 4 is a flowchart of a specific example of a method for changing a business system according to an embodiment of the present invention;

[0033] Figure 5 is a schematic diagram of a business system change device according to an embodiment of the present invention;

[0034] Figure 6 Schematic diagram of the computer device structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0037] With the development of informatization, the workload of information system maintenance has gradually increased, and the time window for operation and maintenance has been shrinking. The complexity of business superposition has also been increasing. In actual operation and maintenance, there are occasional production events caused by routine maintenance. In order to avoid such events, the best change time window can be automatically matched by sorting out the production change process.

[0038] The embodiments of the present invention aim to utilize existing data resources for analysis and sorting, accurately locating the time period for change execution, improving production efficiency, and reducing production incidents caused by subjective assumptions. By sorting out the production change process and utilizing monitoring data, business batch data, data affected by other upstream and downstream business systems, and data required for special business management such as supervision, the optimal change time window is automatically matched. The system then automatically executes the predetermined plan, collects data, and continuously optimizes and improves adaptability.

[0039] The embodiment of the present invention analyzes existing change factors by using the total probability formula and the Bayesian formula principle to determine the optimal time point, and uses the Six Sigma principle to calculate the effective duration of the operation that allows the change. Using these three principles, the optimal time window can be obtained, so that the change can be effectively implemented automatically.

[0040] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0041] Figure 1 This is a flow chart of the business system change method according to an embodiment of the present invention. Figure 1As shown, the method may include:

[0042] Step 101: Obtain a business system change application and business system monitoring data for changing the business system;

[0043] Step 102: Determine the type of the business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications. A no-impact change application has no impact on the business system during the change execution process; an impact change application has an impact on the business system during the change execution process.

[0044] Step 103: Analyze the impact scope of the impact change application and determine the change elements and their corresponding weights;

[0045] Step 104 , using Six Sigma principles, based on business system monitoring data and a historical business system change database, predicting the change duration of the business system change application;

[0046] Step 105 , using the total probability formula, based on the predicted change duration and business system monitoring data, determine a recommended change time period that will not affect the change application;

[0047] Step 106 , using Bayesian theorem, based on the predicted change duration, business system monitoring data, change elements, and their corresponding weights, determines a recommended change time period that will impact the change application.

[0048] Step 107: During the recommended change time period for the non-impact change application, the business system is changed according to the non-impact change application;

[0049] Step 108 : During the recommended change time period of the impacting change application, the business system is changed according to the impacting change application.

[0050] At present, the degree of informatization is relatively high, which brings with it a large amount of data redundancy. Effective analysis of data can not only save a lot of manpower and material costs, but also improve production efficiency. The focus of this patent is to use existing data resources for analysis and sorting, accurately locate the time period for change execution, improve production efficiency, and reduce production incidents caused by human subjective conjecture.

[0051] Important factors in executing changes during the operation and maintenance process include the time window, the quality of the change plan, and the change environment. The present invention focuses on identifying the change time window as a breakthrough point, analyzing the impact of the change operating environment on change execution to determine the change time. Changes can be categorized based on whether they affect the business system.

[0052] Figure 2FIG. 1 is a flow chart of a specific example of a method for changing a business system according to an embodiment of the present invention. Figure 2 As shown, the business system change method may specifically include: receiving and reviewing change applications; classifying business system change applications into non-impact change applications and impact change applications; determining the change execution status for non-impact change applications based on the change time period automatically recommended by the data; determining the change execution status for impact change applications based on the time period with the least impact recommended by the data; if the change execution status is normal, collecting the change data element data, conducting a summary and review of the change, analyzing and storing the data referenced in the change review, and returning to the step of receiving and reviewing the change application to continue processing the new change application. If the change execution status is abnormal, modify the change type. If the modification is approved after review, process the change application as an impact change application, and recommend the time period with the least impact based on the data; focus on analyzing the abnormal change, analyze and store the data referenced in the change review, and return to the step of receiving and reviewing the change application.

[0053] In one embodiment, the types of business system change applications may include non-impact change applications and / or impact change applications. Non-impact change applications have no impact on the business in the business system during the change execution process; impact change applications have an impact on the business in the business system during the change execution process. For non-impact changes, during the change application and review process, this type of change is classified through the pre-stored historical business system change database. Changes that will not affect the business during the change execution process and can be successfully executed are defined as non-impact changes. Such changes only need to be executed on time within the automatically matched time period. For impact changes, during the change application and review process, this type of change is classified through the historical database. Changes that will have an impact on the business during the change execution process require matching a no-business event segment or a business low-end event segment to perform the change operation.

