Information processing method and device, equipment and storage medium

Abstract

The invention discloses an information processing method and device, equipment and a storage medium, and relates to the technical field of big data processing. The method comprises the following steps: acquiring detection stream data of a detection object comprising technical features and IT infrastructure component information corresponding to the technical features, wherein the technical features are features with technical attributes generated in the operation process of the detection object; if the technical features are matched with the fault judgment rule, service graph information is determined from a service topological graph according to the IT infrastructure component information, the service graph information comprises service component nodes corresponding to the IT infrastructure component information and service conduction paths where the service component nodes are located, and the service conduction paths are used for achieving M types of services; and determining business influence degree information of the technical features on each type of business according to the influence coefficient of the business conduction path on each type of business under the P business influence evaluation dimensions. Therefore, the service influenced by the technical features can be determined according to the technical features, and the efficiency and accuracy of processing the service exception are improved.

Description

Technical Field

[0001] The present application belongs to the field of big data processing technology, and in particular relates to an information processing method, apparatus, device and storage medium. Background Art

[0002] Multiple detection systems are commonly deployed in information technology (IT) environments, covering multiple dimensions such as infrastructure, application performance, and log analysis. These detection systems focus on technical feature detection at the technical level, forming a relatively comprehensive technical feature detection system.

[0003] However, there is often a complex nonlinear relationship between the anomalies of technical characteristics and their business impact. It is impossible to determine the business that may be affected based on the anomaly's technical characteristics, resulting in low efficiency and accuracy in operation and maintenance personnel handling business anomalies. Summary of the Invention

[0004] The embodiments of the present application provide an information processing method, apparatus, device, and storage medium, which can solve the problem in the prior art that it is impossible to determine the business that may be affected by an abnormality based on its technical characteristics.

[0005] In a first aspect, an embodiment of the present application provides an information processing method, which may include:

[0006] Acquire detection flow data of the detection object, the detection flow data including technical features and IT infrastructure component information corresponding to the technical features. The technical features are features with technical attributes generated during the operation of the detection object;

[0007] When the technical features match the fault determination rules, determine the business graph information from the business service topology diagram based on the IT infrastructure component information. The business graph information includes the business component nodes corresponding to the IT infrastructure component information and the business transmission paths where the business component nodes are located. The business transmission paths are used to implement Class M services.

[0008] Based on the impact coefficient of the business transmission path on each type of business in M ​​types of business under P business impact assessment dimensions, the business impact degree of the technical characteristics on each type of business is determined, where P is an integer greater than 1.

[0009] In a second aspect, an embodiment of the present application provides an information processing device, which may include:

[0010] An acquisition module is used to acquire detection flow data of the detection object, where the detection flow data includes technical features and IT infrastructure component information corresponding to the technical features. The technical features are features with technical attributes generated during the operation of the detection object;

[0011] a determination module configured to determine, when the technical features match the fault determination rules, business graph information from the business service topology diagram based on the IT infrastructure component information, the business graph information including the business component nodes corresponding to the IT infrastructure component information and the business transmission paths on which the business component nodes are located, the business transmission paths being used to implement Class M services;

[0012] The determination module is also used to determine the business impact degree information of the technical characteristics on each type of business based on the impact coefficient of the business conduction path on each type of business in M ​​types of business under P business impact assessment dimensions, where P is an integer greater than 1.

[0013] In a third aspect, an embodiment of the present application provides a computing device, the computing device comprising: a processor and a memory storing computer program instructions;

[0014] When the processor executes the computer program instructions, the information processing method shown in the first aspect is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the information processing method shown in the first aspect.

[0016] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the information processing method shown in the first aspect.

[0017] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the information processing method shown in the first aspect.

[0018] The information processing method, apparatus, device, and storage medium of the embodiments of the present application can obtain detection flow data of a detection object, where the detection flow data includes technical features and IT infrastructure component information corresponding to the technical features, where the technical features are used to characterize features with technical attributes generated by the detection object during operation; when the technical features match the fault judgment rules, business graph information is determined from a business service topology diagram based on the IT infrastructure component information, where the business graph information includes business component nodes corresponding to the IT infrastructure component information and business transmission paths where the business component nodes are located, where the business transmission paths are used to implement M types of business; and information on the degree of business impact of the technical features on each type of business is determined based on the impact coefficient of the business transmission path on each type of business in the M types of business under at least two business impact assessment dimensions. In this way, by supporting stream data processing, second-level computing can be achieved, which significantly shortens fault discovery and response time and reduces business losses. In addition, a mapping relationship between IT infrastructure component information of technical characteristics and business component nodes is established through a business service topology diagram, realizing real-time correlation mapping between technical characteristics and business component nodes. This can quickly identify the transmission effect of technical anomalies on the business link, and quantify its impact on the business through business impact assessment dimensions and business impact coefficients, thereby improving the efficiency of determining the business affected by abnormal technical characteristics. Moreover, a comprehensive assessment can be conducted on each type of business affected by the technical characteristics through at least two business impact assessment dimensions, further improving the accuracy of the information on the business impact degree of the technical characteristics on each type of business, which is conducive to the operation and maintenance personnel to quickly and accurately handle business anomalies based on the business impact degree information of each type of business. Moreover, if the technical characteristics have an impact on at least two businesses, the operation and maintenance personnel can give priority to processing the business with a greater degree of impact based on the business impact degree information of each type of business, thereby reducing business losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 is a schematic diagram of an information processing architecture according to an embodiment of the information processing method provided by the present application;

[0021] Figure 2 is a flow chart of an information processing method according to an embodiment of the information processing method provided by the present application;

[0022] Figure 3 A schematic diagram of a heat map of business impact of an embodiment of an information processing method provided in an embodiment of the present application;

[0023] Figure 4 is a structural diagram of an information processing device provided by an embodiment of the present application;

