Business process burying point analysis method and device, equipment and storage medium

Abstract

The invention provides a business process burying point analysis method and device, equipment and a storage medium, and relates to the technical field of data collection. According to the method, the business process information of the current business process is obtained by performing request analysis on the business request; and according to the business process information, the target burying point strategy matched with the current business process is loaded from the strategy database, so that the correlation and the analysis efficiency of the data are improved. Based on the target point burying strategy, point burying codes are injected into the process nodes, and real-time data collection of the key nodes of the business process is achieved. And performing real-time index calculation on the acquired node data by using the distributed stream processing model to obtain a node operation index value. Through a visualization tool, node operation index values are subjected to visualization state conversion, a process thermodynamic diagram is generated, and complex data are displayed in a visual mode, so that business personnel can rapidly understand and respond, and the real-time analysis efficiency and the business response speed of the business process in the financial and medical fields are further improved.

Description

Technical Field

[0001] This application relates to the field of data acquisition technology, and in particular to a method, apparatus, equipment and storage medium for business process data tracking analysis. Background Technology

[0002] In the development of the financial and insurance industries, operations management systems play a crucial role, helping companies optimize business processes, improve service quality, and conduct precise data analysis. Currently, data collection through event tracking is widely used in operations management systems; however, it often employs a decentralized design. This design primarily involves developing separate event tracking logic for each single business scenario, such as the insurance application process or claims review, relying on manual hard-coding to achieve data collection.

[0003] However, this distributed tracking design has the following problems: First, business processes are fragmented. Most existing tracking solutions only focus on local business nodes, such as simply recording user click behavior. However, they lack effective tracking capabilities for cross-system and cross-role collaborative processes, such as end-to-end scenarios involving manual review and automated risk control. It is difficult to connect the entire complex business process for monitoring and analysis.

[0004] Secondly, the configuration flexibility is poor. Because manual strategies and automated configurations are separate, when different business rules need to be adapted, such as dealing with differentiated review processes for insurance products, it is necessary to repeatedly develop code. This undoubtedly increases the development cycle and reduces the overall response speed and adaptability of the system.

[0005] Furthermore, timeliness analysis has limitations. Currently, it only records the timestamp of the event, without linking it to relevant business context information, such as the person in charge of the current node and the status of the preconditions. This makes it difficult to accurately pinpoint the root cause of process blockages during the analysis process, making it impossible to quickly and effectively solve problems and affecting the smooth operation of business.

[0006] Finally, there is a lack of consistent data standards. Different modules use their own independent tracking standards, which requires additional time and effort for data cleaning and alignment when conducting cross-process data analysis. This significantly increases the cost of analysis and also affects the accuracy and reliability of the data, hindering enterprises from making data-driven, informed decisions.

[0007] In summary, distributed data tracking solutions in the operational management systems of the financial and insurance industries suffer from technical problems such as fragmented business processes, poor configuration flexibility, limited real-time analysis, and inconsistent data definitions. These problems severely restrict the improvement of business process data analysis efficiency. Therefore, improving the real-time analysis efficiency of business process data has become an urgent technical problem to be solved. Summary of the Invention

[0008] This application provides a method, apparatus, device, and storage medium for business process data tracking analysis, aiming to improve the efficiency of real-time analysis of business process data.

[0009] Firstly, this application provides a business process tracking point analysis method, which includes the following steps: Upon receiving a business request submitted by a client, the business process information of the current business process is obtained by parsing the business request. Based on the business process information, load the target tracking strategy that matches the current business process from the strategy database; Based on the target tracking strategy, tracking code is injected into the process nodes of the current business process to collect node data of each process node through the tracking code. Based on the distributed stream processing model, real-time index calculations are performed on the node data of each process node to obtain the node operation index values ​​of each process node. Based on visualization tools, the node operation index values ​​of each process node are visualized and their states are transformed to generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

[0010] Secondly, this application also provides a business process tracking point analysis device, the business process tracking point analysis device comprising: The request parsing module is used to obtain the business process information of the current business process by parsing the business request submitted by the client when it receives the business request. The event tracking strategy loading module is used to load a target event tracking strategy that matches the current business process from the strategy database based on the business process information. The event tracking strategy execution module is used to inject event tracking code into the process nodes of the current business process based on the target event tracking strategy, so as to collect node data of each process node through the event tracking code. The node indicator calculation module is used to perform real-time indicator calculation on the node data of each process node based on the distributed stream processing model, and obtain the node operation indicator value of each process node. The process visualization module is used to visualize the state transformation of the node operation index values ​​of each process node based on visualization tools, and generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

[0011] Thirdly, this application also provides a computer device, the computer device including a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the steps of the business process tracking analysis method described above.

[0012] Fourthly, this application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the business process data tracking analysis method described above.

[0013] This application provides a business process data tracking analysis method, apparatus, computer equipment, and storage medium. Upon receiving a business request from a client, the method parses the request to obtain the current business process information, ensuring accurate understanding and positioning of the business process. Based on the business process information, a target data tracking strategy matching the current business process is loaded from a strategy database, enabling precise data tracking adaptation to the current business process, avoiding unnecessary data collection, and improving data relevance and analysis efficiency. Based on the target data tracking strategy, data tracking code is injected into process nodes to collect node data in real time, achieving real-time data collection of key nodes in the business process and providing a detailed data foundation for real-time analysis. A distributed stream processing model is used to calculate real-time indicators from the collected node data, obtaining the node operation indicator values ​​for each process node. Leveraging the efficiency and real-time nature of distributed stream processing, large amounts of data can be processed and calculated quickly, providing timely operational status indicators for the business process. By using visualization tools to visualize the status transitions of node operation metrics and generate process heatmaps, complex data is displayed in an intuitive graphical way, enabling business personnel to quickly identify bottlenecks and problem nodes in the process, thereby making timely decisions and optimizations, and further improving the real-time analysis efficiency and business response speed of the business process. Attached Figure Description

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

[0015] Figure 1 This is a schematic diagram of an application environment for a business process data tracking analysis method according to an embodiment of the present invention; Figure 2 A flowchart illustrating an embodiment of a business process data tracking analysis method provided in this application; Figure 3 This is a schematic diagram of the structure of a business process heatmap provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an embodiment of a business process tracking analysis device provided in this application. Figure 5 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0019] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The business process tracking analysis method provided in this invention can be applied to, for example... Figure 1In this application environment, the client communicates with the server via a network. When the server receives a business request submitted by the client, it parses the request to obtain the business process information of the current business process. Based on the business process information, it loads a target tracking strategy matching the current business process from the strategy database. Based on the target tracking strategy, it injects tracking code into the process nodes of the current business process to collect node data of each process node. Based on a distributed stream processing model, it performs real-time indicator calculations on the node data of each process node to obtain the node operation indicator value of each process node. Based on a visualization tool, it performs a visualization state transformation on the node operation indicator value of each process node to generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node of the current business process.

