Change risk analysis method and device, equipment, storage medium and program product

Abstract

The invention provides a change risk analysis method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring change work order information of a current change of an application system; generating a topological graph of the application system based on the asset list and the component information of the configuration management information system of the application system; the topological graph comprises a plurality of connected nodes, and the nodes correspond to components or assets of the application system; determining nodes influenced by the change from the topological graph based on the change work order information; and determining a change risk value of the change based on the node influenced by the change. According to the method, through the generated topological graph and the determined change risk value, comprehensive and effective analysis of the change risk is realized, the situation of inaccurate change risk analysis in a complex scene is avoided, and the accuracy of change risk analysis is improved.

Description

Technical Field

[0001] The present application relates to the field of system operation and maintenance technology, and in particular to a change risk analysis method, device, equipment, storage medium and program product. Background Art

[0002] With the development of information technology, enterprise application systems often face the need to introduce new functions, fix errors or upgrade systems, which requires system changes. In the software development process, due to the huge system architecture, system changes may have an impact on the existing functions of the system. In order to ensure the stable operation of the application system, effective analysis of system changes is required.

[0003] At present, the change risk analysis method mainly relies on manual experience or simple rule judgment, and finds the impact scope by setting impact rules. This method cannot cope with complex systems with large-scale, multi-source heterogeneous data, and it is easy to ignore the potential risks of system changes, resulting in inaccurate change analysis results.

[0004] Therefore, there is an urgent need to provide a comprehensive and accurate change analysis method for complex systems. Summary of the invention

[0005] The embodiments of the present application provide a change risk analysis method, apparatus, device, storage medium and program product. By constructing a topology map of the application system, a comprehensive analysis of the nodes affected by the change is achieved, thereby obtaining a change risk value, and the analysis results are comprehensive and accurate.

[0006] In a first aspect, an embodiment of the present application provides a change risk analysis method, including:

[0007] Get the change work order information of this change in the application system;

[0008] Generate a topology diagram of the application system based on the asset list and component information of the configuration management information system of the application system; the topology diagram includes multiple connected nodes, and the nodes correspond to components or assets of the application system;

[0009] Based on the change work order information, determine the nodes affected by the change from the topology map;

[0010] Based on the nodes affected by this change, determine the change risk value of this change.

[0011] In a possible implementation, generating a topology map of the application system based on the asset list and component information of the configuration management information system of the application system includes:

[0012] Clean and standardize the asset list and component information to obtain a basic data set with a unified format;

[0013] Determine classification information of assets and components in the configuration management information system based on attribute information recorded in the basic data set; wherein the attribute information includes at least one of function type, role and location;

[0014] Determine the relationship between various assets and components in the configuration management information system based on basic data sets and classification information;

[0015] Based on the relationship between various assets and components in the configuration management information system, a topology diagram of the application system is generated.

[0016] In a possible implementation, determining the association relationship between various types of assets and components in the configuration management information system based on the basic data set and classification information includes:

[0017] Using graph algorithms and rule engines, we can identify the relationships between various assets and components in the configuration management information system based on basic data sets and classification information.

[0018] In a possible implementation, based on the association relationship between various assets and components in the configuration management information system, a topology diagram of the application system is generated, including:

[0019] Generate an architecture model based on the relationships between assets and components in the configuration management information system;

[0020] Based on the architecture model, an initial topology diagram with assets and components as nodes is obtained;

[0021] Collect network traffic between components in the initial topology diagram;

[0022] Based on network traffic, determine the dynamic interaction information between components, where the dynamic interaction information includes communication frequency, data flow direction, and interaction volume;

[0023] The dynamic interaction information is integrated into the initial topology map to obtain the topology map of the application system.

[0024] In a possible implementation, based on the change work order information, determining the nodes affected by the change from the topology map includes:

[0025] Based on the description information of the changed object in the change work order information, determine the nodes matching the changed object from the topology map to obtain the change start node set;

[0026] Based on the paths of the nodes in the change start node set in the topology graph, the nodes affected by the change are determined.

[0027] In a possible implementation, determining nodes affected by the change based on paths of nodes in the change start node set in the topology graph includes:

[0028] Based on the paths of each node in the change starting node set in the topological graph, the weights of the connecting edges between nodes in the influence propagation network are determined to obtain a weighted influence propagation network; wherein the influence propagation network is a network composed of the paths of each node in the change starting node set in the topological graph;

[0029] Based on the weighted influence propagation network, determine the nodes affected by this change.

[0030] In a possible implementation, determining nodes affected by the change based on a weighted influence propagation network includes:

[0031] The weighted influence propagation network is pruned and merged to obtain a change influence graph; the pruning process is used to delete redundant branches and branches whose weights of the connecting edges between nodes are less than the weight threshold, and the merging process is used to merge branches with high similarity into a high-level virtual node;

[0032] Based on the change impact diagram, determine the nodes affected by this change.

[0033] In a possible implementation manner, the node affected by the change is a key node in the change impact graph, and based on the change impact graph, determining the node affected by the change includes:

[0034] Perform topological sorting based on the change impact graph to obtain a node hierarchy sequence;

[0035] Based on the node hierarchy sequence, calculate the centrality index of each node in the change impact graph; wherein the centrality index includes at least one of degree centrality, betweenness centrality and eigenvector centrality;

[0036] Based on the centrality index of each node in the change impact graph, key nodes are determined from the nodes in the change impact graph.

[0037] In a possible implementation, determining the change risk value of the change based on the nodes affected by the change includes:

[0038] The determined key nodes, change impact diagram, and change work order information are input into the change risk assessment model to obtain the change risk value of this change.

[0039] In a possible implementation, determining the change risk value of the change based on the nodes affected by the change includes:

[0040] Obtain the review materials submitted during this change;

[0041] Conduct integrity check on review materials and obtain a completeness report;

[0042] Based on the completeness report and the nodes affected by this change, determine the change risk value of this change.

[0043] In a possible implementation, the change risk analysis method further includes:

[0044] When the change risk value is higher than the first risk value, a risk warning is issued based on the nodes affected by the change;

[0045] When the change risk value is higher than the second risk value, a change cancellation suggestion is generated and returned.

[0046] In a second aspect, an embodiment of the present application provides a change risk analysis device, including:

[0047] The change work order acquisition module is used to obtain the change work order information of the current change of the application system;

[0048] A topology map generation module is used to generate a topology map of the application system based on the asset list and component information of the configuration management database of the application system; the topology map includes a plurality of connected nodes, and the nodes correspond to components or assets of the application system;

[0049] An impact node determination module is used to determine the nodes affected by the change from the topology map based on the change work order information;

[0050] The risk value calculation module is used to determine the change risk value of the change based on the nodes affected by the change.

[0051] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0052] Memory stores computer-executable instructions;

[0053] The processor executes the computer-executable instructions stored in the memory, so that the processor executes various possible implementations provided in the first aspect above.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement various possible implementation methods provided in the first aspect above.

[0055] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements various possible implementation methods provided in the first aspect above.

[0056] The present application provides a change risk analysis method, device, equipment, storage medium and program product, which generates a topology map of the application system based on the asset information and component information of the configuration management information system of the application system, and clearly displays the system architecture; based on the topology map of the application system and the change work order information of this change, the nodes in the application system affected by this change are determined, and finally based on the nodes affected by this change, the change risk value of this change is determined. By generating a topology map of the application system, the relationship between each component and asset of a complex system with large-scale, multi-source heterogeneous data can be comprehensively analyzed. At the same time, by outputting a quantitative change risk value, the problems of omissions and unclear focus caused by a large number of affected nodes in complex scenarios are avoided, and the accuracy and intuitiveness of change risk analysis are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0058] Figure 1 A schematic diagram of a scenario provided for an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a change risk analysis process provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of a process for generating a topology map of an application system provided in an embodiment of the present application;

[0061] Figure 4 A flowchart of a method for determining nodes affected by this change provided in an embodiment of the present application;

[0062] Figure 5 A schematic diagram of the structure of a change risk analysis device provided in an embodiment of the present application;

[0063] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0064] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0065] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application.

