A knowledge graph-based inspection method and device

Abstract

The application discloses a kind of based on knowledge graph's inspection method and device, by constructing the knowledge graph including fault state, so that staff can directly view each node in knowledge graph and the fault state of each node, view the monitoring data associated with each node and individually retrieve a node, reduce the difficulty of staff's inspection, improve the work efficiency of inspection.

Description

Technical Field

[0001] This invention belongs to the field of operation and maintenance, and specifically relates to a knowledge graph-based inspection method and device. Background Technology

[0002] In financial systems, regular health checks are a crucial part of ensuring operational stability, especially pre-holiday system checks. These checks primarily involve inspecting various monitoring metrics within the system for anomalies. Currently available monitoring products often only check the operational status of a single node, failing to simultaneously check and display the system under inspection along with its associated systems, middleware, databases, and other related nodes. With the rise of microservices, the number of system nodes has increased exponentially, and the inter-system communication relationships have become increasingly complex. When conducting system health checks, operations personnel often need to check every single node and its associated nodes, which is clearly inefficient and prone to oversights in practice. Summary of the Invention

[0003] The purpose of this application is to provide a knowledge graph-based inspection method that solves the problem of low inspection efficiency in the prior art.

[0004] This invention is achieved through the following technical solution:

[0005] A knowledge graph-based inspection method includes:

[0006] Obtain object parameters for at least one object to be inspected, wherein the object parameters are used to characterize the attributes of the object to be inspected and the relationships between different objects to be inspected;

[0007] A knowledge graph is constructed based on the object parameters of the at least one object to be inspected;

[0008] Based on the knowledge graph, the target inspection object is inspected, and the inspection results are visualized to complete the inspection. The target inspection object represents an object to be inspected.

[0009] In one possible implementation, the attributes of the object to be inspected represent the fault status of the object to be inspected, and the object to be inspected includes the system to be inspected, the middleware to be inspected, and the database to be inspected.

[0010] In one possible implementation, a knowledge graph is constructed based on the object parameters of the at least one object to be inspected, including:

[0011] Using the system to be inspected, the middleware to be inspected, and the database to be inspected as nodes in the knowledge graph, the relationships between the system to be inspected, the middleware to be inspected, and the database to be inspected as edges in the knowledge graph, and the fault status of the system to be inspected, the middleware to be inspected, and the database to be inspected as node attributes in the knowledge graph, a knowledge graph of the object to be inspected is obtained. The fault status includes whether the object to be inspected is normal or faulty. The nodes include the name of the object to be inspected and a unique identifier character code.

[0012] In one possible implementation, after constructing the knowledge graph based on the object parameters of the at least one object to be inspected, the method further includes:

[0013] Monitor the properties of the object to be inspected in real time and obtain the monitoring results;

[0014] Based on the monitoring results, the attributes of the objects to be inspected in the knowledge graph are updated.

[0015] In one possible implementation, after constructing the knowledge graph based on the object parameters of the at least one object to be inspected, the method further includes:

[0016] The monitoring data of the object to be inspected is acquired in real time, stored in a time-series database, and associated with the monitoring data.

[0017] In one possible implementation, the monitoring data includes system metrics, performance metrics, and anomaly logs corresponding to the object to be inspected. The system metrics include CPU utilization, memory utilization, disk utilization, and network metrics. The performance metrics include interface success rate, number of interface calls, and interface call time.

[0018] In one possible implementation, the target inspection object is inspected based on the knowledge graph, and the inspection results are visualized, including:

[0019] Using unique identifier character codes as retrieval data, the target inspection object is retrieved from the knowledge graph to obtain the fault status corresponding to the target inspection object and obtain the inspection results.

[0020] The target inspection object and the corresponding fault status of the target inspection object in the inspection results are visualized.

[0021] In one possible implementation, visualizing the target inspection object and the fault status corresponding to the target inspection object in the inspection results includes:

[0022] If the fault status of the target inspection object in the inspection results is that the object to be inspected is normal, then the knowledge graph node corresponding to the target inspection object will be represented in green.

[0023] If the fault status of the target inspection object in the inspection results is the fault of the object to be inspected, then the knowledge graph node corresponding to the target inspection object will be marked in red.

[0024] In one possible implementation, it also includes: visualizing all nodes and their corresponding node attributes.

[0025] Secondly, this application provides an inspection device based on a knowledge graph, including an object parameter acquisition module, a knowledge graph construction module, and an inspection module.

[0026] The object parameter acquisition module is used to acquire object parameters of at least one object to be inspected. The object parameters are used to characterize the attributes of the object to be inspected and the relationship between different objects to be inspected.

[0027] The knowledge graph construction module is used to construct a knowledge graph based on the object parameters of the at least one object to be inspected.

