Data tracking and analysis method and device, equipment and storage medium

Abstract

The invention provides a data tracking and analysis method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting tracking data in a target process through a Skwalking distributed tracking system, and achieving the real-time monitoring of an insurance claim settlement process; the collected tracking data are imported into a graph database, and the target process is modeled and stored by utilizing graph storage, so that refined tracking of the claim settlement task is realized; analyzing the tracking data stored in a graph database through a graph analysis algorithm to obtain an analysis result, and performing deep mining on the claim settlement data by utilizing a graph storage and analysis technology; according to the analysis result, a target analysis report is generated, key business analysis and decision support are provided, through deep analysis and reporting, potential problems can be found and solved in time, and task processing efficiency and transparency are improved. The method has high expansibility and can be applied to tracking and analysis of other business processes.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data tracking and analysis method, device, equipment and computer-readable storage medium. Background Art

[0002] In the field of data processing, the data processing capabilities based on traditional relational databases are limited and cannot meet complex analysis needs.

[0003] Specifically, in the insurance industry, timely and accurate execution of claims tasks is crucial to customer satisfaction and company reputation. However, traditional claims processing systems usually lack real-time monitoring and in-depth analysis of the task execution process, making it difficult to discover and solve potential problems. The insurance industry is a typical financial services industry, and the transparency and efficiency of the claims process directly affect the reputation and service quality of insurance companies. However, traditional claims management methods often lack real-time monitoring and in-depth analysis of the claims process, making it difficult to discover and solve potential problems, affecting claims efficiency and customer satisfaction.

[0004] The existing claims task tracking and analysis technology has the following problems: it is impossible to achieve real-time monitoring, and it is impossible to discover and solve problems in a timely manner. It is impossible to achieve detailed tracking of claims tasks and find potential optimization points. The existing claims management methods mainly rely on manual management and regular report analysis, which makes it difficult to achieve real-time monitoring and in-depth analysis. In addition, traditional relational databases have certain limitations when dealing with complex relationships and related data in the claims process, and it is difficult to provide comprehensive analysis and decision support. The above problems are currently in urgent need of resolution. Summary of the invention

[0005] The present application provides a data tracking and analysis method, device, equipment and storage medium, aiming to solve the current technical problem of being unable to comprehensively process data when processing complex relationships and related data.

[0006] In a first aspect, an embodiment of the present application provides a data tracking and analysis method, the method comprising:

[0007] Collect tracking data from the target process through the Skywalking distributed tracking system;

[0008] Importing the collected tracking data into a graph database, and using graph storage to model and store the target process;

[0009] Analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result;

[0010] A target analysis report is generated according to the analysis results.

[0011] Preferably, the Skywalking distributed tracking system comprises: a probe;

[0012] The steps of collecting tracking data in the target process through the Skywalking distributed tracking system include:

[0013] Select the target probe and configure it;

[0014] When the application in the target process is started, the tracking data in the target process is collected by the target probe, and the tracking data includes key performance indicators and call chain information.

[0015] Preferably, the Skywalking distributed tracking system further comprises: a backend component;

[0016] After the step of collecting the tracking data in the target process by the target probe when the application in the target process is started, the method further includes:

[0017] The tracking data is sent to the OAP server through the target probe, so that the OAP server integrates and analyzes the tracking data and stores the integrated and analyzed data in the backend component.

[0018] Preferably, the Skywalking distributed tracking system further comprises: a user interface;

[0019] After the step of sending the tracking data to the OAP server through the target probe, the method further includes:

[0020] The tracking data is displayed in real time through the user interface of the Skywalking distributed tracking system.

[0021] Preferably, the step of analyzing the tracking data stored in the graph database by a graph analysis algorithm to obtain the analysis result includes:

[0022] Preprocessing the tracking data stored in the graph database to obtain preprocessed data;

[0023] Constructing a data graph structure according to the preprocessed data;

[0024] Applying a clustering algorithm on the data graph structure to cluster similar nodes into clusters;

[0025] Analyze the clustering results, identify abnormal clusters, and obtain analysis results.