[0022] This invention addresses the technical problem of low real-time analysis efficiency of business process data in the financial and medical fields in existing technologies. Upon receiving a business request from a client, the invention parses the request to obtain the current business process information, ensuring accurate understanding and positioning of the business process. Based on this business process information, a target tracking strategy matching the current business process is loaded from the strategy database. This ensures precise adaptation of tracking points to the current business process, avoiding unnecessary data collection and improving data relevance and analysis efficiency. Based on the target tracking strategy, tracking code is injected into process nodes to collect node data in real time, achieving real-time data collection of key nodes in the business process and providing a detailed data foundation for real-time analysis. A distributed stream processing model is used to calculate real-time indicators from the collected node data, obtaining node operation indicator values ​​for each process node. Leveraging the efficiency and real-time nature of distributed stream processing, large amounts of data can be processed and calculated quickly, providing timely operational status indicators for the business process. By using visualization tools to visualize the status transitions of node operation metrics and generate process heatmaps, complex data is displayed in an intuitive graphical way, enabling business personnel to quickly identify bottlenecks and problem nodes in the process, thereby making timely decisions and optimizations, and further improving the real-time analysis efficiency and business response speed of the business process.

[0023] The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0024] Please refer to Figure 2 , Figure 2This is a flowchart illustrating an embodiment of a business process tracking analysis method provided in this application.

[0025] like Figure 2 As shown, the business process tracking analysis method includes steps S101 to S105.

[0026] S101. Upon receiving a business request submitted by the client, the business process information of the current business process is obtained by parsing the business request. In one embodiment, the server receives business request data from the client through a network communication interface. The business request data can be transmitted in the form of HTTP requests, HTTPS requests, TCP / IP packets, etc., and the business request can include request headers, request bodies, etc.

[0027] Business requests can contain a lot of information, such as the user's basic information, the type of operation, and relevant business parameters. The server can parse the business request to extract key content, such as the type of operation the user wants to perform and the specific business data involved in that operation.

[0028] Specifically, the business request header is parsed to extract key information. The request header typically contains important information such as user identification (e.g., user ID, session token), business type (e.g., claim application, insurance application), request timestamp, and client IP address, which helps the server identify the source, type, and context of the request.

[0029] The server parses the request body to extract the specific business data it contains. The format of the request body varies depending on the business requirements; common formats include JSON, XML, and form data. During parsing, the server extracts key business parameters based on the request body's format, such as the accident description in a claim, the policyholder information and amount in an insurance policy.

[0030] Based on the information obtained from the parsed request headers and request body, extract the business process information for the current business process. This business process information includes, but is not limited to, business process identifier, business process status, list of process nodes, node execution order, preconditions, and postconditions.

[0031] Specifically, the business process identifier is used to uniquely identify the ID or name of the current business process, such as "health insurance claims process" or "car insurance application process". The business process status indicates the current state of the business process, such as "initial state", "processing", or "completed". The process node list records the identifiers and attributes of each node in the business process, such as "hospital information synchronization node", "claims application submission node", or "automatic claims verification node". The node execution order defines the execution order of the process nodes and the execution path of the process. Preconditions and postconditions refer to the preconditions (such as the conditions that must be met before the node can be executed) and postconditions (such as the conditions that must be met after the node is completed) of each process node.

[0032] After parsing the business request, the business process information can be supplemented and enriched by combining the server-side internal business context data. Business context information may include the user's historical business records, current business environment configuration, and relevant business rules. For example, in the claims process, the user's historical claims records can be linked to understand the user's claims frequency and historical case details. The parsed business process information and the associated business context information are integrated into a business process context object. This object contains a complete description of the current business process and all the key information required for execution, providing a foundation for subsequent data tracking strategy loading and process execution.

[0033] Before the server receives and parses the business request submitted by the client, it needs to complete the construction of the event tracking strategy in advance. This process is based on the analysis of historical business requirements. Through steps such as defining process node information, converting business rules into DSL expressions, and generating basic event tracking templates, a preset event tracking strategy is finally formed and stored in the strategy database, laying the foundation for the loading of event tracking strategies after the business request is parsed.

[0034] In one embodiment, historical business requirements are obtained, and based on these requirements, process node information for at least one business process is defined, wherein the process node information includes key nodes, node attributes, and business rules; the business rules are converted into Domain Feature Language (DSL) expressions based on a mapping function, and a basic tracking template is generated by combining the key nodes and the node attributes; the DSL expressions corresponding to the same process node are associated with the basic tracking template to generate a preset tracking strategy for the business process, and the preset tracking strategy is stored in the strategy database.

[0035] Historical business requirements information can be collected from sources such as historical business data, user feedback, and business department requirement documents. This information reflects the key stages, common problems, and evolution of business rules in actual operation. A thorough analysis of the collected historical business requirements should be conducted to identify the complete execution path of at least one business process. Through communication with business experts, developers, and operations personnel, the starting point, ending point, and key intermediate nodes of the business process should be clearly defined.

[0036] For each business process in historical business data, identify the key nodes within the process. The identification of key nodes can be based on the importance of the business and the monitoring requirements. These key nodes can be important turning points or decision points in the business logic, such as the "hospital information synchronization node," "claims application submission node," "automatic claims verification node," and "manual review node" in the claims process.