[0024] Figure 5 It is a structural diagram of an information processing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0026] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0027] The acquisition, storage, use, and processing of data (including but not limited to the features and information herein) in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0028] Multiple detection systems are commonly deployed in current IT environments, covering multiple dimensions such as infrastructure, application performance, and log analysis, forming a relatively comprehensive technical feature detection system. However, these detection systems focus on technical metrics such as the number of error logs, response time, and throughput, lacking the ability to quantitatively assess the impact on business operations. This limitation stems from the design paradigm of traditional detection systems, which employ a "bottom-up" approach. This approach focuses on detecting technical features at the technical level and determines the business impact based on the detection results of a single technical feature. From the perspective of systems theory and cybernetics, there is a complex, nonlinear relationship between technical feature anomalies and business impact. For example, when a technical feature fails, the impact coefficient on payment operations can be as high as 0.9, while the impact coefficient on internal backend systems is only 0.2. This discrepancy stems from the varying criticality of the technical feature in different businesses, resulting in varying degrees of impact on sales representatives. It can be seen that the current detection system lacks the ability to model business context, cannot establish a mapping relationship between technical features and business impact, and cannot determine the possible impact of the business based on the abnormal technical features. This reduces the efficiency and accuracy of operation and maintenance personnel in handling business anomalies. Moreover, since it is impossible to evaluate the priority of the impact of technical features on the business, it makes it difficult for operation and maintenance personnel to apply the Pareto principle to make priority judgments when handling faults. Operation and maintenance personnel will first handle the business with an impact coefficient of only 0.2, and then handle the business with an impact coefficient of up to 0.9, increasing business losses.

[0029] Based on this, in order to solve the above-mentioned problems, an information processing method is provided in an embodiment of the present application. The method adopts a multi-level modeling method. First, a business service topology diagram is constructed, and then a real-time mapping of technical features to business impacts is realized based on a complex event processing engine. In terms of quantitative models, the fuzzy comprehensive evaluation method and the hierarchical analysis method are introduced. By establishing a weight matrix and an impact evaluation function, the dynamic calculation of the fault business impact coefficient is realized. In this way, IT operation and maintenance can be transformed from a traditional technical feature-driven mode to a business value-driven mode. By establishing a business impact quantitative indicator system, the optimal configuration of operation and maintenance resources is achieved, so that fault handling can follow the business priority principle, and it is expected that the identification accuracy of high-priority faults can be improved to a higher level, the average fault location time can be shortened, and the operation and maintenance efficiency and business continuity guarantee capabilities can be significantly improved. In the long run, this method can not only optimize the current operation and maintenance practices, but also lay the foundation for building an adaptive operation and maintenance system, which will help to realize the intelligent and value-oriented transformation of operation and maintenance management.

[0030] Based on this, the embodiments of the present application provide an information processing method, apparatus, device and storage medium. Figures 1 to 5, describes in detail the information processing method, device, server and storage medium of the embodiments of the present application. It should be noted that these embodiments are not intended to limit the scope of disclosure of the present application.

[0031] First, the information processing architecture of the information processing method provided in the embodiment of the present application is described.

[0032] like Figure 1 As shown, the information processing architecture includes a data source layer 101, a data fusion layer 102, a business impact analysis engine 103 and a visualization decision support layer 104. Each module in the information processing architecture is described in detail below.

[0033] The data source layer 101 can be used to obtain detection flow data from various channels inside and outside the business system. These data are multi-source heterogeneous, including technical characteristics and business characteristics from various sensors, log files, databases, Application Programming Interface (API) interfaces, etc. The data source layer 101 can efficiently and stably collect various types of data and transmit them to the subsequent data fusion layer 102 and business impact analysis engine 103, providing sufficient and reliable raw data for the entire architecture. This layer has strict requirements on data quality, data transmission speed and data security to ensure data integrity and accuracy in the subsequent analysis process.

[0034] Among them, technical characteristics and business characteristics are two dimensions in data classification, and the difference between the two lies in the different application scenarios, purposes and audiences. Among them, technical characteristics are data generated by the system or platform during operation, which are directly related to the technical architecture and underlying operations. They can be used to monitor, maintain and optimize IT infrastructure components. Their sources include operating systems, servers, databases, network equipment, application logs, etc. Specifically, technical characteristics may include: system logs, such as error logs, access logs, etc.; performance indicators, such as processor (Central Processing Unit, CPU) usage, memory usage, network latency, etc.; code running status, such as the number of API calls, response time, transaction processing time, etc.; hardware device parameters, such as sensor data, server status. In this way, business troubleshooting and system monitoring can be carried out, code performance or resource allocation can be optimized, and the stability, security and scalability of the system can be guaranteed.

[0035] Business features are data directly related to core business activities, reflecting business operational results and user behavior, and used to support business decision-making and analysis. These features can be sourced from business processes, user interactions, transaction systems, and more. Specifically, these features include: user information such as gender, age, and location; transaction data such as order amount, payment status, and product sales; conversion rates, repurchase rates, and customer retention rates.

[0036] The data fusion layer 102, which acquires detection stream data from the data source layer 101, is raw, unorganized data, often suffering from inconsistent formats and incomplete structures. The data fusion layer 102 is responsible for cleaning, converting, and integrating this multi-source, heterogeneous data, transforming it into a data format with business semantics. Through the data fusion layer 102, originally dispersed and complex data can be transformed into a unified, standardized knowledge system. This layer not only handles data merging and normalization but also uses data mining, machine learning, and other technologies to identify and extract potential value from the data, thereby constructing fault determination rules for business analysis and decision-making.

[0037] The business impact analysis engine 103, the core of the information processing architecture, processes the converted detection stream data transmitted from the data fusion layer 102 and determines in real time the impact of technical features on each type of business. This layer uses technology and other means to detect and predict potential problems in business processes in real time. Based on real-time data, it can determine the actual impact of technical feature failures on the business and predict the potential business losses if a failure occurs. The output of this layer provides important decision-making basis for business operations personnel, helping them identify high-risk areas and take timely and effective countermeasures.