[0066] First, the terms involved in this application are explained:

[0067] Change Management Ticket System: A tool used to track and manage change requests, commonly used in information technology, engineering, manufacturing and other fields.

[0068] Configuration Management Database (CMDB): is an information system used to support the configuration management process, especially in Information Technology (IT) service management and systems engineering, which helps organizations manage and maintain the configuration items of their IT infrastructure and other complex systems.

[0069] In information systems, system changes may have an impact on the existing functions of the system. Therefore, before the system is changed, it is usually necessary to analyze and assess the risks of the system change to confirm whether to adopt the corresponding change strategy. However, in the existing technology, the change risk analysis method mainly sets the impact rules through manual experience or simple rule judgment, such as defining the relationship between assets and components, and the change risk results of the existing technology usually only include the scope of change impact.

[0070] However, for complex systems with large-scale, multi-source heterogeneous data, there are many assets and components in the application system. Manual experience or simple rule judgment may lead to the neglect of the relationship between certain assets and components in the application system, or the wrong judgment of the relationship between certain assets and components, which ultimately leads to incomplete or even inaccurate analysis results. In addition, the impact range of changes output by existing change analysis is not intuitive enough, and further analysis by relevant personnel is required to draw conclusions about the change risk.

[0071] In response to the above problems, an embodiment of the present application provides a change risk analysis method, which generates a topology map of the application system based on the asset list and component information of the configuration management information system of the application system. Through the topology map, the connection relationship between each asset and component in the application system in complex scenarios is comprehensively analyzed and displayed, thereby avoiding errors or omissions in the connection relationship between assets and components, and laying a foundation for improving the accuracy of the change analysis results; based on the change work order information, the assets and components affected by the change are determined from the topology map, and finally the specific change risk value of the change is determined based on the assets and components affected by the change; by comprehensively considering the affected assets and components, a quantitative change risk value is finally determined, which intuitively reflects the risk level of the change, avoids the problem that there are many affected assets and components in complex scenarios, resulting in complex output results and inability to quickly draw change risk conclusions, thereby improving the accuracy and efficiency of change risk analysis.

[0072] First, the application scenario of the embodiment of the present application is explained:

[0073] Figure 1 A schematic diagram of a scenario provided in an embodiment of the present application, such as Figure 1 As shown, the change risk analysis method provided in the embodiment of the present application can be applied to a change management work order system that analyzes and evaluates changes. After receiving the change application, the change management work order system needs to analyze the possible impact of the change. The change can be a functional change, a repair change, a software and hardware upgrade, a configuration change (such as a database change), etc. In order to implement the change risk analysis, the change management work order system can communicate and interact with the application system to obtain the configuration information of the application system. First, the designer needs to submit a change application on the change management work order system and upload relevant materials, such as submitting a change work order. Then, the change management work order system receives the change application and receives the configuration information of the application system. Next, the change management work order system analyzes the change according to the change risk analysis method provided in the present application, determines the part that may be affected in the application system, and finally obtains the result of the change risk analysis based on the part that may be affected.

[0074] Taking the financial system as an example, changes to the financial system can be to improve system performance, user experience or security, and can also be to adapt to the ever-changing needs of the financial market. For example, the risk control system has been continuously upgraded from the traditional single system, gradually adding technologies such as intelligent decision-making and privacy computing, and the anti-money laundering system is also constantly changing to adapt to the monitoring and analysis of abnormal customer transactions under the new management model. When the financial system needs to be changed, a risk analysis of the change is required to help enterprises identify and assess potential risks, avoid losses to enterprises and users, and ensure the smooth progress of business.

[0075] For example, assuming that the financial system is changed by adding artificial intelligence technology, artificial intelligence technology can be used to predict possible failures through historical data analysis, can also be used to analyze the cause of failures through failure information, and can also be used to monitor network security. The designer first submits the financial system change application to the change management work order system. The change may affect the existing financial system functions. The change management work order system analyzes the change based on the change risk analysis method provided by this application. Through this method, it is analyzed that the change may affect the historical data storage and reading function, the fault reporting function or the network connection function, etc. Based on the possible impact of the change, the result of the change risk analysis is finally obtained, and a comprehensive and accurate change risk analysis is achieved.

[0076] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0077] Figure 2 A flow chart of a change risk analysis method provided in an embodiment of the present application, which method can be executed by the aforementioned change management work order system, such as Figure 2 As shown, the change risk analysis method includes:

[0078] Step S201: Obtain the change work order information of the current change of the application system.

[0079] The change work order information refers to the information in the change work order submitted for this change application in the change management work order system, which can be structured data. Structured data is data with a clear, predefined data model and follows a consistent order, such as change time, change object, etc., and specifically refers to data that can be mapped to a predefined data model.

[0080] Specifically, the change management work order system obtains the change application and related materials of this change, including the change work order and the change review materials. The change work order information is extracted from the change work order, such as the work order number, change type, change time and change object.

[0081] Among them, the change type can be divided based on parameters such as the change content and importance. For example, it can be divided into multiple types such as functional change, repair change, software and hardware upgrade and configuration change based on the change content. It can be divided into major changes, minor changes and general changes according to the importance, or divided based on multiple parameters, such as major functional changes, general configuration changes, etc. The change time can be the time when the change is made or the time when the change work order is submitted. The change object is the object of the change, which can be a server, database, component, etc.

[0082] Change review materials are materials such as preliminary analysis and test reports of this change submitted by the designer when making this change, which may include technical solution description, impact analysis report, rollback plan and test report, etc. Change work order or change review materials can be in the form of documents, uploaded to the change management work order system through the attachment upload function, or directly filled in and submitted in the change management work order system template.

[0083] After obtaining the change work order and change review materials of this change, information is extracted from the change work order and change review materials to obtain the change work order information. For example, a natural language processing algorithm can be used to extract keywords and semantic information from the change work order and change review materials to obtain the change work order information.

[0084] Specifically, pre-trained machine learning models, such as deep learning networks or support vector machines, can be used to preliminarily classify and deconstruct the contents of change orders and change review materials to obtain change order information for this change in the application system.

[0085] For structured data in change work orders and change review materials, such as work order numbers, change types, planned time, and change objects, relevant data can be directly extracted and mapped to predefined data models. For unstructured data, such as text content such as change descriptions and impact analysis, which cannot be directly identified and extracted, natural language processing technology, including but not limited to named entity recognition, relationship extraction, semantic role labeling, etc., can be used to perform preliminary semantic analysis, extract key information, and map the key information to a predefined data model. Among them, key information may include change objects, scope of impact, risk points, preliminary analysis of change risks, application system functions affected by the change, and content that failed testing.

[0086] According to the data mapped to the predefined data model, the change work order information of the current change of the application system can be obtained. The change work order information is structured data, which contains key information such as the basic attributes and main contents of this change, so that it can be directly used by subsequent steps.

[0087] Step S202: Generate a topology map of the application system based on the asset list and component information of the configuration management database of the application system.

[0088] The topology diagram includes multiple connected nodes, and the nodes may be components or assets of the application system.