[0037] During the data entry point construction phase, domain experts define the attributes of each key node based on actual business processes. These attributes include node name (e.g., "Health Declaration Filling", "Intelligent Underwriting Assessment", "Manual Review Submission"), node type (automatic / manual), node identifier (e.g., ID, SN code, etc., used to uniquely identify the node), preconditions (conditions that must be met before the node is executed, such as "Risk Control Score ≥ 60" or "User Age > 18"), postconditions (conditions that must be met after the node is executed), execution role (the system or personnel role responsible for executing the node's operation), and triggering conditions (e.g., "Form Field Change" or "Page Redirect"). For example, an "Automatic Claims Settlement Node" might have an automatic node type, a precondition of "Claims Application Submitted with Complete Information", a postcondition of "Claims Settlement Result Generated", and an execution role of the claims settlement system.

[0038] In the visualization canvas, node types (automatic / manual), preconditions (such as risk control score ≥ 60 points), and event types (start / end / abnormal) are defined. Based on these definitions, the system can automatically build corresponding business rules. The rules define the behavior and decision logic of nodes under different conditions, such as "automatically triggering the manual review node when the claim amount exceeds 50,000 yuan." This achieves an efficient closed loop from process design to event implementation, improves the flexibility and convenience of event configuration, and reduces errors and long development cycles caused by manual hard coding.

[0039] In addition, this solution can also preset basic event tracking templates for core scenarios such as "insurance application", "policy modification", and "claim submission". These basic event tracking templates not only define a unified event structure, but also standardize context fields, greatly improving the standardization and comparability of data collection.

[0040] Specifically, the structure of the basic event tracking template can be designed, including the template's fields, format, and data types. The template should be able to accommodate relevant information about key nodes and node attributes, such as node identifier, node name, node type, preconditions, postconditions, and execution role. The defined key nodes and their attribute information are then populated into the basic event tracking template to generate a corresponding basic event tracking template instance. Each instance corresponds to the basic event tracking configuration for one key node.

[0041] In one embodiment, a layered event tracking template library can be defined to store and manage the generated basic event tracking templates, precisely adapting to the complex needs of financial and insurance businesses. The layered event tracking template library can also define a three-layer event tracking model, including a basic event layer, a business process layer, and a cross-system layer, covering the entire process monitoring from business operations to cross-system collaboration.

[0042] The basic event layer focuses on atomic operation tracking, comprehensively covering basic interactive behaviors such as button clicks and page loads. These basic tracking points are the cornerstone of building complex business process monitoring, providing detailed data for subsequent business process analysis by accurately capturing every subtle user action.

[0043] The business process layer focuses on node-level tracking, enabling a complete representation of business scenarios such as "insurance application submission → risk control verification → manual review." In actual business operations, by setting tracking points at each key business node, the progress of the business process can be tracked in real time, allowing for timely identification of process bottlenecks.

[0044] The cross-system collaboration layer includes event tracking for inter-system calls, such as interactions with third-party credit reporting systems and reinsurance systems. Financial and insurance businesses often involve collaboration across multiple systems; this layer of event tracking ensures transparency in cross-system operations and provides crucial data support for troubleshooting inter-system communication issues and optimizing collaboration efficiency.

[0045] In one embodiment, to address frequent changes in financial and insurance business rules, this solution utilizes a dynamic strategy-driven engine to transform manually configured business rules (e.g., "trigger manual review when claim amount > 100,000") into executable DSL (Domain Specific Language) expressions. These rules can then be dynamically bound to corresponding business process nodes. In this way, when business rules change, the system can quickly adapt to the new business logic without cumbersome code refactoring, ensuring the accuracy and timeliness of the data tracking.

[0046] Specifically, this can be achieved through predefined mapping functions:

[0047] Where R represents a business rule (e.g., "Claim amount > 100,000 triggers manual review"), Indicates rule conditions (such as monetary thresholds). This indicates the corresponding action (such as generating a manual review node).

[0048] This mapping function takes into account the semantics of business rules, the context of business processes, and the syntax structure of the DSL. Therefore, it can be used to convert each extracted business rule into a DSL expression. For example, for the rule "trigger manual review when the claim amount exceeds 50,000 yuan," the mapping function needs to map the condition part "claim amount exceeds 50,000 yuan" and the action part "trigger manual review" to the condition expression and action expression of the DSL, respectively.

[0049] In one embodiment, for the same process node, its corresponding DSL expression is associated with a basic event tracking template. This association can be achieved by referencing the identifier of the DSL expression in the template or by directly embedding the DSL expression in a specified position within the template. The associated event tracking template not only contains the basic information of the node but also the behavioral logic of the node under different business rules.

[0050] After associating the DSL expressions of all key nodes with the basic event tracking template, a complete preset event tracking strategy is formed. This strategy covers the event tracking configuration and DSL expressions of business rules for all key nodes in the business process, defining the data to be collected and the actions to be triggered during the execution of the business process.

[0051] Design the structure of the strategy database to ensure efficient storage, retrieval, and management of preset tracking strategies. The database table structure should reflect the hierarchical and relational relationships of the tracking strategies. For example, it can be designed to include multiple tables such as a basic strategy information table, a node information table, and a DSL expression table, and linked through primary keys and foreign keys.

[0052] The generated preset tracking strategies are stored in the strategy database. During storage, strategies can be uniquely identified, categorized, and version controlled for easier management and use later. Simultaneously, the integrity and accuracy of the stored strategy data are ensured so that the tracking strategies can be correctly loaded and executed during business request processing.

[0053] In addition, this solution introduces a visual orchestration tool, allowing business personnel to quickly build a business process topology diagram by dragging and dropping pre-built strategy templates and process nodes from the strategy database through intuitive canvas drag-and-drop operations. This achieves seamless integration from process design to event tracking, significantly shortening the event tracking configuration cycle and improving business response speed.

[0054] This embodiment achieves standardization, dynamism, and intelligence in data tracking for financial and insurance business processes by constructing a layered data tracking template library and a dynamic strategy-driven engine. The layered data tracking model comprehensively covers all levels of business operations, while preset templates and visual orchestration tools significantly improve the efficiency and standardization of data tracking configuration. The dynamic strategy-driven engine ensures flexible adjustment and smooth transition of data tracking strategies when business rules change.

[0055] S102. Based on the business process information, load the target data entry strategy that matches the current business process from the strategy database; After parsing the request, the server performs structured parsing of the business process information, extracting key elements of the business process, including but not limited to the type of business process, the identifiers and attributes of process nodes, the execution path of the process, the execution order of each node, and the dependencies between nodes. For example, in a claims process, key nodes identified include "hospital information synchronization," "claims application submission," "automatic claims verification," and "manual review."

