Test scenario optimization method, system, computer device and readable storage medium

Abstract

This application discloses a test scenario optimization method, system, computer equipment, and readable storage medium, relating to the fields of digital healthcare and internet technology. It optimizes test scenarios for digital healthcare services using user experience data, forming a closed loop to continuously optimize user experience and improve test scenarios. The method includes: a data collection module acquiring multiple raw data sets and sending them to a data analysis module; the data analysis module receiving the raw data sets, performing data cleaning, and synchronizing data whose ranking falls within a preset range as target data to a scenario optimization module according to preset comparison rules; the scenario optimization module classifying the target data into scenarios to obtain the target scenarios corresponding to the target data; and the scenario optimization module triggering test cases corresponding to the target scenarios, obtaining test execution results, and updating the test case set based on the test execution results to complete the optimization of the target scenarios.

Description

Technical Field

[0001] This application relates to the fields of digital healthcare and internet technology, and in particular to a test scenario optimization method, system, computer equipment, and readable storage medium. Background Technology

[0002] With the continuous development of internet technology and the ongoing improvement and progress of the digital healthcare field, an increasing number of medical applications are emerging. Like other types of applications, medical applications require testing before launch. However, in actual operation, the launch of a medical application does not signify the end of testing. A continuous monitoring mechanism is needed to focus on user pain points, high-value, and frequently used features, and to continuously identify areas for improvement. Therefore, there is an urgent need for a continuous testing scenario optimization method that leverages existing user experience data to optimize testing scenarios and improve user experience. Summary of the Invention

[0003] In view of this, this application provides a test scenario optimization method, system, computer equipment, and readable storage medium. The main purpose is to solve the current problem of needing to establish a continuous monitoring mechanism, focusing on user pain points, high-value and high-usage functions, and continuously optimizing and improving test scenarios.

[0004] According to the first aspect of this application, a test scenario optimization method is provided, the method comprising:

[0005] The data collection module acquires multiple raw data sets and sends them to the data analysis module. The raw data sets include operational data, feedback data, and complaint data.

[0006] The data analysis module receives the multiple raw data, performs data cleaning to obtain multiple data to be synchronized, and determines the ranking of the multiple data to be synchronized according to the preset comparison rules. The data to be synchronized that whose ranking matches the preset range is used as the target data and synchronized to the scene optimization module.

[0007] The scene optimization module uses artificial intelligence natural language processing technology to classify the target data into scenes and obtain the target scenes corresponding to the target data.

[0008] The scenario optimization module triggers the test cases corresponding to the target scenario, obtains the test execution results, and updates the test case set based on the test execution results to complete the optimization of the target scenario.

[0009] Optionally, the data analysis module receives the multiple raw data sets, performs data cleaning, and obtains multiple data sets to be synchronized, including:

[0010] The data analysis module sets multiple granularities to clean the sample data and obtain multiple screening results. The multiple granularities are arranged in order from coarse granularity to fine granularity.

[0011] The data analysis module determines a target granularity among the multiple granularities based on the multiple filtering results, and performs data cleaning on the original data according to the target granularity to obtain the multiple data to be synchronized.

[0012] Optionally, determining the ranking of the plurality of data to be synchronized according to a preset comparison rule includes:

[0013] The indicator module sends preset comparison rules to the data analysis module;

[0014] For each piece of data to be synchronized, the data analysis module reads a first specified data and a second specified data from the data to be synchronized. The first specified data includes at least the number of operations, the number of repetitions, and the number of rollbacks of the process node. The second specified data is used to indicate the operation duration of the process node.

[0015] The data analysis module receives the preset comparison rules and compares the first specified data and the second specified data according to the preset comparison rules to obtain the first evaluation value and the second evaluation value.

[0016] The data analysis module calculates the first evaluation value and the second evaluation value according to preset weights to obtain the total score corresponding to the data to be synchronized.

[0017] The data analysis module determines multiple total scores corresponding to the multiple data to be synchronized, sorts the multiple data to be synchronized according to the multiple total scores, and obtains the ranking corresponding to each data to be synchronized.

[0018] Optionally, the scene optimization module utilizes artificial intelligence's natural language processing technology to classify the target data into scenes, including:

[0019] The scenario optimization module calculates the vector between the target data and each use case library by accumulating word vectors bit by bit;

[0020] The scenario optimization module uses cosine similarity to calculate the similarity between the target data vector and the use case library vector, thereby determining the similarity between the target data and each use case library and obtaining multiple similarity scores.

[0021] The scene optimization module determines the target similarity with a similarity greater than other similarities, and uses the scene corresponding to the target similarity as the target scene corresponding to the target data.