[0054] In one embodiment, as the business environment continues to change, the time period suitable for operation also changes continuously. After each change, data on the factors that determine the change time window can be collected, analyzed, and archived as a reference for the change review process.

[0055] In one embodiment, obtaining a business system change application and business system monitoring data for a business system change may include: obtaining a business system change application for the business system change; and determining the business system monitoring data after the time the business system change application was obtained. For example, if a business system change application is obtained at 9:00 AM on the same day, business system monitoring data after 9:00 AM on the same day may be obtained to serve as the basis for subsequent data analysis. The business system monitoring data can be obtained using different data collection methods depending on the data type and characteristics, and will not be further described here.

[0056] In order to improve the accuracy of locating the change execution time period that minimizes the impact on the business system, in one embodiment, the business system monitoring data includes one or any combination of the business occurrence frequency, the business operation time period, the low-frequency time period for interaction between the business system and upstream and downstream systems, and the scheduled execution time period for unexecuted historical business system change applications. The determination of the change execution time period needs to consider not only the business busyness of the business system and the execution of other changes in the business system, but also the possibility that the change may cause alarms in upstream and downstream related systems and even affect their business. Therefore, it is also necessary to push the appropriate change time period based on the business processes and system characteristics of the upstream and downstream systems. If the change involves hardware updates, it is also necessary to consider factors such as whether the spare parts for the hardware replacement are in place and the delivery time. Changes may also have other special requirements, namely time nodes such as regulatory authorities and financial statements.

[0057] In one embodiment, determining the type of a business system change application based on a pre-stored historical business system change database may include: using a random forest algorithm and historical business system change applications in the historical business system change database to train a random forest model to determine a prediction model; inputting the business system change application into the prediction model to determine the type of the business system change application.

[0058] In this embodiment, determining the type of a business system change application based on a pre-stored historical business system change database may include the following steps:

[0059] 1. Data Collection and Preprocessing

[0060] 1. Historical data collection:

[0061] Data source: Historical change records can be extracted from the change management system or module, including the following fields: Basic information: application code, submission time, submitter, urgency (high / medium / low); Change content: change type (configuration modification / code deployment / database change, etc.), involved modules (such as gateway, user center), number of code modified lines; Dependency: number of affected upstream and downstream services, associated configuration file path; Text description: reason for change, operation steps (such as "update interface timeout configuration"); Result label: whether a fault is caused (impact = 1, no impact = 0).

[0062] 2. Data cleaning:

[0063] Missing value handling: Numerical fields (such as the number of modified lines of code) are filled with the mean value of similar changes. Categorical fields (such as urgency) are individually marked as "unknown".

[0064] Outlier processing: Detect the number of modified lines of code and truncate values ​​that exceed a reasonable range (for example, lines exceeding 10,000 are considered abnormal).

[0065] Deduplication: Remove duplicated change records (only the latest version of the same application code is retained).

[0066] 2. Feature Engineering

[0067] 1. Structured feature extraction: Direct fields: Numerical: Number of lines of code modified, number of dependent services, change duration (minutes). Categorical: Change type, involved modules, submitter department.

[0068] Derived features: Time features: whether the submission time is during business peak hours (such as 9:00-18:00 on weekdays).

[0069] Historical statistical features: Average failure rate of the same committer in the past three months. Number of changes to the same module in the past 30 days.

[0070] Combined features: Cross-marks with a high urgency level and involving core modules.

[0071] 2. Text feature processing (can be used if there is a change description): Keyword extraction: Extract risk keywords (such as "rollback", "test not covered", "emergency repair") from the change description and convert them into Boolean features.

[0072] TF-IDF vectorization: After tokenization and stop word filtering of the change description, a TF-IDF vector (the dimension can be set to 50-100) is generated.

[0073] 3. Dataset Partitioning and Balancing

[0074] 1. Divide the training and test sets: Divide by time window (e.g., use the first 11 months of data for training and the last month for testing) to prevent future data leakage. If the data has no time correlation, you can randomly divide it into a 7:3 ratio.

