Knowledge graph construction method and device for call chain data, equipment and medium

Abstract

The invention discloses a knowledge graph construction method and device for call chain data, equipment and a medium. The method comprises the steps of obtaining a basic call chain data set corresponding to a target distributed system, and performing data grouping on the basic call chain data set based on a target identifier corresponding to basic call chain data to obtain a basic call chain data array; performing data enhancement processing on the basic call chain data array based on a preset multi-dimensional data processing rule to obtain a target call chain data array; performing semantic reasoning on the target call chain data array based on a target semantic model and a preset frequent item set algorithm to determine target business semantic information; and on the basis of a preset knowledge graph construction rule, performing graph construction on the target call chain data array and the target business semantic information to obtain a target knowledge graph. According to the technical scheme, precious knowledge which is contained in the call chain data and can reflect the actual operation service logic of the system can be effectively extracted, and the working efficiency of an automatic test task is improved.

Description

Technical Field

[0001] This invention relates to the field of knowledge graph construction technology, and in particular to a method, apparatus, device and medium for constructing a knowledge graph using call chain data. Background Technology

[0002] With the increasing adoption of information technology (IT) service architectures centered on microservices, containerization, and cloud-native technologies, the call relationships between services within systems are becoming increasingly complex. Observability of distributed systems has become crucial for ensuring system stability and performance. Call chain tracing has thus become one of the core pillars of observability.

[0003] In existing technologies, call chain data is typically tested using two methods: traffic replay and fault injection. Traffic replay testing captures real online traffic, anonymizes it, and replays it in a test environment to verify whether the system behavior meets expectations. Fault injection testing artificially injects faults such as delays and anomalies into specific nodes of the call chain to observe the system's fault tolerance and self-healing capabilities.

[0004] However, existing testing methods treat the call chain merely as a purely technical topology path consisting of service nodes and call edges, limiting their analysis to the physical or logical dependencies of services. The system cannot automatically infer the underlying business semantics from frequent call patterns and stable execution sequences, making it impossible to directly correlate call chain data with business requirements and processes, thus restricting the depth of alignment between testing activities and business value.

[0005] Therefore, how to effectively extract the valuable knowledge contained in the call chain data that reflects the actual business logic of the system and improve the efficiency of automated testing tasks is an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a method, apparatus, device, and medium for constructing a knowledge graph from call chain data, which can solve the problem of low extraction efficiency of valuable knowledge contained in call chain data that reflects the actual business logic of the system in the prior art.

[0007] According to one aspect of the present invention, a method for constructing a knowledge graph of call chain data is provided, comprising: Obtain the basic call chain data set corresponding to the target distributed system, and group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array; The basic call chain data array is augmented based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation. Based on the target semantic model and the preset frequent itemset algorithm, semantic reasoning is performed on the target call chain data array to determine the target business semantic information corresponding to the target call chain data array; Based on preset knowledge graph construction rules, a graph is constructed from the target call chain data array and the target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set.

[0008] According to another aspect of the present invention, a knowledge graph construction apparatus for calling chain data is provided, comprising: The data acquisition module is used to acquire the basic call chain data set corresponding to the target distributed system, and to group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array; The data augmentation module is used to perform data augmentation processing on the basic call chain data array based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation. The semantic reasoning module is used to perform semantic reasoning on the target call chain data array based on the target semantic model and the preset frequent itemset algorithm, and to determine the target business semantic information corresponding to the target call chain data array. The graph construction module is used to construct a graph of the target call chain data array and the target business semantic information based on preset knowledge graph construction rules, so as to obtain the target knowledge graph corresponding to the basic call chain data set.

[0009] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the knowledge graph construction method for call chain data according to any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the knowledge graph construction method for call chain data as described in any embodiment of the present invention.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the knowledge graph construction method for call chain data as described in any embodiment of the present invention.

[0012] The technical solution of this invention involves obtaining a basic call chain data set corresponding to a target distributed system, and grouping the basic call chain data set based on the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array. Then, data augmentation processing is performed on the basic call chain data array based on preset multi-dimensional data processing rules to obtain a data-augmented target call chain data array. Further, semantic reasoning is performed on the target call chain data array based on a target semantic model and a preset frequent itemset algorithm to determine the target business semantic information corresponding to the target call chain data array. Finally, a graph is constructed on the target call chain data array and the target business semantic information based on preset knowledge graph construction rules to obtain a target knowledge graph corresponding to the basic call chain data set. By mining the call chain data at the business semantic level and performing data augmentation, the constructed knowledge graph can elevate the system from the "observable" level to the "understandable" level. This transforms the testing paradigm from passive, interface-level verification that relies on human experience to proactive, business scenario-level reasoning and assurance driven by data and knowledge. It solves the problem of low efficiency in extracting valuable knowledge from the call chain data that reflects the actual business logic of the system, which is present in existing technologies, and improves the efficiency of subsequent automated testing tasks.

[0013] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a knowledge graph construction method for call chain data provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a knowledge graph construction method for call chain data provided in Embodiment 2 of the present invention; Figure 3This is a schematic diagram of a knowledge graph construction device for call chain data provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the knowledge graph construction method for call chain data according to embodiments of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "objective," "basic," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] Example 1 Figure 1 This is a flowchart of a method for constructing a knowledge graph of call chain data according to Embodiment 1 of the present invention. This embodiment is applicable to the case of knowledge extraction from call chain data. The method can be executed by a knowledge graph construction device for call chain data, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the basic call chain data set corresponding to the target distributed system, and group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array.