[0038] The visual decision support layer 104 converts the analysis results of the business impact analysis engine 103, i.e., the business impact of technical features on each type of business, into intuitive and easy-to-understand business decision support information, such as a visual business impact heat map, a business impact root cause location map, technical feature alarm information, and resource allocation recommendation information. The visual decision support layer 104 allows operations and maintenance personnel to clearly see the impact of technical feature failures on various businesses, quickly identify key issues and high-priority businesses, i.e., businesses with a high impact coefficient, and make reasonable business decisions based on the visual impact prediction results. This layer not only provides data-driven decision support, but also continuously optimizes business processes through real-time feedback, improving overall business efficiency and decision-making accuracy.

[0039] Therefore, the information processing architecture provided by the embodiment of the present application is a real-time impact quantification architecture for multi-level business topology. By constructing a dynamically updated business service topology map, combined with a complex event processing engine and a machine learning model, it realizes real-time correlation mapping between technical features and business functions, and can automatically identify the transmission effect of technical anomalies on the business chain and quantify its impact on business value.

[0040] Based on the above information processing architecture, the following Figure 2 The information processing method provided in the embodiments of the present application is described in detail.

[0041] Figure 2 A flowchart of an information processing method provided in an embodiment of the present application.

[0042] like Figure 2 As shown, the information processing method can be applied to Figure 1 The information processing architecture shown in FIG. 1 may include the following steps:

[0043] Step 210: Acquire detection flow data of the detection object, where the detection flow data includes technical features and IT infrastructure component information corresponding to the technical features. The technical features are features used to characterize the technical attributes generated by the detection object during operation. Step 220: When the technical features match the fault judgment rules, determine the business graph information from the business service topology diagram based on the IT infrastructure component information. The business graph information includes the business component nodes corresponding to the IT infrastructure component information and the business transmission paths where the business component nodes are located. The business transmission paths are used to implement M types of business. Step 230: Determine the business impact degree of the technical features on each type of business based on the impact coefficient of the business transmission path on each type of business in the M types of business under P business impact assessment dimensions, where P is an integer greater than 1.

[0044] In this way, by supporting stream data processing, second-level computing can be achieved, which significantly shortens fault discovery and response time and reduces business losses. In addition, a mapping relationship between IT infrastructure component information of technical characteristics and business component nodes is established through a business service topology diagram, realizing real-time correlation mapping between technical characteristics and business component nodes. This can quickly identify the transmission effect of technical anomalies on the business link, and quantify its impact on the business through business impact assessment dimensions and business impact coefficients, thereby improving the efficiency of determining the business affected by abnormal technical characteristics. Moreover, a comprehensive assessment can be conducted on each type of business affected by the technical characteristics through at least two business impact assessment dimensions, further improving the accuracy of the information on the business impact degree of the technical characteristics on each type of business, which is conducive to the operation and maintenance personnel to quickly and accurately handle business anomalies based on the business impact degree information of each type of business. Moreover, if the technical characteristics have an impact on at least two businesses, the operation and maintenance personnel can give priority to processing the business with a greater degree of impact based on the business impact degree information of each type of business, thereby reducing business losses.

[0045] The above steps are explained in detail below:

[0046] First, referring to step 210, in one or more possible embodiments, the IT infrastructure component information in the embodiments of the present application is information used to characterize the IT infrastructure components that generate technical features. Specifically, the IT infrastructure components may include: hardware devices, such as servers, storage devices, routers, switches, firewalls, load balancers, etc.; network devices, such as routers, switches, firewalls, network device controllers, etc.; software components, such as operating systems (such as Windows, Linux, MacOS, etc.), service programs, database management systems (such as MySQL, Oracle, SQL Server, etc.), middleware (such as web servers, message queues, cache servers, etc.), applications, etc.; data storage, such as databases, file servers, disk arrays, backup storage systems, etc.; and middleware services, such as Domain Name System (DNS) servers, Dynamic Host Configuration Protocol (DHCP) servers, proxy servers, log servers, detection and alarm systems, etc.

[0047] Therefore, by supporting stream data processing, computing can be achieved in seconds, significantly shortening fault discovery and response time and reducing business losses.

[0048] Next, referring to step 220, the embodiment of the present application provides the following three methods for determining business graph information, as shown below.

[0049] In one or more possible embodiments, the historical service transmission path may be determined as the service transmission path where the service component node is located. Based on this, step 220 may specifically include step 2201 and step 2202 .

[0050] Step 2201 : Determine a business component node from the business service topology diagram based on the IT infrastructure component information. The component information of the business component node is the IT infrastructure component information.

[0051] For example, if the IT infrastructure component information is virtual machine A, then a node providing services for virtual machine A may be determined from the business service topology map and used as a business component node.

[0052] Step 2202: Determine the service transmission path where the service component node is located based on the historical service transmission path of the service component node.

[0053] For example, if business component node 1 has implemented payment services, data storage services, and password verification services, the historical business transmission path corresponding to each of the payment services, data storage services, and password verification services can be determined as the business transmission path. Here, the historical business transmission path includes business component node 1.

[0054] In this way, the historical service transmission path for the service component node to implement the service can be determined as the service transmission path where the service component node is located.

[0055] In one or more possible embodiments, the estimated business conduction path determined by the graph neural network model can be adjusted through the historical business conduction path to obtain the business conduction path where the business component node is located. Based on this, before step 2202, the information processing method may also include step 2203, according to the business component node, determining the estimated business conduction path where the business component node is located through the graph neural network model, wherein the graph neural network model is trained by sample business and sample business component nodes used to implement the sample business, the sample business includes at least M types of business, and the sample business component nodes include at least nodes in the business service topology diagram.

[0056] Here, it can be understood that its graph neural network model can include a mapping relationship between a sample component node and a business conduction path of a sample business implemented by the sample component node. In this way, the estimated business conduction path where the business component node is located can be determined based on the business component node.

[0057] Based on this, step 2202 may specifically include:

[0058] The estimated business conduction path is adjusted through the historical business conduction path, and the adjusted conduction path is determined as the business conduction path where the business component node is located.

[0059] Among them, adjusting the estimated business transmission path may specifically include deleting the business component nodes in the estimated business transmission path or adding the business component nodes in the historical business transmission path to the estimated business transmission path.

[0060] For example, if the historical service transmission path of service component node 1 is a→b→d→c and the estimated service transmission path is a→b→c, then the adjusted transmission path may be a→b→d→c.