[0075] 2. Handling class imbalance: If the proportion of positive samples (influential) is less than 10%, use the following methods: Oversampling: Use SMOTE to generate synthetic data for minority class samples. Adjust class weights.

[0076] 4. Model Training and Validation

[0077] Training the random forest model; Model validation: Evaluation metrics: Core metrics: Recall (reducing the risk of missed reports impacting changes). Auxiliary metrics: Precision, etc.

[0078] Cross-validation: Use 5-fold cross-validation to ensure stability and observe whether the indicator variance is within an acceptable range (such as AUC standard deviation < 0.05).

[0079] 5. New Application Data Processing and Forecasting

[0080] 1. New data preprocessing: Feature alignment: Ensure that the fields and encoding methods of the new data are exactly the same as those of the training set.

[0081] Example: If the "Change Type" during training includes [code deployment, configuration modification, database change], unknown types appearing in new data need to be mapped to "Other".

[0082] Real-time feature calculation: Dynamically generate historical statistical features (such as the number of changes made by the current submitter in the past 30 days), which requires connecting to the database for real-time query.

[0083] 2. Prediction and output: Probabilistic output: Input the business system change application into the prediction model to determine the type of business system change application.

[0084] In this embodiment, other algorithms besides the random forest algorithm may be used to determine the type of the business system change application, which will not be described in detail here.

[0085] In one embodiment, analyzing the scope of influence of an influential change application and determining the change elements and the weights corresponding to the change elements may include: analyzing the scope of influence of an influential change application and determining the change elements; determining the weights corresponding to the change elements based on historical business system change applications in a historical business system change database; or receiving the set weights corresponding to the change elements.

[0086] With the advancement of informatization, many businesses have evolved from a 5 / 8 hour operating window to 24 / 7 service. Their service orientation has also evolved from being enterprise-oriented to being human-oriented, serving consumers. The connections have evolved from connecting production factors such as people, machines, and objects to primarily connecting people, reflecting their various social activities in a virtual space. The impact on operations and maintenance personnel can be broadly categorized into two types: no business impact and business impact. From the perspective of change analysis, the technical difficulty can be categorized as a wide variety of equipment, specialized applications, simple application scenarios, and relatively low technical requirements. From the perspective of data characteristics, the data volume can be categorized as large and complex, requiring customized analysis, and the data is more centralized and homogenized, facilitating big data analysis and application.

[0087] Given the wide variety of factors and the varying impacts on changes in different scenarios, weights are necessary to quantify the impact of changes and facilitate approval by change reviewers. Weights can also be dynamically adjusted to allow the system to intelligently prioritize optimal change timeframes. This dynamic adjustment falls into two categories: one is for known change types that have undergone extensive execution and have been thoroughly understood to determine weights; the other is for unexpected changes that require specific analysis.

[0088] The weight value configuration table is shown in Table 1 below:

[0089] Table 1

[0090] No impact influential Weight value server Storage devices Network equipment operating system database middleware

[0091] In one embodiment, using Six Sigma criteria, based on business system monitoring data and a historical business system change database, the change duration of a business system change application is predicted, which can include: using Six Sigma criteria, analyzing the average processing time of historical business system change applications of the same type as the business system change application in the historical business system change database; and determining the average processing time as the change duration of the business system change application.

[0092] Six Sigma is a statistical method used in quality management. Based on normal distribution theory, it defines an interval using three key values: μ-6σ, μ, and μ+6σ, within which 99.99966% of data points fall. This method is used to identify outliers, assess process stability, and set quality targets. By applying Six Sigma, statistical analysis of the time required for different types of changes can be performed to predict the duration of each change. Since changes are made within a dense timeframe, the optimal window for making changes is relatively small. By analyzing the average processing time for the same type of change, a roughly accurate estimate of the required change duration can be obtained. For example, when replacing memory, after multiple sampling, using Six Sigma, it is calculated that the replacement time is approximately 3 minutes. Therefore, 3 minutes can be used as the normal time required for memory replacement, allowing for precise manipulation of limited time.