[0019] In this context, a distributed system refers to a large software system composed of components distributed across different computers. Components may include services or databases. Typically, the components in a distributed system can communicate and coordinate through a network to jointly complete a common task. For example, taking a logistics system as an example, each service in the distributed system, such as user service, order service, and payment service, is responsible for a specific duty. Services call each other through Application Programming Interfaces (APIs) to complete complex business processes. The target distributed system can refer to the distributed system selected for testing. Typically, the target distributed system can be determined based on actual application requirements; this embodiment of the invention does not impose specific limitations on this. Call chain data refers to data used to record the complete flow path and details between all relevant services and components in the distributed system for an end-to-end request, such as a user clicking a purchase button. Typically, call chain data uses traces as the basic unit, with each trace containing multiple spans. A trace can represent a complete distributed request and has a globally unique trace identifier (TraceID). A span can represent a single operation within a service or component. Typically, a Span can include a unique identifier (SpanID), the parent Span ID (ParentSpanId), the Trace ID (TraceID), the operation name (Name), such as the interface name or method name, the start timestamp (StartTime), the end timestamp (EndTime), the status code (Status), and key event logs (Events). Basic call chain data can refer to the initially collected, unprocessed call chain data. For example, in this embodiment of the invention, basic call chain data can include complete Trace and Span data. This includes, for example, service identifiers, method information, time information, relationship information, tag information, and business parameters. The service identifier includes the service name, instance ID, and Internet Protocol (IP) port; the method information includes the class name, method name, and operation name; the time information includes the start time, end time, and duration; the relationship information includes the Trace ID, Parent Span ID, and Span ID; the tag information includes the Hypertext Transfer Protocol (HTTP) method, path, status code, and error flag; and the business parameters include request parameters and response results. A basic call chain data set can refer to a collection of basic call chain data corresponding to the same distributed system over a period of time. A target identifier can refer to identification information used to distinguish call chain data from different call paths. For example, a target identifier can be an operation name. A call chain data array can refer to a data combination consisting of call chain data corresponding to the same target identifier.Typically, one target identifier corresponds to one call chain data array. The base call chain data array can refer to the call chain data array corresponding to the base call chain data set.

[0020] S120. Perform data augmentation processing on the basic call chain data array based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation processing.

[0021] Among them, the preset multidimensional data processing rules can refer to the pre-defined rules used to limit the data augmentation process. The target call chain data array can refer to the call chain data array obtained after performing data augmentation processing on the basic call chain data array.

[0022] In an optional implementation, preset multidimensional data processing rules are included, such as: preset call pattern enhancement rules, preset parameter pattern enhancement rules, preset temporal relationship enhancement rules, and preset anomaly pattern enhancement rules. Preset call pattern enhancement rules can refer to pre-defined algorithmic rules used to identify, quantify, and classify stable call combinations and sequences between services. Typically, preset call pattern enhancement rules can determine the classification information of stable call combinations between services. Preset parameter pattern enhancement rules can refer to pre-defined inductive rules used for automated analysis of the value range, format, type, and logical constraints between interface parameters. Typically, preset parameter pattern enhancement rules can help understand the business meaning and effective boundaries of data. Preset temporal relationship enhancement rules can refer to pre-defined logical and time window rules used to accurately determine the type of temporal constraints between spans that transcend parent-child relationships. Typically, preset temporal relationship enhancement rules can reveal complex runtime dependencies such as synchronous or asynchronous operations. Preset anomaly pattern enhancement rules can refer to pre-defined rules and classification systems used for in-depth classification, root cause induction, and impact scope association of failed spans. Typically, preset anomaly pattern enhancement rules can go beyond binary labels to understand the business essence of failures.

[0023] S130. Based on the target semantic model and the preset frequent itemset algorithm, perform semantic reasoning on the target call chain data array to determine the target business semantic information corresponding to the target call chain data array.

[0024] Semantic models can refer to specially trained artificial intelligence models capable of understanding human language, including technical terms and business vocabulary, and using this understanding for reasoning and classification. Target semantic models can refer to machine learning models specifically pre-trained or fine-tuned for IT architecture, microservices, and business domain text. Typically, target semantic models can infer the functional role of a service within the overall business architecture based on the semantics and context of service and method names. Pre-set frequent itemset algorithms can refer to pre-trained data mining algorithms. Typically, these algorithms automatically identify frequently occurring service call combinations from large amounts of data. Business semantic information refers to the meaning, purpose, and rules hidden behind technical call data, related to specific business activities and logic. Target business semantic information refers to the business semantic information corresponding to the target call chain data array. For example, target business semantic information can include service roles, business processes, and business constraint rules.

[0025] S140. Based on the preset knowledge graph construction rules, construct a graph of the target call chain data array and the target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set.

[0026] The preset knowledge graph construction rules refer to pre-defined rules that define the knowledge graph construction process. For example, these rules may include connection rules between nodes. A knowledge graph can be a multi-dimensional, multi-relational, attribute-rich, and reasoning-supporting structured knowledge base. Typically, knowledge graphs can elevate a system from observable to understandable and reasonable levels. The target knowledge graph can refer to the knowledge graph corresponding to the basic call chain data set.