[0028] The inspection module is used to inspect the target inspection object according to the knowledge graph, visualize the inspection results, and complete the inspection. The target inspection object represents an object to be inspected.

[0029] This application provides a knowledge graph-based inspection method and apparatus. By constructing a knowledge graph containing fault states, staff can directly view each node in the knowledge graph and its fault state, view the monitoring data associated with each node, and retrieve a specific node, thereby reducing the difficulty of inspections and improving the efficiency of inspection work. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0031] Figure 1 A flowchart of a knowledge graph-based inspection method provided in this application embodiment.

[0032] Figure 2 This is a schematic diagram of the structure of a knowledge graph-based inspection device provided in an embodiment of this application.

[0033] Figure 3 This is a schematic diagram of the structure of a knowledge graph-based inspection device provided in an embodiment of this application.

[0034] The attached diagram shows the markings and corresponding component names:

[0035] 21-Object parameter acquisition module, 22-Knowledge graph construction module, 23-Inspection module, 31-Memory, 32-Processor, 33-Bus. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0037] Example 1

[0038] like Figure 1 As shown, a knowledge graph-based inspection method includes:

[0039] S11. Obtain at least one object parameter of the object to be inspected. The object parameter is used to characterize the attributes of the object to be inspected and the relationship between different objects to be inspected.

[0040] In this embodiment, the attributes corresponding to each object to be inspected can be monitored uniformly and updated in real time to realize the update of the knowledge graph. This allows staff to complete the inspection of each object to be inspected simply by viewing the knowledge graph. The object to be inspected can be a system, middleware, or database. The object parameters include the fault status, the name of the object to be inspected, and the unique identifier character code of the object to be inspected.

[0041] S12. Construct a knowledge graph based on the object parameters of at least one object to be inspected.

[0042] For example, a knowledge graph can be constructed by using the objects to be inspected as nodes, the object parameters of the objects to be inspected as node attributes, and the relationships between the objects to be inspected as edges between nodes.

[0043] S13. Based on the knowledge graph, conduct inspections on the target inspection objects and visualize the inspection results to complete the inspection. The target inspection object represents an object to be inspected.

[0044] For example, a knowledge graph can be displayed through a visualization device. When a node is selected, it can be highlighted, and information associated with that node can be viewed.

[0045] Optionally, information from different systems, middleware, and databases can be collected through a configuration management database (CMDB).

[0046] In one possible implementation, the attributes of the object to be inspected represent the fault status of the object to be inspected, and the object to be inspected includes the system to be inspected, the middleware to be inspected, and the database to be inspected.

[0047] By constructing a knowledge graph based on all systems, middleware, and databases to be inspected, rapid inspection of all objects to be inspected can be performed through the knowledge graph, improving the efficiency of the inspection work. Furthermore, a specific target object can be identified, and the target object and its attributes can be retrieved from the knowledge graph.

[0048] In one possible implementation, a knowledge graph is constructed based on object parameters of at least one object to be inspected, including:

[0049] Using the system to be inspected, the middleware to be inspected, and the database to be inspected as nodes in the knowledge graph, the relationships between the system to be inspected, the middleware to be inspected, and the database to be inspected as edges in the knowledge graph, and the fault status of the system to be inspected, the middleware to be inspected, and the database to be inspected as node attributes in the knowledge graph, a knowledge graph of the object to be inspected is obtained. The fault status includes whether the object to be inspected is normal or faulty. The nodes include the name of the object to be inspected and its unique identifier character code.

[0050] For example, the system, middleware, and database to be inspected each include a corresponding name (systemName) and a unique identifier (systemCode). Therefore, nodes can be created using these names and unique identifiers. Simultaneously, a node attribute (faultStatus) is created. When faultStatus = 1, it indicates that the fault status of the node corresponds to a fault in the object to be inspected; when faultStatus = 0, it indicates that the fault status of the node corresponds to a normal state in the object to be inspected. After creating nodes, edges are created between nodes with related relationships, thus completing the construction of the knowledge graph.

[0051] In one possible implementation, after constructing a knowledge graph based on the object parameters of at least one object to be inspected, the method further includes: real-time monitoring of the attributes of the object to be inspected to obtain monitoring results; and updating the attributes of the object to be inspected in the knowledge graph based on the monitoring results.

[0052] Monitor system alarms and anomaly logs, and update the fault status of the corresponding nodes in the graph database in real time. When the system receives a fault notification, update faultStatus to "1" (faulting); when the system receives a recovery notification, update faultStatus to "0" (normal).

[0053] In one possible implementation, after constructing the knowledge graph based on the object parameters of at least one object to be inspected, the method further includes:

[0054] The system acquires real-time monitoring data of the objects to be inspected, stores the monitoring data in a time-series database, and associates the monitoring data with the objects to be inspected.