[0093] In one embodiment, a total probability formula is used to determine a recommended change time period that does not affect the change application based on the predicted change duration and business system monitoring data. This can include: using the total probability formula to determine a first idle time period based on the business occurrence frequency, the business operation time period, and the scheduled execution time period of unexecuted historical business system change applications; the first idle time period is a time period in which the business system does not execute changes or the business occurrence frequency is lower than the preset business occurrence frequency; and the time period in the idle time period that is greater than the predicted change duration is determined as a recommended change time period that does not affect the change application.

[0094] Total probability formula: P(A) = ∑P(A∩B) or P(A) = ∑P(B)P(A|B); Function: If events B1, B2, ..., Bn form a complete event set (i.e., they are mutually exclusive and form the complete set), then the probability of event A occurring, P(A), equals the sum of the probabilities P(Bi) of each event Bi occurring and the conditional probability P(A|Bi) of A occurring conditional on Bi occurring. Leveraging the total probability formula, we recommend optimal time periods for changes with no business impact and for systems temporarily inactive. Operating within this time period can reduce the occurrence of production incidents and ensure safe and stable system operation. We also collect and summarize production data elements involved in the changes, analyzing factors such as the duration of the change, monitoring data during the change, and the impact between related systems. We focus on the differences before and after each change, providing effective data support for change review and building a global change knowledge base.

[0095] In this embodiment, the total probability formula can be combined with the exhaustive method to list all changes of the system within a certain period of time, sort these changes, and thus find the idle time periods. For example, system A has three changes in the time period from 10:00 to 11:00. Change 1 is from 10:00 to 10:15, change 2 is from 10:20 to 10:30, and change 3 is from 10:50 to 11:00. In the time period from 10:00 to 11:00, the idle time periods are 10:15 to 10:20 and 10:30 to 10:50.

[0096] In one embodiment, the Bayesian rule is used to determine the recommended change time period that affects the change application based on the predicted change duration, business system monitoring data, change elements and the weights corresponding to the change elements. This can include: using the Bayesian rule to determine the second idle time period based on the business occurrence frequency, business operation time period, low-frequency time period for interaction between the business system and upstream and downstream systems, and the scheduled execution time period for unexecuted historical business system change applications; the second idle time period is the time period when the business system executes the change and the business occurrence frequency is lower than the preset business occurrence frequency; based on the predicted change duration, the second idle time period, the change elements and the weights corresponding to the change elements, the recommended change time period that affects the change application is determined.

[0097] Bayesian formula: P(B|A) = P(A|B) × P(B) / P(A); Function: Used to calculate the probability of an event occurring given certain conditions. It reveals the logical relationship of "seeking cause from effect." Parameters: P(A|B) represents the probability of event A occurring given event B; P(B) and P(A) represent the probabilities of event B and event A, respectively.

[0098] Using the principles of the Bayesian formula, we derive the optimal change window for business-impacting changes from known conditions. We also collect and analyze production data elements during each change execution process, analyzing and sorting out key factors influencing the change and documenting the change environment and conditions. This provides a basis for future change reviews and ensures the accuracy, effectiveness, and feasibility of decisions. The Bayesian formula derives the optimal time period from known conditions, effectively selecting the optimal time period for changes that minimize business impact. For example, system A has three changes between 10:00 and 12:00. Change 1 is from 10:00 to 10:45, Change 2 is from 10:45 to 11:30, and Change 3 is from 11:30 to 12:00. The peak business period between 10:00 and 11:00 is from 10:30 to 11:30, so subsequent changes can only be made during the idle time periods of 10:00 to 10:30 and 11:30 to 12:00.

[0099] By utilizing the principles and characteristics of the total probability formula, the Bayesian formula principle, and the Six Sigma principle, we can calculate the optimal time point for the change and whether the change duration can be met. In this way, during the automatic change execution, we can fully utilize the advantages of automation to arrange the changes for production in an orderly manner, reduce production accidents caused by human judgment, and at the same time reduce labor costs, which can not only improve production efficiency but also ensure the safe and stable operation of the system.