[0027] The technical solution of this invention involves obtaining a basic call chain data set corresponding to a target distributed system, and grouping the basic call chain data set based on the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array. Then, data augmentation processing is performed on the basic call chain data array based on preset multi-dimensional data processing rules to obtain a data-augmented target call chain data array. Further, semantic reasoning is performed on the target call chain data array based on a target semantic model and a preset frequent itemset algorithm to determine the target business semantic information corresponding to the target call chain data array. Finally, a graph is constructed on the target call chain data array and the target business semantic information based on preset knowledge graph construction rules to obtain a target knowledge graph corresponding to the basic call chain data set. By mining the call chain data at the business semantic level and performing data augmentation, the constructed knowledge graph can elevate the system from the "observable" level to the "understandable" level. This transforms the testing paradigm from passive, interface-level verification that relies on human experience to proactive, business scenario-level reasoning and assurance driven by data and knowledge. It solves the problem of low efficiency in extracting valuable knowledge from the call chain data that reflects the actual business logic of the system, which is present in existing technologies, and improves the efficiency of subsequent automated testing tasks.

[0028] Example 2 Figure 2 This is a flowchart of a method for constructing a knowledge graph of call chain data according to Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Specifically, this embodiment refines the step of "constructing a knowledge graph based on preset knowledge graph construction rules for the target call chain data array and target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set". Specifically, it may include: using the target operation name in the basic call chain data array as the target graph node, and combining the target call chain data array and target business semantic information as a set of node attributes; determining the target call relationship edge, target data flow edge, and target dependency relationship edge corresponding to the basic call chain data array based on the set of node attributes; and connecting the target graph nodes based on the target call relationship edge, target data flow edge, and target dependency relationship edge to obtain the target knowledge graph corresponding to the basic call chain data set. Figure 2 As shown, the method includes: S210. Obtain the basic call chain data set corresponding to the target distributed system, and group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array.

[0029] S220. Based on the preset call mode enhancement rules, perform statistical analysis on the call mode of the basic call chain data array to determine the target call mode information corresponding to the basic call chain data array.

[0030] The call pattern information can refer to the statistical results obtained after statistical analysis of the call chain data array using preset call pattern enhancement rules. The target call pattern information can refer to the call pattern information corresponding to the basic call chain data array. For example, if the preset call pattern enhancement rules include the call frequency and call stability of the statistical call chain data array, and the call chain corresponding to the basic call chain data array is A->B->C, then the number of times A->B is called each day can be counted as the call frequency, and the frequency of successful A->B calls can be calculated as the call stability.

[0031] S230. Based on the preset parameter pattern enhancement rules, perform statistical analysis on the parameter patterns of the basic call chain data array to determine the target parameter pattern information corresponding to the basic call chain data array.

[0032] Here, parameter pattern information can refer to the statistical results obtained after statistical analysis of the parameter patterns of the call chain data array using preset parameter pattern enhancement rules. Target parameter pattern information can refer to the parameter pattern information corresponding to the basic call chain data array. For example, taking a preset parameter pattern enhancement rule that includes parameter distribution analysis, parameter type inference, and parameter correlation analysis, parameter distribution analysis can be completed by statistically analyzing the frequency of parameter occurrence, data range, and whether each method call parameter is mandatory; parameter type can be inferred through value patterns; and parameter correlation analysis can be completed by discovering parameters passed across services, such as the return value of A being the input of B.

[0033] S240. Based on preset temporal relationship enhancement rules, perform statistical analysis on the temporal relationship of the basic call chain data array to determine the target temporal relationship information corresponding to the basic call chain data array.

[0034] The temporal relationship information refers to the statistical results obtained after statistical analysis of the temporal relationship of the call chain data array using preset temporal relationship enhancement rules. The target temporal relationship information refers to the temporal relationship information corresponding to the basic call chain data array. For example, taking the preset temporal relationship enhancement rules that include synchronous or asynchronous identification and timeout pattern discovery as an example, synchronous or asynchronous calls can be identified by response time and call relationship. For example, if span A calls span B, and A finishes earlier than B, it is asynchronous; if A finishes later than B, it is synchronous. The timeout pattern is determined by judging the response time between the two calls through a preset timeout threshold.

[0035] S250. Based on preset exception mode enhancement rules, perform statistical analysis on the exception modes of the basic call chain data array to determine the target exception mode information corresponding to the basic call chain data array.

[0036] Here, "abnormal pattern information" can refer to the statistical results obtained after statistical analysis of the abnormal patterns in the call chain data array using preset abnormal pattern enhancement rules. "Target abnormal pattern information" can refer to the abnormal pattern information corresponding to the basic call chain data array. For example, taking the preset abnormal pattern enhancement rules that include error type clustering and failure propagation analysis as an example, failure types can be classified based on status codes and error information, and the propagation path of failed calls in the call chain can be analyzed. For example, if C fails in A->B->C, it may cause both A and B to fail, or it may cause B to fail and A not to fail.

[0037] S260. Summarize and process the target call mode information, target parameter mode information, target timing relationship information and target anomaly mode information corresponding to the basic call chain data array to obtain the target call chain data array after data augmentation.