[0026] Preferably, the step of preprocessing the tracking data stored in the graph database to obtain preprocessed data includes:

[0027] The key fields of the tracking data stored in the graph database are extracted, and the key fields are cleaned and standardized to obtain preprocessed data.

[0028] Preferably, the target process is a claims settlement task process;

[0029] The step of constructing a data graph structure according to the preprocessed data comprises:

[0030] The entities in the preprocessed data are used as nodes, and the relationships between the entities are used as edges to construct a graph structure of the claims task process.

[0031] In a second aspect, an embodiment of the present application provides a data tracking and analysis device, the device comprising:

[0032] The collection module is used to collect tracking data in the target process through the Skywalking distributed tracing system;

[0033] A modeling and storage module, used to import the collected tracking data into a graph database, and use the graph storage to model and store the target process;

[0034] An analysis module, used to analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result;

[0035] A generation module is used to generate a target analysis report according to the analysis results.

[0036] In a third aspect, an embodiment of the present application provides a data tracking and analysis device, which includes a processor, a memory, and a data tracking and analysis program stored in the memory and executable by the processor, wherein when the data tracking and analysis program is executed by the processor, the steps of the data tracking and analysis method as described above are implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the data tracking and analysis method as described above are implemented.

[0038] The embodiment of the present application provides a data tracking and analysis method, device, equipment and storage medium. The method collects tracking data in the target process through the Skywalking distributed tracking system to realize real-time monitoring of the insurance claims process; imports the collected tracking data into a graph database, models and stores the target process using graph storage, and realizes refined tracking of the claims task; analyzes the tracking data stored in the graph database through a graph analysis algorithm to obtain analysis results, and uses graph storage and analysis technology to deeply mine the claims data; generates a target analysis report based on the analysis results to provide key business analysis and decision support for insurance companies. Through in-depth analysis and reporting, potential problems can be discovered and solved in a timely manner, and the efficiency and transparency of claims task processing can be improved, thereby improving customer satisfaction and company reputation. In addition, the present application has strong scalability and can be easily applied to the tracking and analysis of other business processes.

[0039] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory and cannot limit the disclosure of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 It is a schematic diagram of the hardware structure of the data tracking and analysis device provided in the embodiment of the present application;

[0042] Figure 2 It is a flowchart of the first embodiment of the data tracking and analysis method provided by the embodiment of the present application;

[0043] Figure 3 A schematic diagram of the components of the SkyWalking distributed link tracking system for claims settlement in the data tracking and analysis method embodiment provided in the embodiment of the present application;

[0044] Figure 4 A schematic diagram of the relationship between components of the SkyWalking distributed link tracking system for claims settlement in the data tracking and analysis method embodiment provided in the embodiment of the present application;

[0045] Figure 5 It is a schematic diagram of the functional modules of the data tracking and analysis device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0048] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0049] Reference Figure 1 , Figure 1 Schematic diagram of the hardware structure of the data tracking and analysis device involved in the embodiment of the present invention. In the embodiment of the present invention, the data tracking and analysis device may include a processor 1001 (such as a CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components; the user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface); the memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory, and the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The hardware structure shown in the figure does not constitute a limitation on the data tracking and analysis device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0051] Reference Figure 1 , Figure 1 The memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, and a data tracking and analysis program.

[0052] exist Figure 1In the embodiment, the network communication module is mainly used to connect to the server and perform data communication with the server; and the processor 1001 can call the data tracking and analysis program stored in the memory 1005, and execute the data tracking and analysis method provided in the embodiment of the present invention.

[0053] Reference Figure 2 , Figure 2 Schematic diagram of the flow chart of the first embodiment of the data tracking and analysis method of the present invention.

[0054] like Figure 2 As shown, the data tracking and analysis method includes steps S100 to S600.

[0055] S100, collects tracking data in the target process through the Skywalking distributed tracking system.