[0055] In one possible implementation, the monitoring data includes system metrics, performance metrics, and anomaly logs corresponding to the object to be inspected. System metrics include CPU utilization, memory utilization, disk utilization, and network metrics. Performance metrics include interface success rate, number of interface calls, and interface call time.

[0056] During the inspection process, a specific object to be inspected can be selected and the monitoring data associated with that object can be retrieved from the time series database for viewing. It is worth noting that in addition to the above-mentioned indicators, system indicators and performance indicators can also include other system indicators. For example, when the object to be inspected is a Java application, JVM GC indicators can also be included.

[0057] In one possible implementation, the target inspection object is inspected based on a knowledge graph, and the inspection results are visualized, including:

[0058] Using unique identifier character codes as retrieval data, the target inspection object is retrieved from the knowledge graph to obtain the fault status corresponding to the target inspection object and obtain the inspection results; the target inspection object and the fault status corresponding to the target inspection object in the inspection results are visualized.

[0059] Knowledge graphs can be built on graph databases. When retrieving target inspection objects from a knowledge graph, a graph database query statement containing a unique identifier character code can be constructed. This allows data to be queried in the graph database using the unique identifier character code, and the query results can be displayed in the form of a graph.

[0060] In one possible implementation, the target inspection object and the corresponding fault status of the target inspection object in the inspection results are visualized, including:

[0061] If the fault status of the target inspection object in the inspection results is that the object to be inspected is normal, then the knowledge graph node corresponding to the target inspection object will be represented in green.

[0062] If the fault status of the target inspection object in the inspection results is the fault of the object to be inspected, then the knowledge graph node corresponding to the target inspection object will be marked in red.

[0063] For example, when the node attribute `faultStatus` = 1, the knowledge graph node corresponding to the target inspection object is represented in red. When the node attribute `faultStatus` = 0, the knowledge graph node corresponding to the target inspection object is represented in green. It's worth noting that green and red can be replaced with other colors, but it's crucial to ensure that the colors corresponding to a normal inspection object and a faulty inspection object are different.

[0064] In one possible implementation, the system further includes visualizing all nodes and their corresponding node attributes. This allows staff to directly view the knowledge graph, perform system inspections using colors within the knowledge graph, and quickly locate faulty nodes.

[0065] This application features a high degree of centralized inspection, aggregating various operational data required for inspection. Inspections can be completed through a single system, eliminating the need to log into individual systems (such as alarm systems, monitoring systems, and log systems) for inspections. The application also boasts a high degree of automation, automatically inspecting associated nodes and marking faulty nodes with special colors, eliminating the need for individual node inspections.

[0066] This application provides a knowledge graph-based inspection method. By constructing a knowledge graph containing fault states, it enables staff to directly view each node in the knowledge graph and its fault state, view the monitoring data associated with each node, and retrieve a specific node. This reduces the difficulty of inspections and improves work efficiency.

[0067] Example 2

[0068] like Figure 2 As shown in the figure, an inspection device based on a knowledge graph according to an embodiment of this application includes an object parameter acquisition module 21, a knowledge graph construction module 22, and an inspection module 23.

[0069] The object parameter acquisition module 21 is used to acquire object parameters of at least one object to be inspected. The object parameters are used to characterize the attributes of the object to be inspected and the relationship between different objects to be inspected.

[0070] The knowledge graph construction module 22 is used to construct a knowledge graph based on the object parameters of the at least one object to be inspected.

[0071] The inspection module 23 is used to inspect the target inspection object according to the knowledge graph, visualize the inspection results, and complete the inspection. The target inspection object represents an object to be inspected.

[0072] The knowledge graph-based inspection device provided in this embodiment can execute the technical solution described in claim 1. Its principle and beneficial effects are similar and will not be repeated here.

[0073] Example 3

[0074] like Figure 3 As shown, this embodiment provides a knowledge graph-based inspection device, including a memory 31 and a processor 32, which are interconnected via a bus 33.

[0075] Memory 31 stores computer-executed instructions.

[0076] The processor 32 executes computer execution instructions stored in the memory, causing the processor to perform a knowledge graph-based inspection method as described in Embodiment 1.

[0077] For specific examples, memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0078] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0079] Example 4

[0080] This embodiment provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement a knowledge graph-based inspection method as described in Embodiment 1.