[0038] Specifically, after obtaining the basic call chain data arrays, preset exception mode enhancement rules can be used to statistically analyze the exception modes of the basic call chain data arrays, obtaining target call mode information including specific call frequencies and call success rates. Preset parameter mode enhancement rules can be used to statistically analyze the parameter modes of the basic call chain data arrays, obtaining target parameter mode information including the frequency of parameter occurrence, data range, whether it is required, parameter type, and parameter association information for each method call. Preset timing relationship enhancement rules can be used to statistically analyze the timing relationships of the basic call chain data arrays, obtaining a set of spans including asynchronous calls, a set of spans including synchronous calls, spans with timeout modes, and their corresponding specific timeout modes. Preset exception mode enhancement rules can be used to statistically analyze the exception modes of the basic call chain data arrays, obtaining error types and the propagation path of failures in the call chain. This provides a valid foundation for subsequent operations.

[0039] It is worth noting that, in this embodiment of the invention, the execution order of the preset call mode enhancement rule, the preset parameter mode enhancement rule, the preset timing relationship enhancement rule, and the preset exception mode enhancement rule can be executed in parallel, and this embodiment does not impose specific limitations on this.

[0040] S270. Based on the target semantic model, perform service role inference on the target call chain data array to determine the target business role corresponding to the target call chain data array.

[0041] Here, "business role" refers to information used to qualitatively categorize the role of a service method in business activities. "Target business role" refers to the business role corresponding to the target call chain data array. For example, the target business role could be user query, registration, or data retrieval.

[0042] S280. Based on a preset frequent itemset algorithm, the target call chain data array is used to identify the business process and determine the target business process corresponding to the target call chain data array.

[0043] In this context, a business process can refer to information used to identify and name the complete business objective achieved by a series of ordered service calls. A target business process can refer to the business processes that frequently appear in the target call chain data array.

[0044] S290. Based on preset business constraint rules, perform constraint judgment on the target call chain data array to determine the target constraint result corresponding to the target call chain data array.

[0045] Among them, preset business constraint rules can refer to pre-defined rules used to limit the data mining process of business processes. For example, preset business constraint rules can include call constraint rules and data constraint rules. Call constraint rules can refer to rules used to represent the business logic order and conditional dependencies of interactions between services. For example, call constraint rules can be determined according to actual business applications. For instance, if serviceA fails to call serviceB, it will most likely call serviceC; therefore, calling serviceC when serviceA fails to call serviceB can be used as a call constraint rule. Data constraint rules can refer to rules used to represent the validity rules and relationships that parameters must adhere to at the business level. For example, data constraints can be derived from parameter patterns. For example, order_id always starts with 0, and the amount parameter is always greater than 0. Constraint results can refer to the results obtained after constraining the target call chain data array using preset business constraint rules. Target constraint results can refer to the constraint results corresponding to the target call chain data array.

[0046] S2100, Combine and process the target business role, target business process and target constraint results to obtain the target business semantic information corresponding to the target call chain data array.

[0047] Specifically, after obtaining the target call chain data array, the service names and method names in the array can be input into the target semantic model. The target semantic model then infers the target business role corresponding to the target call chain data array. A pre-defined frequent itemset algorithm is used to mine common business processes within the target call chain data array. Pre-defined business constraint rules are then used to derive business constraints and data constraints. This provides a solid foundation for subsequent operations.

[0048] It is worth noting that, in this embodiment of the invention, the execution order of the target semantic model, the preset frequent itemset algorithm, and the preset business constraint rules can be executed in parallel, and this embodiment does not impose specific limitations on this.

[0049] S2110. Take the target operation name in the basic call chain data array as the target graph node, and combine the target call chain data array and the target business semantic information as the node attribute set.

[0050] The target operation name can refer to the operation name matched by the underlying call chain data array. A graph node can refer to the smallest unit of code that can be independently invoked and performs a specific function. Typically, a graph node represents an entity in the graph. The target graph node can refer to the graph node corresponding to the underlying call chain data array. Node attributes can refer to the set of key-value pairs attached to a graph node, used to describe the comprehensive characteristics of that graph node. Typically, node attributes can include the original span metadata, the data-enhanced target call chain data array, and the inferred business semantics. The node attribute set can refer to the set of node attributes corresponding to the same underlying call chain data array. For example, the node attribute set can include basic attributes, business semantics, input patterns, output patterns, exception characteristics, and contextual characteristics. Basic attributes can include service name, method name, and call parameters. Business semantics can include the inferred business role. Input patterns can include parameter name, type, value range, and mandatory requirement. Output patterns can include return fields and types. Exception characteristics can include error type and propagation method. Contextual characteristics can include common upstream services and common downstream services.

[0051] S2120. Based on the node attribute set, determine the target call relationship edge, target data flow edge, and target dependency relationship edge corresponding to the basic call chain data array.

[0052] In this context, a call relationship edge can refer to a technical call fact edge established based on the parent Span ID link between Spans. Typically, a call relationship edge describes a technically direct call initiated by one service method to another. Call relationship edges can reflect information such as direction, call frequency, success rate, average response time, and call time period distribution. A target call relationship edge can refer to the call relationship edge corresponding to the base call chain data array. For example, all Span data in the base call chain data array can be read first. Then, for each Span, its parent Span ID is checked to find the node corresponding to its parent Span. Finally, a call relationship edge is established between the parent node and the current node.