[0100] Figure 3 FIG. 1 is a flow chart of a specific example of a method for changing a business system according to an embodiment of the present invention. Figure 3 As shown, the business system change method can specifically include: operations personnel initiate change requests based on business needs; once the change request enters the review phase, the change approver can review it based on the change type, impact scope, and historical experience data automatically pushed by the system. After the change is reviewed, the change is pre-launched. The system automatically matches the change operation time based on an algorithm that considers the change time period provided by monitoring data, the time period allowed by business batch operations, and the time period allowed by other related systems or external conditions. When the change time window arrives, a launch decision is made. The system automatically approves the pre-launch time window a second time, and the change is now pending launch, awaiting a decision. If the execution conditions are met, the change can be executed and launched; otherwise, it must wait for the next change time window before execution. Meeting the execution conditions means that the change time window exists and is optimal, minimizing the business impact. Change factor data is collected and analyzed to assist in change review and approval. Change factors after implementation are collected, analyzed, summarized, and provided as feedback to form a change knowledge base.

[0101] Determine whether the change will impact business operations based on the type of change. For example, a database switch typically takes several seconds to complete. Even if this can be reduced to milliseconds in the future, business interruption is inevitable. This is unavoidable. Distributed solutions can only alleviate the impact of business interruptions.

[0102] In one embodiment, the weights corresponding to the change elements may include: one or any combination of the first-level weight, the second-level weight and the third-level weight; in the recommended change time period of the influential change application, the business system is changed according to the influential change application, which may include: in the recommended change time period of the influential change application, the change elements corresponding to the first-level weight are changed according to the influential change application; the change elements corresponding to the second-level weight are approved according to the influential change application, and the change is made after the approval is passed; the change elements corresponding to the third-level weight are approved according to the influential change application at the second level, and the change is made after the second level approval is passed.

[0103] Changes are categorized into two types: impactful and non-impactful. Approval for non-impactful changes is more favorable for business systems with relatively relaxed business downtime periods, making the approval process relatively straightforward. However, for 24 / 7 operations, quantitative indicators are required to facilitate change approval. Risk weights range from 1 to 9, with increasing risk leading to increased risk. The approval process for different risk weights varies. Risk weights of 1 to 3 automatically recommend time periods, which can be changed and automatically executed. Risk weights of 4 to 6 require approval after analysis by the approver (first-level approval). Risk weights of 7 to 9 require feedback from the approver before proceeding to second-level approval. Weights and risk values ​​are correlated; larger weights correspond to greater risk. For example, a server has a smaller weight than a network device. A server may be a single point of service, while a network switch, which handles a greater number of servers and other end devices, has a larger weight. Consequently, the business risk associated with a single server and a network switch differs. First-level and second-level approvals can utilize existing approval algorithms or technologies, which will not be elaborated here.

[0104] The risk value configuration table of a specific embodiment is shown in Table 2 below:

[0105] Table 2

[0106]

[0107] Figure 4 FIG. 1 is a flow chart of a specific example of a method for changing a business system according to an embodiment of the present invention. Figure 4 As shown below, the following introduces the change operation function based on the hardware change application:

[0108] Hardware changes are categorized into non-redundant server hardware risk changes, redundant server hardware low-risk changes, redundant server high-risk changes, switch hardware operation changes, storage hardware operation changes, and other hardware operation changes.

[0109] The change conditions are based on the change time period provided by monitoring data, the time period allowed by business batch operations, the time period allowed by other related systems, the time period allowed by external conditions, and the time period allowed by other special needs.

[0110] Taking the risk change of redundant server hardware as an example, the operation and maintenance personnel initiate a change application, enter the change review stage, and then enter the inspection and review stage of the following steps.

[0111] 1. The system automatically provides time periods of low business volume or no business through monitoring data, and conducts a second review before the change is implemented to ensure that there are no other factors affecting the change.

[0112] 2. Push the non-business time period based on the business batch operation working hours.

[0113] 3. Since system changes may cause alarms in upstream and downstream related systems and even affect their business, it is also necessary to push appropriate change time periods based on business processes and system characteristics.

[0114] 4. External conditions refer to factors such as whether spare parts for hardware replacement are in place and delivery time.

[0115] 5. Other special needs refer to time nodes such as regulatory authorities and financial statements, which require clear time periods.

[0116] Based on the total probability formula principle or the Bayesian formula principle, these known change factors are comprehensively analyzed to automatically recommend one or several optimal change operation event points. At the same time, the most reliable change duration is determined according to the Three Sigma principle to ensure that the change operation can be completed in sufficient time within a time period with no business impact or low business impact and controllable risks.