[0056] It is understandable that the executor of this embodiment is the data tracking and analysis device, which is usually an electronic device such as a personal computer or a server, and this embodiment does not limit this. The target process can be a claim task process, a loan task process, and other task processes containing complex relationships and related data, and there is no limitation on this. Through Skywalking's distributed tracking system, the execution process of the claim task is monitored in real time, and relevant data of the task is collected. SkyWalking provides comprehensive performance monitoring, distributed tracking, business analysis and other functions to help claims operators effectively monitor, analyze and optimize the performance of microservice applications. The tracking data includes key performance indicators and call chain information such as method calls, HTTP requests, and database access.

[0057] SkyWalking's architecture mainly includes the following parts:

[0058] 1. Probe (Agent):

[0059] The probe is responsible for collecting monitoring data and tracking information in the application. SkyWalking provides probes in multiple languages, including Java, .NET Core, Node.js, etc. The probe collects application call link information in a non-invasive way, including method calls, HTTP requests, etc., and formats this information into a format suitable for SkyWalking.

[0060] 2. Backend components (OAP Server):

[0061] The backend component is used to receive, process, and store data from the probes. It supports data aggregation, data analysis, and drives the flow of data from the probes to the user interface. The backend component adopts a modular design, including receivers, analyzers, storage modules, etc.

[0062] 3. Storage:

[0063] SkyWalking supports multiple storage backends, such as Elasticsearch, MySQL, H2, etc., for persistent storage of monitoring data and tracking information. Users can choose the appropriate storage system according to actual needs and make corresponding configurations.

[0064] 4. User Interface (UI):

[0065] SkyWalking provides an intuitive web interface, through which users can view monitoring data, service topology, tracking details and other information in real time. The interface is highly customizable, and users can adjust the displayed content and layout as needed.

[0066] Further, in this embodiment, the Skywalking distributed tracking system includes: a probe;

[0067] The step of S100 includes:

[0068] Select the target probe and configure it;

[0069] When the application in the target process is started, the tracking data in the target process is collected by the target probe, and the tracking data includes key performance indicators and call chain information.

[0070] It should be noted that the working steps of SkyWalking are: 1. Deploy probes: select appropriate probes according to the language of the application and configure them. For Java applications, it is usually necessary to add the probe Jar package to the startup parameters of the application and configure the corresponding agent.config file. Applications in other languages ​​also have similar configuration methods. 2. Data collection: When the application starts, the probe will automatically collect monitoring data and tracking information. The tracking data collected in the target process includes key performance indicators and call chain information such as method calls, HTTP requests, and database access.

[0071] Further, in this embodiment, the Skywalking distributed tracking system further includes: a backend component;

[0072] After the step of collecting the tracking data in the target process by the target probe when the application in the target process is started, the method further includes:

[0073] The tracking data is sent to the OAP server through the target probe, so that the OAP server integrates and analyzes the tracking data and stores the integrated and analyzed data in the backend component.

[0074] In the specific implementation, SkyWalking's working steps also include: 3. Data processing and storage: The probe sends the collected data to the OAP server via HTTP or gRPC. The OAP server integrates and analyzes the received data and stores the processed data in the configured backend component.

[0075] Further, in this embodiment, the Skywalking distributed tracking system further includes: a user interface;

[0076] After the step of sending the tracking data to the OAP server through the target probe, the method further includes:

[0077] The tracking data is displayed in real time through the user interface of the Skywalking distributed tracking system.

[0078] It is understandable that SkyWalking's working steps also include: 4. Data display: Users can view monitoring data, service topology, tracking details and other information in real time through SkyWalking's Web interface. The tracking details include the tracking data. The interface provides a wealth of visual charts and reports to help users quickly locate performance bottlenecks and root causes of problems.

[0079] SkyWalking's advantages include: 1. Multi-language support: supports probes in multiple programming languages ​​and is suitable for microservice architectures of different technology stacks. 2. High performance: can process monitoring data of large-scale distributed systems and provide real-time performance monitoring and analysis. 3. Visualization: provides an intuitive web interface, allowing users to easily view and analyze monitoring data.