[0053] In this context, a data flow edge refers to a logical data dependency edge established between service method nodes that produce and consume data, based on business-level data constraint rules. Typically, a data flow edge reveals the flow path of business parameters or objects within the system. Data flow edges can reflect data transfer relationships. A target data flow edge can refer to the data flow edge corresponding to the underlying call chain data array. For example, data constraint rules can be obtained first from preset business constraint rules. For instance, rule R: The "order number" input of the payment service must equal the "generate order number" output of the order service. Then, in the knowledge graph, the specific method nodes corresponding to the "payment service" and "order service" are located. Finally, a "data flow" edge is established between these two nodes, using rule R or the data object "order number" as the edge's label attribute.

[0054] In this context, dependency edges refer to implicit logical dependencies established between service methods based on invocation constraint rules and temporal relationships. Typically, dependency edges describe a condition, order, or resource dependency for successful execution; this dependency is not necessarily reflected in a direct call in every instance. Target dependency edges refer to dependency edges corresponding to the underlying call chain data array. For example, invocation constraint rules can be obtained first from preset business constraint rules, such as rule S: the inventory deduction service must be called before the payment service. Then, temporal patterns are obtained from the target call chain data array, such as: in 100 transactions, the order of inventory service -> payment service appears 95 times. Finally, a dependency edge is established between the corresponding inventory service node and payment service node.

[0055] S2130. Based on the target call relationship edge, target data flow edge and target dependency relationship edge, connect the target graph nodes to obtain the target knowledge graph corresponding to the basic call chain data set.

[0056] Specifically, after obtaining the target call chain data array and the target business semantic information, the target operation names in the basic call chain data array can be used as target graph nodes, and the target call chain data array and the target business semantic information can be combined and processed into a node attribute set. Then, the node attribute set is used to determine the target call relationship edges, target data flow edges, and target dependency relationship edges corresponding to the basic call chain data array. Finally, the target call relationship edges, target data flow edges, and target dependency relationship edges are used to connect each target graph node, resulting in the target knowledge graph corresponding to the basic call chain data set.

[0057] It is worth noting that, in this embodiment of the invention, the basic call chain data in the same basic call chain data set must ultimately be reflected in the same knowledge graph, that is, one target distributed system corresponds to one target knowledge graph.

[0058] S2140. Based on the preset scenario construction rules, the target knowledge graph is traversed and processed to generate candidate path scenarios corresponding to the target knowledge graph.

[0059] Among them, preset scenario construction rules refer to pre-defined rules used to define the process of automatically discovering and inferring valuable test scenarios. For example, preset scenario construction rules may include coverage gap analysis rules, data flow coverage analysis rules, business scenario mining rules, and abnormal scenario construction rules. Coverage gap analysis rules can identify low-frequency call paths by statistically analyzing call frequency, thereby finding paths that are rarely executed. Data flow coverage analysis rules can identify abnormal data transmission paths and uncovered parameters at boundary values. Business scenario mining rules can extract typical business scenarios from high-frequency call chains and label them with business semantics and key parameters. Alternatively, they can infer boundary scenarios based on parameter value distribution and response time distribution. Abnormal scenario construction rules can extract abnormal scenarios from failed call chains or construct new abnormal scenarios based on dependencies. Path scenarios can refer to an abstract description of a specific business situation or system state derived through graph algorithms and business rules. Typically, path scenarios define what to test and under what conditions, but do not yet include specific execution data. For example, path scenarios may include scenario objectives, trigger entry points, execution paths, preset conditions, simulated states, and expected results. Candidate path scenarios can refer to test scenarios that are automatically discovered and constructed after graph traversal and rule reasoning of the target knowledge graph.

[0060] S2150. Match the corresponding test script template based on the service call type corresponding to the candidate path scenario, and instantiate the candidate path scenario to obtain the test parameter data corresponding to the candidate path scenario.

[0061] In this context, "service call type" refers to the specific technical protocol and interaction mode used by a service method node to communicate or interact with its downstream dependent nodes in a distributed system. Typically, the service call type can be determined through logical inference based on the node attributes integrated into the knowledge graph. "Test script template" refers to a pre-designed, parameterized code framework specific to a particular test type. Typically, a test script template consists of fixed parts and variable placeholders; the system automatically fills the placeholders with instantiated content. One test script template corresponds to one service call type. "Instantiation" refers to the process of filling in all necessary specific details of an abstract test scenario, binding it to actual technical components, and assembling it into program code that can be run immediately under a specific programming language and test framework. "Test parameter data" refers to the specific, valid, and business-rule-compliant set of input values ​​generated for the service interface calls involved in the test scenario when generating executable test cases.

[0062] S2160. Fill the test parameter data and preset simulation strategy into the test script template to obtain basic test cases, and process the basic test cases based on preset data constraint rules to obtain target test cases that can run independently.

[0063] Among these, a pre-defined simulation strategy refers to a set of systematic rules and methods used to create virtual objects in the test environment to simulate the behavior of real dependent components, thereby isolating the system under test from its external dependencies and achieving controllability, predictability, and repeatability of the test. A test case refers to a complete, independent, and executable verification unit. Typically, a test case may include the environment state before the test begins, specific input parameters, how to call the system under test, the system's expected response and behavior, and environment recovery after the test. Basic test cases refer to the initially obtained test cases. Pre-defined data constraint rules refer to pre-defined restrictions imposed on the validity, completeness, consistency, and business relationships of data in the business logic. For example, pre-defined data constraint rules can be obtained from historical call chain data through pattern reasoning and statistical learning. Target test cases refer to the finally generated test cases.