[0117] After the change is approved by the change approver, it enters the stage of waiting for production. Before the change time window is reached, the system automatically rechecks the change elements involved in the review stage. If it does not meet the production requirements, it will need to wait for the next best execution point, or a senior leader will make a decision manually. Once the recheck is passed, the change will be automatically executed after the time window is reached. The operation steps are as follows:

[0118] 1. Perform business isolation on the target device, and then perform physical isolation on the target device after business isolation.

[0119] 2. When the target device is out of the business production environment, perform hardware replacement operations on it.

[0120] 3. After the hardware replacement is completed, perform change verification. After the verification passes, prepare to restore the target device to the production environment.

[0121] 4. Collect the production factors of each link in this change.

[0122] 5. Summarize and analyze the operational procedures of this change and record them in the change knowledge base for reference in subsequent change review stages.

[0123] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0124] After introducing the method of the exemplary embodiment of the present invention, next, reference is made to Figure 5 A business system changing device according to an exemplary embodiment of the present invention is introduced.

[0125] The implementation of the business system change device can refer to the implementation of the above method, and the repeated parts will not be repeated here. The terms "module" or "unit" used below can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0126] Based on the same inventive concept, the present invention also proposes a business system change device. Figure 5 Schematic diagram of a business system change device according to an embodiment of the present invention. Figure 5 As shown, the device includes:

[0127] Acquisition module 501, used to acquire business system change applications and business system monitoring data for changing the business system;

[0128] A type determination module 502 is configured to determine the type of a business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; a no-impact change application has no impact on the business system during the change execution process; an impact change application has an impact on the business system during the change execution process;

[0129] Impact scope analysis module 503, used to analyze the impact scope of the impact change application and determine the change elements and their corresponding weights;

[0130] The change duration prediction module 504 is used to predict the change duration of the business system change application based on the business system monitoring data and the historical business system change database using Six Sigma principles;

[0131] The first change time period recommendation module 505 is used to determine a recommended change time period that does not affect the change application based on the predicted change duration and business system monitoring data using a full probability formula;

[0132] The second change time period recommendation module 506 is configured to use the Bayesian theorem to determine a recommended change time period that affects the change application based on the predicted change duration, business system monitoring data, change elements, and their corresponding weights;

[0133] A non-impact change application change module 507 is configured to modify the business system according to the non-impact change application within the recommended change time period of the non-impact change application;

[0134] The impact change application modification module 508 is configured to modify the business system according to the impact change application within the recommended modification time period of the impact change application.

[0135] In one embodiment, the acquisition module 501 is specifically configured to:

[0136] Obtain business system change applications for changes to business systems;

[0137] Based on the acquisition time of the business system change application, the business system monitoring data after the acquisition time is determined.

[0138] In one embodiment, the business system monitoring data includes one or any combination of business occurrence frequency, business operation time period, low-frequency time period of interaction between the business system and upstream and downstream systems, and scheduled execution time period of unexecuted historical business system change applications.

[0139] In one embodiment, the type determination module 502 is specifically configured to:

[0140] Use the random forest algorithm and historical business system change applications in the historical business system change database to train the random forest model and determine the prediction model;

[0141] The business system change request is input into the prediction model to determine the type of the business system change request.

[0142] In one embodiment, the impact range analysis module 503 is specifically configured to:

[0143] Analyze the scope of impact of the impact change application and determine the change elements;

[0144] Determine weights corresponding to change elements according to historical business system change applications in a historical business system change database; or receive set weights corresponding to change elements.

[0145] In one embodiment, the change duration prediction module 504 is specifically configured to:

[0146] Using Six Sigma principles, analyze the average processing time of historical business system change applications of the same type as business system change applications in the historical business system change database;

[0147] The average processing time is determined as the change time of the business system change application.

[0148] In one embodiment, the first change time period recommendation module 505 is specifically configured to:

[0149] Using a total probability formula, a first idle time period is determined based on the business occurrence frequency, the business operation time period, and the scheduled execution time period for unexecuted historical business system change applications. The first idle time period is a time period when the business system does not execute changes or when the business occurrence frequency is lower than the preset business occurrence frequency.

[0150] The time period in the idle time period that is longer than the predicted change duration is determined as the recommended change time period that does not affect the change application.

