Processing method and device for data product subscription approval process and related equipment

Abstract

The invention provides a data product subscription approval process processing method and device and related equipment, and the method comprises the steps: collecting related data of a subscription approval process from a multi-source heterogeneous data source, and converting the collected data into data in a uniform format; performing process mining on the data in the uniform format by using a preset process mining algorithm, and generating a process model of the subscription approval process; analyzing the process approval model to obtain a potential process mode, an abnormal path and a bottleneck link; extracting an enhanced approval rule by utilizing an intelligent rule engine in combination with a business rule base of the user, an approval strategy and a process mining result; a process optimization scheme is formulated in combination with a process mining result and an enhanced approval rule, an existing approval process is optimized, the method is suitable for a scene of processing multi-source heterogeneous data, the problems that the existing approval process is fixed, low in efficiency and insufficient in flexibility are efficiently solved, and the approval efficiency is improved. And it is ensured that the processed approval process is highly fit with an actual application scene.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus and related equipment for processing a data product subscription approval process. Background Technology

[0002] In the data-driven era, the data product subscription approval process is a core mechanism for enterprises and organizations to manage the circulation of data assets. Its main purpose is to manage data consumers' subscription requests for access to "data products" (such as datasets, data service interfaces, or analytical reports). An excellent approval process needs to strike a balance between promoting the release of data value and ensuring data security and compliance. However, current technologies for implementing such processes are generally quite rudimentary. The approval processes of most organizations rely primarily on fixed processes set by human experience. This means that the approval paths, nodes, and rules are predefined based on the experience of managers, and once the process is set, it lacks flexibility. This stems from a lack of in-depth analysis capabilities of actual process execution data. To improve fixed processes, some systems have introduced simple process mining techniques, but these techniques have limited capabilities, mostly only able to perform preliminary analysis on process data from a single source, and unable to effectively handle multi-source heterogeneous process data. This results in a coarse process model that deviates significantly from the complex real-world business scenarios. Due to its limited analytical methods, coarse models, weak rule extraction capabilities, and rigid processes, existing technologies can no longer meet users' urgent needs for efficient, accurate, and flexible data product subscription approval processes. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, and related equipment for processing data product subscription approval processes, so as to at least partially solve the above problems.

[0004] In a first aspect, embodiments of this application provide a method for processing a data product subscription approval process, including: Data related to the subscription approval process is collected from multiple heterogeneous data sources and converted into a unified format. The data related to the subscription approval process includes at least one of the following: subscription application data, approval process data, and user behavior data. A process mining algorithm is used to mine the data in the unified format to generate a process model of the subscription approval process. The process approval model is used to display the various stages, approval actions and flow paths of the subscription approval process. The process approval model is analyzed to obtain potential process patterns, abnormal paths, and bottlenecks. By using an intelligent rule engine, combined with the user's business rule library, approval strategy, potential process patterns, abnormal paths and bottlenecks, approval rules are extracted from the process model to extract enhanced approval rules. Based on the results of the process mining and the enhanced approval rules, a process optimization plan is formulated to optimize the existing data product subscription approval process.

[0005] Optionally, in one embodiment of this application, the multi-source heterogeneous data source includes at least one of relational databases, non-relational databases, log files, and API interfaces.

[0006] Optionally, in one embodiment of this application, the analysis of the process approval model to obtain potential process patterns, abnormal paths, and bottlenecks includes: The process model is analyzed and optimized in depth using machine learning algorithms to identify abnormal paths and bottlenecks in the approval process and to determine potential process patterns that can improve the efficiency of the current approval process.

[0007] Optionally, in one embodiment of this application, the method further includes: verifying and optimizing the extracted enhanced approval rules before formulating a process optimization scheme based on the results of the process mining and the enhanced approval rules.

[0008] Optionally, in one embodiment of this application, optimizing the existing data product subscription approval process includes: Implement at least one of the following strategies for the existing subscription approval process: process simplification, process reengineering, or resource allocation optimization.

[0009] Optionally, in one embodiment of this application, the method further includes: real-time monitoring and evaluation of the optimized subscription approval process, so as to continuously improve the subscription approval process based on the evaluation results.