[0080] S200: Import the collected tracking data into a graph database, and use graph storage to model and store the target process.

[0081] It should be noted that the collected tracking data is imported into the graph database, and the advantages of graph storage are used to model and store the claims process. When the target process is the claims task process, the graph database can better handle the complex relationships and associated data in the claims process, providing faster and more flexible data query and analysis.

[0082] like Figure 3 As shown, in the Skywalking distributed link tracking system for claims, components such as the client, Ocelot, aggregation microservices (members, teams, videos, locations, etc.), Consul, claims link monitoring system, RabbitMQ, Alpha, etc. work together to support efficient and reliable claims processing and monitoring.

[0083] In the Skywalking distributed link tracking system for claims, each component interacts through clear interfaces and protocols to jointly support the efficient operation of the claims process. The client initiates the request, Ocelot performs routing and preprocessing, the aggregated microservices handle the business logic, Consul provides service discovery and configuration management, RabbitMQ implements asynchronous communication, the claims link monitoring system monitors the entire process, and Alpha (or other specific components) may be responsible for specific business logic or data processing. This distributed architecture improves the scalability, reliability and flexibility of the system.

[0084] The following is a detailed description of the relationship between these components:

[0085] 1. Client

[0086] Role: The user initiates a claim request through a client (such as a browser, mobile application, etc.).

[0087] Interaction: The client sends the claim request to the front-end service of the system. The front-end service may forward the request to the Ocelot gateway for further processing.

[0088] 2. Ocelot

[0089] Role: As an API gateway, responsible for request routing, conversion, verification, and current limiting.

[0090] Interaction: Receive requests from front-end services or clients. Route requests to corresponding aggregated microservices based on configuration. May perform authentication, current limiting, etc.

[0091] 3. Aggregate microservices (members, teams, videos, locations, etc.)

[0092] Role: Handles business logic in specific areas, such as member information, team information, video evidence, location information, etc.

[0093] Interaction: Receive requests from the Ocelot gateway. Process the business logic in the request, such as verifying claim eligibility, calculating compensation amount, etc. Interact with components such as databases and caches to obtain or store data. May communicate asynchronously with other microservices through RabbitMQ.

[0094] 4. Consul

[0095] Role: Distributed system as service discovery and configuration management.

[0096] Interaction: Microservices register their own information with Consul so that other services can discover and call them. Consul provides health check functions to ensure the availability of services. Microservices obtain configuration information such as database connection strings, cache addresses, etc. through Consul.

[0097] 5. Claims Link Monitoring System

[0098] Role: Responsible for monitoring the call links in the entire claims process to ensure high availability and performance of the system.

[0099] Interaction: Capture and record requests and responses from clients, Ocelot, aggregated microservices, etc. Trace the propagation path of requests in the system, including calls across multiple microservices. Provide a visual interface to display detailed information about the call link to help developers locate problems.

[0100] 6. RabbitMQ

[0101] Role: As a message queue, used for asynchronous communication between microservices.

[0102] Interaction: Microservices send tasks that need to be processed asynchronously to RabbitMQ. Other microservices listen to messages in RabbitMQ and process them as needed. For example, when a claim is approved, a message can be sent to the payment system through RabbitMQ for payment.

[0103] 7. Alpha (assuming it is a specific component in the system)

[0104] Role: Since the specific role of "Alpha" is not explicitly mentioned in the reference article, it is assumed here that it is a key component in the system, which may be responsible for specific business logic or data processing.

[0105] Interaction: Interacts with other components (such as Ocelot, aggregate microservices, RabbitMQ, etc.) to complete its tasks. It may be a key node in the claims process, such as an approval center, a data analysis center, etc.

[0106] S300: Analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain analysis results.

[0107] It should be understood that when the target process is a claims task process, the claims data stored in the graph database is deeply mined and analyzed through a graph analysis algorithm. When deeply mining and analyzing claims data, a cluster analysis algorithm is used. The graph analysis algorithm can identify key nodes, bottlenecks, and potential problems in the claims process, providing valuable information for claims management and decision-making.