[0151] In one embodiment, the second change time period recommendation module 506 is specifically configured to:

[0152] Using Bayes' theorem, a second idle time period is determined based on the business frequency, business operation time period, low-frequency interaction time period between the business system and upstream and downstream systems, and scheduled execution time period when historical business system change applications have not been executed. The second idle time period is a time period when the business system is executing changes and the business frequency is lower than the preset business frequency.

[0153] Based on the predicted change duration, the second idle time period, the change elements and the weights corresponding to the change elements, the recommended change time period that affects the change application is determined.

[0154] In one embodiment, the weight corresponding to the change factor includes: one or any combination of the first-level weight, the second-level weight, and the third-level weight;

[0155] The impact change application change module 508 is specifically used to:

[0156] During the recommended change period for impact change applications, change the change elements corresponding to the first-level weight according to the impact change application;

[0157] Approve the change elements corresponding to the secondary weight according to the impact change application, and make the change after approval;

[0158] Based on the application for impactful changes, the change elements corresponding to the three-level weights will be subject to secondary approval, and the changes will be made after the secondary approval is passed.

[0159] It should be noted that while the detailed description above mentions several modules of the business system change device, this division is merely exemplary and not mandatory. In practice, according to embodiments of the present invention, the features and functions of two or more modules described above may be embodied in a single module. Conversely, the features and functions of a single module described above may be further divided and embodied by multiple modules.

[0160] Based on the above invention concept, Figure 6 As shown, the present invention also proposes a computer device 600, including a memory 610, a processor 620 and a computer program 630 stored in the memory 610 and executable on the processor 620, wherein the processor 620 implements the aforementioned business system change method when executing the computer program 630.

[0161] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the aforementioned business system change method is implemented.

[0162] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, a method for changing a business system is implemented.

[0163] Compared with the technical solutions in the prior art that rely on personnel to proactively request offline business functions or invalid data nodes, the embodiments of the present invention obtain business system change applications and business system monitoring data for changes to the business system; determine the type of business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; no-impact change applications have no impact on the business in the business system during the change execution process; impact change applications have an impact on the business in the business system during the change execution process; analyze the impact scope of the impact change application, determine the change elements and the corresponding weights of the change elements; use Six Sigma principles, based on the business system monitoring data and the historical business system change database, to evaluate the business system change application. Predict the change duration; use the total probability formula to determine the recommended change time period for non-impact change applications based on the predicted change duration and business system monitoring data; use Bayesian law to determine the recommended change time period for impact change applications based on the predicted change duration, business system monitoring data, change elements and the weights corresponding to the change elements; in the recommended change time period for non-impact change applications, make changes to the business system based on non-impact change applications; in the recommended change time period for impact change applications, make changes to the business system based on impact change applications. This can accurately locate the change execution time period that minimizes the impact on the business system, improve production efficiency, avoid production incidents caused by human subjective assumptions, reduce labor costs, and meet the needs of large-scale operation and refined management of data centers.

[0164] The embodiment of the present invention proposes and implements a method for matching the time period of a change operation, and proposes and implements a method for determining the decision of a change operation. Beneficial effects are achieved:

[0165] 1. By collecting existing monitoring data, you can use mathematical probability formulas to calculate the optimal time point and duration for change operations.

[0166] 2. By collecting existing monitoring data and using mathematical probability formulas to assist in reviewing changes, the probability of production incidents can be reduced.

[0167] 3. It can automatically match the change time period and automatically execute it within the predetermined time period after the approval is completed, reducing labor costs and human accidents.

[0168] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0169] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for changing a business system, characterized in that: include: Obtain business system change applications and business system monitoring data for changes to business systems; Determining the type of the business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; a no-impact change application has no impact on the business in the business system during the change execution process; an impact change application has an impact on the business in the business system during the change execution process; Analyze the impact scope of the impact change application and determine the change elements and their corresponding weights; Using Six Sigma principles, based on business system monitoring data and historical business system change databases, we can predict the duration of business system change applications. Using the total probability formula, based on the predicted change duration and business system monitoring data, determine the recommended change time period that will not affect the change application; Using Bayesian theorem, based on the predicted change duration, business system monitoring data, change elements, and their corresponding weights, we determine the recommended change timeframe that will impact the change application. During the recommended change time period for the non-impact change application, make changes to the business system according to the non-impact change application; During the recommended change time period for the impact change application, the business system is changed according to the impact change application.