[0022] According to a second aspect of this application, a test scenario optimization system is provided, the system comprising: a data collection module, a data analysis module, and a scenario optimization module;

[0023] The data collection module is used to acquire raw user data and send the raw user data to the data analysis module. The raw user data includes operation data, feedback data, and complaint data.

[0024] The data analysis module is used to receive the multiple raw data, perform data cleaning to obtain multiple data to be synchronized, and determine the ranking of the multiple data to be synchronized according to the preset comparison rules, and synchronize the data to be synchronized with the ranking within the preset range as the target data to the scene optimization module.

[0025] The scenario optimization module is used to classify the target data into scenarios using artificial intelligence natural language processing technology, obtain the target scenarios corresponding to the target data, trigger the test cases corresponding to the target scenarios, obtain test execution results, and update the test case set according to the test execution results to complete the optimization of the target scenarios.

[0026] Optionally, the data analysis module is configured to set multiple granularities to perform data cleaning on the sample data to obtain multiple filtering results, and to determine a target granularity among the multiple granularities based on the multiple filtering results, and to perform data cleaning on the original data according to the target granularity to obtain the multiple data to be synchronized, wherein the multiple granularities are arranged in order from coarse granularity to fine granularity.

[0027] Optionally, the system further includes: an indicator module;

[0028] The indicator module is used to send preset comparison rules to the data analysis module;

[0029] The data analysis module is configured to, for each piece of data to be synchronized, read first specified data and second specified data from the data to be synchronized, and receive the preset comparison rules, compare the first specified data and the second specified data according to the preset comparison rules to obtain a first evaluation value and a second evaluation value, calculate the first evaluation value and the second evaluation value according to preset weights to obtain the total score corresponding to the data to be synchronized, determine multiple total scores corresponding to the multiple pieces of data to be synchronized, and sort the multiple pieces of data to be synchronized according to the multiple total scores to obtain the ranking corresponding to each piece of data to be synchronized. The first specified data includes at least the number of operations, the number of repetitions, and the number of rollbacks of the process node, and the second specified data is used to indicate the operation time of the process node.

[0030] Optionally, the scenario optimization module is used to calculate the vectors of the user data and each use case library by accumulating word vectors bit by bit, and to calculate the similarity between the vectors of the user data and the vectors of the use case library using cosine similarity to obtain multiple similarities, and to determine the target similarity that is greater than other similarities, and to take the scenario corresponding to the target similarity as the target scenario corresponding to the user data.

[0031] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0032] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0033] By employing the aforementioned technical solution, this application provides a test scenario optimization method, system, computer equipment, and readable storage medium. The data collection module acquires multiple raw data sets and sends them to the data analysis module. These raw data sets include operational data, feedback data, and complaint data. Next, the data analysis module receives the raw data, performs data cleaning to obtain multiple data sets to be synchronized, and determines the ranking of these data sets according to preset comparison rules. Data sets whose rankings fall within a preset range are then synchronized to the scenario optimization module as target data. Further, the scenario optimization module utilizes artificial intelligence's natural language processing technology to classify the target data into scenarios, obtaining the target scenarios corresponding to the target data. Finally, the scenario optimization module triggers the test cases corresponding to the target scenarios, obtains the test execution results, and updates the test case set based on the results, completing the optimization of the target scenarios. By classifying and summarizing user experience data as target data to determine corresponding test scenarios, the test scenarios for digital healthcare services can be improved in a targeted manner. Further optimization of these test scenarios enhances the user experience, forming a closed loop that continuously optimizes the user experience and continuously improves the test scenarios.

[0034] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0035] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0036] Figure 1A This paper illustrates a flowchart of a test scenario optimization method provided in an embodiment of this application.

[0037] Figure 1B This illustration shows a business architecture diagram of a test scenario optimization method provided in an embodiment of this application;

[0038] Figure 2A This illustration shows a schematic diagram of a test scenario optimization system provided in an embodiment of this application;

[0039] Figure 2B This illustration shows a schematic diagram of a test scenario optimization system provided in an embodiment of this application;

[0040] Figure 3 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0041] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0042] This application provides a test scenario optimization method, such as... Figure 1A As shown, the method includes:

[0043] 101. The data collection module acquires multiple raw data sets and sends them to the data analysis module. The raw data sets include operational data, feedback data, and complaint data.

[0044] With the continuous development of internet technology and the ongoing improvement and progress of the digital healthcare field, an increasing number of medical applications are emerging. Like other types of applications, medical applications require testing before launch. However, in actual operation, the launch of a medical application does not signify the end of testing. A continuous monitoring mechanism is needed to focus on user pain points, high-value, and frequently used features, and to continuously identify areas for improvement. Therefore, there is an urgent need for a continuous testing scenario optimization method that leverages existing user experience data to optimize testing scenarios and improve user experience.