[0108] S400: Generate a target analysis report according to the analysis results.

[0109] In the specific implementation, when the target process is the claims task process, real-time claims monitoring and early warning functions are provided according to the analysis results to timely discover and handle potential problems, improve claims efficiency and customer satisfaction. Generate a detailed claims analysis report to provide business analysis and decision support for insurance companies. The report can count and analyze claims data according to different dimensions, such as by time period, product type, region, etc., in order to find problems and improvement directions in the claims process.

[0110] The claims task tracking and analysis algorithm based on Skywalking and graph storage has the following advantages and effects: it can realize real-time monitoring, timely discover and solve problems, and improve the execution efficiency and quality of claims tasks. By taking advantage of graph storage, it can realize fast query and in-depth analysis of data, and improve data processing efficiency and accuracy. Through the graph analysis algorithm, key indicators can be extracted, potential problems and optimization points can be found, and targeted improvement suggestions can be provided to improve the execution efficiency of claims tasks and customer satisfaction. The visual display of analysis results is convenient for managers and staff to understand and operate, and improve work efficiency and synergy.

[0111] In this embodiment, the Skywalking distributed tracking system is used to realize real-time monitoring of the claims process, and potential problems are discovered and handled in a timely manner. In-depth analysis: Graph storage and analysis technology are used to deeply mine and analyze claims data to provide more comprehensive and in-depth business analysis and decision support. Optimize decisions: The analysis results can provide insurance companies with key business information and decision support, and help optimize the claims process and management decisions. Improve efficiency: Through real-time monitoring and in-depth analysis, claims efficiency and customer satisfaction can be improved, and claims costs and risks can be reduced. Strong scalability: The algorithm can be extended to the analysis and optimization of other insurance business processes, such as sales, underwriting, customer service, etc., and has broad application prospects.

[0112] Continue to refer to Figure 2 Based on the above first embodiment of the method, a second embodiment of the data tracking and analysis method of the present invention is proposed.

[0113] In this embodiment, the S300 includes:

[0114] Preprocessing the tracking data stored in the graph database to obtain preprocessed data;

[0115] Constructing a data graph structure according to the preprocessed data;

[0116] Applying a clustering algorithm on the data graph structure to cluster similar nodes into clusters;

[0117] Analyze the clustering results, identify abnormal clusters, and obtain analysis results.

[0118] It should be understood that when the target process is the claims task process, a cluster analysis algorithm is used to conduct in-depth mining and analysis of claims data. The cluster analysis algorithm is based on graph theory and discovers hidden patterns and abnormal behaviors in the data by constructing a graph structure of entities in the claims data (such as policyholders, medical institutions, claims items, etc.) and their relationships (such as insurance relationships, payment relationships, etc.). The following are the detailed steps of the cluster analysis algorithm and its use in claims data analysis:

[0119] Cluster analysis algorithm execution steps:

[0120] 1. Data preprocessing: Extract key fields from the claims data (such as policyholder information, compensation amount, compensation time, etc.), and clean and standardize them to eliminate noise and outliers.

[0121] 2. Build a graph structure: Take entities such as policyholders and medical institutions as nodes, and payment relationships as edges to build a graph structure for claims data.

[0122] 3. Select a clustering algorithm: Select a suitable clustering algorithm based on the data characteristics, such as K-means, DBSCAN, etc.

[0123] 4. Perform cluster analysis: Apply clustering algorithms on the graph structure to cluster similar nodes (i.e., policyholders, medical institutions, etc. with similar claims behaviors or characteristics) into clusters.

[0124] 5. Result analysis: Analyze the clustering results and identify clusters with abnormal claims behavior (such as frequent claims, high claims, etc.) for further investigation.