[0064] Specifically, after obtaining the target knowledge graph corresponding to the basic call chain data set, the target knowledge graph can be traversed and processed based on preset scenario construction rules to generate candidate path scenarios corresponding to the target knowledge graph. Then, each service node in the candidate path scenario is traversed, its tag attributes in the knowledge graph are checked to determine the service call type, and the corresponding test script template is matched according to the service call type. Simultaneously, the candidate path scenario is instantiated to obtain the corresponding test parameter data. Further, the test parameter data and preset simulation strategies are populated into the test script template to obtain basic test cases. Finally, the basic test cases are processed based on preset data constraint rules to obtain independently runnable target test cases.

[0065] The technical solution of this invention involves obtaining a basic call chain data set corresponding to a target distributed system, and grouping the basic call chain data set based on the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array. Then, based on preset call mode enhancement rules, statistical analysis is performed on the call modes of the basic call chain data array to determine the target call mode information corresponding to the basic call chain data array. Based on preset parameter mode enhancement rules, statistical analysis is performed on the parameter modes of the basic call chain data array to determine the target parameter mode information corresponding to the basic call chain data array. Based on preset temporal relationship enhancement rules, statistical analysis is performed on the temporal relationships of the basic call chain data array to determine the target temporal relationship information corresponding to the basic call chain data array. Based on preset anomaly mode enhancement rules, statistical analysis is performed on the anomaly modes of the basic call chain data array to determine the target anomaly mode information corresponding to the basic call chain data array. Finally, the target call mode information, target parameter mode information, target temporal relationship information, and target anomaly mode information corresponding to the basic call chain data array are summarized and processed to obtain the data-enhanced target call chain data array. Furthermore, based on the target semantic model, service roles are inferred from the target call chain data array to determine the target business roles corresponding to the target call chain data array. A pre-defined frequent itemset algorithm is used to identify the business processes corresponding to the target call chain data array, determining the target business processes corresponding to the target call chain data array. Pre-defined business constraint rules are used to determine the constraint results corresponding to the target call chain data array. The target business roles, target business processes, and target constraint results are then combined to obtain the target business semantic information corresponding to the target call chain data array. Further, the target operation names in the basic call chain data array are used as target graph nodes, and the target call chain data array and target business semantic information are combined to form a set of node attributes. Based on the node attribute set, the target call relationship edges, target data flow edges, and target dependency relationship edges corresponding to the basic call chain data array are determined. Node connections are then made between the target graph nodes based on the target call relationship edges, target data flow edges, and target dependency relationship edges to obtain the target knowledge graph corresponding to the basic call chain data set. Finally, the target knowledge graph is traversed and processed based on pre-defined scenario construction rules to generate candidate path scenarios corresponding to the target knowledge graph. Based on the service call type corresponding to the candidate path scenario, a corresponding test script template is matched, and the candidate path scenario is instantiated to obtain the test parameter data corresponding to the candidate path scenario. The test parameter data and preset simulation strategies are populated into the test script template to obtain basic test cases. Based on preset data constraint rules, the basic test cases are processed to obtain target test cases that can be run independently.By mining the call chain data at the business semantic level and performing data augmentation, the constructed knowledge graph can elevate the system from the "observable" level to the "understandable" level. This transforms the testing paradigm from passive, interface-level verification that relies on human experience to proactive, business scenario-level reasoning and assurance driven by data and knowledge. It solves the problem of low efficiency in extracting valuable knowledge from the call chain data that reflects the actual business logic of the system, which is present in existing technologies, and improves the efficiency of subsequent automated testing tasks.

[0066] Example 3 Figure 3 This is a schematic diagram of a knowledge graph construction device for call chain data provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data acquisition module 310, a data augmentation module 320, a semantic reasoning module 330, and a graph construction module 340; The data acquisition module 310 is used to acquire the basic call chain data set corresponding to the target distributed system, and to group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array. Data augmentation module 320 is used to perform data augmentation processing on the basic call chain data array based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation processing; Semantic reasoning module 330 is used to perform semantic reasoning on the target call chain data array based on the target semantic model and a preset frequent itemset algorithm to determine the target business semantic information corresponding to the target call chain data array; The graph construction module 340 is used to construct a graph of the target call chain data array and the target business semantic information based on preset knowledge graph construction rules, so as to obtain the target knowledge graph corresponding to the basic call chain data set.

[0067] The technical solution of this invention involves obtaining a basic call chain data set corresponding to a target distributed system, and grouping the basic call chain data set based on the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array. Then, data augmentation processing is performed on the basic call chain data array based on preset multi-dimensional data processing rules to obtain a data-augmented target call chain data array. Further, semantic reasoning is performed on the target call chain data array based on a target semantic model and a preset frequent itemset algorithm to determine the target business semantic information corresponding to the target call chain data array. Finally, a graph is constructed on the target call chain data array and the target business semantic information based on preset knowledge graph construction rules to obtain a target knowledge graph corresponding to the basic call chain data set. By mining the call chain data at the business semantic level and performing data augmentation, the constructed knowledge graph can elevate the system from the "observable" level to the "understandable" level. This transforms the testing paradigm from passive, interface-level verification that relies on human experience to proactive, business scenario-level reasoning and assurance driven by data and knowledge. It solves the problem of low efficiency in extracting valuable knowledge from the call chain data that reflects the actual business logic of the system, which is present in existing technologies, and improves the efficiency of subsequent automated testing tasks.