[0010] Optionally, in one embodiment of this application, the step of continuously improving the subscription approval process based on the evaluation results includes: Using the operational data related to the subscription approval process collected during the evaluation as new input, the steps of process mining, approval rule extraction, and process optimization are repeated.

[0011] Optionally, in one embodiment of this application, the method further includes: visually displaying one or more of the information in the process model, approval rules, process optimization scheme, and the real-time monitoring and evaluation process operation data.

[0012] Secondly, based on the data product subscription approval process processing method described in the first aspect of this application, embodiments of this application also provide a data product subscription approval process processing apparatus, including: The acquisition module is used to collect relevant data of the subscription approval process from multiple heterogeneous data sources and convert the collected heterogeneous data into data in a unified format. The subscription approval process related data includes at least one of subscription application data, approval process data and user behavior data. The construction module is used to perform process mining on the unified format data using a preset process mining algorithm to generate a process model of the subscription approval process. The process approval model is used to display the various stages, approval actions and flow paths of the subscription approval process. The analysis module is used to analyze the process approval model to obtain potential process patterns, abnormal paths, and bottlenecks. The extraction module is used to extract approval rules from the process model by utilizing the intelligent rule engine, combined with the user's business rule library, approval strategy, potential process patterns, abnormal paths and bottlenecks, and extracting enhanced approval rules. The processing module is used to formulate a process optimization plan based on the results of the process mining and the enhanced approval rules, so as to optimize the existing data product subscription approval process.

[0013] Thirdly, embodiments of this application also provide a computer storage medium storing computer-executable instructions, which, when executed, perform a processing method for any data product subscription approval process as described in the first aspect of embodiments of this application.

[0014] Fourthly, embodiments of this application also provide an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to execute a processing method for any data product subscription approval process as described in the first aspect of the embodiments of this application.

[0015] This application provides a method, apparatus, and related equipment for processing data product subscription approval processes. It involves collecting relevant data from multiple heterogeneous data sources and converting this data into a unified format. The relevant data includes at least one of subscription application data, approval process data, and user behavior data. A preset process mining algorithm is used to perform process mining on the unified format data to generate a process model for the subscription approval process. This model displays each stage, approval action, and flow path of the subscription approval process. The process model is analyzed to obtain potential process patterns, abnormal paths, and bottlenecks. An intelligent rule engine, combined with the user's business rule base, approval strategy, and the potential process patterns, abnormal paths, and bottlenecks, extracts approval rules from the process model to obtain enhanced approval rules. Based on the results of the process mining and the enhanced approval rules, a process optimization plan is formulated to optimize the existing data product subscription approval process. This method addresses the shortcomings of existing technologies, such as the inability to handle single data sources and significant discrepancies between process models and actual business operations, by integrating heterogeneous subscription and approval data from multiple sources and performing unified format conversion. It generates a basic process model to accurately identify potential patterns, abnormal paths, and bottlenecks within the process, overcoming the limitations of existing technologies that suffer from coarse process models and a lack of diagnostic depth. Next, by introducing an intelligent rule engine and combining it with a business rule base, it automatically extracts precise approval rules, solving the problem of inaccurate approval rules in existing technologies. Finally, based on the identified bottlenecks and the extracted precise rules, strategies such as process simplification, restructuring, and resource allocation optimization are implemented, enabling real-time dynamic adjustments to the approval process. This ultimately overcomes the technical challenges of fixed, inefficient, and inflexible subscription and approval processes, ensuring that the optimized approval process remains in a superior state. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 A schematic diagram illustrating the workflow of a data product subscription approval process provided in this application embodiment; Figure 2 A schematic diagram of the structure of a data product subscription approval process processing device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in 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, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0019] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0020] Example 1 This application provides a method for processing a data product subscription approval process, such as... Figure 1 As shown, Figure 1 This illustration shows a flowchart of a data product subscription approval process according to an embodiment of this application, including: Step S101: Collect relevant data from multi-source heterogeneous data sources for the subscription approval process, and convert the collected multi-source heterogeneous data into data in a unified format. The subscription approval process related data includes at least one of the following: subscription application data, approval process data, and user behavior data. In this embodiment, multi-source heterogeneous data refers to data from diverse sources (multi-source) and with different data formats and structures (heterogeneous). Specifically, the subscription application data refers to relevant data containing information such as the applicant, application time, requested data volume, data type, and application purpose. The approval process data essentially includes relevant data such as the approval workflow history, the personnel handling each stage, the dwell time at each stage (time consumed), the approval operation (approval / rejection), and the result. User behavior data refers to relevant data such as the applicant's or approver's historical operation records and data usage, mainly used for risk assessment of the subscription approval process. For example, a system implementing the method described in this application can collect structured "subscription application form" information from MySQL (a relational database) in Department A; collect semi-structured "user profile data" from MongoDB (a non-relational database) in System B; collect unstructured "user click behavior" data from log files on Server C; or obtain "risk rating" information from external systems via API interfaces. After obtaining this collected data, it undergoes data cleaning (e.g., removing duplicate application records, missing values, and outliers) and preprocessing. Then, the preprocessed data in different formats is converted to a unified format (e.g., JSON or CSV) and encoding rules for subsequent module processing. The key to this step in this application embodiment is solving the problem that existing technologies can only handle a single data source by collecting multi-source heterogeneous data and performing format conversion. For example, different systems may use different timestamp formats or user ID encodings. Preprocessing must map them to a unified standard format to form a high-quality event log data (a structured dataset that records each approval action in the process - Activity, the instance that performed the approval action - Case ID) and execution timestamp - Timestamp - as input for process mining, thereby ensuring the comprehensiveness and efficiency of subsequent analysis.