[0056] In one embodiment, a corresponding business process instance can be created for the current business request based on the business type and related parameters in the business process information. This instance serves as the current business process corresponding to the request. A unique identifier, the process instance ID, is generated for each business process instance. This ID is used throughout the entire business process, serving as a unique identifier and linking operation logs scattered across different systems. For example, in a claims process, from the user submitting a claims application on the front end, to the core business system registering the case, and then to the reinsurance ceding system processing the reinsurance, all relevant operation logs throughout the process carry the process instance ID.

[0057] In financial insurance operations, a complete business process often involves interactions between multiple systems, such as the insurance application front-end system, core business system, reinsurance ceding system, and financial system. By recording a process instance ID in each system, the operation logs scattered across different systems can be linked together to form a complete end-to-end view of the business process. Using process instance IDs, end-to-end tracing of the process is achieved from the insurance application front-end to the core business system and then to the reinsurance ceding system. By uniformly querying the process instance ID, the operation logs of each system can be linked together chronologically and logically, fully reconstructing the execution path and status changes of the business process.

[0058] In-depth analysis of the contextual information of the business process is crucial, including user roles (such as agents, customers, and underwriters), business data characteristics (such as claim amount and insurance type), the execution environment of the process (such as mobile and PC), and the business rule constraints of the process (such as amount thresholds and review levels). For example, it might be determined that the current user is an agent and the claim amount is 120,000 yuan, which is considered a high-amount claim.

[0059] The server retrieves the tracking strategies from the strategy database based on the key elements and contextual information obtained from parsing and analysis, such as policy ID and claim amount.

[0060] In one embodiment, the business process information is matched and analyzed to determine the business process type and business process attributes of the current business process; based on the business process type and business process attributes, the adaptability of each preset tracking strategy in the strategy database is evaluated, and the target tracking strategy with the highest matching degree with the current business process is output.

[0061] In one embodiment, the strategy database stores a variety of predefined tracking strategies, each matched with a specific business process type and attributes. For example, in a claims process, all tracking strategies related to "health insurance claims" can be retrieved. Therefore, tracking strategies relevant to the current business process can be filtered based on the current business process type and attributes.

[0062] Specifically, key elements for matching are extracted from business process information, including but not limited to business process type (such as insurance application, claims, policy maintenance, etc.) and business process attributes (such as insurance type, business urgency, and range of involved amounts, etc.). The extracted key elements are then used to construct a matching feature vector for subsequent matching analysis with preset tracking strategies in the strategy database.

[0063] Further, at least one preset tracking strategy that matches the business process type is selected from the strategy database to obtain a candidate tracking strategy set; for each candidate tracking strategy in the candidate tracking strategy set, the indicator score of the candidate tracking strategy and at least one evaluation indicator of the current business type is calculated; based on the weight indicator corresponding to each evaluation indicator, the indicator score of each evaluation indicator is weighted and calculated to obtain the matching degree corresponding to each candidate tracking strategy; the candidate tracking strategy with the highest matching degree is selected as the target tracking strategy corresponding to the current business process.

[0064] In one embodiment, in the strategy database, preset tracking strategies that match the current business process type (such as insurance application, claims settlement, policy maintenance, etc.) are filtered out to obtain a set of candidate tracking strategies. The tracking strategies in the strategy database are usually labeled with the applicable business process type, which can be used to quickly narrow down the scope.

[0065] In addition to business process type, preliminary screening can be conducted by combining some basic attributes of the business process (such as insurance type, business urgency, etc.) to further narrow down the range of candidate tracking strategies and improve screening efficiency.

[0066] For each candidate event tracking strategy, we analyze its suitability for the current business process. Evaluation metrics include, but are not limited to, the strategy's coverage of process nodes, its compliance with business rules, and its impact on process performance.

[0067] Node coverage reflects the degree of match between the process nodes defined in the event tracking strategy and the actual nodes in the current business process. For example, if a certain event tracking strategy covers three nodes, namely "hospital information synchronization", "claims application submission" and "automatic claims verification", and the current business process has four key nodes, then the node coverage is 3 / 4.

[0068] Business rule compliance is used to check whether the business rules in the event tracking strategy are consistent with the rules of the current business process. For example, if the rule in the event tracking strategy is "trigger manual review when the claim amount exceeds 100,000 yuan", and it is completely consistent with the rules of the current business process, then the compliance rate is 100%.

[0069] The process attribute matching degree is used to compare the attributes of the business process to which the tracking strategy applies with the actual attributes of the current business process. For example, if the tracking strategy applies to the business process of "health insurance claims" and the amount involved is "greater than or equal to 100,000 yuan", and it completely matches the type of insurance and the amount involved in the current business process, then the matching degree is 100%.

[0070] For each candidate tracking strategy, calculate its score on each evaluation metric according to the definitions above. The score range is usually set between 0 and 100, or other suitable scoring criteria can be used.

[0071] Based on business needs and the importance of each evaluation indicator, the corresponding weight indicators are determined. These weight indicators reflect the relative importance of different evaluation indicators in the final matching degree calculation. For example, node coverage has a weight of 40%, business rule compliance has a weight of 30%, and process attribute matching degree has a weight of 30%. Based on the weight indicators of each evaluation indicator, the indicator scores for each evaluation indicator are calculated using a weighted average. The specific formula is: Matching Degree = Σ(Evaluation Indicator Score × Evaluation Indicator Weight Indicator).

[0072] Sort all candidate event tracking strategies from highest to lowest according to their calculated matching scores, and select the candidate event tracking strategy with the highest matching score as the target event tracking strategy for the current business process. If multiple strategies with the same matching score exist, select the strategy that covers the most nodes or has the most complete business rules as the target event tracking strategy.

[0073] S103. Based on the target data collection strategy, inject data collection code into the process nodes of the current business process to collect node data of each process node through the data collection code. Based on the current process instance status (e.g., policy ID=2023XXXX), the matching event tracking strategy DSL (domain-specific language) is dynamically loaded from the strategy repository, and the business code is intercepted through AOP aspect technology to automatically inject the event tracking code without intruding on the original logic.