[0089] Assets refer to IT assets of application systems, including hosts (physical machines, virtual machines), networks (network equipment, IP network resources, dedicated line exports, Internet exports), infrastructure (computer rooms) and other hardware facilities. Component information includes information on the entire life cycle of components and the relationships between components, including physical relationships, communication relationships, and association relationships. Components include hardware components, software components, network equipment, service components, documents, and processes.

[0090] The topology diagram is used to represent the connection relationship between the assets and components of the application system, such as the data transmission path between routers, switches, servers, etc. The topology diagram includes nodes and connection edges. Among them, the node specifically refers to the connection point in the application system topology diagram. The node corresponds to the asset or component of the application system. The nodes are connected by connection edges, which are used to represent the data transmission path between the assets or components corresponding to the nodes.

[0091] The topology map can be generated specifically by obtaining the asset list and component information of the configuration management database of the application system, processing the assets and components in the configuration management database, such as classification and association analysis, obtaining the association relationship between the assets and components, taking each asset and component as a node in the topology map, determining the connection relationship of the nodes based on the association relationship between the assets and components, obtaining the connection edges in the topology map, and connecting each node through the connection edges to obtain the topology map of the application system.

[0092] The topology can also be determined by splitting the application system from different angles. For example, assets and components can be divided into different subsystems according to logical functions, and the topology between subsystems can be generated through the relationship between assets and components in the subsystems. Then, the topology of each subsystem can be spliced ​​together to obtain a complete topology of the application system.

[0093] Step S203: Based on the change work order information, determine the nodes affected by the change from the topology map.

[0094] The nodes affected by this change refer to the nodes that will be directly or indirectly affected by this change. The directly affected nodes specifically refer to the nodes that are directly affected by the components involved in this change, and the indirectly affected nodes specifically refer to the nodes that are indirectly affected by the components involved in this change through intermediate components.

[0095] Specifically, firstly, key features are extracted according to the change work order information, and based on the extracted key features, nodes directly or indirectly affected by the change are determined from the topology map to constitute nodes affected by the change.

[0096] Among them, the key features are data related to the changed object in the change work order information, such as the name of the component involved in the change, IP address, function description, etc. The nodes in the topology map correspond to attribute information, which is used to describe the attributes of the assets or components corresponding to the nodes, such as name, function, address, etc. The nodes affected by this change can be determined based on the matching results of the key features and the attribute information of the nodes in the topology map.

[0097] Based on the key features, the nodes in the topology map can be matched, and the node whose attribute information matches the key features successfully can be determined as the change start node. The change start node and the nodes connected to the change start node in the topology map can be determined as the nodes affected by the change, or the more important nodes among the change start node and the nodes connected to the change start node in the topology map can be determined as the nodes affected by the change.

[0098] The key features and the attribute information of the nodes can be matched by exact matching or fuzzy matching. Exact matching can compare the key features of the change work order information with the attribute information of each node in the topology map, and the match is successful when the two are consistent; fuzzy matching can compare the key features of the change work order information with the attribute information of each node in the topology map to obtain the similarity between the key features and the attribute information, and the match is successful when the similarity is greater than or equal to the set value. Fuzzy matching can also be matched according to relevant algorithms, such as regular expressions, distance algorithms, or similarity algorithms.

[0099] Natural language processing technology can also be used to analyze the functional description of the changed object in the change work order information, and semantically match it with the attribute information of each node in the topology map, and add the successfully matched nodes to the change start node. For example, when changing the function of the application system, the components involved in the change are not clearly stated in the change application. It is necessary to use natural language processing technology to analyze the functional content of the change, obtain semantic features, and semantically match it with the functional attributes in the attribute information of each node, so as to obtain the change start node.

[0100] After obtaining the change start node, the path affected by the change start node in the topology graph is explored, and the nodes or parts of the explored path can be used as nodes affected by the change.

[0101] In some embodiments, after obtaining the change starting node, the node connected to the change starting node in the topology graph can be obtained based on the connection edge of the change starting node, and regarded as the second node. Then, the node connected to the second node is obtained in the topology graph, and so on, until there is no connected node in the topology graph. The change starting node and the aforementioned obtained nodes, or parts of them, are regarded as the nodes affected this time.

[0102] Step S204: Determine the change risk value of the change based on the nodes affected by the change.

[0103] The change risk value is used to assess the risk level of the change. It can be a numerical score between 0 and 100, where 0 represents the lowest risk and 100 represents the highest risk. The change risk value can also be compared with a predefined risk level range to determine the risk level, such as low, medium, or high risk.

[0104] The change risk value of this change can be determined based on parameters such as the level and total number of nodes affected by this change.

[0105] Specifically, a multi-dimensional quantitative evaluation is performed on the change according to the nodes affected by the change, and the multi-dimensional quantitative evaluation indicators are input into a pre-trained change risk assessment model to output the change risk value of the change. The change risk assessment model can be a deep neural network or an integrated learning model.

[0106] The multi-dimensional quantitative assessment process may include several items of change complexity index, impact scope index, core business impact index and change readiness index.

[0107] The specific method for obtaining the change complexity index is to extract the key features of the change work order information. The key features refer to the attributes that characterize the complexity of the change, such as the type of change, the number of system components involved, the complexity of the change operation, etc. The attribute that characterizes the complexity of the change is quantified, and the change complexity index is determined based on the quantification result. For example, the change complexity index can be determined based on the weighted average of each item after quantification. It is also possible to input a multidimensional vector obtained after quantifying the attributes of the change complexity into a pre-trained complexity assessment model, and analyze and process it through the model, such as a support vector machine or a random forest, to obtain a change complexity index that integrates various attributes.

[0108] The quantification method of the change type can be quantified using a predefined change type weight table. For example: based on the average complexity of different change types, important changes are predefined as a certain score, minor changes are predefined as a certain score, and the change type and its score are pre-stored in the change type weight table, and the score can be directly extracted according to the change type. The number of components involved can be directly counted to obtain the number of components involved parameter. The quantification method of the complexity of the change operation can be based on several key features of this change, and the predefined complexity score table corresponding to the key features can be read. The predefined complexity score table can be determined by extracting the key features of historical changes based on natural language processing technology, and at the same time determining the change complexity of multiple historical changes, and counting and processing their data to finally obtain the complexity score.

[0109] The specific method for obtaining the impact range indicator is to quantitatively analyze the impact range based on the nodes affected by this change. According to the nodes affected by this change, the total number of affected nodes is calculated. It is also possible to generate a change impact graph based on the nodes affected by this change and the paths they affect. The change impact graph is an information visualization chart containing nodes and their connecting edges. According to the change impact graph, calculate the depth and breadth of the impact. The depth can be understood as the length of the longest impact path, and the breadth can be understood as the maximum width of the impact spread, such as the number of change starting nodes. Determine the impact range indicator based on the above results. For example, the impact range can be determined based on the weighted average of the items after the above indicators. These results can also be combined into a multidimensional vector, input into a pre-trained impact range evaluation model, and analyzed and processed by the model, such as a regression model, to obtain the impact range indicator.

[0110] The impact range index can also include the calculation of the overall impact strength based on the weight of the connecting edges in the change impact graph, and can also include the calculation of the impact dispersion based on the structural characteristics of the change impact graph, such as connectivity and clustering coefficient. Impact dispersion means that the nodes involved in the change impact are dispersed, which may make it difficult to effectively exchange and integrate data. For example, for the same number of nodes affected by this change, when the degree of dispersion is higher, the impact range is wider.