[0021] In one optional implementation of this application's embodiments, the multi-source heterogeneous data source includes at least one of relational databases, non-relational databases, log files, and API interfaces. This results in the method described in this application having better compatibility and more comprehensive data acquisition.

[0022] Step S102: Utilize a preset process mining algorithm to perform process mining on the unified format data to generate a process model for the subscription approval process. This process approval model displays the various stages, approval actions, and flow paths of the subscription approval process. In this embodiment, process mining is implemented by analyzing event logs using a preset process mining algorithm (such as Alpha algorithm, Heuristics Miner, or fuzzy mining algorithm). This involves discovering, monitoring, and constructing actual business processes based on event log data, thereby generating an initial process model that reflects the current situation. Specifically, in this embodiment, this process model is typically displayed in the form of a Petri net or activity flowchart, which can completely and clearly depict the various stages, execution order, and flow paths of the subscription approval process. This embodiment exemplifies the generated process model. For example, the mined model might show that after submission, 70% of "data subscription applications" flow to "department manager approval," while 30% flow to "data specialist approval," thus intuitively and clearly revealing the branching of the process. The process mining described in this application is generated based on actual operational data (event logs) rather than a pre-set flowchart. Therefore, generating this initial process model can eliminate the deviation between the model and the actual business scenario, ensuring that all subsequent analyses are based on reality and reliability. This effectively solves the problem that approval processes in traditional technologies mainly rely on fixed processes set by human experience. The model is automatically generated through a pre-set algorithm, which can objectively and accurately reflect the actual operation mechanism of the process.

[0023] Step S103: Analyze the process approval model to obtain potential process patterns, abnormal paths, and bottlenecks. For example, as mentioned earlier, the constructed process approval model shows that after submitting all "data subscription requests," 70% of the requests go to "department manager approval," and 30% go to "data specialist approval." Analysis revealed that the average time for the "data specialist approval" step is 3 days, making it a bottleneck in the entire process. It also revealed abnormal paths such as "applications being rejected and then resubmitted by the same person."