[0074] Furthermore, based on the target tracking strategy, tracking code corresponding to each process node in the current business process is generated; using aspect-oriented programming technology, the tracking code corresponding to each process node is injected into the corresponding process node; when the current business process executes to the process node where the tracking code is injected, the execution of the tracking code is triggered, and the node data of the process node is collected, wherein the node data includes five-dimensional information of timestamp, node ID, execution role, business tag, and context state.

[0075] Based on the event tracking configuration for each process node, corresponding event tracking code snippets are dynamically generated. These code snippets include data acquisition logic, context-aware logic, and data transmission logic.

[0076] Specifically, relevant information about process nodes is extracted from the target event tracking strategy, including the node's unique identifier, node name, node type (e.g., automatic or manual node), and preconditions and postconditions. In typical event tracking data collection, basic event attributes are usually collected, such as user ID, timestamp, event type (e.g., click, submit, load), data transmission method, and target.

[0077] To more comprehensively reflect the actual situation and underlying context of business processes, business context tags are automatically injected on top of traditional event attributes. These tags include, but are not limited to: current policy status (e.g., whether the policy is valid, expired, or pending claims), business process stage (e.g., "information entry stage," "confirmation stage," "payment stage," etc. in the insurance application process), user role information (e.g., agent, policyholder, insured, beneficiary, etc.), and business product line (distinguishing different insurance product lines, such as health insurance, auto insurance, life insurance, etc.). By integrating business context-aware logic into the event tracking code, the corresponding business context tags can be automatically obtained and added when an event occurs. When the event tracking code is triggered, the tag information is obtained by calling the context management module's interface or directly reading the context storage, and then packaged together with the traditional event attributes and sent to the data acquisition system.

[0078] By leveraging AOP (Aspect-Oriented Programming) technology to intercept business logic code, tracking code is woven into key nodes of the business process code. This allows for the automatic triggering of tracking code execution when the business process reaches specific nodes, completing the data collection task, without modifying the original business logic code. Based on the node information in the target tracking strategy, the tracking code is precisely injected into the corresponding process nodes.

[0079] As the business process progresses, the server, following the data tracking strategy, triggers the execution of the data tracking code when the process reaches a node where the tracking code is injected. This process collects five dimensions of information from that node: timestamp, node ID, execution role, business tag, and context state. This multi-dimensional data collection method enriches the data's content, enabling the collected data to more comprehensively reflect each stage and detail of the business process, providing ample data support for in-depth analysis of problems and bottlenecks within the business process.

[0080] The collected node data is stored in a time-series database (such as InfluxDB) and then analyzed in depth using association rule mining algorithms (such as the Apriori algorithm) to identify process bottlenecks. For example, analysis may reveal that "when the number of health declaration modifications exceeds 3, the underwriting approval rate drops by 40%", providing strong evidence for optimizing business processes and helping companies to improve processes in a targeted manner and enhance operational efficiency.

[0081] This embodiment can flexibly adapt to different data tracking strategies according to different business scenarios and rule changes, without requiring extensive modifications to the original code, thus improving the maintainability and scalability of the system.

[0082] S104. Based on the distributed stream processing model, perform real-time index calculation on the node data of each process node to obtain the node operation index value of each process node. In one embodiment, a mature distributed stream processing framework, such as Apache Flink, is employed, which features high throughput, low latency, support for event-driven processing, and state management. The input data stream is partitioned according to certain rules (such as process instance ID, node type, etc.), allowing different partitions to be processed in parallel, thereby improving data processing efficiency.

[0083] Retrieve real-time node data for each process node from the event tracking code or message queue, including timestamps, node IDs, execution roles, business tags, and context states. Define the node performance metrics to be calculated based on business requirements, such as processing time, blocking rate, throughput, and error rate.

[0084] By leveraging the windowing functionality of distributed stream processing frameworks, data is divided into time windows (such as scrolling windows or sliding windows) or counting windows to facilitate aggregation calculations within each window. Specifically, the server utilizes tools such as the Flink streaming engine to perform real-time calculations on key metrics such as processing time and blocking rate for each node.

[0085] For example, for each process node, record the start and end timestamps of the node and calculate its processing time. Analyze the node's waiting time and processing time to calculate the node's blocking rate, where blocking rate = (waiting time / (waiting time + processing time)) × 100%. Statistically calculate the amount of data or requests passing through a given process node per unit time to calculate the node's throughput. Record the number of errors that occur during node execution and the total number of executions, and calculate the error rate, where error rate = (number of errors / total number of executions) × 100%.

[0086] Meanwhile, the server will also use data mining techniques such as the Apriori algorithm to analyze the collected data in order to discover potential bottlenecks in the process, such as "when the health declaration is modified more than 3 times, the underwriting pass rate drops by 40%", thereby identifying nodes that take too long or frequently make mistakes.

[0087] Specifically, the Apriori algorithm is used to mine frequent itemsets in node data, identifying frequently occurring node combinations or event patterns. Based on these frequent itemsets, association rules are generated to reveal potential relationships and influencing factors between nodes. By analyzing metrics such as processing time and blocking rate, nodes with excessively long processing times are identified, such as a node whose average processing time is significantly higher than other nodes. Based on error rate metrics, frequently failing nodes are located, the causes of errors are analyzed, and optimization measures are implemented. Combining the association rule mining results, node combinations or business patterns that lead to process bottlenecks are discovered; for example, in a certain business scenario, frequent blocking and errors of multiple nodes result in low overall process efficiency.

[0088] This embodiment can reflect the operational status of business processes in a timely manner through real-time indicator calculation, enabling enterprises to quickly identify anomalies or potential problems in the processes, so as to take timely measures to adjust and optimize them, and ensure the smooth operation of business processes.

[0089] S105. Based on visualization tools, the node operation index values ​​of each process node are visualized and their states are transformed to generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

[0090] Based on the definition of the business process and the information of its nodes, the topology of the current business process is drawn. The process heatmap graphically displays each process node and its connections, forming a structure like... Figure 3 The business process diagram shown.

[0091] Choose powerful visualization tools to implement visualization components and interactive functions. The server displays the execution status and analysis results of the process through visualization tools. For example, on the process heatmap, nodes with high timeout rates can be highlighted in red, allowing business personnel to intuitively see where the problems occur, thus providing a basis for process optimization.