[0111] The core business impact index is obtained by identifying key nodes based on the nodes affected by the change and obtaining key nodes. The business importance of each key node is quantified based on the predefined business function importance score table. Then, according to the position of each key node in the change impact graph and the connection relationship with other nodes, the criticality of each key node in the overall impact propagation is calculated. The criticality of each key node in the overall impact propagation can also take into account factors such as the historical failure rate and average repair time of the key node. The core business impact index is determined based on the criticality of each node. For example, the change complexity index can be determined based on the weighted average of the criticality of each key node. The criticality of each key node can also be input into a pre-trained core business impact assessment model, and the core business impact score of each key node is obtained through analysis and processing by the model, such as a gradient boosting decision tree, and the core business impact score of each key node is weighted averaged to obtain the overall core business impact index.

[0112] Among them, the key nodes are the nodes with higher influence among the nodes affected by this change. The influence degree may include the centrality of the node, the frequency of node occurrence, and the importance index of the business, etc. The key node identification process can be performed by quantifying the above influence degree and performing comprehensive scoring process, and selecting a group of nodes with the highest score as the key nodes. It can also be performed by predefined algorithms, such as cluster analysis, to select representative nodes as key nodes.

[0113] The method of obtaining the change readiness index specifically refers to determining whether the relevant materials submitted for the change application are complete and obtaining the change readiness index. A material completeness report can also be generated based on the determination result.

[0114] Scoring rules for material completeness can be predefined. For each evaluation item in the material completeness report, such as document completeness, test coverage, and the level of detail of the rollback plan, each evaluation item can be converted into a quantitative score according to the scoring rules. Based on the scores of each item, a change readiness index can be obtained. For example, the change readiness index can be determined based on the weighted average of the scores of each item. The scores of each item can also be input into a pre-trained change readiness assessment model, which is analyzed and processed by the model to finally output the change readiness index. The model can also consider the relationship between the success rate of historical changes and readiness to quantify the change readiness index.

[0115] The scores of each evaluation item can also be assigned different weights according to the change type and complexity. For example, for highly complex changes, test plans and rollback plans will be given higher weights. Finally, the weighted scores are input into the pre-trained change readiness assessment model to output a comprehensive change readiness index.

[0116] Among them, the pre-trained change risk assessment model, pre-trained complexity assessment model, pre-trained impact scope assessment model, pre-trained core business impact assessment model, and pre-trained change readiness assessment model are all complex machine learning models trained with a large amount of historical data, and at the same time, the nonlinear relationship and interaction between indicators are considered. The implementation method can use the various algorithms in the above steps, or can be selected according to the actual scenario, and there is no limitation on this.

[0117] In this embodiment, in addition to outputting the change risk value of this change, the pre-trained change risk assessment model can also output the contribution of each indicator to the change risk value of this change, which is used to reflect the main source of risk. For example, in this change risk analysis, the contribution of the change complexity indicator is 40, the contribution of the impact range indicator is 80, the contribution of the core business impact index is 55, and the contribution of the change readiness index is 57, which means that the main source of change risk causing this change is the impact range indicator. Based on the change risk value, risk level, and contribution analysis of each indicator, a risk assessment report can also be generated to intuitively reflect the risk situation of this change, as well as to generate recommended measures based on the risk analysis results.

[0118] The change risk analysis method, device, equipment, storage medium and program product provided in the embodiment of the present application generate a topology map of the application system based on the asset information and component information of the configuration management information system of the application system, and clearly display the system architecture; based on the topology map of the application system and the change work order information of this change, determine the nodes in the application system affected by this change, and finally determine the change risk value of this change based on the nodes affected by this change. By generating a topology map of the application system, the relationship between each component and asset of a complex system with large-scale, multi-source heterogeneous data can be comprehensively analyzed. At the same time, by outputting a quantitative change risk value, the problems of omissions and unclear focus caused by a large number of affected nodes in complex scenarios are avoided, thereby improving the accuracy and intuitiveness of the change risk analysis.

[0119] Optional, Figure 3 A schematic diagram of the process of generating a topology diagram of the application system provided in this application, such as Figure 3 As shown, based on the asset list and component information of the configuration management information system of the application system, a topology diagram of the application system is generated, including:

[0120] Step S301: clean and standardize the asset list and component information to obtain a basic data set in a unified format.

[0121] The basic data set with unified format refers to the data set that contains asset and component information and their associated relationships.

[0122] Data cleaning specifically refers to processing asset lists and component information according to predefined rules and algorithms to identify and process duplicate, erroneous, and formatted information, such as using regular expressions to standardize IP address formats, using fuzzy matching algorithms to identify and merge duplicate asset records, and using preset data validity rules to filter out outliers.

[0123] Standardization processing specifically refers to converting data in different formats into a consistent representation, such as unified date format, standardized naming rules, etc.

[0124] Specifically, the asset list and component information in the configuration management database are obtained. This information usually contains inconsistent formats and redundant or erroneous information. It is necessary to perform data cleaning and standardization on it to finally obtain a basic data set in a unified format to facilitate subsequent analysis.

[0125] Step S302: Determine the classification information of assets and components in the configuration management database based on the attribute information recorded in the basic data set.

[0126] The attribute information includes at least one of function type, role and location.

[0127] Specifically, the attribute information of assets and components is obtained from the basic data set, and the assets and components are classified according to their functions, roles, locations and other attributes using a predefined classification system to determine the classification information of assets and components in the configuration management information system. For example, servers are classified into application servers, database servers, network devices and other categories.

[0128] Step S303: Based on the basic data set and classification information, determine the association relationship between various types of assets and components in the configuration management information system.

[0129] Specifically, based on the basic data set and classification information, the association relationship between assets and components is obtained. The association relationship includes direct association relationship and indirect association relationship. For example, the association of an application to a database is a direct association relationship, and the association of an application to a database through middleware is an indirect association relationship.

[0130] The association relationship between various assets and components can be obtained through the configuration information in the configuration management database. For example, the association relationship between various assets and components can be determined through the relationship between components defined in the configuration information. It can also be obtained through the data communication between various assets and components. For example, the association relationship between various assets and components can be determined based on whether there is communication and interaction between the assets and components.

[0131] In this step, the hierarchical relationship between assets and components can also be obtained based on the asset list and component information. The hierarchical relationship can be obtained by layering assets and components through the system architecture, such as application layer, middle layer and driver layer. For each layer, determine the assets and components of the layer, their associations, and the associations between the layers.

[0132] Optionally, based on the basic data set and classification information, determine the association relationship between various assets and components in the configuration management information system, including:

[0133] Using graph algorithms and rule engines, we can identify the relationships between various assets and components in the configuration management information system based on basic data sets and classification information.

[0134] Specifically, based on the classification information of the basic data set, the relationship between the configuration information of assets and components is extracted, and the relationship between various assets and components in the configuration management information system is identified using graph algorithms and rule engines. For example, using graph algorithms, all possible paths between two nodes are found to infer the relationship between the nodes.

[0135] In this embodiment, the use of graph algorithms and rule engines is very effective in processing complex relationship networks, avoiding omissions or errors caused by manual determination of association relationships, and achieving rapid acquisition of association relationships between different assets and components.

[0136] Step S304: Generate a topology diagram of the application system based on the association relationship between various assets and components in the configuration management information system.

[0137] Specifically, various assets and components in the configuration management information system are used as nodes in the topology map, connecting edges are added between nodes with associated relationships, and the nodes are connected to the connecting edges to generate a topology map of the application system.

[0138] In this step, for nodes with direct associations, connection edges can be directly added between nodes. For some nodes with indirect associations, heuristic algorithms can also be used for inference. For example, if two application components frequently exchange data but have no direct association, the system can infer that there is a middleware connecting the two application components, temporarily determine a middleware in the topology map, and through the subsequent topology relationship inference process, determine the component connecting the two application components, replace the middleware, and generate a complete system topology map. By considering implicit connection relationships, the topology map generated in this way is more accurate and avoids missing connection edges in the topology map when the connection relationship is complex.