[0024] Further, in an optional implementation of this application embodiment, the process approval model is analyzed to obtain potential process patterns, abnormal paths, and bottlenecks. This includes: performing in-depth analysis and optimization of the process model using machine learning algorithms to identify abnormal paths and bottlenecks in the approval process, and determining potential process patterns that can improve the efficiency of the current approval process. In the practical application scenario of this application embodiment, to address the problem that traditional process models are coarse and deviate significantly from reality, this solution further introduces machine learning algorithms for in-depth diagnostic analysis of the process model. For example, for the identification process of abnormal paths, Markov Models can be used to model the state transition information in the process model, treating each approval step shown in the process model as a state and assigning probability values ​​to the transitions between states. When the transition probability value of a process instance's transition path (such as "preliminary review" skipping "secondary review" and going directly to "final review") is extremely low, it can be marked as an abnormal path. For the text content in the constructed process model, data sparsity processing (text content transformation) can be used for in-depth analysis. Specifically, when constructing the transition probability model, to avoid incorrectly assigning zero probability to process paths that do not appear in the training data (the sparse data problem), statistical techniques such as linear interpolation smoothing can be used. This technique combines univariate, bivariate, and ternary maximum likelihood estimates (corresponding to a single step, two consecutive steps, and three consecutive steps) to estimate more reliable probability values, thereby better ensuring the accuracy of modeling the process transition patterns. The final process analysis and mining results not only include the initial flowchart, but more importantly, they identify abnormal paths, inefficient steps (bottlenecks), and potential process patterns hidden in the data (for example, discovering that approvals in a specific department always use the same fast-track approval channel).

[0025] Step S104: Using an intelligent rule engine, combined with the user's business rule base, approval strategy, potential process patterns, abnormal paths, and bottlenecks, approval rules are extracted from the process model to obtain enhanced approval rules. In this embodiment, the intelligent rule engine is a software component that can automatically execute decisions and operations based on predefined business rules. The engine analyzes the constructed process model based on the user's business rules (e.g., "applications exceeding 10,000 yuan must be approved by the director") and approval strategy, combined with potential process patterns, abnormal paths, and bottlenecks, and extracts precise enhanced approval rules based on the analysis results. This intelligent rule engine transforms the process mining results shown in the process model obtained in step S102 into executable, accurate, and more efficient business rules, thereby solving the deficiency of inaccurate rules in existing technologies.

[0026] This embodiment of the application exemplifies the process of extracting enhanced approval rules: Assuming that the analysis of the process mining results after steps S102 and S103 shows that applications for "subscribing to specific highly sensitive data (such as financial customer data)" spend the longest average time in the "legal review" stage, the system, combining business rules related to "data sensitivity," extracts the following enhanced approval rule: IF Subscription data sensitivity = extremely high AND historical rejection rate of the applicant > 10% THEN Automatically trigger the "legal department priority processing" tag to shorten the approval time to 4 hours. For example, the intelligent engine can analyze historical data through machine learning and discover that all subscriptions "approved by Team Leader A" and "application amount less than 5000 yuan" have a 100% pass rate in the subsequent "director approval" stage. Therefore, the intelligent engine can extract a new rule: "If the application is approved by Team Leader A and the amount is less than 5000 yuan, skip the director approval and automatically pass," thus improving approval efficiency while ensuring the accuracy of the subscription process approval.

[0027] Step S105: Based on the results of the process mining and the enhanced approval rules, formulate a process optimization plan to optimize the existing data product subscription approval process. In this embodiment, this step dynamically adjusts and optimizes the approval process shown in the current process model based on the potential process patterns, abnormal paths, and bottlenecks discovered in step S103, and the more precise enhanced approval rules extracted in step S104. This improves the flexibility and efficiency of subscription approval.

[0028] Optionally, in one embodiment of this application, optimizing the existing data product subscription approval process includes: implementing at least one strategy among process simplification, process reorganization, or resource allocation optimization in the existing subscription approval process. The aforementioned optimization strategies are exemplarily illustrated herein in this application embodiment: For optimization solutions that include process simplification strategies: eliminate unnecessary steps based on approval rules. For example, subscription requests with low risk (such as those requesting internal test data with a good user history) can be extracted using the aforementioned intelligent rule engine and bypass the "junior administrator approval" step, proceeding directly to the "department manager approval" step after system verification, thereby simplifying the process and shortening the cycle.

[0029] For optimization solutions that include process reengineering strategies: adjust the order or parallelism of approval activities. For example, if process mining results reveal that the "compliance review" and "security review" steps are often executed sequentially, but they have no dependency on each other and take a long time in total, the optimization solution can reorganize them into parallel execution, with both steps starting simultaneously, thereby effectively reducing the total approval time.