[0092] Based on predefined performance thresholds and business rules, the node operation indicators of each process node are evaluated to determine the current operating status of each node. For example, whether the response time exceeds the set threshold, whether the throughput is lower than normal, whether the error rate is too high, and whether resource utilization has reached a bottleneck.

[0093] Furthermore, by comparing the node operation index values ​​of each process node with preset node index thresholds, the current node status of each process node is determined; based on the visualization tool, each process node is marked with its corresponding current node status to generate the process heatmap.

[0094] In one embodiment, threshold values ​​for node metrics are set based on historical data of the business process, business requirements, and experience. For example, for the processing time metric, threshold values ​​of different levels are determined by referring to the average processing time and fluctuation range of each node in historical data and combining the business's requirements for process efficiency; for the error rate metric, it is set based on the business's tolerance for errors.

[0095] For example, multiple levels of node indicator thresholds can be set to reflect different levels of node status. For instance, a first node indicator threshold (upper limit of normal threshold) and a second node indicator threshold (upper limit of warning threshold) can be set to distinguish between normal, warning, and abnormal states.

[0096] In one embodiment, when the node operation index value of the current process node is less than or equal to a first node index threshold, the current node state of the current process node is determined to be a first node state; when the node operation index value of the current process node is greater than the first node index threshold and less than a second node index threshold, the current node state of the current process node is determined to be a second node state; when the node operation index value of the current process node is greater than or equal to the second node index threshold, the current node state of the current process node is determined to be a third node state.

[0097] In one embodiment, the operating status of each node can be divided into different levels, such as the first node status (normal), the second node status (warning), and the third node status (abnormal).

[0098] Specifically, when the node operation metric value of the current process node is less than or equal to the threshold value of the first node metric, the current node state is determined to be the first node state. For example, if the processing time metric is within the normal range and the error rate is low, it indicates that the node is operating normally and there are no obvious problems.

[0099] When the node operation metric value of the current process node is greater than the threshold value of the first node metric but less than the threshold value of the second node metric, the current node state is determined to be the second node state. This indicates that the node operation has some anomalies that require attention but have not yet reached a serious level.

[0100] When the node operation metric value of the current process node is greater than or equal to the threshold value of the second node metric, the current node state is determined to be the third node state. At this point, the node operation is severely abnormal and may have a significant impact on the business process, requiring priority handling.

[0101] In one embodiment, in practical applications, multiple node operation metrics can be combined to determine the node's state. For example, processing time and error rate can be considered simultaneously. Only when both metrics are within the normal range is the node determined to be in the first node state. If one metric exceeds the normal range but does not reach the warning range, the node is in an intermediate state. If any metric reaches the warning or abnormal range, the final state of the node is determined according to the corresponding rules.

[0102] Based on the operational status of each node, different color codes and node size variations are used to mark the corresponding nodes in the process heatmap. For example, green represents a normal state, yellow represents a warning state, and red represents an abnormal state, while the node size can be adjusted according to the severity of the indicator value.

[0103] Specifically, nodes are marked with different colors based on their current state. For example, the first node is marked in green, indicating normal operation; the second node is marked in yellow, indicating a warning; and the third node is marked in red, indicating an anomaly.

[0104] In addition to color coding, the display size of nodes can be dynamically adjusted based on their performance metrics. For example, nodes with longer processing times or higher error rates are displayed larger, thus visually reflecting the severity of a node's problems.

[0105] Different shapes or border styles can also be used to distinguish node types or states. For example, a circle represents a normal node, a diamond represents a decision node; a solid border represents a node without alarms, and a dashed border represents a node with alarms, etc.

[0106] In the process heatmap, each process node is represented by a graphic (such as a rectangle, circle, etc.), and according to the status marking method mentioned above, the node is filled with corresponding colors, and its size and shape are set as visual attributes to intuitively display the current running status of the node.

[0107] The visualization tool supports automatic updates based on predefined rules and dynamic refreshes triggered by user interaction, ensuring that the process heatmap reflects the latest status of each node in the current business process in real time. Specifically, as the node's performance metrics are updated in real time, the node status markers in the heatmap are also dynamically updated accordingly, automatically highlighting high-frequency timeout nodes (such as those whose average manual review time exceeds the threshold by 200%). Through intuitive visualization, relevant business and management personnel can quickly locate bottleneck nodes and problems in the process, facilitating further analysis and decision-making, and improving operational management level and efficiency.

[0108] For example, in the health insurance claims process, a heatmap can visually show the status of each node. For instance, the processing time of the "Hospital Information Synchronization" node is normal (less than or equal to the first threshold), displayed in green, indicating that the node is operating normally; the error rate of the "Automatic Claims Verification" node is slightly higher than the first threshold but lower than the second threshold, displayed in yellow, indicating that attention needs to be paid to errors at this node; the processing time of the "Manual Review" node is much higher than the second threshold, displayed in red, indicating a serious bottleneck at this node, possibly due to task backlog or complex review standards. Claims specialists can use this information to take timely measures, such as optimizing the "Hospital Information Synchronization" interface to prevent potential problems, investigating the causes of errors in "Automatic Claims Verification" and adjusting relevant rules, increasing manpower for "Manual Review" or optimizing the review process, thereby improving overall claims efficiency and service quality.

[0109] In one embodiment, warning information can be visually displayed on the process heatmap by combining warning rules. When the indicator value of a certain node exceeds the normal range, the user is alerted through flashing, pop-up prompts, or notifications. Warning prompts can be displayed in a tiered manner, with different reminder methods used for different levels of alarm status. For example, minor alarms only display a warning sign on the heatmap, while serious alarms are marked on the heatmap and also send SMS or email notifications to relevant personnel.

[0110] In addition to node status, information such as traffic volume and data transmission latency can be displayed on the connection lines based on the data flow and relationships between nodes, further enriching the visualization content of the process heatmap. For example, different colors or thicknesses of connection lines can be used to represent different traffic volumes or latency levels.

[0111] In addition, the process heatmap can also include hyperlinks and hover tooltips, allowing users to view detailed node information and historical trend data by clicking or hovering over nodes. Specifically, when a user hovers the mouse over a node or connection, detailed information about that node or connection is displayed, including node name, processing time, blocking rate, error rate, current status, and comparison of metrics with thresholds.

