Core call link topology generation method and device, equipment, medium and product

Abstract

The invention relates to a core calling link topology generation method and device, equipment, a medium and a product. The method comprises the following steps of: aggregating calling links of the same service into a weighted directed graph according to calling times; generating a random network of the original call link directed graph based on the zero model, wherein the random network comprises nodes and edges which are the same as those of the original call link directed graph; comparing the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the whole random network; and filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the whole random network to obtain the core call link topology. Therefore, the core call chain topology can be mined from top to bottom, the dependency relationship of a service system is understood from the overall perspective by means of a backbone network, the call chain topology can be effectively extracted and simplified, and key services and main call paths are highlighted.

Description

Technical Field

[0001] The present application relates to the technical field of computer network architecture, and in particular to a method, apparatus, computer device, computer-readable storage medium and computer program product for generating a core call link topology. Background Art

[0002] With the development of microservice technology in backend systems, a large number of business applications have been refined into more sophisticated microservices. Microservice architecture can improve the flexibility, scalability and versatility of the system. In view of the characteristics of microservice architecture, call chain tracing technology is introduced to help outline the complete dependency path in the business system, making it easier to view the call chain of the entire request.

[0003] In traditional technology, a call chain directed graph is usually generated according to the call sequence of each service in the business interface. When a new transaction is triggered, the business interface will generate a new call chain directed graph. Each directed graph node is a service, and each service can be described by multiple features. Based on these features, the cosine similarity of the two directed graphs is compared to determine the difference between the old transaction call link and the new transaction call link at the call layer. If the difference is small, the call layer of the new transaction call link and the call layer of the old transaction call link can be merged to obtain an updated call link topology.

[0004] However, the above method does not take into account the network characteristics of the call chain, nor does it take into account the role played by each service node in the entire call chain network, resulting in the generated call chain topology being inaccurate. Summary of the invention

[0005] Based on this, it is necessary to provide a core call link topology generation method, device, computer equipment, computer-readable storage medium and computer program product that can generate a more effective and accurate core call chain in response to the above technical problems.

[0006] In a first aspect, the present application provides a core call link topology generation method, comprising:

[0007] According to the number of calls, the call links of the same service are aggregated into a weighted directed graph;

[0008] Generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0009] Comparing the edges in the weighted directed graph with the edges in the random network, obtaining the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0010] The edges in the weighted directed graph are filtered according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0011] In one embodiment, aggregating the call links of the same service into a weighted directed graph according to the number of calls includes:

[0012] Generate a directed graph of call links based on the calling sequence of services in the same business context.

[0013] Counting the number of times the downstream node in each call link is called by the upstream node; wherein the call link includes the call relationship between the upstream node and the downstream node;

[0014] The weight of the corresponding call link is determined according to the number of times the downstream node is called; the higher the number of times the downstream node is called, the higher the weight of the corresponding call link;

[0015] The weights of the call links are integrated into the generated directed graph to obtain a weighted directed graph.

[0016] In one embodiment, comparing the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network includes:

[0017] Assuming that the weights of the edges in the weighted directed graph are uniformly distributed, for each node in the weighted directed graph, the outbound edge weight and the inbound edge weight of each edge connected to the node are determined respectively;

[0018] Determine the ratio of the outgoing edge weight of each edge to the total outgoing edge weight of the node, and the ratio of the incoming edge weight of each edge to the total incoming edge weight of the node;

[0019] Determine the significance probability of each edge according to the out-degree of the node, the in-degree of the node, the definite integral of the uniform distribution assumption, the ratio of the out-edge weight of each edge to the total out-edge weight of the node, and the ratio of the in-edge weight of each edge to the total in-edge weight of the node;

[0020] According to the significance probability of each edge, the percentile of the significance probability of each edge in the entire random network is determined.

[0021] In one embodiment, determining the percentile of the significance probability of each edge in the entire random network according to the significance probability of each edge includes:

[0022] Set the significance probability of a given edge;

[0023] Comparing the significance probabilities of all edges in the weighted directed graph with the significance probability of the given edge;

[0024] Obtain the number of all edges whose significance probability is not greater than the given edge;

[0025] The ratio of the number of all edges whose significance probability is not greater than the given edge to the total number of edges in the weighted directed graph is taken as the percentile of the significance probability of each edge in the entire random network.