[0139] Optionally, based on the associations between various assets and components in the configuration management information system, a topology diagram of the application system is generated, including:

[0140] Based on the association relationship between assets and components in the configuration management information system, an architecture model is generated; based on the architecture model, an initial topology map with assets and components as nodes is obtained; the network traffic between components in the initial topology map is collected; based on the network traffic, the dynamic interaction information between components is determined, wherein the dynamic interaction information includes communication frequency, data flow direction and interaction volume; the dynamic interaction information is integrated into the initial topology map to obtain the topology map of the application system.

[0141] The architecture model refers to a comprehensive model used to fully describe the entire application system, including many structural elements, namely nodes, and the relationship between nodes. The initial topology diagram refers to a topology diagram generated based on the relationship between assets and components, which is used to represent the static connection relationship between assets and components. Dynamic interaction information is specifically used to reflect the behavioral characteristics of each asset and component in actual operation, namely the communication mode, including communication frequency, data flow direction, protocol type, etc.

[0142] Specifically, through the classification information and association relationship of assets and components, a multi-level architecture model can be obtained, which can be implemented by any modeling tool of the architecture model. The architecture model is subjected to topological relationship inference. Based on graph theory and network topology algorithm, each asset and component is represented as a node in the topological graph. Then, according to the association relationship between assets and components in the architecture model of the application system, connection edges are added between each node to generate the initial topological graph of the application system. The topological graph contains multiple nodes and the connection relationship between nodes.

[0143] Next, deploy traffic collection points in the application system and perform network packet capture and processing based on the traffic collection points, including configuring network devices or deploying dedicated network probes. Based on the collected network traffic, identify the communication mode between assets and components and obtain dynamic interaction information between components. Perform graph fusion processing on the dynamic interaction information and the initial topology map to obtain the topology map of the application system. For example, adjust the weight of the connection between nodes according to the communication mode, or add previously undiscovered connection edges.

[0144] In this step, the location of the traffic collection point is determined by identifying key network nodes and paths based on the node connection relationship in the initial topology diagram. Graph theory algorithms, such as the minimum spanning tree or maximum flow algorithm, can be used to determine the minimum number of sampling points that can cover the most paths. At the same time, the location of the traffic collection point is generated based on the performance of the network equipment and the supported monitoring methods, such as port mirroring, NetFlow or sFlow, to ensure the comprehensiveness of network traffic collection while reducing the impact on network performance.

[0145] In this step, network packet capture processing specifically refers to capturing network traffic by configuring ports or software, using a high-performance packet capture library to prevent packet loss in a high-speed network environment. The captured data packets are written into the storage system, and the data packets contain network communication records. The network communication records can then be further processed, including protocol identification, session reconstruction, communication data statistical analysis, and pattern recognition.

[0146] Protocol identification uses multi-layer protocol analysis technology. The system uses a combination of signature-based identification methods and behavior-based heuristic algorithms to accurately identify various protocols. For encrypted traffic, the system infers the protocol type by analyzing traffic characteristics and certificate information.

[0147] Session reconstruction uses state machines and timeout mechanisms to process TCP (Transmission Control Protocol) sessions and UDP (User Datagram Protocol) sessions, combining data packets of the same communication session to reproduce the complete communication process. At the same time, it can be based on the influence of complex network environments such as NAT conversion (Network Address Translation) and load balancing. Through session reconstruction, data packets are converted into structured inter-component communication data, and each record contains information such as source address, destination address, protocol type, session duration, and amount of data transmitted.

[0148] Communication data statistical analysis uses big data processing frameworks, such as the Apache Spark framework or the Flink framework, to process communication data in parallel. The system calculates various statistical indicators, including but not limited to communication frequency, average data transfer volume, peak transfer rate, etc. At the same time, the system uses time series analysis technology to identify periodic communication patterns and abnormal traffic patterns.

[0149] Pattern recognition uses machine learning algorithms, such as cluster analysis and association rule mining, to discover interaction patterns between components. In addition, the system combines information from the initial topology map to map the identified communication patterns to specific business processes or application functions.

[0150] By introducing the architectural model to read the classification information, association relationships and hierarchical relationships of assets and components, and integrating multi-level information, the asset list and component information of the application system can be clearly displayed. Through the initial topology map and dynamic interaction information, the static structure and dynamic operation characteristics of the application system are combined together. The generated topology map is both intuitive and rich.

[0151] In this embodiment, erroneous and redundant data are avoided by performing data cleaning on the asset list and component information, and a basic data set with a unified format is generated through standardization processing, thereby avoiding the problem of incorrect recognition of the same information in different formats during subsequent processing, thereby improving data quality. At the same time, by determining the classification information and association relationships of assets and components, a systematic connection between assets and components is achieved, making the asset and component information more orderly and easy to process, laying the foundation for subsequent analysis.

[0152] Optional, Figure 4 A flowchart of the method for determining nodes affected by this change provided for this application is as follows: Figure 4 As shown, based on the change work order information, the nodes affected by this change are determined from the topology map, including:

[0153] Step S401: Based on the description information of the changed object in the change work order information, determine the node matching the changed object from the topology map to obtain a change start node set.

[0154] The change object refers to the object of this design change, such as the changed asset, component or function. The description information of the change object specifically refers to the key information related to the change object, such as the component name or IP address.

[0155] The change start node set is a set consisting of the change start nodes or part of the change start nodes.

[0156] Specifically, based on the description information of the changed object, such as component name, IP address, etc., a multi-level matching strategy is adopted for processing, including exact matching and fuzzy matching. First, exact matching is performed, such as directly locating the node according to the component name or IP address. For the case where exact matching cannot be performed, a fuzzy matching algorithm is used, such as edit distance or cosine similarity, to find the most similar nodes. Through the above matching operations, the set of change starting nodes is obtained.

[0157] Step S402: Determine the nodes affected by the change based on the paths of the nodes in the change start node set in the topology graph.

[0158] Specifically, the nodes in the change start node set are obtained, and the paths of the nodes in the topology map are obtained. The nodes in the change start node set and the nodes included in the paths of the nodes in the topology map together constitute the nodes affected by this change.

[0159] The path acquisition method is to perform a depth-first traversal on the topology graph, that is, starting from each change start node, along the direction of increasing depth in the topology graph, find all possible paths explored by the change start node along the connection edges in the topology graph. During the traversal process, each path is recorded, including the nodes and connection edges on the path.

[0160] In the depth-first traversal process, due to complex associations, there may be circular dependencies, resulting in infinite recursion. A circular detection mechanism can be added to stop exploration when a circular is detected to avoid infinite recursion. You can also set a maximum depth limit and branching factor limit to control the scope and complexity of the traversal.

[0161] Based on the above steps, the path can also be obtained through an improved depth-first search algorithm based on cross-level component dependencies. For example, a change in the application layer may affect the underlying components or the upper-level business. The improved depth-first search algorithm can be used to obtain the cross-level path of functions, components or businesses.

[0162] By obtaining the change start node that matches the change object, the node directly associated with the change object is obtained, and then all its paths in the topology map are explored according to the start node. Through the nodes on all paths, the nodes affected by this change are obtained. This method achieves accurate and comprehensive acquisition of the nodes affected by this change, avoids the problem of incomplete subsequent analysis caused by missing nodes far away from the start node, and improves the accuracy of change risk analysis.

[0163] Optionally, based on the path of each node in the change start node set in the topology graph, determining the nodes affected by the change includes:

[0164] Based on the paths of each node in the change starting node set in the topological graph, the weights of the connecting edges between nodes in the influence propagation network are determined to obtain a weighted influence propagation network; wherein the influence propagation network is a network composed of the paths of each node in the change starting node set in the topological graph; based on the weighted influence propagation network, the nodes affected by this change are determined.