[0061] It should be noted that the Graph Neural Network (GNN) model in the embodiments of this application is a technology for analyzing and predicting how faults propagate in complex networks. In many business systems, such as power grids and communication networks, a fault may originate from a single node and propagate through a series of interconnected nodes, leading to a chain reaction. GNN can effectively capture the relationships and propagation patterns between these nodes, thereby predicting the propagation path of the fault.

[0062] In one or more possible embodiments, the estimated business transmission path determined by the graph neural network model can be determined as the business transmission path where the business component node is located. Based on this, step 220 can specifically include steps 2204 to 2206, as shown below.

[0063] Step 2204: Determine a business component node from the business service topology diagram based on the IT infrastructure component information. The component information of the business component node is the IT infrastructure component information.

[0064] Step 2205: Determine the estimated business conduction path of the business component node through a graph neural network model based on the business component node, wherein the graph neural network model is trained by a sample business and a sample business component node used to implement the sample business.

[0065] Step 2206: Determine the estimated service transmission path as the service transmission path where the service component node is located.

[0066] In this way, a mapping relationship between business systems and IT infrastructure can be established, and support can be provided for estimating business transmission path predictions and potential impact assessments, thereby providing early warnings and improving the efficiency of determining businesses affected by abnormal technical features.

[0067] It should be noted that M is a positive integer. The relationship between the service transmission path and M types of services in the embodiment of the present application can be that one path implements multiple types of services, one path implements one type of service, multiple paths implement one type of service, or multiple service paths implement multiple types of services.

[0068] Among them, the realization of multiple types of services on one path can be achieved through the following methods (1) to (2).

[0069] (1) Based on different domain names, on the same port, different services are distinguished by different domain names such as lvan.service1.com and lvan.service2.com.

[0070] (2) Based on different paths such as URL routing. For example, under the same domain name, different services can be distinguished by different URL paths, such as path1 and path2.

[0071] (3) Protocol upgrade. HTTP and HTTPS protocols usually require different ports, such as 80 and 443, but multiple services can be implemented on the same port through protocol upgrades such as WebSocket or different routing logic under the same protocol.

[0072] In this way, a mapping relationship between technical features and business is established to achieve end-to-end impact analysis, thereby avoiding blind spots such as abnormal technical features but normal business, or normal technical features but abnormal business.

[0073] Furthermore, in one or more possible embodiments, to reduce the computational cost of determining business graph information from the business service topology graph, a determination may be made before step 220 as to whether the technical features match the fault determination rules. Based on this, the fault determination rules in the embodiments of the present application are used to reflect the mapping relationship between the reference technical features and the business. Prior to step 220, the information processing method may further include steps 3101 and 3102, as specifically described below.

[0074] Step 3101 , screening candidate fault determination rules that match the technical features from the fault determination rules, wherein the candidate fault determination rules include candidate technical features that match the technical features, feature value fault ranges of the candidate technical features, and mapping information of services affected by the feature value fault ranges.

[0075] For example, if the database connection timeout is 5 seconds, the technical feature is the database connection timeout, and the characteristic value of the technical feature is 5 seconds. In this case, the fault determination rules first screen candidate fault determination rules that match the database connection timeout. The candidate fault determination rules include: Candidate Fault Determination Rule 1: Database connection timeout / characteristic value fault range [2, 3], [3, 8] / characteristic value fault range [2, 3] affects service a; characteristic value fault range [3, 8] affects services a, b, and c; Candidate Fault Determination Rule 2: Database connection timeout / characteristic value fault range [2, 4] / characteristic value fault range [2, 4] affects service d.

[0076] Step 3102: When the characteristic value of the technical characteristic is within the characteristic value fault range, it is determined that the technical characteristic matches the fault judgment rule.

[0077] For example, since the characteristic value of the technical feature is 5 seconds, at this time, it can be determined that the fault determination rule matching the technical feature is candidate fault determination rule 1.

[0078] On the contrary, when the characteristic value of the technical feature is not within the characteristic value fault range, it is determined that the technical feature does not match the fault determination rule.

[0079] For example, if the characteristic value of the technical feature is 1 second, it is determined that the technical feature does not match the fault judgment rule.

[0080] In some embodiments, different range values ​​may affect different services, that is, different ranges may correspond to different services. The characteristic value fault range of a candidate technical feature includes at least two characteristic value fault ranges, including a first characteristic value fault range and a second characteristic value fault range. The first characteristic value fault range affects the first service, while the second characteristic value fault range affects the second service. In this case, the first service and the second service are not the same service.

[0081] Therefore, different businesses affected by the characteristic values ​​of technical features can be determined. In this way, the efficiency of determining businesses affected by abnormal technical features can be improved by quantifying their impact on the business through the impact coefficient of the business.

[0082] In one or more possible embodiments, a business service topology diagram may be constructed through the following steps. Based on this, before step 220, the information processing method may further include steps 3201 to 3203, as shown below.

[0083] Step 3201: Build a microservice call link for processing business through the operating data of the microservice nodes in the microservice architecture.

[0084] Step 3202: Construct an IT infrastructure topology map based on the preset mapping relationship between the services and IT infrastructure components stored in the configuration management database. The IT infrastructure topology map includes nodes corresponding to at least two IT infrastructure components and a connection line connecting two nodes corresponding to each of the at least two IT infrastructure components.

[0085] Step 3203: Using the nodes with the same component information in the microservice call link and the IT infrastructure topology map as anchor points, fit the microservice call link and the IT infrastructure topology map to obtain a business service topology map.

[0086] For example, service mesh technology can be used to build a microservice call link for processing business, and the configuration management database (CMDB) can be combined to obtain an IT infrastructure topology map, and the microservice call link and the IT infrastructure topology map can be integrated to obtain a new business service topology map.

[0087] Therefore, a mapping relationship between IT infrastructure component information of technical characteristics and business component nodes is established through the business service topology diagram, and real-time correlation mapping between technical characteristics and business component nodes is realized. This can quickly identify the transmission effect of technical anomalies on the business chain, and quantify its impact on the business through business impact assessment dimensions and business impact coefficients, thereby improving the efficiency of determining the business affected by abnormal technical characteristics.