[0081] Example 5

[0082] This application embodiment can also provide a computer program product, including a computer program that, when executed by a processor, implements a knowledge graph-based inspection method as described in Embodiment 1.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1.A knowledge graph-based inspection method, characterized in that, The method comprises the following steps: acquiring object parameters of at least one to-be-inspected object, the object parameters being used to represent attributes of the to-be-inspected objects and the correlation between different to-be-inspected objects, wherein the attributes of the to-be-inspected objects represent fault states of the to-be-inspected objects, and the to-be-inspected objects include to-be-inspected systems, to-be-inspected middleware and to-be-inspected databases; constructing a knowledge graph based on the object parameters of the at least one to-be-inspected object; inspecting a target to-be-inspected object according to the knowledge graph, visualizing an inspection result, and completing the inspection, wherein the target to-be-inspected object represents one to-be-inspected object, wherein the step of constructing the knowledge graph based on the object parameters of the at least one to-be-inspected object comprises the following steps: taking the to-be-inspected systems, the to-be-inspected middleware and the to-be-inspected databases as nodes in the knowledge graph, taking the correlation between the to-be-inspected systems, the to-be-inspected middleware and the to-be-inspected databases as edges of the knowledge graph, and taking the fault states of the to-be-inspected systems, the to-be-inspected middleware and the to-be-inspected databases as node attributes of the knowledge graph, to obtain the knowledge graph of the to-be-inspected objects, wherein the fault states include normal to-be-inspected objects or faulty to-be-inspected objects, and the nodes include names and unique character codes of the to-be-inspected objects. 2.The knowledge graph-based inspection method of claim 1, wherein, After the step of constructing the knowledge graph based on the object parameters of the at least one to-be-inspected object, the method further comprises the following steps: real-time monitoring of the attributes of the to-be-inspected objects to obtain monitoring results; updating the attributes of the to-be-inspected objects in the knowledge graph according to the monitoring results. 3.The knowledge graph-based inspection method of claim 2, wherein, After the step of constructing the knowledge graph based on the object parameters of the at least one to-be-inspected object, the method further comprises the following steps: real-time acquisition of monitoring data of the to-be-inspected objects, storage of the monitoring data in a time-series database, and association of the monitoring data with the to-be-inspected objects. 4.The knowledge graph-based inspection method of claim 3, wherein, The monitoring data includes system indicators, performance indicators and abnormal logs corresponding to the to-be-inspected objects, the system indicators include CPU occupancy, memory occupancy, disk occupancy and network indicators, and the performance indicators include interface success rates, interface call frequencies and interface call time consumptions. 5.The knowledge graph based inspection method of claim 1, wherein, The step of inspecting the target to-be-inspected object according to the knowledge graph and visualizing the inspection result comprises the following steps: taking the unique character codes as search data to search the target to-be-inspected object from the knowledge graph, to obtain a fault state corresponding to the target to-be-inspected object, and to obtain the inspection result; visualizing the target to-be-inspected object and the fault state corresponding to the target to-be-inspected object in the inspection result. 6.The knowledge graph-based inspection method of claim 5, wherein, The step of visualizing the target to-be-inspected object and the fault state corresponding to the target to-be-inspected object in the inspection result comprises the following steps: if the fault state corresponding to the target to-be-inspected object in the inspection result is that the to-be-inspected object is normal, then the knowledge graph node corresponding to the target to-be-inspected object is represented by green color; if the fault state corresponding to the target to-be-inspected object in the inspection result is that the to-be-inspected object is faulty, then the knowledge graph node corresponding to the target to-be-inspected object is represented by red color. 7.The knowledge graph-based inspection method of claim 6, wherein, The method further comprises the following step: visualizing all nodes and node attributes corresponding to the nodes. 8.A knowledge graph based inspection device, characterized in that, The method comprises an object parameter acquisition module, a knowledge graph construction module and an inspection module. The object parameter acquisition module is configured to acquire object parameters of at least one to-be-inspected object, the object parameters being used to represent attributes of the to-be-inspected objects and correlation relationships among different to-be-inspected objects, wherein the attributes of the to-be-inspected objects represent fault states of the to-be-inspected objects, and the to-be-inspected objects include to-be-inspected systems, to-be-inspected middleware, and to-be-inspected databases; The knowledge graph construction module is configured to construct a knowledge graph based on the object parameters of the at least one to-be-inspected object; The inspection module is configured to inspect a target to-be-inspected object according to the knowledge graph, visualize an inspection result, and complete the inspection, wherein the target to-be-inspected object represents one to-be-inspected object. The construction of the knowledge graph based on the object parameters of the at least one to-be-inspected object includes: Taking the to-be-inspected systems, the to-be-inspected middleware, and the to-be-inspected databases as nodes in the knowledge graph, taking the correlation relationships among the to-be-inspected systems, the to-be-inspected middleware, and the to-be-inspected databases as edges of the knowledge graph, and taking the fault states of the to-be-inspected systems, the to-be-inspected middleware, and the to-be-inspected databases as node attributes of the knowledge graph, a knowledge graph of the to-be-inspected objects is obtained, wherein the fault states include normal states of the to-be-inspected objects or fault states of the to-be-inspected objects, and a node includes a name and a unique character code of a to-be-inspected object.

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