[0045] Therefore, this application provides a test scenario optimization method, system, computer equipment, and readable storage medium. The data collection module acquires multiple raw data sets and sends them to the data analysis module. These raw data sets include operational data, feedback data, and complaint data. Next, the data analysis module receives the raw data, performs data cleaning to obtain multiple data sets to be synchronized, and determines the ranking of these data sets according to preset comparison rules. Data sets whose rankings fall within a preset range are then synchronized to the scenario optimization module as target data. Further, the scenario optimization module utilizes artificial intelligence's natural language processing technology to classify the target data into scenarios, obtaining the target scenarios corresponding to the target data. Finally, the scenario optimization module triggers the test cases corresponding to the target scenarios, obtains the test execution results, and updates the test case set based on the test execution results, completing the optimization of the target scenarios. By classifying and summarizing user experience data as target data to determine corresponding test scenarios, the test scenarios for digital healthcare services can be improved in a targeted manner. Further optimization of the test scenarios for digital healthcare services enhances user experience, forming a closed loop that continuously optimizes user experience and continuously improves test scenarios.

[0046] Considering that data-driven healthcare services may include online physical examinations, this method uses the physical examination process as an example for explanation. The raw data can be user experience data generated by the user at various stages of the physical examination process, specifically including operational data, feedback data, and complaint data.

[0047] In this step, the data collection module employs various strategies and tools to collect raw data from multiple channels, namely user experience data. Specifically, it continuously collects event tracking data, sets measurements based on user behavior patterns, and determines user action data through metrics such as click-through rate, conversion rate, time spent, and number of operation steps. It's important to note that event tracking (data collection points) occurs when users perform actions within the product, such as clicking a button, entering a page, or opening a pop-up. The front-end requests the tracking interface to record these actions. Data personnel use the data recorded by the interface to understand user behavior, enabling data analysis to better support product optimization and development, attract users, and increase retention. The module also continuously collects user feedback regarding operational difficulties or unfriendly experiences during the product check-in process, identifying feedback data. Finally, it continuously collects complaint data received by customer service via email, reviews, or the customer service backend system. Finally, the action data, feedback data, and complaint data are aggregated to obtain the raw data.

[0048] 102. The data analysis module receives multiple raw data, performs data cleaning, obtains multiple data to be synchronized, and determines the ranking of the multiple data to be synchronized according to the preset comparison rules. The data to be synchronized that whose ranking matches the preset range is used as the target data and synchronized to the scene optimization module.

[0049] In this step, the data analysis module first needs to determine the target granularity from multiple granularities to achieve the optimal filtering effect. In other words, by cleaning the raw data using the target granularity, the most effective data, i.e., the data to be synchronized, can be obtained. Automated cleaning reduces the cost of manual processing and improves data cleaning efficiency.

[0050] First, the data analysis module cleans the sample data by setting multiple granularities from coarse to fine, resulting in multiple filtering results. Based on these results, the target granularity, which is the granularity with the best filtering effect, is determined.

[0051] Subsequently, according to the target granularity, the raw data is cleaned, splitting it into multiple sets of data to be synchronized that have reference value and invalid, dirty data. In actual operation, before receiving the raw data for data cleaning, the data module needs to obtain comparison rules from the indicator module. The indicator module responds to the retrieval command sent by the data analysis module by sending the corresponding comparison rules to the data analysis module. It should be noted that the indicator module first sets the original comparison rules based on business experience or by extracting high-frequency words through unsupervised clustering. These original comparison rules include comparison items and corresponding comparison indicators. Furthermore, the comparison indicators in the original comparison rules are continuously tested and optimized to ultimately form reasonable comparison rules.

[0052] Furthermore, for each piece of data to be synchronized, the data analysis module reads first and second specified data from the data to be synchronized. The first specified data includes at least the number of operations, repetitions, and rollbacks of each process node, while the second specified data indicates the operation duration of each process node. The data analysis module receives preset comparison rules and compares the first and second specified data according to these rules to obtain a first evaluation value and a second evaluation value. In essence, this calculates the overall effort and complexity of the business process based on the user's repeated operations, resulting in the first evaluation value. Specifically, it counts the number of steps the user takes from entering to exiting the health check process, the number of operations at each node, checks for repetitions and rollbacks, identifies which user actions were repeated, and identifies which nodes were repeatedly performed. The second evaluation value is obtained by scoring the user's operation time. Specifically, by examining the time consumption of relevant indicators, the rationality of the health check business process is assessed. Additionally, during the comparison process, warning values ​​can be set for process nodes with concentrated negative feedback. If the effort or time consumption of a process node triggers the corresponding warning value, an automatic warning notification is sent to the relevant product department.