[0088] Furthermore, it should be noted that, in addition to step 220, a situation may also occur where the technical features do not match the fault determination rules. Based on this, after step 210, the information processing method may further include:

[0089] If the technical characteristics do not match the fault determination rules, perform at least one of the following steps:

[0090] The technical features, IT infrastructure component information and the business component nodes corresponding to the IT infrastructure component information are used as negative samples to update the graph neural network model. The graph neural network model is a model used to determine the business transmission path where the business component nodes are located.

[0091] The technical features and candidate businesses affected by the technical features are used as negative samples to update the fault judgment rules.

[0092] Update the fault severity coefficient corresponding to the connection used to connect the business component nodes in the business service topology diagram.

[0093] As a result, the graph neural network model, fault judgment rules and fault severity coefficients can be frequently updated to improve the accuracy of determining the business impacted by abnormal technical characteristics.

[0094] Then, referring to step 230, in one or more possible embodiments, the service transmission path includes at least two service component nodes and a connection line for connecting every two service component nodes of the at least two service component nodes. Based on this, step 230 may specifically include step 2301 and step 2302.

[0095] Step 2301 : Determine a weight coefficient of a connection line according to the impact coefficient of each type of business in each of P business impact assessment dimensions between two target business component nodes connected by the connection line in the business transmission path.

[0096] Step 2302: Determine the business impact of the technical feature on each type of business based on the weight coefficient, the fault severity coefficient corresponding to the connection line, and the time screening coefficient; wherein the fault severity coefficient is used to characterize the degree of fault impact of the two target business component nodes on each type of business, and the time screening coefficient is used to reflect the cumulative degree of impact of the duration of the continuous abnormality of the technical feature on the business.

[0097] Therefore, through at least two business impact assessment dimensions, a comprehensive assessment can be conducted on each type of business affected by the technical features, further improving the accuracy of the information on the business impact of the technical features on each type of business, which is conducive to the operation and maintenance personnel to quickly and accurately handle business anomalies based on the business impact information of each type of business. If the technical features have an impact on at least two businesses, the operation and maintenance personnel can give priority to processing the business with a greater degree of impact based on the business impact information of each type of business, thereby reducing business losses.

[0098] Specifically, the P business impact assessment dimensions in the embodiment of the present application include at least two of the following: business operation status dimension, service level agreement (SLA) dimension, and user experience dimension. The user experience dimension includes at least one of the following items: business public sentiment, business service user scale, and business service time period. The business operation status dimension may include transaction volume.

[0099] Based on this, in some embodiments, the above step 2301 may specifically include:

[0100] According to the impact coefficient of the two target business component nodes on each type of business under each business impact assessment dimension and the reference adjustment weight of each business impact assessment dimension, the impact coefficient of the two target business component nodes on each type of business under each business impact assessment dimension is weighted and summed to obtain the weight coefficient of the connecting line.

[0101] For example, the weight coefficient of the connecting line can be calculated by the following formula (1):

[0102] W = α*business operation status dimension + β*SLA dimension + γuser experience dimension (1)

[0103] Among them, α, β, and γ are preset reference adjustment weights.

[0104] In some embodiments, the service transmission path in the embodiments of the present application includes at least two connection lines. Based on this, the above step 2302 may specifically include step 23021 and step 23022.

[0105] Step 23021: Determine the business impact of the failure of the two target business component nodes connected by each connection line on each type of business based on the weight coefficient of each connection line in the at least two connection lines, the fault severity coefficient corresponding to each connection line, and the time screening coefficient corresponding to the first time period.

[0106] Step 23022: Summarize the information on the impact of the failure of two target service component nodes connected by each connection line in at least two connection lines on each type of service to obtain information on the impact of the technical characteristics on each type of service.

[0107] For example, the impact of technical features on each type of business can be calculated using the following formula (2):

[0108]

[0109] Where Wi is the weight coefficient of the i-th connection line, Si is the fault severity coefficient, and F(t) is the time screening coefficient.

[0110] It should be noted that the automatic adjustment of the weight coefficient of the connecting line based on time series analysis is achieved by analyzing historical data to identify its changing trends and periodic characteristics, and then adjusting the weight according to the prediction results. In time series analysis, weight adjustment can be adopted in various ways. Here, the exponential smoothing method is used. This method assigns greater weight to the latest data points and gradually reduces the weight of historical data. It is suitable for situations where the data shows a certain trend. It can be achieved by the following formula (3):

[0111] w t =α·x t -(1-α)·w t-1 (3)

[0112] Where wt is the updated weight at the current time t, Xt is the observed value at the current time t, wt-1 is the weight value at the previous time t-1, and α is a smoothing factor that controls the relative influence of the current observation and historical weights. α typically ranges from 0 to 1, with larger values ​​indicating a greater influence of the current data on the weight, and smaller values ​​indicating a greater influence of historical data.

[0113] Therefore, through at least two business impact assessment dimensions, a comprehensive assessment can be conducted on each type of business affected by the technical features, further improving the accuracy of the information on the business impact of the technical features on each type of business, which is conducive to the operation and maintenance personnel to quickly and accurately handle business anomalies based on the business impact information of each type of business. If the technical features have an impact on at least two businesses, the operation and maintenance personnel can give priority to processing the business with a greater degree of impact based on the business impact information of each type of business, thereby reducing business losses.

[0114] In addition, in one or more possible embodiments, in order to facilitate users to view the business impact information of each type of business, the obtained business impact information of each type of business can be visualized. After step 230, the information processing method can also include step 240, as shown below.

[0115] Step 240, based on the business impact information of the technical characteristics on each type of business in the M types of business, displays at least one of the following: a business impact heat map corresponding to the business impact information of each type of business in the M types of business, a business impact root cause location map corresponding to the business transmission path for implementing each type of business, technical characteristic alarm information corresponding to the business impact information, and resource allocation recommendation information, the resource allocation recommendation information including resource allocation recommendation information for the business transmission path of the business when the technical characteristics are abnormal.