[0068] Optionally, preset multidimensional data processing rules can be included, such as: preset call mode enhancement rules, preset parameter mode enhancement rules, preset time sequence relationship enhancement rules, and preset exception mode enhancement rules.

[0069] Optional, the data augmentation module 320 can be used for: Based on preset call pattern enhancement rules, statistical analysis is performed on the call patterns of the basic call chain data array to determine the target call pattern information corresponding to the basic call chain data array; Based on preset parameter pattern enhancement rules, statistical analysis is performed on the parameter patterns of the basic call chain data array to determine the target parameter pattern information corresponding to the basic call chain data array; Based on preset temporal relationship enhancement rules, statistical analysis is performed on the temporal relationship of the basic call chain data array to determine the target temporal relationship information corresponding to the basic call chain data array; Based on preset exception mode enhancement rules, statistical analysis is performed on the exception modes of the basic call chain data array to determine the target exception mode information corresponding to the basic call chain data array; The target call pattern information, target parameter pattern information, target timing relationship information, and target anomaly pattern information corresponding to the basic call chain data array are summarized and processed to obtain the target call chain data array after data augmentation.

[0070] Optional, semantic reasoning module 330, which can be used for: Based on the target semantic model, service role inference is performed on the target call chain data array to determine the target business role corresponding to the target call chain data array; Based on a preset frequent itemset algorithm, the target call chain data array is used to identify the target business process and determine the target business process corresponding to the target call chain data array. Based on preset business constraint rules, the target call chain data array is constrained to determine the target constraint result corresponding to the target call chain data array; By combining and processing the target business role, target business process, and target constraint results, the target business semantic information corresponding to the target call chain data array is obtained.

[0071] Optionally, the map building module 340 can be used for: The target operation name in the basic call chain data array is used as the target graph node, and the target call chain data array and target business semantic information are combined and processed as a set of node attributes. Based on the node attribute set, determine the target call relationship edge, target data flow edge, and target dependency relationship edge corresponding to the basic call chain data array; Based on the target call relationship edge, target data flow edge, and target dependency relationship edge, the target graph nodes are connected to obtain the target knowledge graph corresponding to the basic call chain data set.

[0072] Optionally, the knowledge graph construction device for call chain data may further include: a scenario construction module, used to construct a graph of the target call chain data array and target business semantic information based on preset knowledge graph construction rules to obtain the target knowledge graph corresponding to the basic call chain data set, and then traverse and process the target knowledge graph based on preset scenario construction rules to generate candidate path scenarios corresponding to the target knowledge graph.

[0073] Optionally, the knowledge graph construction device for calling chain data may further include: a test case generation module, used to, after traversing and processing the target knowledge graph based on preset scenario construction rules to generate candidate path scenarios corresponding to the target knowledge graph, match the corresponding test script template based on the service call type corresponding to the candidate path scenario, and instantiate the candidate path scenario to obtain test parameter data corresponding to the candidate path scenario; fill the test parameter data and preset simulation strategy into the test script template to obtain basic test cases, and perform data processing on the basic test cases based on preset data constraint rules to obtain independently runnable target test cases.

[0074] The knowledge graph construction apparatus for call chain data provided in the embodiments of the present invention can execute the knowledge graph construction method for call chain data provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0075] Example 4 Figure 4 A schematic diagram of an electronic device 410 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0076] like Figure 4 As shown, the electronic device 410 includes at least one processor 420 and a memory, such as a read-only memory (ROM) 430 or a random access memory (RAM) 440, communicatively connected to the at least one processor 420. The memory stores computer programs executable by the at least one processor. The processor 420 can perform various appropriate actions and processes based on the computer program stored in the ROM 430 or loaded into the RAM 440 from storage unit 490. The RAM 440 may also store various programs and data required for the operation of the electronic device 410. The processor 420, ROM 430, and RAM 440 are interconnected via a bus 450. An input / output (I / O) interface 460 is also connected to the bus 450.

[0077] Multiple components in electronic device 410 are connected to I / O interface 460, including: input unit 470, such as keyboard, mouse, etc.; output unit 480, such as various types of monitors, speakers, etc.; storage unit 490, such as disk, optical disk, etc.; and communication unit 4100, such as network card, modem, wireless transceiver, etc. Communication unit 4100 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0078] Processor 420 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 420 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 420 performs the various methods and processes described above, such as invoking knowledge graph construction methods for chained data.

[0079] The method includes: Obtain the basic call chain data set corresponding to the target distributed system, and group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array; The basic call chain data array is augmented based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation. Based on the target semantic model and the preset frequent itemset algorithm, semantic reasoning is performed on the target call chain data array to determine the target business semantic information corresponding to the target call chain data array; Based on preset knowledge graph construction rules, a graph is constructed from the target call chain data array and the target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set.

[0080] In some embodiments, the knowledge graph construction method for call chain data can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 490. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via ROM 430 and / or communication unit 4100. When the computer program is loaded into RAM 440 and executed by processor 420, one or more steps of the knowledge graph construction method for call chain data described above can be performed. Alternatively, in other embodiments, processor 420 can be configured to execute the knowledge graph construction method for call chain data by any other suitable means (e.g., by means of firmware).