[0026] In one embodiment, before filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network, the method further includes:

[0027] According to business requirements, a percentile filter value or a percentile filter range for filtering the weighted directed graph is determined.

[0028] In one embodiment, filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology includes:

[0029] filtering the edges in the weighted directed graph according to the percentile filter value, retaining only the edges whose percentile of significance probability in the entire random network is equal to the percentile filter value, or

[0030] According to the percentile screening range, only the edges whose percentile of significance probability in the entire random network is within the percentile screening range are retained to obtain the core call link topology.

[0031] In a second aspect, the present application also provides a core call link topology generation device, the device comprising:

[0032] The aggregation module is used to aggregate the call links of the same service into a weighted directed graph according to the number of calls;

[0033] A random network generation module, used to generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0034] A comparison module, used for comparing the edges in the weighted directed graph with the edges in the random network, to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0035] The filtering module is used to filter the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] According to the number of calls, the call links of the same service are aggregated into a weighted directed graph;

[0038] Generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0039] Comparing the edges in the weighted directed graph with the edges in the random network, obtaining the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0040] The edges in the weighted directed graph are filtered according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0042] According to the number of calls, the call links of the same service are aggregated into a weighted directed graph;

[0043] Generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0044] Comparing the edges in the weighted directed graph with the edges in the random network, obtaining the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0045] The edges in the weighted directed graph are filtered according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0046] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0047] According to the number of calls, the call links of the same service are aggregated into a weighted directed graph;

[0048] Generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0049] Comparing the edges in the weighted directed graph with the edges in the random network, obtaining the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0050] The edges in the weighted directed graph are filtered according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0051] The core call link topology generation method, device, computer equipment, computer readable storage medium and computer program product, according to the number of calls, aggregate the call links of the same business into a weighted directed graph; thus, according to the call sequence of services under the same business, the call link network associated with the number of calls can be obtained, which is convenient for the subsequent extraction of core call links. A random network of the original call link directed graph is generated based on the zero model, and the random network contains the same nodes and edges as the original call link directed graph; the edges in the weighted directed graph are compared with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network; thus, the influence of randomly generated features on the call link can be eliminated, making the core call link extracted later more effective and accurate. According to the percentile of the significance probability of each edge in the entire random network, the edges in the weighted directed graph are filtered to obtain the core call link topology. Thus, it is possible to focus on top-down mining of the core call chain topology, understand the dependency of the business system from a holistic perspective with the help of the backbone network, effectively extract and simplify the call chain topology, and highlight key services and main call paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of the generation principle of a weighted directed graph in one embodiment;

[0054] Figure 2 A schematic diagram of a process of a core calling link topology generation method in one embodiment;

[0055] Figure 3 A schematic diagram of the principle of a core call link topology generation method in one embodiment;

[0056] Figure 4 A schematic diagram of a process of a core calling link topology generation method in another embodiment;

[0057] Figure 5 A structural block diagram of a core call link topology generating device in one embodiment;

[0058] Figure 6 It is a structural block diagram of a core call link topology generating device in another embodiment;

[0059] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] In order to facilitate understanding of the technical solutions in the embodiments of the present application, the professional terms appearing in the embodiments of the present application are briefly described as follows:

[0062] Backbone Network: In network science, it refers to the most important and critical connected part or subset of a network, usually composed of a small number of nodes and the connections between them. Backbone networks are often used to highlight the core structure and key components of a network in network analysis and visualization.

[0063] Call Chain Tracing is a technology used to analyze and monitor the execution process and call relationships of distributed systems or applications. It can provide detailed visualization and analysis of system performance and behavior by recording and tracing the transfer path and call links of requests or transactions in the system.

[0064] Call Chain Topology: refers to the topological structure formed by the mutual calling relationship between functions or services in a computer system or software application. It describes the calling sequence and dependency relationship between functions or services.

[0065] Weighted Directed Graph: A directed graph in which each edge is assigned a weight or cost.

[0066] Null Model: In network science and complex network analysis, it is a random model or benchmark model used to compare and evaluate the actual network structure. The purpose of the Null Model is to provide a reference benchmark so that researchers can determine whether the observed characteristics in the network are due to randomness or reflect real structural characteristics.