[0165] Specifically, based on the paths of each node in the change starting node set in the topological graph, the dependency relationships between the nodes are weighted and processed to quantify the association relationships between the nodes, and finally a weighted influence propagation network is output, in which the connecting edges in the influence propagation network are assigned a weight value, and the nodes affected by the change are determined based on the nodes in the weighted influence propagation network.

[0166] In this step, shorter paths and nodes at the same level can be given higher weights based on the length of the path and the hierarchical relationship of the nodes. Frequently communicating nodes can also be given higher weights based on the communication frequency and data traffic between nodes. Node importance, such as core business or key components, can also be given higher weights. The weight calculation process can combine the above factors to calculate the weight value of the connection edge between adjacent nodes through weighted summation or more complex machine learning models, such as random forests or neural networks.

[0167] By introducing a weighted influence propagation network, the influence strength of each connection edge is quantified, which can intuitively reflect the influence strength of each path. The degree of influence of each node can be obtained from the weighted influence propagation network. Subsequently, targeted processing can be performed based on the degree of influence of each node, such as deleting nodes with less influence, etc., which improves the efficiency of change risk analysis.

[0168] Optionally, based on a weighted influence propagation network, determine the nodes affected by the change, including:

[0169] The weighted influence propagation network is pruned and merged to obtain a change impact graph; the pruning process is used to delete redundant branches and branches whose weights of the connecting edges between nodes are less than the weight threshold, and the merging process is used to merge branches with high similarity into a high-level virtual node; based on the change impact graph, the nodes affected by this change are determined.

[0170] In the process of exploring the impact paths of each change start node in the topology graph, there may be redundant impact paths. In order to simplify the impact paths, the original impact path set can also be pruned and merged, such as removing original impact paths with less impact, or merging similar original impact paths.

[0171] Pruning specifically refers to deleting the connecting edges between nodes with weights less than a preset weight threshold, and deleting paths containing the same nodes and connecting edges. At the same time, a pruning algorithm based on importance is used to remove branches with less impact and retain the paths that have the greatest impact on the core business.

[0172] Merge processing specifically refers to identifying closely connected nodes based on graph clustering algorithms, such as spectral clustering or Louvain algorithm, and identifying the similarity of the branches where the nodes are located according to the functions and associations of the nodes, merging the branches with high similarity into a high-level virtual node. The merged node inherits the key attributes and accumulated influence weights of the original node. For example, if the data of an application component flows to several databases with similar functions, the nodes and connection edges corresponding to these databases can be merged into one middleware.

[0173] Specifically, the weighted influence propagation network is pruned and merged to obtain a change impact graph, which specifically refers to a graph that reflects a collection of nodes affected by the change and paths of the nodes, and based on the change impact graph, the nodes affected by the change are determined.

[0174] The change impact diagram is presented in a graphical form. The nodes are nodes affected by the change, indicating the affected assets or components. The connecting edges indicate the impact relationship. The thickness or color of the connecting edges can be used to indicate the intensity of the impact. The change impact diagram can also include the hierarchical relationship of components to show the impact of the change at different levels. Based on the change impact diagram, an impact analysis report can also be generated, including a description of each affected node, a description of the impact path, and a quantitative impact assessment.

[0175] The change impact diagram obtained by pruning and merging the impact propagation network simplifies the application system structure without missing key information, avoids the inefficiency of the analysis process caused by the overly complex change impact diagram, and improves the efficiency of change analysis. At the same time, the change impact diagram is presented in a graphical way, clearly showing the propagation of impacts between different architectural levels.

[0176] Optionally, the nodes affected by the change are key nodes in the change impact graph. Based on the change impact graph, the nodes affected by the change are determined, including:

[0177] A topological sorting is performed based on the change impact graph to obtain a node hierarchy sequence; based on the node hierarchy sequence, a centrality index of each node in the change impact graph is calculated; wherein the centrality index includes at least one of degree centrality, betweenness centrality and eigenvector centrality; based on the centrality index of each node in the change impact graph, key nodes are determined from the nodes in the change impact graph.

[0178] The node hierarchy sequence refers to a sequence in which each node is sorted according to a preset order, and the preset order may be the importance of the node, the degree of the node, etc. The degree specifically refers to the sum of the number of times the node serves as the end point and the starting point of the connecting edge.

[0179] Centrality indicators are indicators used to measure the importance of nodes, mainly including degree centrality, betweenness centrality and eigenvector centrality. Among them, degree centrality specifically refers to the number of nodes directly connected to a node in the topology graph, which is used to indicate the number of nodes directly associated with the node in the application system; betweenness centrality specifically refers to the frequency of the node appearing in the shortest path between other nodes, which is used to measure the degree to which the node acts as an intermediary in the application system; eigenvector centrality specifically refers to measuring the importance of a node based on the importance of the nodes directly associated with it, which is used to indicate the connectivity of the node and its connection with other nodes.

[0180] Specifically, the Kahn algorithm is used to sort the nodes in the change impact graph, sort the nodes according to their in-degree, identify the nodes with in-degree 0, remove the nodes from the change impact graph, and add them to the node hierarchy sequence, and recalculate the in-degree of other nodes, that is, reduce the in-degree of its adjacent nodes. Next, identify the nodes with in-degree 0, repeat the above steps until there are no nodes in the change impact graph, and obtain a node hierarchy sequence containing all affected nodes. In the process of sorting the nodes, there may be a situation where not all affected nodes have been processed, indicating that there is a loop in the change impact graph. At this time, an alternative strategy can be adopted and a warning can be issued. The alternative strategy can be a depth-first search.

[0181] Based on the node hierarchy sequence, the centrality index of each node in the change impact graph is calculated, and multiple centrality indexes of each node are combined into a multidimensional vector, and the multiple centrality indexes include at least one of degree centrality, betweenness centrality and eigenvector centrality. In addition, the PageRank value of each node can also be considered, using a random walk model and iterative calculation until convergence. The PageRank value is used to measure the importance of a web page. In addition, different weights can be given to each node according to its position in the hierarchy sequence.

[0182] Degree centrality can directly count the sum of the in-degree and out-degree of each node; betweenness centrality can use the Floyd-Warshall algorithm to find all shortest paths and calculate the frequency of each node appearing in the path; eigenvector centrality can use the power iteration method.

[0183] Next, based on the multidimensional vector composed of the centrality index of each node, a multidimensional clustering analysis is performed on the affected nodes to determine the key nodes from the nodes in the change impact graph.

[0184] For multi-dimensional clustering analysis, the indicators of each dimension can be standardized first, for example, using the Z-score method, and then clustering using the K-means++ algorithm to determine the optimal number of clusters. Specifically, the elbow rule is used to calculate the intra-group sum of squares under different numbers of clusters, and the number of clusters at the inflection point of the curve is selected. The optimal number of clusters can also be verified through silhouette coefficient analysis.

[0185] After clustering is completed, the center point of each cluster is calculated, and the node closest to the center point is selected as the candidate key node. At the same time, in order to ensure that key nodes are not missed, the local anomaly factor algorithm can be used to identify nodes that significantly deviate from the main cluster during the clustering process as candidate key nodes.

[0186] Finally, based on the candidate key nodes and the preset business importance weights, the business importance weights are used to reflect the importance of the node to the overall business. The candidate key nodes and the corresponding business importance weights are input into the comprehensive scoring model to obtain the final scoring results of each candidate key node. The group of nodes with the highest scores can be selected as key nodes based on the preset number of key nodes, or a group of nodes above the static threshold can be selected as key nodes based on the preset static threshold.