[0030] For optimization schemes that include resource allocation optimization, the allocation relationship between tasks and approvers is dynamically adjusted. For example, if process mining, determined through the process model, identifies a specific approver as being in a saturated state for an extended period, while other approvers with the same permissions are relatively idle, the optimization scheme could be: real-time monitoring of approvers' workload and areas of expertise, prioritizing the allocation of new, specific types of data subscription requests, such as those involving artificial intelligence applications, to currently idle approvers with AI approval experience, thus achieving optimal resource allocation.

[0031] Optionally, in one embodiment of this application, the method further includes: verifying and optimizing the extracted enhanced approval rules before formulating a process optimization plan based on the results of the process mining and the enhanced approval rules. In this embodiment, the extracted rules are verified and optimized in advance to ensure their accuracy and effectiveness. For example, by backtesting with historical data, the extracted new rules (enhanced approval rules) are applied to historical approval records to verify whether they can produce a judgment consistent with the actual approval result under the new rules. If a deviation is found, the enhanced approval rule is fine-tuned or optimized. This ensures the effectiveness and accuracy of subsequent optimization of the subscription approval process.

[0032] Optionally, in one embodiment of this application, the method further includes: real-time monitoring and evaluation of the optimized subscription approval process to continuously improve the subscription approval process based on the evaluation results. This embodiment of the application defines a cyclical feedback step, that is, after the optimized approval process is put into use, various key indicators (such as average approval time and abnormal rejection rate) are monitored in real time. By comparing the running data with the baseline before optimization, the effectiveness of the optimization scheme is evaluated to further improve the automation capability and optimization accuracy of using this method to optimize the subscription approval process. At the same time, this process of continuous improvement of the approval process also ensures that the approval process can adapt to business changes and always remain in or near the optimal state.

[0033] Specifically, in one implementation of this application embodiment, the continuous improvement of the subscription approval process based on the evaluation results includes: using the operational data related to the subscription approval process collected in the evaluation as new input, and repeatedly executing the steps of process mining, approval rule extraction, and process optimization. In this application embodiment, this step limits the process to repeating steps S102-S104 if the evaluation discovers new inefficient steps or changes in business rules, forming a continuously self-optimizing closed loop to ensure the long-term efficient operation of the process. This aims to provide users with an optimized approval process that best meets their current actual needs.

[0034] Optionally, in one embodiment of this application, the method further includes: visualizing one or more of the information from the process model, approval rules, process optimization scheme, and the real-time monitoring and evaluation process operation data. The visualization includes, but is not limited to, using a visual interface (flowchart, bar chart, line chart, etc.) to display the process model, extracted rules, and operational indicators to users or managers. Users can conveniently view and analyze the operational status of the approval process through the visual interface. Simultaneously, the system also supports retrospective querying and analysis of historical data, providing data support for managerial decision-making.

[0035] Optionally, in one implementation of this application embodiment, the method further includes: using a combination of a distributed file system and a relational database to correspondingly store the data collected in steps S101-S105, as well as various data generated during the implementation of this solution. Specifically, for example, the raw log files (heterogeneous data) of the collected and generated data are stored in HDFS (a distributed file system) to ensure storage scalability; while one or more of the preprocessed structured data, as well as the subsequently generated process model, extracted approval rules, user information, etc., are stored in PostgreSQL (a relational database) to ensure data security and to ensure that this solution can support relatively efficient query access.

[0036] Optionally, in one implementation of this application embodiment, the method further includes the following steps in the approval rule extraction process of step S104: employing a reinforcement learning model to feed back the evaluation results (such as approval efficiency improvement and risk occurrence rate) obtained during process monitoring and evaluation as a reward signal to the intelligent rule engine; so that the intelligent rule engine dynamically adjusts the trigger threshold and priority of the approval rule according to the reward signal. This makes the intelligent rule engine an adaptive rule engine, enabling the rules themselves to learn and evolve, and automatically fine-tune the rules based on the actual optimized effect (reward / penalty) (e.g., whether to automatically approve "VIP" or adjust the threshold to "VIP and no violations in the past 3 months" before automatic approval), thereby further improving the accuracy of the extracted enhanced approval rules and their adaptability to business changes, achieving a deeper level of dynamic optimization.