[0067] For example, Figure 1 FIG. 1 is a schematic diagram of the generation principle of a weighted directed graph in an embodiment, such as Figure 1 As shown in the figure, in order to analyze the dependency path of the business system from a global perspective, a group of call links under the same business field can be aggregated into a directed graph. By analyzing the shape and flow of this graph, the key nodes and dependency paths can be quickly sorted out, and this is the call chain link topology. Figure 1 As shown in the figure, application a has complex dependencies with multiple downstream applications (b, c, d, e), and the paths of each call are different. In order to sort out the complete call dependencies of application a, multiple call chains are aggregated into a weighted directed graph according to the number of calls. By further mining the directed graph, the core applications and key paths in the call chain can be obtained. It should be noted that in actual production environments, link topology diagrams are often more complex than Figure 1 The presentation is more complex, but the principle of analyzing call dependencies is the same.

[0068] In this embodiment, when the business system is very complex, it is necessary to simplify the weighted directed graph of the intricate call links and mine the core call links, so that when executing the business, the core and non-core links can be protected through a hierarchical strategy, thereby improving the stability and availability of the entire business system while reducing labor costs.

[0069] In an exemplary embodiment, Figure 2 As shown, a core call link topology generation method is provided. The method in this embodiment may include the following steps 201 to 204.

[0070] Step 201: Aggregate the call links of the same service into a weighted directed graph according to the number of calls.

[0071] In this embodiment, it can be combined Figure 1 As shown in the figure, firstly sort out all the call links of application a, and then count the number of calls from the upstream node to the downstream node. For example, the number of times a calls b is 3, the number of times a calls c is 7, the number of times c calls d is 3, and the number of times c calls e is 4, thus obtaining the number of calls of each call link in the directed graph. The more the number of calls, the higher the degree of dependence of the business system on the call link. In order to reflect such a relationship in the figure, the weight of each call link is set according to the number of calls.

[0072] Exemplarily, according to the calling sequence of services in the same business scenario, a directed graph of call links is generated, and the number of times the downstream node in each call link is called by the upstream node is counted respectively; wherein the call link includes the call relationship between the upstream node and the downstream node; according to the number of times the downstream node is called, the weight of the corresponding call link is determined; wherein, the higher the number of times the downstream node is called, the higher the weight of the corresponding call link; the weight of the call link is integrated into the generated directed graph to obtain a weighted directed graph.

[0073] Step 202: Generate a random network of the original call link directed graph based on the zero model.

[0074] The random network contains the same nodes and edges as the original call link directed graph.

[0075] In this embodiment, the original call link directed graph refers to an unweighted directed graph, and its construction method is generally: according to the call order of services under the same business field, a directed graph of the call link is generated. For example, the upstream node and the downstream node can be determined according to the service call situation of the business, and then the connection relationship between all nodes is composed of a directed graph. A random network (which is actually a node network similar to a directed graph) is generated by the zero model based on the original call link. The features generated by randomness in the weighted directed graph can be filtered out through the random network, so that the true features of the network results can be extracted.

[0076] Step 203 : Compare the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network.

[0077] In this embodiment, assuming that the weights of the edges in the weighted directed graph are uniformly distributed, for each node in the weighted directed graph, the outbound edge weight and the inbound edge weight of each edge connected to the node are determined respectively; the ratio of the outbound edge weight of each edge to the total outbound edge weight of the node, and the ratio of the inbound edge weight of each edge to the total inbound edge weight of the node are determined; the significance probability of each edge is determined according to the out-degree of the node, the in-degree of the node, the definite integral of the uniform distribution assumption, the ratio of the outbound edge weight of each edge to the total outbound edge weight of the node, and the ratio of the inbound edge weight of each edge to the total inbound edge weight of the node; according to the significance probability of each edge, the percentile of the significance probability of each edge in the entire random network is determined.

[0078] For example, based on the local significance theory, we determine whether the weight of a particular edge is significant for all connections of a node. By assuming that the weight of the edge is evenly distributed, we calculate the significance probability of each edge under this distribution, thereby identifying those significant edges that contribute significantly to the network structure.

[0079] For example, for each node i, calculate its outgoing edge weight w ij and the incoming edge weight w ji The total weight and total input weight Ratio:

[0080]

[0081] Among them, j represents the jth edge connected to node i, Indicates the edge weight w ij Total output weight The proportion of represents the incoming edge weight w ji Total Incoming Weight proportion.