[0053] The data analysis module performs a weighted calculation on the first and second evaluation values ​​according to preset weights to obtain the total score corresponding to the data to be synchronized. The data analysis module determines multiple total scores corresponding to multiple data sets to be synchronized, and sorts the data sets according to these total scores to obtain the ranking for each data set. To select the most valuable user data to send to the scene optimization module, this method also sets user record limits, synchronizing the top-ranked data (those falling within a preset ranking range) to the scene optimization module.

[0054] This step analyzes the sample data from coarse-grained to fine-grained levels to find the appropriate granularity, forming a comprehensive data cleaning mechanism to clean the raw data and obtain data with analytical value to be synchronized. Then, based on a ranking mechanism, the target data is selected from multiple datasets to be synchronized and sent to the scene optimization module for subsequent test scene optimization steps.

[0055] 103. The scene optimization module uses artificial intelligence's natural language processing technology to classify the target data into scenes and obtain the target scenes corresponding to the target data.

[0056] In this step, the scene optimization module uses Natural Language Processing (NLP) technology to intelligently analyze the target data, classifying the target data synchronized to this module into scene categories to obtain the target scene corresponding to the target data. Based on... Figure 1BAs shown, the health checkup service includes three main parts: pre-checkup, during checkup, and post-checkup. The pre-checkup part includes scenarios such as checkup benefits, multiple package selection, selecting the examinee, selecting the institution, package upgrade, adding items to the package, order submission, rescheduling the checkup, and canceling the checkup. The during-checkup part includes in-store verification. The post-checkup part includes scenarios such as uploading reports, report tagging, customized treatment plans, and report interpretation.

[0057] Specifically, the scenario optimization module calculates the vectors of the target data and each use case library by accumulating word vectors positionally. Using cosine similarity, it calculates the similarity between the target data vector and the use case library vectors to determine the similarity between the target data and each use case library, resulting in multiple similarity scores. The target similarity score, which is greater than the others, is determined, and the scenario corresponding to this target similarity score is taken as the target scenario for the target data.

[0058] The code snippet for calculating cosine similarity is as follows:

[0059]

[0060]

[0061] In addition, for more difficult text matching tasks, consider introducing supervised signals and using more complex neural networks such as "DSSM" or "CLSM" to calculate semantic relevance.

[0062] By continuously collecting user data and observing user feedback, and using NLP technology (recommendation algorithm as follows) to intelligently recommend relevant test scenarios, we continuously improve the test case set for physical examination services and automatically improve the test scenarios for physical examination services, forming a mature implementation solution for improving test scenarios based on user experience.

[0063] 104. The scenario optimization module triggers the test cases corresponding to the target scenario, obtains the test execution results, and updates the test case set based on the test execution results to complete the optimization of the target scenario.

[0064] In this step, the scenario optimization module triggers the test case set corresponding to the target scenario, obtains the test execution results, and updates the test case set based on the test execution results to complete the optimization of the target scenario. In actual operation, the scenario optimization module can also first identify target scenarios where problems are concentrated, then trigger the test case sets corresponding to these target scenarios, and finally expand or refine the test case sets based on the execution results, thereby improving the physical examination business test scenarios.

[0065] Furthermore, this method also enables visualization of synchronized data through a data visualization module. Taking a physical examination business as an example, through the analysis and processing of raw user physical examination data, it ultimately provides multi-dimensional analysis directions, flexible filtering conditions, and rich visualization reports. Specifically, it displays user data distribution from multiple dimensions, such as by physical examination business process, by business operation time, and by operation cost, helping product, operations, and testing departments identify problems and find solutions, thereby improving user experience and refining physical examination business testing scenarios. Relevant personnel can use the data visualization module to regularly view the distribution of user feedback, using high-quality user data to understand consumer behavior and improve personalized user interaction experiences.