[0081] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0082] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0083] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0084] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0085] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0086] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0087] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the knowledge graph construction method for call chain data provided in any embodiment of this application. This program product and the knowledge graph construction method for call chain data disclosed in the embodiments of this application belong to the same inventive concept, and therefore will not be described in detail here.

[0088] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0089] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for constructing a knowledge graph using chained data, characterized in that, include: Obtain the basic call chain data set corresponding to the target distributed system, and group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array; The basic call chain data array is augmented based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation. Based on the target semantic model and the preset frequent itemset algorithm, semantic reasoning is performed on the target call chain data array to determine the target business semantic information corresponding to the target call chain data array; Based on preset knowledge graph construction rules, a graph is constructed from the target call chain data array and the target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set.

2. The method according to claim 1, characterized in that, The preset multidimensional data processing rules include: preset call mode enhancement rules, preset parameter mode enhancement rules, preset temporal relationship enhancement rules, and preset exception mode enhancement rules.

3. The method according to claim 2, characterized in that, The process of performing data augmentation on the basic call chain data array based on preset multidimensional data processing rules to obtain the data-augmented target call chain data array includes: Based on preset call pattern enhancement rules, statistical analysis is performed on the call patterns of the basic call chain data array to determine the target call pattern information corresponding to the basic call chain data array; Based on preset parameter pattern enhancement rules, statistical analysis is performed on the parameter patterns of the basic call chain data array to determine the target parameter pattern information corresponding to the basic call chain data array; Based on preset temporal relationship enhancement rules, statistical analysis is performed on the temporal relationship of the basic call chain data array to determine the target temporal relationship information corresponding to the basic call chain data array; Based on preset exception mode enhancement rules, statistical analysis is performed on the exception modes of the basic call chain data array to determine the target exception mode information corresponding to the basic call chain data array; The target call pattern information, target parameter pattern information, target timing relationship information, and target anomaly pattern information corresponding to the basic call chain data array are summarized and processed to obtain the target call chain data array after data augmentation.

4. The method according to claim 1, characterized in that, The semantic reasoning of the target call chain data array based on the target semantic model and the preset frequent itemset algorithm to determine the target business semantic information corresponding to the target call chain data array includes: Based on the target semantic model, service role inference is performed on the target call chain data array to determine the target business role corresponding to the target call chain data array; Based on a preset frequent itemset algorithm, the target call chain data array is used to identify the target business process and determine the target business process corresponding to the target call chain data array. Based on preset business constraint rules, the target call chain data array is constrained to determine the target constraint result corresponding to the target call chain data array; By combining and processing the target business role, target business process, and target constraint results, the target business semantic information corresponding to the target call chain data array is obtained.

5. The method according to claim 1, characterized in that, The step of constructing a knowledge graph based on preset knowledge graph construction rules for the target call chain data array and target business semantic information to obtain the target knowledge graph corresponding to the basic call chain data set includes: The target operation name in the basic call chain data array is used as the target graph node, and the target call chain data array and target business semantic information are combined and processed as a set of node attributes. Based on the node attribute set, determine the target call relationship edge, target data flow edge, and target dependency relationship edge corresponding to the basic call chain data array; Based on the target call relationship edge, target data flow edge, and target dependency relationship edge, the target graph nodes are connected to obtain the target knowledge graph corresponding to the basic call chain data set.

6. The method according to claim 1, characterized in that, After constructing the target knowledge graph corresponding to the basic call chain data set by constructing the target call chain data array and the target business semantic information based on the preset knowledge graph construction rules, the method further includes: Based on preset scenario construction rules, the target knowledge graph is traversed and processed to generate candidate path scenarios corresponding to the target knowledge graph.

7. The method according to claim 6, characterized in that, After the target knowledge graph is traversed and processed based on the preset scenario construction rules to generate candidate path scenarios corresponding to the target knowledge graph, the method further includes: Based on the service call type corresponding to the candidate path scenario, a corresponding test script template is matched, and the candidate path scenario is instantiated to obtain the test parameter data corresponding to the candidate path scenario. The test parameter data and preset simulation strategies are filled into the test script template to obtain basic test cases. Based on preset data constraint rules, the basic test cases are processed to obtain target test cases that can run independently.

8. A knowledge graph construction device for calling chain data, characterized in that, include: The data acquisition module is used to acquire the basic call chain data set corresponding to the target distributed system, and to group the basic call chain data set according to the target identifier corresponding to each basic call chain data in the basic call chain data set to obtain a basic call chain data array; The data augmentation module is used to perform data augmentation processing on the basic call chain data array based on preset multidimensional data processing rules to obtain the target call chain data array after data augmentation. The semantic reasoning module is used to perform semantic reasoning on the target call chain data array based on the target semantic model and the preset frequent itemset algorithm, and to determine the target business semantic information corresponding to the target call chain data array. The graph construction module is used to construct a graph of the target call chain data array and the target business semantic information based on preset knowledge graph construction rules, so as to obtain the target knowledge graph corresponding to the basic call chain data set.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the knowledge graph construction method for call chain data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the knowledge graph construction method for call chain data as described in any one of claims 1-7.

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