[0082] Assuming that edge weights are uniformly distributed, calculate the significance of each edge and Indicates edge significance, Indicates the significance of the input edge. The lower the significance, the less the edge weight is a random value and the stronger the significance.

[0083] For the outgoing edge significance, the calculation formula is as follows:

[0084]

[0085] in, is the out-degree of node i, and * represents the multiplication sign.

[0086] For the in-edge significance, the calculation formula is as follows:

[0087]

[0088] in, is the in-degree of node i.

[0089] Among them, DI represents the definite integral of the probability density function (uniform distribution assumption), and its calculation formula is as follows:

[0090]

[0091] Among them: x represents the weight variable, k represents the angle variable.

[0092] The above formula is derived from the cumulative distribution function, which is used to evaluate the probability of significance of an edge weight under the uniform distribution assumption. This significance reflects the relative importance of the edge in the network.

[0093] Exemplarily, the significance probability of a given edge is set; the significance probabilities of all edges in the weighted directed graph are compared with the significance probability of the given edge; the number of all edges whose significance probability is not greater than the given edge is obtained; and the ratio of the number of all edges whose significance probability is not greater than the given edge to the total number of edges in the weighted directed graph is used as the percentile of the significance probability of each edge in the entire random network.

[0094] In this embodiment, the calculation formula for the percentile of the significance probability of each edge in the entire random network is as follows:

[0095] Percentile of edge significance = (number of edges not greater than a given edge significance / total number of edges) * 100%

[0096] Step 204 , filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0097] For example, Figure 3 As shown in FIG. 1 , the whole principle of extracting the core call chain is given. First, the call chain data is obtained, then a service call directed graph is constructed based on the call chain data, and finally the core call chain is extracted using the backbone network algorithm. Figure 3 In the embodiment, the backbone network algorithm is the method for filtering the call links in step 203 and step 204.

[0098] In this embodiment, based on the percentile of edge significance obtained in step 203, the edges in the weighted directed graph are screened, and some unimportant edges or edges with low contribution are removed to obtain the core call link topology.

[0099] In the above core call link topology generation method, the call links of the same business are aggregated into a weighted directed graph according to the number of calls; thus, the call link network associated with the number of calls can be obtained according to the call sequence of services under the same business, which is convenient for the subsequent extraction of core call links. A random network of the original call link directed graph is generated based on the zero model, and the random network contains the same nodes and edges as the original call link directed graph; the edges in the weighted directed graph are compared with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network; thus, the influence of randomly generated features on the call link can be eliminated, making the core call link extracted later more effective and accurate. According to the percentile of the significance probability of each edge in the entire random network, the edges in the weighted directed graph are filtered to obtain the core call link topology. Thus, it is possible to focus on top-down mining of the core call chain topology, understand the dependency of the business system from a holistic perspective with the help of the backbone network, effectively extract and simplify the call chain topology, and highlight key services and main call paths.

[0100] In another exemplary embodiment, Figure 4 As shown, a core call link topology generation method is provided. The method in this embodiment may include the following steps 401 to 405.

[0101] Step 401, according to the number of calls, aggregate the call links of the same service into a weighted directed graph.

[0102] Step 402: Generate a random network of the original call link directed graph based on the zero model.

[0103] The random network contains the same nodes and edges as the original call link directed graph.

[0104] Step 403: compare the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network.

[0105] For the specific implementation process and technical effects of steps 401 to 403 in this embodiment, please refer to Figure 2 The relevant contents of steps 201 to 203 in the illustrated method embodiment will not be repeated here.

[0106] Step 404: Determine a percentile filter value or a percentile filter range for filtering the weighted directed graph according to business requirements.

[0107] In this embodiment, assuming that there are 100 service nodes and 540 weighted call edges, the calculated significance and percentile of each edge are shown in Table 1.

[0108] Table 1

[0109] Percentile Edge saliency 0.00 0.0000 0.10 0.0189 0.20 0.0559 0.30 0.0948 0.40 0.1570 0.50 0.2125 0.60 0.3238 0.70 0.4777 0.80 0.6972 0.90 0.9783

[0110] For the above Table 1, a percentile screening value or a percentile screening range for filtering the weighted directed graph may be set. Exemplarily, a difference filter is made based on the percentile screening value, and the call link of the weighted directed graph is filtered by the filter.