[0125] Furthermore, in this embodiment, the step of preprocessing the tracking data stored in the graph database to obtain preprocessed data includes:

[0126] The key fields of the tracking data stored in the graph database are extracted, and the key fields are cleaned and standardized to obtain preprocessed data.

[0127] Furthermore, in this embodiment, the target process is a claims settlement task process;

[0128] The step of constructing a data graph structure according to the preprocessed data comprises:

[0129] The entities in the preprocessed data are used as nodes, and the relationships between the entities are used as edges to construct a graph structure of the claims task process.

[0130] It should be noted that when the target process is the claim settlement task process, the insured, medical institution and other entities are used as nodes, and the payment relationship and other entities are used as edges to construct the graph structure of the claim settlement data. Figure 4 As shown, Figure 4 In the Skywalking distributed link tracking system for claims settlement, the relationship between the user, gateway, oauth, mysql, and redis components work together to complete data processing, verification, storage, and retrieval in the claims settlement process.

[0131] In the Skywalking distributed link tracking system for claims settlement, the relationship between these components is closely linked. Users initiate requests through the front-end interface, and the requests are processed by the gateway and forwarded to the back-end microservices for processing. During the processing, it is necessary to authenticate and authorize with the oauth service, store and retrieve data with the MySQL database, and cache and quickly access data with the Redis cache. Throughout the process, the distributed link tracking system will record the call link of each request so that problems can be quickly located and resolved when they occur.

[0132] The following is a detailed description of the relationship between these components:

[0133] 1. User

[0134] Role: As the initiator of the claims process, the user submits the claims request through the front-end interface.

[0135] Interaction: Users initiate requests through browsers or mobile applications, which are first sent to the Gateway for processing.

[0136] 2. Gateway

[0137] Role: As the entry point of the system, it is responsible for receiving requests from users and routing them to the corresponding backend services based on the type and destination of the request.

[0138] Interaction:

[0139] Receive claims requests from users. Perform preliminary processing on the requests, such as load balancing and security verification (possibly combined with oauth services). Forward the processed requests to backend microservices, such as microservices that handle claims logic.

[0140] 3. Authentication and Authorization (OAuth)

[0141] Role: Responsible for user authentication and authorization, ensuring that only authenticated users can access specific resources in the system.

[0142] Interaction: When a user first accesses the system or performs a sensitive operation (such as submitting a claim), the gateway may forward the request to the oauth service for identity authentication. After the oauth service verifies the user's identity and permissions, it returns the corresponding token to the gateway or directly to the requested service. Subsequent service calls can use the token to verify the user's identity and permissions.

[0143] 4. Database (MySQL)

[0144] Role: As the core of data storage, it is responsible for storing all data generated in the claims process, including user information, claims applications, processing results, etc.

[0145] Interaction: When a user submits a claim request, the microservice that processes the request will query or update relevant data from the MySQL database.

[0146] For example, query the user's insurance information, store new claim application records, update claim status, etc.

[0147] 5. Cache (Redis)

[0148] Role: As a data cache layer, it is used to store frequently accessed data to reduce the number of database accesses and improve the system's response speed.

[0149] Interaction:

[0150] When processing a claim request, the microservice may first query the relevant data from Redis. If the data exists in the cache, it will be returned directly. If not, it will be queried from the MySQL database and updated to Redis. The cache can also be used to store temporary data, such as user session information, tokens, etc.

[0151] In this embodiment, based on Skywalking and graph storage, the tracking and analysis of claims tasks can realize real-time monitoring, timely discover and solve problems, and improve the execution efficiency and quality of claims tasks. Taking advantage of graph storage, fast query and deep analysis of data can be realized, and data processing efficiency and accuracy can be improved. Through the graph analysis algorithm, key indicators are extracted, potential problems and optimization points are discovered, and targeted improvement suggestions are provided to improve the execution efficiency and customer satisfaction of claims tasks. The visual display of analysis results is convenient for managers and staff to understand and operate, and improve work efficiency and synergy. Combining the distributed tracking function of Skywalking and the storage and query capabilities of the graph database, real-time tracking and analysis of claims tasks are realized. Through in-depth analysis and reporting, potential problems can be discovered and solved in a timely manner, and the efficiency and transparency of claims task processing can be improved, thereby improving customer satisfaction and company reputation. In addition, this embodiment has strong scalability and can be easily applied to the tracking and analysis of other business processes.