[0037] Optionally, in a practical application scenario of this application embodiment, the process mining process in step S102 further includes: using a process deviation detection (Conformance Checking) algorithm to compare the mined actual process model with the preset standard process model (i.e., the expected model) in real time; the process optimization process in step S104 further includes: when the detected deviation (e.g., a violation of approval skipping occurred) exceeds a preset threshold, a process compliance alarm is triggered, and the deviation link is prioritized for resource reorganization or rule strengthening. This step in this application embodiment elevates process mining from "model discovery" to the technical level of real-time deviation monitoring. It solves the "compliance" problem that may occur during the optimization process, ensuring that while pursuing efficiency (optimization), the process does not deviate from approval security and compliance requirements, thereby significantly enhancing the system's risk management capabilities.

[0038] Optionally, in a practical application scenario of this application embodiment, the process of collecting relevant data on the subscription approval process from multi-source heterogeneous data sources further includes: collecting operational behavior data of approvers on the approval interface (such as mouse hover time, approval decision time, and text length of the approval reason); correspondingly, using a preset process mining algorithm to perform process mining on the unified format data further includes: using the process mining algorithm in conjunction with the operational behavior data to perform micro-bottleneck analysis on the approval process. In practical application scenarios, traditional process mining can only pinpoint which approval action (such as manager approval) is the bottleneck. This application embodiment, by integrating the micro-behavioral data of approvers, can further analyze whether the bottleneck is due to insufficient resources such as a shortage of approval personnel or tool-related issues such as a complex approval interface leading to long processing times. This enables subsequent process optimization processes to make more accurate decisions (whether to increase manpower or optimize the UI), thereby further improving the targeting and effectiveness of optimization.

[0039] This application provides a method for processing a data product subscription approval process. The method involves collecting relevant data from multiple heterogeneous data sources and converting this data into a unified format. The relevant data includes at least one of subscription application data, approval process data, and user behavior data. A pre-defined process mining algorithm is used to mine the unified format data, generating a process model for the subscription approval process. This model displays each stage, approval action, and flow path of the subscription approval process. The process model is analyzed to identify potential process patterns, abnormal paths, and bottlenecks. An intelligent rule engine, combined with the user's business rule base, approval strategies, and the potential process patterns, abnormal paths, and bottlenecks, extracts approval rules from the process model to create enhanced approval rules. Based on the results of the process mining and the enhanced approval rules, a process optimization plan is developed to optimize the existing data product subscription approval process. This method addresses the shortcomings of existing technologies, such as the inability to handle single data sources and significant discrepancies between process models and actual business operations, by integrating heterogeneous subscription and approval data from multiple sources and performing unified format conversion. It generates a basic process model to accurately identify potential patterns, abnormal paths, and bottlenecks within the process, overcoming the limitations of existing technologies that suffer from coarse process models and a lack of diagnostic depth. Next, by introducing an intelligent rule engine and combining it with a business rule base, it automatically extracts precise approval rules, solving the problem of inaccurate approval rules in existing technologies. Finally, based on the identified bottlenecks and the extracted precise rules, strategies such as process simplification, restructuring, and resource allocation optimization are implemented, enabling real-time dynamic adjustments to the approval process. This ultimately overcomes the technical challenges of fixed, inefficient, and inflexible subscription and approval processes, ensuring that the optimized approval process remains in a superior state.