[0111] Step 405 , filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0112] In this embodiment, the edges in the weighted directed graph are filtered according to the percentile filtering value, and only the edges whose percentile of significance probability in the entire random network is equal to the percentile filtering value are retained, or according to the percentile filtering range, only the edges whose percentile of significance probability in the entire random network is within the percentile filtering range are retained to obtain the core call link topology.

[0113] Exemplarily, as shown in Table 2, the number of edges and nodes remaining after filtering with different percentile filter values ​​are given.

[0114] Table 2

[0115] Difference Filter Number of edges Number of nodes Percentile of edge significance <= 0.50 266 96 Percentile of edge significance <= 0.30 146 79 Percentile of edge significance <= 0.10 24 24

[0116] According to Table 2 above, different core call link topologies can be obtained by setting different percentile filter values ​​or percentile filter ranges.

[0117] In this embodiment, the mined core call link topology (backbone network) can help identify services that play a key role in the call chain. These services are usually the core components of the system and are crucial to the performance and reliability of the system. After identifying key services, more resources can be allocated to these services on a priority basis to ensure their high availability and high performance. For example, in a complex call chain, the backbone network can help extract the main call path, simplify the call chain topology, and make the analysis more intuitive. Performance optimization can be focused on the main call path, which helps to improve the overall performance of the system. In addition, the simplified call chain topology is more suitable for visual display, helping developers and operation and maintenance personnel to better understand the system structure.

[0118] In addition, in distributed systems, the complexity of the call chain increases the difficulty of fault location. The backbone network can help simplify this process. After identifying key services and paths, fault recovery strategies can be formulated more efficiently to reduce system downtime.

[0119] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0120] Based on the same inventive concept, the embodiment of the present application also provides a core call link topology generation device for implementing the core call link topology generation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more core call link topology generation device embodiments provided below can refer to the limitations of the core call link topology generation method above, and will not be repeated here.

[0121] In an exemplary embodiment, Figure 5 As shown, a core call link topology generation device is provided, including: an aggregation module 501, a random network generation module 502, a comparison module 503 and a filtering module 504, wherein:

[0122] Aggregation module 501, used to aggregate the call links of the same service into a weighted directed graph according to the number of calls;

[0123] A random network generation module 502, used to generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph;

[0124] A comparison module 503 is used to compare the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network;

[0125] The filtering module 504 is used to filter the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

[0126] Exemplarily, the aggregation module 501 is specifically used to: generate a directed graph of call links according to the call sequence of services under the same business scenario, and count the number of times the downstream node in each call link is called by the upstream node; wherein the call link includes the call relationship between the upstream node and the downstream node; determine the weight of the corresponding call link according to the number of times the downstream node is called; wherein, the higher the number of times the downstream node is called, the higher the weight of the corresponding call link; and fuse the weight of the call link into the generated directed graph to obtain a weighted directed graph.

[0127] Exemplarily, the comparison module 503 is specifically used to: assuming that the weights of the edges in the weighted directed graph are uniformly distributed, for each node in the weighted directed graph, determine the outbound edge weight and the inbound edge weight of each edge connected to the node respectively; determine the ratio of the outbound edge weight of each edge to the total outbound edge weight of the node, and the ratio of the inbound edge weight of each edge to the total inbound edge weight of the node; determine the significance probability of each edge according to the out-degree of the node, the in-degree of the node, the definite integral of the uniform distribution assumption, the ratio of the outbound edge weight of each edge to the total outbound edge weight of the node, and the ratio of the inbound edge weight of each edge to the total inbound edge weight of the node; and determine the percentile of the significance probability of each edge in the entire random network according to the significance probability of each edge.

[0128] Exemplarily, determining the percentile of the significance probability of each edge in the entire random network based on the significance probability of each edge includes: setting the significance probability of a given edge; comparing the significance probability of all edges in the weighted directed graph with the significance probability of the given edge; obtaining the number of all edges whose significance probability is not greater than the given edge; and taking the ratio of the number of all edges whose significance probability is not greater than the given edge to the total number of edges in the weighted directed graph as the percentile of the significance probability of each edge in the entire random network.