[0116] For example, it can assist in locating the root cause of the business transmission path; generate resource allocation suggestion information according to the Pareto principle, and recommend processing priority resources. Figure 3 As shown, it is a heat map of business impact, where users can intuitively see the impact of technical features on M types of businesses, such as business a, business b, business c, and business d.

[0117] Therefore, intuitive visualization tools such as business impact heat maps and loss amount predictions are provided to help operation and maintenance personnel understand the business value of operation and maintenance work, support the optimal allocation of operation and maintenance resources, and reduce business losses.

[0118] In summary, the information processing method provided by the embodiments of the present application can improve the speed, accuracy, and comprehensiveness of business impact assessment. By constructing a multi-level, multi-dimensional business impact assessment model, it deeply associates technical anomalies with business functions and realizes real-time quantitative analysis of the impact of failures. Specifically, the method can dynamically calculate the actual impact of failures on the business, such as the amount of loss per minute, user churn rate, order failure rate, etc., based on real-time detection of streaming data and combining business context such as transaction volume, user scale, and business criticality. This real-time computing capability not only greatly improves the assessment speed, but also significantly improves the accuracy of the assessment by introducing graph neural network model reasoning. In terms of comprehensiveness, the present application not only considers the direct business operation status, but also incorporates indirect impact indicators, such as user experience dimensions, which are difficult to quantify. Operation and maintenance personnel can prioritize the business with greater impact based on the business impact information of each type of business. For example, when a database connection timeout occurs, the business impact information on the payment business, such as 0.9, can be immediately assessed and compared with the business impact information on the internal backend system, such as 0.2, thereby providing clear priority guidance for operation and maintenance personnel.

[0119] Based on the same inventive concept, the present application also provides an information processing device. Figure 4 Provide detailed explanation.

[0120] Figure 4 It is a structural diagram of an information processing device provided by an embodiment of the present application.

[0121] In some embodiments of the present application, Figure 4 The information processing device shown can be set as Figure 1 In the information processing architecture shown.

[0122] like Figure 4 As shown, the information processing device 40 may specifically include:

[0123] An acquisition module 401 is configured to acquire detection flow data of a detection object, the detection flow data including technical features and IT infrastructure component information corresponding to the technical features. The technical features are features that characterize the technical properties of the detection object during operation.

[0124] Determination module 402 is configured to determine, if the technical characteristics match the fault determination rules, business graph information from the business service topology graph based on the IT infrastructure component information. The business graph information includes business component nodes corresponding to the IT infrastructure component information and business transmission paths on which the business component nodes are located. The business transmission paths are used to implement Class M services.

[0125] The determination module 402 is further configured to determine information on the business impact of the technical feature on each type of business based on an impact coefficient of the business conduction path on each type of business in the M types of business under P business impact assessment dimensions, where P is an integer greater than 1.

[0126] In an embodiment of the present application, by supporting stream data processing, second-level computing is achieved, fault discovery and response time is significantly shortened, business losses are reduced, and a mapping relationship between IT infrastructure component information of technical features and business component nodes is established through a business service topology diagram, realizing real-time correlation mapping between technical features and business component nodes, which can quickly identify the conduction effect of technical anomalies on the business link, and quantify its impact on the business through business impact assessment dimensions and business impact coefficients, thereby improving the efficiency of determining the business affected by abnormal technical features. Moreover, a comprehensive assessment can be performed on each type of business affected by the technical features through at least two business impact assessment dimensions, further improving the accuracy of the information on the business impact degree of the technical features on each type of business, which is beneficial for operation and maintenance personnel to quickly and accurately handle business anomalies based on the business impact degree information of each type of business, and if the technical feature has an impact on at least two businesses, the operation and maintenance personnel can give priority to processing the business with a greater degree of impact based on the business impact degree information of each type of business, thereby reducing business losses.

[0127] The information processing device 40 in the embodiments of the present application is described in detail below.

[0128] In one or more optional embodiments, the information processing device 40 in the embodiment of the present application may further include a screening module, which is used to screen candidate fault determination rules that match the technical features from the fault determination rules when the fault determination rules are used to reflect the mapping relationship between the reference technical features and the services, wherein the candidate fault determination rules include candidate technical features that match the technical features, feature value fault ranges of the candidate technical features, and mapping information of the services affected by the feature value fault ranges;

[0129] When the characteristic value of the technical characteristic is within the characteristic value fault range, it is determined that the technical characteristic matches the fault determination rule.

[0130] In one or more optional embodiments, the characteristic value fault range of the candidate technical feature includes at least two characteristic value fault ranges, and the at least two characteristic value fault ranges include a first characteristic value fault range and a second characteristic value fault range; the business affected by the first characteristic value fault range is the first business, and the business affected by the second characteristic value fault range is the second business.

[0131] In one or more optional embodiments, the determining module 402 may be specifically configured to determine a business component node from the business service topology diagram based on the IT infrastructure component information, where the component information of the business component node is the IT infrastructure component information;

[0132] Determine the business transmission path where the business component node is located based on the historical business transmission path of the business component node.

[0133] In one or more optional embodiments, the determination module 402 may be specifically configured to determine, based on the service component node, an estimated service transmission path on which the service component node is located using a graph neural network model, wherein the graph neural network model is trained using sample services and sample service component nodes for implementing the sample services, the sample services include at least M types of services, and the sample service component nodes include at least nodes in a service service topology graph;

[0134] In the embodiment of the present application, the information processing device 40 may further include an adjustment module for adjusting the estimated service transmission path based on the historical service transmission path, and determining the adjusted transmission path as the service transmission path where the service component node is located.

[0135] In one or more optional embodiments, the determining module 402 may be specifically configured to determine a business component node from the business service topology diagram based on the IT infrastructure component information, where the component information of the business component node is the IT infrastructure component information;

[0136] Based on the business component nodes, an estimated business transmission path of the business component nodes is determined using a graph neural network model, wherein the graph neural network model is trained using sample businesses and sample business component nodes used to implement the sample businesses;

[0137] The estimated business transmission path is determined as the business transmission path where the business component node is located.