[0040] Example 2 Based on the data product subscription approval process processing method provided in Embodiment 1 of this application, this embodiment also provides a corresponding data product subscription approval process processing device, such as... Figure 2 As shown, Figure 2 A schematic diagram of a data product subscription approval process processing device 20 provided in this application embodiment. The data product subscription approval process processing device 20 includes: The acquisition module 201 is used to collect relevant data of the subscription approval process from multiple heterogeneous data sources and convert the collected heterogeneous data into data in a unified format. The relevant data of the subscription approval process includes at least one of subscription application data, approval process data and user behavior data. The construction module 202 is used to perform process mining on the unified format data using a preset process mining algorithm to generate a process model of the subscription approval process, wherein the process approval model is used to display the various stages, approval actions and flow paths of the subscription approval process. Analysis module 203 is used to analyze the process approval model to obtain potential process patterns, abnormal paths and bottlenecks. The extraction module 204 is used to extract approval rules from the process model by using the intelligent rule engine, combined with the user's business rule library, approval strategy, potential process patterns, abnormal paths and bottleneck links, and extracting enhanced approval rules. The processing module 205 is used to formulate a process optimization plan based on the results of the process mining and the enhanced approval rules, so as to optimize the existing data product subscription approval process.

[0041] Optionally, in one implementation of this application embodiment, the multi-source heterogeneous data source includes at least one of relational databases, non-relational databases, log files, and API interfaces.

[0042] Optionally, in one embodiment of this application, the extraction module 204 is further configured to perform in-depth analysis and optimization of the process model using machine learning algorithms to identify abnormal paths and bottlenecks in the approval process and determine potential process patterns that can improve the efficiency of the current approval process.

[0043] Optionally, in one embodiment of this application, the device 20 further includes a verification module (not shown in the figures), which is used to verify and optimize the extracted enhanced approval rules before formulating a process optimization scheme based on the results of the process mining and the enhanced approval rules.

[0044] Optionally, in one embodiment of this application, the verification module is further configured to: perform at least one strategy among process simplification, process reengineering, or resource allocation optimization on the existing subscription approval process.

[0045] Optionally, in one embodiment of this application, the device 20 further includes an evaluation module (not shown in the figures), which is used to monitor and evaluate the optimized subscription approval process in real time, so as to continuously improve the subscription approval process based on the evaluation results.

[0046] Optionally, in one embodiment of this application, the evaluation module is further configured to use the operational data related to the subscription approval process collected in the evaluation as new input, and repeatedly execute the steps of process mining, approval rule extraction and process optimization.

[0047] Optionally, in one embodiment of this application, the device 20 further includes a display module (not shown in the figures), which is used to: visually display one or more of the information in the process model, approval rules, process optimization scheme and the real-time monitoring and evaluation process operation data.

[0048] This application provides a processing device for a data product subscription approval process. The device acquires relevant data from multiple heterogeneous data sources and converts this data into a unified format. A construction module uses a preset process mining algorithm to perform process mining on the unified format data, generating a process model for the subscription approval process. An analysis module analyzes the approval model to identify potential process patterns, abnormal paths, and bottlenecks. An extraction module uses an intelligent rule engine, combined with the user's business rule base, approval strategies, and the potential process patterns, abnormal paths, and bottlenecks, to extract approval rules from the process model, resulting in enhanced approval rules. A processing module formulates a process optimization plan based on the process mining results and the enhanced approval rules to optimize the existing data product subscription approval process. This device has a simple structure and overcomes the shortcomings of existing technologies, such as the ability to handle only a single data source and significant deviations between the process model and actual business, by integrating multi-source heterogeneous subscription approval data and performing unified format conversion. It generates a basic process model to more accurately identify potential patterns, abnormal paths, and bottlenecks in the process, overcoming the shortcomings of existing technologies, such as coarse process models and a lack of diagnostic depth. Next, by introducing an intelligent rule engine and combining it with a business rule base, accurate approval rules were automatically extracted, solving the problem of inaccurate approval rules in existing technologies. Finally, based on the identified bottlenecks and the extracted accurate rules, strategies such as process simplification, restructuring, and resource allocation optimization were implemented, enabling real-time dynamic adjustments to the approval process. This ultimately overcame the technical challenges of the existing subscription approval process being fixed, inefficient, and lacking flexibility, ensuring that the optimized approval process is always in a superior state.