[0129] In another exemplary embodiment, Figure 6 As shown, a core call link topology generation device is provided. Figure 5 Based on the device shown, it can also include: a determination module 505, which is used to determine the percentile screening value or percentile screening range used to filter the weighted directed graph according to business needs.

[0130] Exemplarily, the filtering module 504 is specifically used to: filter the edges in the weighted directed graph according to the percentile filtering value, and only retain the edges whose percentile of significance probability in the entire random network is equal to the percentile filtering value, or according to the percentile filtering range, only retain the edges whose percentile of significance probability in the entire random network is within the percentile filtering range, to obtain the core call link topology.

[0131] Each module in the above core call link topology generation device can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0132] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a core call link topology generation method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0133] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0136] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0139] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0140] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A core call link topology generation method, characterized in that: The method comprises: According to the number of calls, the call links of the same service are aggregated into a weighted directed graph; Generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph; Comparing the edges in the weighted directed graph with the edges in the random network, obtaining the percentile of the significance probability of each edge in the weighted directed graph in the entire random network; The edges in the weighted directed graph are filtered according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

2. The method according to claim 1, characterized in that According to the number of calls, the call links of the same service are aggregated into a weighted directed graph, including: Generate a directed graph of call links based on the calling sequence of services in the same business context. Counting the number of times the downstream node in each call link is called by the upstream node; wherein the call link includes the call relationship between the upstream node and the downstream node; The weight of the corresponding call link is determined according to the number of times the downstream node is called; the higher the number of times the downstream node is called, the higher the weight of the corresponding call link; The weights of the call links are integrated into the generated directed graph to obtain a weighted directed graph.

3. The method according to claim 1, characterized in that The step of comparing the edges in the weighted directed graph with the edges in the random network to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network comprises: Assuming that the weights of the edges in the weighted directed graph are uniformly distributed, for each node in the weighted directed graph, the outbound edge weight and the inbound edge weight of each edge connected to the node are determined respectively; Determine the ratio of the outgoing edge weight of each edge to the total outgoing edge weight of the node, and the ratio of the incoming edge weight of each edge to the total incoming edge weight of the node; Determine the significance probability of each edge according to the out-degree of the node, the in-degree of the node, the definite integral of the uniform distribution assumption, the ratio of the out-edge weight of each edge to the total out-edge weight of the node, and the ratio of the in-edge weight of each edge to the total in-edge weight of the node; According to the significance probability of each edge, the percentile of the significance probability of each edge in the entire random network is determined.

4. The method according to claim 3, characterized in that Determining the percentile of the significance probability of each edge in the entire random network according to the significance probability of each edge includes: Set the significance probability of a given edge; Comparing the significance probabilities of all edges in the weighted directed graph with the significance probability of the given edge; Obtain the number of all edges whose significance probability is not greater than the given edge; The ratio of the number of all edges whose significance probability is not greater than the given edge to the total number of edges in the weighted directed graph is taken as the percentile of the significance probability of each edge in the entire random network.

5. The method according to any one of claims 1 to 4, characterized in that: Before filtering the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network, the method further comprises: According to business requirements, a percentile filter value or a percentile filter range for filtering the weighted directed graph is determined.

6. The method according to claim 5, characterized in that According to the percentile of the significance probability of each edge in the entire random network, the edges in the weighted directed graph are filtered to obtain the core call link topology, including: filtering the edges in the weighted directed graph according to the percentile filter value, retaining only the edges whose percentile of significance probability in the entire random network is equal to the percentile filter value, or According to the percentile screening range, only the edges whose percentile of significance probability in the entire random network is within the percentile screening range are retained to obtain the core call link topology.

7. A core call link topology generation device, characterized in that: The device comprises: The aggregation module is used to aggregate the call links of the same service into a weighted directed graph according to the number of calls; A random network generation module, used to generate a random network of the original call link directed graph based on the zero model, wherein the random network contains the same nodes and edges as the original call link directed graph; A comparison module, used for comparing the edges in the weighted directed graph with the edges in the random network, to obtain the percentile of the significance probability of each edge in the weighted directed graph in the entire random network; The filtering module is used to filter the edges in the weighted directed graph according to the percentile of the significance probability of each edge in the entire random network to obtain the core call link topology.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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