[0138] In one or more optional embodiments, the information processing device 40 in the embodiment of the present application may further include a construction module for constructing a microservice call link for processing business through the operation data of the microservice node in the microservice architecture;

[0139] The construction module can also be used to construct an IT infrastructure topology map based on the preset mapping relationship between business and IT infrastructure components stored in the configuration management database;

[0140] In the embodiment of the present application, the information processing device 40 may further include a fitting module for fitting the microservice call link and the IT infrastructure topology map using the nodes with the same component information in the microservice call link and the IT infrastructure topology map as anchor points to obtain a business service topology map.

[0141] In one or more optional embodiments, the determining module 402 may be specifically configured to, when a service transmission path includes at least two service component nodes and a connection line connecting every two service component nodes of the at least two service component nodes, determine a weight coefficient of the connection line based on an impact coefficient of two target service component nodes connected by the connection line in the service transmission path on each type of service in each of P service impact assessment dimensions.

[0142] Determine the impact of the technical feature on each type of service based on the weight coefficient, the fault severity coefficient corresponding to the connection line, and the time screening coefficient;

[0143] Among them, the fault severity coefficient is used to characterize the impact of the failure of the two target business component nodes on each type of business, and the time screening coefficient is used to reflect the cumulative impact of the duration of the continuous abnormality of technical characteristics on the business.

[0144] In one or more optional embodiments, the determination module 402 can be specifically used to, when P business impact assessment dimensions include at least two of the following: business operation status dimension, business level agreement grade dimension, and user experience dimension, and the user experience dimension includes at least one of the following: business public opinion, business service user scale, and business service time period, perform weighted summation of the impact coefficients of the two target business component nodes on each type of business under each business impact assessment dimension and the reference adjustment weight of each business impact assessment dimension to obtain the weight coefficient of the connecting line.

[0145] In one or more optional embodiments, the determination module 402 may be specifically configured to determine, based on a weight coefficient of each connection line in the at least two connection lines, a fault severity coefficient corresponding to each connection line, and a time screening coefficient corresponding to the first time period, information on the service impact of the failure of two target service component nodes connected by each connection line on each type of service;

[0146] Business impact information of failures of two target business component nodes connected by each connection line in at least two connection lines on each type of business is summarized to obtain business impact information of technical features on each type of business.

[0147] In one or more optional embodiments, the information processing device 40 in the embodiment of the present application may also include a display module for displaying at least one of the following items based on the business impact information of each type of business in the M types of business according to the technical characteristics: a business impact heat map corresponding to the business impact information of each type of business in the M types of business, a business impact root cause location map corresponding to the business conduction path for implementing each type of business, technical feature alarm information corresponding to the business impact information, and resource allocation recommendation information, where the resource allocation recommendation information includes resource allocation recommendation information for the business conduction path of the business when the technical characteristics are abnormal.

[0148] In one or more optional embodiments, the information processing device 40 in the embodiment of the present application may further include an execution module, configured to execute at least one of the following steps when the technical features do not match the fault determination rules:

[0149] Using technical features, IT infrastructure component information, and the business component nodes corresponding to the IT infrastructure component information as negative samples, the graph neural network model is updated. The graph neural network model is used to determine the business transmission path where the business component nodes are located.

[0150] Update the fault judgment rules by using technical features and candidate businesses affected by the technical features as negative samples;

[0151] Update the fault severity coefficient corresponding to the connection used to connect the business component nodes in the business service topology diagram.

[0152] Based on the same inventive concept, the present application also provides an information processing device. Figure 5 Provide detailed explanation.

[0153] Figure 5 It is a structural diagram of an information processing device provided by an embodiment of the present application.

[0154] like Figure 5 As shown, the information processing device may include at least one of the following involved in the embodiments of the present application: an electronic device, a server. The information processing device may include a processor 501 and a memory 502 storing computer program instructions.

[0155] Specifically, the processor 501 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0156] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 502 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory. In a specific embodiment, the memory 502 includes a solid-state memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0157] The processor 501 implements any one of the information processing methods in the above embodiments by reading and executing computer program instructions stored in the memory 502 .

[0158] In one example, the information processing device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0159] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0160] Bus 510 comprises hardware, software or both, and the parts of flow control device are coupled to each other.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus 510 can comprise one or more buses.Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0161] The information processing device can execute the information processing method in the embodiment of the present application, thereby realizing the combination Figures 1 to 5 Described information processing method and device.

[0162] In addition, in conjunction with the information processing methods in the above embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the information processing methods in the above embodiments is implemented.

[0163] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0164] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0165] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0166] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. An information processing method, comprising: Acquire detection flow data of the detection object, the detection flow data including technical features and IT infrastructure component information corresponding to the technical features, the technical features being features used to characterize the detection object as having technical attributes during operation; When the technical features match the fault determination rules, determining business graph information from a business service topology graph based on the IT infrastructure component information, the business graph information including business component nodes corresponding to the IT infrastructure component information and business transmission paths on which the business component nodes are located, the business transmission paths being used to implement Class M services; According to the impact coefficient of the business transmission path on each type of business in the M types of business under P business impact assessment dimensions, the business impact degree information of the technical feature on each type of business is determined, where P is an integer greater than 1.

2. The method according to claim 1, characterized in that The fault determination rule is used to reflect the mapping relationship between the reference technical characteristics and the service; the method further includes: screening, from the fault determination rules, candidate fault determination rules that match the technical feature, wherein the candidate fault determination rules include candidate technical features that match the technical feature, feature value fault ranges of the candidate technical features, and mapping information of services affected by the feature value fault ranges; In a case where the characteristic value of the technical characteristic is within the characteristic value fault range, it is determined that the technical characteristic matches the fault determination rule.

3. The method according to claim 2, characterized in that The characteristic value fault range of the candidate technical feature includes at least two characteristic value fault ranges, and the at least two characteristic value fault ranges include a first characteristic value fault range and a second characteristic value fault range; the business affected by the first characteristic value fault range is the first business, and the business affected by the second characteristic value fault range is the second business.