[0112] This embodiment uses a process heatmap generated by a visualization tool to intuitively and dynamically display the current status of each process node through various visual means such as color coding and node size adjustment. Combined with early warning and interactive functions, it helps business personnel quickly locate problem nodes, make targeted optimizations and adjustments, and improve the efficiency and stability of business processes.

[0113] Ultimately, the server needs to output the following results: First, it needs to provide feedback on the business processing results to the client, informing the user whether their operation was successful or failed, and the general situation of the operation; second, it needs to store the process data, which provides a foundation for subsequent data analysis and system optimization; and third, it needs to generate real-time process performance analysis reports to help business personnel understand the operation of the process in a timely manner, and then optimize and improve the process.

[0114] The general data tracking framework built in this solution effectively solves the problems of business process fragmentation, poor configuration flexibility, limited timeliness analysis, and inconsistent data standards in traditional data tracking solutions in financial and insurance operation management systems. It enables refined data tracking and real-time performance analysis of complex business processes, providing strong support for the digital transformation and operational optimization of the financial and insurance industries.

[0115] This embodiment provides a business process tracking analysis method. Upon receiving a business request from a client, this method parses the request to obtain the current business process information, ensuring accurate understanding and positioning of the business process. Based on the business process information, a target tracking strategy matching the current business process is loaded from the strategy database. This ensures precise tracking of the current business process, avoiding unnecessary data collection and improving data relevance and analysis efficiency. Based on the target tracking strategy, tracking code is injected into process nodes to collect node data in real time, achieving real-time data collection of key nodes in the business process and providing a detailed data foundation for real-time analysis. A distributed stream processing model is used to calculate real-time indicators from the collected node data, obtaining the node operation indicator values ​​for each process node. Leveraging the efficiency and real-time nature of distributed stream processing, large amounts of data can be processed and calculated quickly, providing timely operational status indicators for the business process. By using visualization tools to visualize the status transitions of node operation metrics and generate process heatmaps, complex data is displayed in an intuitive graphical way, enabling business personnel to quickly identify bottlenecks and problem nodes in the process, thereby making timely decisions and optimizations, and further improving the real-time analysis efficiency and business response speed of the business process.

[0116] Please see Figure 4 , Figure 4 This is a schematic diagram of the current embodiment of a business process tracking analysis device provided in this application. The business process tracking analysis device is used to execute the aforementioned business process tracking analysis method.

[0117] like Figure 4 As shown, the business process tracking analysis device 200 includes: a request parsing module 201, a tracking strategy loading module 202, a tracking strategy execution module 203, a node indicator calculation module 204, and a process visualization module 205.

[0118] The request parsing module 201 is used to obtain the business process information of the current business process by parsing the business request submitted by the client when it receives the business request. The event tracking strategy loading module 202 is used to load a target event tracking strategy that matches the current business process from the strategy database based on the business process information. The event tracking strategy execution module 203 is used to inject event tracking code into the process nodes of the current business process based on the target event tracking strategy, so as to collect node data of each process node through the event tracking code. The node indicator calculation module 204 is used to perform real-time indicator calculation on the node data of each process node based on the distributed stream processing model, and obtain the node operation indicator value of each process node. The process visualization module 205 is used to visualize the state transformation of the node operation index values ​​of each process node based on visualization tools, and generate a process heat map corresponding to the current business process, so as to reflect the current node status of each process node of the current business process through the process heat map.

[0119] In one embodiment, the event tracking strategy loading module 202 includes: A business process matching unit is used to match and analyze the business process information to determine the business process type and business process attributes of the current business process. The target event tracking strategy determination unit is used to evaluate the adaptability of each preset event tracking strategy in the strategy database based on the business process type and business process attributes, and output the target event tracking strategy with the highest matching degree with the current business process.

[0120] In one embodiment, the target embedding strategy determination unit includes: The candidate event tracking strategy filtering subunit is used to filter at least one of the preset event tracking strategies that match the business process type in the strategy database, and obtain a set of candidate event tracking strategies. The indicator scoring calculation subunit is used to calculate the indicator score of each candidate event tracking strategy in the candidate event tracking strategy set and at least one evaluation indicator of the current business type. The matching degree acquisition subunit is used to perform weighted calculation of the indicator scores of each evaluation indicator based on the weight indicators corresponding to each evaluation indicator, and obtain the matching degree corresponding to each candidate tracking point strategy. The target event tracking strategy determination subunit is used to select the candidate event tracking strategy with the highest matching degree as the target event tracking strategy corresponding to the current business process.

[0121] In one embodiment, the process visualization module 205 includes: The current node status determination unit is used to compare the node operation index value of each process node with the preset node index threshold to determine the current node status of each process node. The process heatmap generation unit is used to generate the process heatmap by marking each process node according to its current state based on the visualization tool.

[0122] In one embodiment, the current node state determination unit includes: The first node state determination subunit is used to determine the current node state of the current process node as the first node state when the node operation index value of the current process node is less than or equal to the first node index threshold. The second node state determination subunit is used to determine the current node state of the current process node as the second node state when the node operation index value of the current process node is greater than the first node index threshold and less than the second node index threshold. The third node state determination subunit is used to determine the current node state of the current process node as the third node state when the node operation index value of the current process node is greater than or equal to the second node index threshold.

[0123] In one embodiment, the event tracking strategy execution module 203 includes: The event tracking code generation unit is used to generate event tracking codes corresponding to each process node in the current business process based on the target event tracking strategy. The event tracking code injection unit is used to inject the event tracking code corresponding to each process node into the corresponding process node using aspect-oriented programming technology. The node data acquisition unit is used to trigger the execution of the tracking code when the current business process reaches the process node where the tracking code is injected, and to collect the node data of the process node. The node data includes five-dimensional information: timestamp, node ID, execution role, business tag, and context state.