[0152] In addition, as Figure 2 As a specific implementation of the method shown, an embodiment of the present application provides a data tracking and analysis device.

[0153] See also Figure 5 , Figure 5 : is a functional module diagram of a data tracking and analysis device provided by an embodiment of the present application, the device comprising:

[0154] The collection module 10 is used to collect the tracking data in the target process through the Skywalking distributed tracking system;

[0155] A modeling and storage module 20, used to import the collected tracking data into a graph database, and use the graph storage to model and store the target process;

[0156] An analysis module 30, configured to analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result;

[0157] The generating module 40 is used to generate a target analysis report according to the analysis result.

[0158] Further, the Skywalking distributed tracking system includes: a probe;

[0159] The collection module 10 is also used to select a target probe and configure it;

[0160] When the application in the target process is started, the tracking data in the target process is collected by the target probe, and the tracking data includes key performance indicators and call chain information.

[0161] Furthermore, the Skywalking distributed tracking system further includes: a backend component;

[0162] The sending module is used to send the tracking data to the OAP server through the target probe, so that the OAP server integrates and analyzes the tracking data, and stores the integrated and analyzed data in the back-end component.

[0163] Furthermore, the Skywalking distributed tracking system further includes: a user interface;

[0164] A display module is used to display the tracking data in real time through the user interface of the Skywalking distributed tracking system.

[0165] Furthermore, the analysis module 30 is also used to preprocess the tracking data stored in the graph database to obtain preprocessed data; construct a data graph structure based on the preprocessed data; apply a clustering algorithm on the data graph structure to cluster similar nodes into clusters; analyze the clustering results, identify abnormal clusters, and obtain analysis results.

[0166] Furthermore, the analysis module 30 is also used to extract key fields of the tracking data stored in the graph database, clean and standardize the key fields, and obtain pre-processed data.

[0167] Furthermore, the target process is a claims settlement task process;

[0168] The analysis module 30 is further used to construct a graph structure of the claims settlement task process by taking the entities in the preprocessed data as nodes and the relationships between the entities as edges.

[0169] Other embodiments or specific implementations of the data tracking and analysis device of the present invention can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0170] In addition, an embodiment of the present invention further provides a computer-readable storage medium.

[0171] The computer-readable storage medium of the present invention stores a data tracking and analysis program, wherein when the data tracking and analysis program is executed by a processor, the steps of the data tracking and analysis method described above are implemented.

[0172] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0173] Collect tracking data from the target process through the Skywalking distributed tracking system;

[0174] Importing the collected tracking data into a graph database, and using graph storage to model and store the target process;

[0175] Analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result;

[0176] A target analysis report is generated according to the analysis results.

[0177] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0178] The Skywalking distributed tracking system includes: a probe;

[0179] The steps of collecting tracking data in the target process through the Skywalking distributed tracking system include:

[0180] Select the target probe and configure it;

[0181] When the application in the target process is started, the tracking data in the target process is collected by the target probe, and the tracking data includes key performance indicators and call chain information.

[0182] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0183] The Skywalking distributed tracking system also includes: a backend component;

[0184] After the step of collecting the tracking data in the target process by the target probe when the application in the target process is started, the method further includes:

[0185] The tracking data is sent to the OAP server through the target probe, so that the OAP server integrates and analyzes the tracking data and stores the integrated and analyzed data in the backend component.

[0186] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0187] The Skywalking distributed tracking system further includes: a user interface;

[0188] After the step of sending the tracking data to the OAP server through the target probe, the method further includes:

[0189] The tracking data is displayed in real time through the user interface of the Skywalking distributed tracking system.