[0049] Example 3 This application also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements any of the data product subscription approval process methods described in the foregoing embodiment one of this application. Example 4 This application also provides an electronic device, such as... Figure 3 As shown, Figure 3 This application provides a schematic diagram of the structure of an electronic device 30, which includes: One or more processors 301, communication interface 302, memory 303 and communication bus 304, the processors 301, memory 303 and communication interface 302 communicate with each other through communication bus 304; Memory 303 is used to store one or more programs; When the one or more programs are executed by the one or more processors 301, the one or more processors 301 implement any of the data product subscription approval process processing methods described in Embodiment 1 of this application.

[0050] This application has now described specific embodiments of the subject matter. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0051] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system layer onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0052] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0053] The system layers, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0054] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0056] Those skilled in the art will understand that embodiments of this application can be provided as methods, system-level, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This application can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0058] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system-level embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0059] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing a data product subscription approval process, characterized in that, include: Data related to the subscription approval process is collected from multiple heterogeneous data sources and converted into a unified format. The data related to the subscription approval process includes at least one of the following: subscription application data, approval process data, and user behavior data. A process mining algorithm is used to mine the data in the unified format to generate a process model of the subscription approval process. The process approval model is used to display the various stages, approval actions and flow paths of the subscription approval process. The process approval model is analyzed to obtain potential process patterns, abnormal paths, and bottlenecks. Using an intelligent rule engine, combined with the user's business rule library, approval strategy, potential process patterns, abnormal paths and bottlenecks, approval rules are extracted from the process model to extract enhanced approval rules. Based on the results of the process mining and the enhanced approval rules, a process optimization plan is formulated to optimize the existing data product subscription approval process.

2. The processing method for the data product subscription approval process according to claim 1, characterized in that, The multi-source heterogeneous data sources include at least one of relational databases, non-relational databases, log files, and API interfaces.

3. The processing method for the data product subscription approval process according to claim 1, characterized in that, The analysis of the process approval model to obtain potential process patterns, abnormal paths, and bottlenecks includes: The process model is analyzed and optimized in depth using machine learning algorithms to identify abnormal paths and bottlenecks in the approval process and to determine potential process patterns that can improve the efficiency of the current approval process.

4. The processing method for the data product subscription approval process according to claim 1 or 4, characterized in that, The method further includes: verifying and optimizing the extracted enhanced approval rules before formulating a process optimization plan based on the results of the process mining and the enhanced approval rules.

5. The processing method for the data product subscription approval process according to claim 1, characterized in that, The optimization of the existing data product subscription approval process includes: Implement at least one of the following strategies for the existing subscription approval process: process simplification, process reengineering, or resource allocation optimization.

6. The method for processing the data product subscription approval process according to claim 1, characterized in that, The method further includes: real-time monitoring and evaluation of the optimized subscription approval process, so as to continuously improve the subscription approval process based on the evaluation results.

7. The method for processing the data product subscription approval process according to claim 6, characterized in that, The continuous improvement of the subscription approval process based on the evaluation results includes: Using the operational data related to the subscription approval process collected during the evaluation as new input, the steps of process mining, approval rule extraction, and process optimization are repeated.

8. The method for processing the data product subscription approval process according to claim 6, characterized in that, The method further includes: visualizing one or more of the information from the process model, approval rules, process optimization scheme, and the real-time monitoring and evaluation process operation data.

9. A processing device for a data product subscription approval process, characterized in that, include: The acquisition module is used to collect relevant data of the subscription approval process from multiple heterogeneous data sources and convert the collected heterogeneous data into data in a unified format. The subscription approval process related data includes at least one of subscription application data, approval process data and user behavior data. The construction module is used to perform process mining on the unified format data using a preset process mining algorithm to generate a process model of the subscription approval process. The process approval model is used to display the various stages, approval actions and flow paths of the subscription approval process. The analysis module is used to analyze the process approval model to obtain potential process patterns, abnormal paths, and bottlenecks. The extraction module is used to extract approval rules from the process model by utilizing the intelligent rule engine, combined with the user's business rule library, approval strategy, potential process patterns, abnormal paths and bottlenecks, and extracting enhanced approval rules. The processing module is used to formulate a process optimization plan based on the results of the process mining and the enhanced approval rules, so as to optimize the existing data product subscription approval process.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed, perform the data product subscription approval process processing method as described in any one of claims 1-8.

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