4. The method according to claim 1, wherein Determining the business graph information from the business service topology graph based on the IT infrastructure component information includes: Determining a business component node from the business service topology diagram according to the IT infrastructure component information, wherein the component information of the business component node is the IT infrastructure component information; The service transmission path where the service component node is located is determined according to the historical service transmission path of the service implemented by the service component node.

5. The method according to claim 4, characterized in that Before determining the service transmission path where the service component node is located based on the historical service transmission path of the service component node implementing the service, the method further includes: Determining, based on the service component node, an estimated service transmission path on which the service component node is located using a graph neural network model, wherein the graph neural network model is trained using sample services and sample service component nodes for implementing the sample services, the sample services at least including the M-type services, and the sample service component nodes at least including nodes in the service service topology graph; The determining the service transmission path where the service component node is located according to the historical service transmission path of the service component node implementing the service includes: The estimated service transmission path is adjusted using the historical service transmission path, and the adjusted transmission path is determined as the service transmission path where the service component node is located.

6. The method according to claim 1, characterized in that Determining the business graph information from the business service topology graph based on the IT infrastructure component information includes: Determining a business component node from the business service topology diagram according to the IT infrastructure component information, wherein the component information of the business component node is the IT infrastructure component information; Determining, based on the service component node, an estimated service transmission path on which the service component node is located using a graph neural network model, wherein the graph neural network model is trained using a sample service and a sample service component node for implementing the sample service; The estimated service transmission path is determined as the service transmission path where the service component node is located.

7. The method according to any one of claims 1, 4-6, characterized in that: The method further comprises: Build a microservice call link for processing business through the operating data of microservice nodes in the microservice architecture; Construct an IT infrastructure topology map based on the preset mapping relationships between business and IT infrastructure components stored in the configuration management database; The microservice call link and the IT infrastructure topology map are fitted with the nodes having the same component information in the microservice call link and the IT infrastructure topology map to obtain the business service topology map.

8. The method according to claim 1, characterized in that The service transmission path includes at least two service component nodes and a connection line for connecting every two service component nodes of the at least two service component nodes; The impact coefficient of each type of business in the M types of business according to the business transmission path under P business impact assessment dimensions includes: determining a weight coefficient of the connection line according to an impact coefficient of two target service component nodes connected by the connection line in the service transmission path on each type of service in each of the P service impact assessment dimensions; Determining information on the degree of impact of the technical feature on each type of service based on the weight coefficient, the fault severity coefficient corresponding to the connection line, and the time screening coefficient; Among them, the fault severity coefficient is used to characterize the degree of impact of the failure of the two target business component nodes on each type of business, and the time screening coefficient is used to reflect the cumulative degree of impact of the duration of the continuous abnormality of the technical feature on the business.

9. The method according to claim 8, characterized in that The P business impact assessment dimensions include at least two of the following: a business operation status dimension, a business level agreement level dimension, and a user experience dimension, wherein the user experience dimension includes at least one of the following: business public opinion, business service user scale, and business service time period; Determining a weight coefficient of the connection line according to an impact coefficient of two target service component nodes connected by the connection line in the service transmission path on each type of service in each of the P service impact assessment dimensions includes: According to the impact coefficient of the two target business component nodes on each type of business under each business impact assessment dimension and the reference adjustment weight of each business impact assessment dimension, the impact coefficient of the two target business component nodes on each type of business under each business impact assessment dimension is weighted and summed to obtain the weight coefficient of the connecting line.

10. The method according to claim 8 or 9, characterized in that The service transmission path includes at least two connection lines; and determining, based on the weight coefficient, the fault severity coefficient corresponding to the connection line, and the time screening coefficient, information on the service impact of the technical feature on each type of service includes: Determining, based on a weight coefficient of each connection line among the at least two connection lines, a fault severity coefficient corresponding to each connection line, and a time screening coefficient corresponding to the first time period, information on a service impact degree of the failure of two target service component nodes connected by each connection line on each type of service; The information on the service impact degree of each type of service caused by the failure of two target service component nodes connected by each connection line in the at least two connection lines is summarized to obtain the information on the service impact degree of the technical feature on each type of service.

11. The method according to claim 1, wherein The method further comprises: Based on the business impact information of the technical features on each type of business in the M types of business, at least one of the following is displayed: a business impact heat map corresponding to the business impact information of each type of business in the M types of business, a business impact root cause location map corresponding to the business conduction path for implementing each type of business, technical feature alarm information corresponding to the business impact information, and resource allocation recommendation information, wherein the resource allocation recommendation information includes resource allocation recommendation information for the business conduction path of the business when the technical features are abnormal.

12. The method according to claim 1, characterized in that The method further comprises: If the technical features do not match the fault determination rules, perform at least one of the following steps: Using the technical features, the IT infrastructure component information, and the business component nodes corresponding to the IT infrastructure component information as negative samples, updating a graph neural network model, wherein the graph neural network model is a model for determining the business conduction path where the business component nodes are located; Using the technical features and candidate services affected by the technical features as negative samples to update the fault determination rules; Update the fault severity coefficient corresponding to the connection used to connect the business component node in the business service topology diagram.

13. An information processing device, comprising: An acquisition module, configured to acquire detection flow data of a detection object, wherein the detection flow data includes technical features and IT infrastructure component information corresponding to the technical features, wherein the technical features are features used to characterize the technical attributes generated by the detection object during operation; a determination module configured to, when the technical feature matches the fault determination rule, determine business graph information from a business service topology graph based on the IT infrastructure component information, the business graph information including business component nodes corresponding to the IT infrastructure component information and business conduction paths on which the business component nodes are located, the business conduction paths being used to implement Class M services; The determination module is further configured to determine information on the business impact of the technical feature on each type of business based on an impact coefficient of the business conduction path on each type of business in the M types of business under P business impact assessment dimensions, where P is an integer greater than 1.

14. A computing device, comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the information processing method according to any one of claims 1 to 12 is implemented.

15. A storage medium storing computer program instructions, wherein the computer program instructions, when executed by a processor, implement the information processing method according to any one of claims 1 to 12.

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