[0190] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0191] Preprocessing the tracking data stored in the graph database to obtain preprocessed data;

[0192] Constructing a data graph structure according to the preprocessed data;

[0193] Applying a clustering algorithm on the data graph structure to cluster similar nodes into clusters;

[0194] Analyze the clustering results, identify abnormal clusters, and obtain analysis results.

[0195] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0196] The key fields of the tracking data stored in the graph database are extracted, and the key fields are cleaned and standardized to obtain preprocessed data.

[0197] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0198] The target process is the claims settlement task process;

[0199] The step of constructing a data graph structure according to the preprocessed data comprises:

[0200] The entities in the preprocessed data are used as nodes, and the relationships between the entities are used as edges to construct a graph structure of the claims task process.

[0201] The method implemented when the data tracking and analysis program is executed can refer to the various embodiments of the data tracking and analysis method of the present invention, and will not be repeated here.

[0202] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0203] It will also be understood that the term "and / or" as used in this application and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0204] It should be noted that the descriptions of "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0205] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data tracking and analysis method, It is characterized in that The method comprises: Collect tracking data in the target process through the Skywalking distributed tracking system; Importing the collected tracking data into a graph database, and using the graph storage to model and store the target process; Analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result; A target analysis report is generated according to the analysis results.

2. The data tracking and analysis method according to claim 1, It is characterized in that The Skywalking distributed tracking system includes: a probe; The steps of collecting tracking data in the target process through the Skywalking distributed tracking system include: Select the target probe and configure it; When the application in the target process is started, the tracking data in the target process is collected by the target probe, and the tracking data includes key performance indicators and call chain information.

3. The data tracking and analysis method according to claim 2, It is characterized in that The Skywalking distributed tracking system also includes: a backend component; After the step of collecting the tracking data in the target process by the target probe when the application in the target process is started, the method further includes: The tracking data is sent to the OAP server through the target probe, so that the OAP server integrates and analyzes the tracking data and stores the integrated and analyzed data in the backend component.

4. The data tracking and analysis method according to claim 3, It is characterized in that The Skywalking distributed tracking system further includes: a user interface; After the step of sending the tracking data to the OAP server through the target probe, the method further includes: The tracking data is displayed in real time through the user interface of the Skywalking distributed tracking system.

5. The data tracking and analysis method according to any one of claims 1 to 4, It is characterized in that The step of analyzing the tracking data stored in the graph database by a graph analysis algorithm to obtain an analysis result includes: Preprocessing the tracking data stored in the graph database to obtain preprocessed data; Constructing a data graph structure according to the preprocessed data; Applying a clustering algorithm on the data graph structure to cluster similar nodes into clusters; Analyze the clustering results, identify abnormal clusters, and obtain analysis results.

6. The data tracking and analysis method according to claim 5, It is characterized in that The step of preprocessing the tracking data stored in the graph database to obtain preprocessed data includes: The key fields of the tracking data stored in the graph database are extracted, and the key fields are cleaned and standardized to obtain preprocessed data.

7. The data tracking and analysis method according to claim 5, It is characterized in that The target process is the claims settlement task process; The step of constructing a data graph structure according to the preprocessed data comprises: The entities in the preprocessed data are used as nodes, and the relationships between the entities are used as edges to construct a graph structure of the claims task process.

8. A data tracking and analysis device, It is characterized in that The data tracking and analyzing device comprises: The collection module is used to collect tracking data in the target process through the Skywalking distributed tracing system; A modeling and storage module, used to import the collected tracking data into a graph database, and use the graph storage to model and store the target process; An analysis module, used to analyze the tracking data stored in the graph database by using a graph analysis algorithm to obtain an analysis result; A generation module is used to generate a target analysis report according to the analysis results.

9. A data tracking and analysis device, It is characterized in that The data tracking and analysis device includes a processor, a memory, and a data tracking and analysis program stored in the memory and executable by the processor, wherein when the data tracking and analysis program is executed by the processor, the steps of the data tracking and analysis method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the data tracking and analysis method according to any one of claims 1 to 7 are implemented.

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