2. The method according to claim 1, characterized in that Obtain business system change applications and business system monitoring data for changes to business systems, including: Obtain business system change applications for changes to business systems; Based on the acquisition time of the business system change application, the business system monitoring data after the acquisition time is determined.

3. The method according to claim 2, characterized in that The business system monitoring data includes one or any combination of business occurrence frequency, business operation time period, low-frequency time period for interaction between the business system and upstream and downstream systems, and scheduled execution time period for unexecuted historical business system change applications.

4. The method according to claim 3, characterized in that Determine the type of business system change application based on the pre-stored historical business system change database, including: Use the random forest algorithm and historical business system change applications in the historical business system change database to train the random forest model and determine the prediction model; The business system change request is input into the prediction model to determine the type of the business system change request.

5. The method according to claim 1, wherein Analyze the scope of impact of the impact change application and determine the change elements and their corresponding weights, including: Analyze the scope of impact of the impact change application and determine the change elements; Determine weights corresponding to change elements according to historical business system change applications in a historical business system change database; or receive set weights corresponding to change elements.

6. The method according to claim 1, characterized in that Using Six Sigma principles, based on business system monitoring data and historical business system change databases, we predict the duration of business system change applications, including: Using Six Sigma principles, analyze the average processing time of historical business system change applications of the same type as business system change applications in the historical business system change database; The average processing time is determined as the change time of the business system change application.

7. The method according to claim 3, characterized in that Using the full probability formula, based on the predicted change duration and business system monitoring data, determine the recommended change time period that will not affect the change application, including: Using a total probability formula, a first idle time period is determined based on the business occurrence frequency, the business operation time period, and the scheduled execution time period for unexecuted historical business system change applications. The first idle time period is a time period when the business system does not execute changes or when the business occurrence frequency is lower than the preset business occurrence frequency. The time period in the idle time period that is longer than the predicted change duration is determined as the recommended change time period that does not affect the change application.

8. The method according to claim 3, characterized in that Using Bayesian theorem, based on the predicted change duration, business system monitoring data, change elements, and their corresponding weights, we determine the recommended change timeframe that will impact the change application, including: Using Bayes' theorem, a second idle time period is determined based on the business frequency, business operation time period, low-frequency interaction time period between the business system and upstream and downstream systems, and scheduled execution time period when historical business system change applications have not been executed. The second idle time period is a time period when the business system is executing changes and the business frequency is lower than the preset business frequency. Based on the predicted change duration, the second idle time period, the change elements and the weights corresponding to the change elements, the recommended change time period that affects the change application is determined.

9. The method according to claim 3, characterized in that The weight corresponding to the change factor includes: one or any combination of the first-level weight, the second-level weight and the third-level weight; During the recommended change period for the impact change application, changes will be made to the business system in accordance with the impact change application, including: During the recommended change period for impact change applications, change the change elements corresponding to the first-level weight according to the impact change application; Approve the change elements corresponding to the secondary weight according to the impact change application, and make the change after approval; Based on the application for impactful changes, the change elements corresponding to the three-level weights will be subject to secondary approval, and the changes will be made after the secondary approval is passed.

10. A business system change device, characterized in that: include: The acquisition module is used to obtain business system change applications and business system monitoring data for changes to the business system; A type determination module is configured to determine the type of a business system change application based on a pre-stored historical business system change database; the types include no-impact change applications and / or impact change applications; a no-impact change application has no impact on the business in the business system during the change execution process; an impact change application has an impact on the business in the business system during the change execution process; Impact scope analysis module, used to analyze the impact scope of the impact change application and determine the change elements and their corresponding weights; The change duration prediction module is used to predict the change duration of business system change applications based on business system monitoring data and historical business system change databases using Six Sigma principles; The first change time period recommendation module is used to determine a recommended change time period that does not affect the change application based on the predicted change duration and business system monitoring data using a full probability formula; The second change time period recommendation module is used to determine the recommended change time period that will affect the change application based on the predicted change duration, business system monitoring data, change factors, and their corresponding weights using the Bayesian theorem; A non-impact change application change module, configured to modify the business system according to the non-impact change application within the recommended change time period of the non-impact change application; The impact change application change module is used to change the business system according to the impact change application within the recommended change time period of the impact change application.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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