[0124] In one embodiment, the business process tracking analysis device 200 further includes a preset tracking strategy generation module, comprising: A node information definition unit is used to acquire historical business requirements and, based on the historical business requirements, define process node information for at least one business process, wherein the process node information includes key nodes, node attributes, and business rules. The basic tracking module generation unit is used to convert the business rules into domain feature language (DSL) expressions based on the mapping function, and to generate a basic tracking template by combining the key nodes and the node attributes. The preset tracking strategy generation unit is used to associate the DSL expression corresponding to the same process node with the basic tracking template to generate the preset tracking strategy corresponding to the business process, and store the preset tracking strategy in the strategy database.

[0125] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the device and each module described above can be referred to the corresponding processes in the aforementioned business process data point analysis method embodiments, and will not be repeated here.

[0126] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 5 It runs on the computer device shown.

[0127] Please see Figure 5 , Figure 5 This is a schematic block diagram illustrating the structure of a computer device according to an embodiment of this application. The computer device may be a server.

[0128] See Figure 5 The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0129] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any business process tracking analysis method.

[0130] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0131] Internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to perform any business process tracking analysis method.

[0132] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0134] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Upon receiving a business request submitted by a client, the business process information of the current business process is obtained by parsing the business request. Based on the business process information, load the target tracking strategy that matches the current business process from the strategy database; Based on the target tracking strategy, tracking code is injected into the process nodes of the current business process to collect node data of each process node through the tracking code. Based on the distributed stream processing model, real-time index calculations are performed on the node data of each process node to obtain the node operation index values ​​of each process node. Based on visualization tools, the node operation index values ​​of each process node are visualized and their states are transformed to generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

[0135] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement any of the business process tracking analysis methods provided in the embodiments of this application.

[0136] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard equipped on the computer device.

[0137] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for analyzing data points in a business process, characterized in that, The method includes: Upon receiving a business request submitted by a client, the business process information of the current business process is obtained by parsing the business request. Based on the business process information, load the target tracking strategy that matches the current business process from the strategy database; Based on the target tracking strategy, tracking code is injected into the process nodes of the current business process to collect node data of each process node through the tracking code. Based on the distributed stream processing model, real-time index calculations are performed on the node data of each process node to obtain the node operation index values ​​of each process node. Based on visualization tools, the node operation index values ​​of each process node are visualized and their states are transformed to generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

2. The business process data tracking analysis method according to claim 1, characterized in that, The step of loading a target event tracking strategy that matches the current business process from the strategy database based on the business process information includes: The business process information is matched and analyzed to determine the business process type and business process attributes of the current business process; Based on the business process type and business process attributes, the adaptability of each preset tracking strategy in the strategy database is evaluated, and the target tracking strategy with the highest matching degree with the current business process is output.

3. The business process data tracking analysis method according to claim 2, characterized in that, The process of evaluating the adaptability of each preset tracking strategy in the strategy database based on the business process type and business process attributes, and outputting the target tracking strategy with the highest matching degree to the current business process, includes: Filter at least one of the preset tracking strategies that match the business process type in the strategy database to obtain a set of candidate tracking strategies; For each candidate event tracking strategy in the candidate event tracking strategy set, calculate the indicator score of the candidate event tracking strategy and at least one evaluation indicator of the current business type; Based on the weight index corresponding to each of the evaluation indicators, the index scores of each of the evaluation indicators are weighted and calculated to obtain the matching degree corresponding to each of the candidate tracking strategies. The candidate event tracking strategy with the highest matching degree is selected as the target event tracking strategy corresponding to the current business process.

4. The business process data tracking analysis method according to claim 1, characterized in that, The process of visually transforming the node operation index values ​​of each process node using visualization tools to generate a process heatmap corresponding to the current business process includes: By comparing the node operation index values ​​of each process node with the preset node index threshold, the current node status of each process node is determined. Based on the visualization tool, each process node is marked with its current state to generate the process heatmap.

5. The business process data tracking analysis method according to claim 4, characterized in that, The step of comparing the node operation indicator values ​​of each process node with preset node indicator thresholds to determine the current node status of each process node includes: When the node operation index value of the current process node is less than or equal to the first node index threshold, the current node state of the current process node is determined to be the first node state. When the node operation index value of the current process node is greater than the first node index threshold and less than the second node index threshold, the current node state of the current process node is determined to be the second node state. When the node operation index value of the current process node is greater than or equal to the second node index threshold, the current node state of the current process node is determined to be the third node state.

6. The business process data tracking analysis method according to claim 1, characterized in that, The step of injecting tracking code into the process nodes of the current business process based on the target tracking strategy, so as to collect node data of each process node through the tracking code, includes: Based on the target tracking strategy, generate tracking code corresponding to each process node in the current business process; By using aspect-oriented programming, the embedded code corresponding to each process node is injected into the corresponding process node; When the current business process reaches the process node where the tracking code is injected, the execution of the tracking code is triggered, and the node data of the process node is collected. The node data includes five-dimensional information: timestamp, node ID, execution role, business tag, and context state.

7. The business process data tracking analysis method according to claim 1, characterized in that, Before obtaining the business process information of the current business process by parsing the business request, the method further includes: Obtain historical business requirements, and based on the historical business requirements, define process node information for at least one business process, wherein the process node information includes key nodes, node attributes, and business rules. Based on the mapping function, the business rules are converted into domain feature language (DSL) expressions, and a basic tracking template is generated by combining the key nodes and the node attributes. Associate the DSL expression corresponding to the same process node with the basic tracking template to generate a preset tracking strategy for the business process, and store the preset tracking strategy in the strategy database.

8. A business process data tracking and analysis device, characterized in that, The business process data tracking analysis device includes: The request parsing module is used to obtain the business process information of the current business process by parsing the business request submitted by the client when it receives the business request. The event tracking strategy loading module is used to load a target event tracking strategy that matches the current business process from the strategy database based on the business process information. The event tracking strategy execution module is used to inject event tracking code into the process nodes of the current business process based on the target event tracking strategy, so as to collect node data of each process node through the event tracking code. The node indicator calculation module is used to perform real-time indicator calculation on the node data of each process node based on the distributed stream processing model, and obtain the node operation indicator value of each process node. The process visualization module is used to visualize the state transformation of the node operation index values ​​of each process node based on visualization tools, and generate a process heatmap corresponding to the current business process, so as to reflect the current node status of each process node in the current business process through the process heatmap.

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

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the business process data tracking analysis method as described in any one of claims 1 to 7.

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