[0187] The comprehensive scoring model combines the node centrality index, cluster analysis results and business importance weights, using a weighted summation method, where the weights are determined through sensitivity analysis to balance the impact of different factors. In addition, nodes on the critical path can be given a higher weight based on their position in the change impact diagram. The critical path can be obtained using the longest path algorithm.

[0188] By sorting the nodes in the change impact diagram and calculating the centrality index of each node, the key nodes are determined. The key nodes determined in this way will neither be too many to disperse important risk points, nor too few to cause important risk points to be missed. By analyzing the key nodes, it can also provide important basis for the risk analysis of this change, thereby improving the importance of change risk analysis.

[0189] Optionally, based on the nodes affected by the change, determine the change risk value of the change, including:

[0190] The determined key nodes, change impact diagram, and change work order information are input into the change risk assessment model to obtain the change risk value of this change.

[0191] Specifically, the core business impact index is calculated based on the determined key nodes, the impact scope index is calculated based on the change impact diagram, and the change complexity index can be calculated based on the change work order information. The core business impact index, impact scope index and change complexity index are input into the change risk assessment model to output the change risk value of this change.

[0192] The change risk assessment model specifically refers to a complex machine learning model that has been trained with a large amount of historical data, such as a deep neural network or an integrated learning model. First, the input indicators are standardized, and then each indicator is input into the corresponding sub-module to process risk assessments in different aspects, such as technology, business, and readiness risks. Finally, the outputs of each sub-module are comprehensively processed to obtain the final change risk score.

[0193] In this embodiment, the calculation method of each indicator is similar to the partial implementation method of step S204 in the aforementioned embodiment of the present application, and will not be described in detail here.

[0194] In this embodiment, the change risk value obtained through analysis from multiple aspects draws a conclusion that is closer to the actual situation and has a stronger universality of the results, thus avoiding the situation where the change risk value has no universal reference value for different types of changes and improving the universality and accuracy of the change risk value.

[0195] Optionally, based on the nodes affected by the change, determine the change risk value of the change, including:

[0196] Obtain the review materials submitted for this change; perform integrity check on the review materials to obtain a completeness report; determine the change risk value of this change based on the completeness report and the nodes affected by this change.

[0197] Specifically, the text content of the relevant materials of this change application is obtained, including the text content of the change work order and related review materials, and natural language processing is performed on the text content to extract key fields, such as change object, impact scope, risk points, etc. The semantic information of key fields in the text content can also be analyzed. For example, it can be identified from the text content that the change object is a database server, and it can also be identified whether the change object is an upgraded target or an affected target.

[0198] In this step, natural language processing uses a variety of processing techniques, including but not limited to named entity recognition, relationship extraction, semantic role labeling, etc. Named entity recognition is used to identify specific entities in text content, such as system names, component names, etc.; relationship extraction is used to identify the relationship between entities; semantic role labeling is used to understand the semantic structure, such as identifying what operations a certain entity has performed.

[0199] Next, according to the preset review criteria, which defines the necessary information and documents required for changes of different types, including the necessary review material documents and the key information points that each document should contain, the extracted key fields and semantic information are compared with the review criteria. The rule engine and semantic similarity calculation can be used to determine whether each item in the review criteria exists and is sufficient, and a completeness report is output based on the comparison results.

[0200] In this step, if there are insufficient or inconsistent key information points, this problem will be marked in the completeness report. In addition, the completeness report can not only include the existence of key information points in the document and the device, but also include an assessment of the quality of the information. For example, the report may point out that although a certain key information point exists, the description is too simple. At the same time, the completeness report can also summarize the completeness score based on the comparison results of the review criteria to reflect the adequacy of the preparation of the review materials.

[0201] Finally, the change accuracy index is calculated based on the completeness report, and the core business impact index, impact range index, and change complexity index are calculated based on the nodes affected by this change. The above indicators are input into the change risk assessment model to output the change risk value of this change.

[0202] In this embodiment, the change risk value obtained based on the completeness report of the review materials can reflect the adequacy of the preparatory work before the change, and the completeness report can provide clear guidance to avoid insufficient materials and help improve the quality of the change application.

[0203] Optionally, when the change risk value is higher than the first risk value, a risk warning is issued based on the nodes affected by the change; when the change risk value is higher than the second risk value, a suggestion to cancel the change is generated and returned.

[0204] The first risk value and the second risk value specifically refer to predefined risk level thresholds, wherein the second risk value is higher than the first risk value.

[0205] Specifically, the first risk value and the second risk value can be set according to the actual usage scenario. When the change risk value is not higher than the first risk value, the risk level is determined to be low, indicating that the risk of this change is low, and an approval change instruction is generated and returned. When the change risk value is between the first risk value and the second risk value, the risk level is determined to be medium, indicating that there is a certain risk in this change, but the risk is controllable, and an approval change instruction is generated and returned. At the same time, risk warnings are given based on the nodes affected by this change to remind people to pay attention to related risk items. When the change risk value is higher than the second risk value, a suggestion to cancel the change instruction is generated and returned. At the same time, a risk assessment report can be generated to intuitively illustrate the main sources of risks.

[0206] The risk level can be low, medium, or high, or other levels can be set according to needs, which are not limited here.

[0207] For example, you can set the change risk value to be low when it is less than 60 points, and change is recommended; the change risk value is greater than or equal to 60 points and less than or equal to 80 points, and it is medium, and change is recommended, but you need to pay attention to certain risk items; the change risk value is greater than 80 points, and it is high, and this change is not recommended.

[0208] By directly obtaining the results of the change risk analysis based on the change risk value, the problem of inefficiency or erroneous conclusions caused by analyzing a large number of influencing nodes can be avoided, thereby improving the efficiency of the change risk analysis.

[0209] Figure 5 A schematic diagram of a change risk analysis device provided in this application is shown in FIG. Figure 5As shown, the change risk analysis device provided in this embodiment includes: a change work order acquisition module 501, a topology map generation module 502, an impact node determination module 503, and a risk value calculation module 504.

[0210] The change work order acquisition module 501 is used to obtain the change work order information of the current change of the application system; the topology map generation module 502 is used to generate a topology map of the application system based on the asset list and component information of the configuration management information system of the application system, and the topology map includes multiple connected nodes, and the nodes correspond to the components or assets of the application system; the affected node determination module 503 is used to determine the nodes affected by the current change from the topology map based on the change work order information; the risk value calculation module 504 is used to determine the change risk value of the current change based on the nodes affected by the current change.

[0211] Optionally, the topology map generating module 502 is specifically used for:

[0212] A data processing unit is used to clean and standardize the asset list and component information to obtain a basic data set with a unified format; a classification information determination unit is used to determine the classification information of assets and components in the configuration management information system based on the attribute information recorded in the basic data set; wherein the attribute information includes at least one of the function type, role and location; an association determination unit is used to determine the association between various assets and components in the configuration management information system based on the basic data set and the classification information; a topology map generation unit is used to generate a topology map of the application system based on the association between various assets and components in the configuration management information system.

[0213] Optionally, the association relationship determination unit is specifically used to: utilize graph algorithms and rule engines to identify the association relationships between various assets and components in the configuration management information system based on basic data sets and classification information.

[0214] Optionally, a topology map generating unit is specifically used to:

[0215] Based on the association relationship between assets and components in the configuration management information system, an architecture model is generated; based on the architecture model, an initial topology map with assets and components as nodes is obtained; the network traffic between components in the initial topology map is collected; based on the network traffic, the dynamic interaction information between components is determined, wherein the dynamic interaction information includes communication frequency, data flow direction and interaction volume; the dynamic interaction information is integrated into the initial topology map to obtain the topology map of the application system.

[0216] Optionally, the influencing node determination module 503 is specifically configured to:

[0217] The starting node determination unit is used to determine the nodes matching the changed object from the topological map based on the description information of the changed object in the change work order information, and obtain the change starting node set; the affected node determination unit is used to determine the nodes affected by this change based on the paths of each node in the change starting node set in the topological map.

[0218] Optionally, the influencing node determination unit is specifically used to:

[0219] The influence propagation network acquisition unit is used to determine the weights of the connecting edges between nodes in the influence propagation network based on the paths of each node in the change starting node set in the topological graph, so as to obtain a weighted influence propagation network; wherein the influence propagation network is a network composed of the paths of each node in the change starting node set in the topological graph; the influence propagation network determines the influence node unit, which is used to determine the nodes affected by this change based on the weighted influence propagation network.

[0220] Optionally, the influence propagation network determines the influence node unit, which is specifically used for:

[0221] The change influence diagram acquisition unit is used to perform pruning and merging processing on the weighted influence propagation network to obtain the change influence diagram; wherein the pruning processing is used to delete redundant branches and branches whose weights of the connecting edges between nodes are less than the weight threshold, and the merging processing is used to merge branches with high similarity into a high-level virtual node; the change influence diagram determination influence node unit is used to determine the nodes affected by the current change based on the change influence diagram.

[0222] Optionally, the change impact diagram determines the impact node unit, specifically used for:

[0223] A topological sorting is performed based on the change impact graph to obtain a node hierarchy sequence; based on the node hierarchy sequence, a centrality index of each node in the change impact graph is calculated; wherein the centrality index includes at least one of degree centrality, betweenness centrality and eigenvector centrality; based on the centrality index of each node in the change impact graph, key nodes are determined from the nodes in the change impact graph.

[0224] Optionally, the risk value calculation module 504 is specifically used to: input the determined key nodes, change impact diagram and change work order information into the change risk assessment model to obtain the change risk value of the current change.

[0225] Optionally, the risk value calculation module 504 is specifically used to:

[0226] Obtain the review materials submitted for this change; perform integrity check on the review materials to obtain a completeness report; determine the change risk value of this change based on the completeness report and the nodes affected by this change.

[0227] Optionally, the change risk analysis device further includes a risk prompt module, which is used to:

[0228] When the change risk value is higher than the first risk value, a risk warning is issued based on the nodes affected by the change; when the change risk value is higher than the second risk value, a suggestion to cancel the change is generated and returned.

[0229] The change risk analysis device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0230] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the device 60 also includes a communication component 603. The processor 601, the memory 602 and the communication component 603 are connected via a bus 604.

[0231] In a specific implementation process, at least one processor 601 executes the computer execution instructions stored in the memory 602, so that at least one processor 601 executes the above method.

[0232] The specific implementation process of the processor 601 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0233] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0234] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0235] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0236] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0237] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0238] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0239] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0240] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0241] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0242] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0243] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0244] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0245] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A change risk analysis method, characterized in that: include: Get the change work order information of this change in the application system; Generate a topology map of the application system based on the asset list and component information of the configuration management database of the application system; The topology diagram includes a plurality of connected nodes, the nodes corresponding to components or assets of the application system; Based on the change work order information, determine the nodes affected by the change from the topology map; Based on the nodes affected by this change, determine the change risk value of this change.

2. The method according to claim 1, characterized in that Generating a topology diagram of the application system based on the asset list and component information of the configuration management information system of the application system includes: Performing data cleaning and standardization on the asset list and component information to obtain a basic data set with a unified format; Determining classification information of assets and components in the configuration management information system based on the attribute information recorded in the basic data set; wherein the attribute information includes at least one of function type, role and location; Determining associations between various types of assets and components in the configuration management information system based on the basic data set and the classification information; Based on the association relationship between various assets and components in the configuration management information system, a topology map of the application system is generated.

3. The method according to claim 2, characterized in that Determining the association relationship between various types of assets and components in the configuration management information system based on the basic data set and the classification information includes: By using a graph algorithm and a rule engine, based on the basic data set and the classification information, the association relationship between various types of assets and components in the configuration management information system is identified.

4. The method according to claim 2, characterized in that: Based on the association relationship between various assets and components in the configuration management information system, a topology diagram of the application system is generated, including: generating an architecture model based on associations between assets and components in the configuration management information system; Based on the architecture model, an initial topology diagram with assets and components as nodes is obtained; Collecting network traffic between components in the initial topology diagram; Based on the network traffic, determining dynamic interaction information between components, wherein the dynamic interaction information includes communication frequency, data flow direction, and interaction volume; The dynamic interaction information is integrated into the initial topology map to obtain a topology map of the application system.

5. The method according to claim 1, characterized in that Based on the change work order information, determining nodes affected by the change from the topology map includes: Based on the description information of the changed object in the change work order information, determine the node matching the changed object from the topology map to obtain a change start node set; Based on the path of each node in the change start node set in the topology graph, the nodes affected by the change are determined.

6. The method according to claim 5, characterized in that Determining nodes affected by the change based on the paths of the nodes in the change start node set in the topology graph includes: Based on the paths of each node in the change starting node set in the topological graph, the weights of the connecting edges between nodes in the influence propagation network are determined to obtain a weighted influence propagation network; wherein the influence propagation network is a network composed of the paths of each node in the change starting node set in the topological graph; Based on the weighted influence propagation network, nodes affected by the change are determined.

7. The method according to claim 6, characterized in that Based on the weighted influence propagation network, nodes affected by the change are determined, including: The weighted influence propagation network is pruned and merged to obtain a change influence graph; wherein the pruning process is used to delete redundant branches and branches whose weights of connecting edges between nodes are less than a weight threshold, and the merging process is used to merge branches with high similarity into a high-level virtual node; Based on the change impact graph, nodes affected by the change are determined.

8. The method according to claim 7, characterized in that The nodes affected by this change are key nodes in the change impact graph. Based on the change impact graph, the nodes affected by this change are determined, including: Performing topological sorting based on the change impact graph to obtain a node hierarchy sequence; Based on the node hierarchy sequence, calculating the centrality index of each node in the change impact graph; wherein the centrality index includes at least one of degree centrality, betweenness centrality and eigenvector centrality; Based on the centrality index of each node in the change impact graph, key nodes are determined from the nodes in the change impact graph.

9. The method according to claim 8, characterized in that Based on the nodes affected by this change, determine the change risk value of this change, including: The determined key nodes, the change impact diagram, and the change work order information are input into the change risk assessment model to obtain the change risk value of this change.

10. The method according to any one of claims 1 to 8, characterized in that: Based on the nodes affected by this change, determine the change risk value of this change, including: Obtain the review materials submitted during this change; Conduct a completeness check on the review materials and obtain a completeness report; Based on the completeness report and the nodes affected by the change, a change risk value of the change is determined.

11. The method according to any one of claims 1 to 9, characterized in that: The method further comprises: When the change risk value is higher than the first risk value, a risk warning is issued based on the nodes affected by the change; When the change risk value is higher than the second risk value, a change cancellation suggestion is generated and returned.

12. A change risk analysis device, characterized in that: include: The change work order acquisition module is used to obtain the change work order information of the current change of the application system; A topology map generating module, used to generate a topology map of the application system based on the asset list and component information of the configuration management information system of the application system; The topology diagram includes a plurality of connected nodes, the nodes corresponding to components or assets of the application system; An affected node determination module, used to determine the nodes affected by the change from the topology map based on the change work order information; The risk value calculation module is used to determine the change risk value of the change based on the nodes affected by the change.

13. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 11 when executed by a processor.

15. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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