[0066] The method provided in this application involves a data collection module acquiring multiple raw data sets and sending them to a data analysis module. These raw data sets include operational data, feedback data, and complaint data. Next, the data analysis module receives the raw data, performs data cleaning to obtain multiple sets of data to be synchronized, and determines the ranking of these sets according to preset comparison rules. Data sets whose rankings fall within a preset range are then synchronized to the scenario optimization module as target data. Further, the scenario optimization module utilizes natural language processing technology from artificial intelligence to categorize the target data into scenarios, obtaining the corresponding target scenarios. Finally, the scenario optimization module triggers test cases corresponding to the target scenarios, obtains test results, and updates the test case set based on these results, thus completing the optimization of the target scenarios. By classifying and summarizing user experience data as target data to determine corresponding test scenarios, the testing scenarios for digital healthcare services can be improved in a targeted manner. Further optimization of these test scenarios enhances the user experience, forming a closed loop that continuously optimizes the user experience and continuously improves the test scenarios.

[0067] This application provides a test scenario optimization system. A data collection module acquires multiple raw data sets and sends them to a data analysis module. The raw data includes operational data, feedback data, and complaint data. Next, the data analysis module receives the raw data, performs data cleaning to obtain multiple sets of data to be synchronized, and determines the ranking of these sets according to preset comparison rules. Data sets whose rankings fall within a preset range are synchronized to the scenario optimization module as target data. Further, the scenario optimization module uses artificial intelligence's natural language processing technology to classify the target data into scenarios, obtaining the target scenarios corresponding to the target data. Finally, the scenario optimization module triggers the test cases corresponding to the target scenarios, obtains the test execution results, and updates the test case set based on the results, completing the optimization of the target scenarios. By classifying and summarizing user experience data as target data to determine corresponding test scenarios, the system can specifically improve the test scenarios for digital healthcare services. Further optimization of these test scenarios enhances the user experience, forming a closed loop that continuously optimizes the user experience and improves the test scenarios. Figure 2A As shown, the system includes: a data collection module 201, a data analysis module 202, and a scene optimization module 203.

[0068] In this embodiment, the data collection module 201 is used to acquire raw user data and send it to the data analysis module 202. The raw user data includes operation data, feedback data, and complaint data. The data collection module 201 employs various strategies and tools to collect raw data from multiple channels, i.e., user experience data. Specifically, it continuously collects tracking data, sets measurements based on user behavior trajectories, and determines user operation data through click-through rates, conversion rates, time consumption, and number of operation steps. It should be noted that data tracking (event tracking) occurs when users perform certain actions within the product, such as clicking a button, entering a page, or opening a pop-up. The front-end requests the event tracking interface to record user actions. Data personnel use the data recorded by the interface to understand user behavior, thereby performing data statistics on the product to better assist in product optimization and development, attracting users, and increasing user retention. The module also continuously collects user feedback regarding operational difficulties or unfriendly experiences during the product check-up process, determining the feedback data. We continuously collect complaint data received by customer service via email, reviews, or the customer service backend system. Finally, we aggregate the operation data, feedback data, and complaint data to obtain the raw data.

[0069] The data analysis module 202 is used to receive multiple raw data, perform data cleaning, obtain multiple data to be synchronized, and determine the ranking of the multiple data to be synchronized according to preset comparison rules. The data to be synchronized that whose ranking matches the preset range is used as the target data and synchronized to the scene optimization module 203.

[0070] The scenario optimization module 203 is used to classify the target data into scenarios using the natural language processing technology of artificial intelligence, obtain the target scenarios corresponding to the target data, trigger the test cases corresponding to the target scenarios, obtain the test execution results, and update the test case set according to the test execution results to complete the optimization of the target scenarios.

[0071] In another embodiment, such as Figure 2B As shown, the test scenario optimization system also includes: indicator module 204 and data display module 205.

[0072] In this embodiment, the data analysis module 202 is used to first clean the sample data by setting multiple granularities in order from coarse to fine granularity, resulting in multiple screening results. Based on these screening results, a target granularity, i.e., the granularity with the optimal screening effect, is determined among the multiple granularities. Subsequently, according to the target granularity, the original data is cleaned, splitting it into multiple data sets to be synchronized with reference value and invalid, dirty data. In actual operation, before receiving the original data for data cleaning, the data module needs to obtain comparison rules from the indicator module. The indicator module responds to the acquisition instruction sent by the data analysis module by sending the corresponding comparison rules to the data analysis module. It should be noted that the indicator module first sets the original comparison rules by extracting high-frequency words through business experience or unsupervised clustering. The original comparison rules include comparison items and corresponding comparison indicators. Furthermore, the comparison indicators in the original comparison rules are continuously tested and optimized to ultimately form reasonable comparison rules.

[0073] The indicator module 204 is used to send preset comparison rules to the data analysis module 202.

[0074] The data analysis module 202 reads first and second specified data from each piece of data to be synchronized. The first specified data includes at least the number of operations, repetitions, and rollbacks of each process node. The second specified data indicates the operation duration of each process node. The data analysis module 202 receives preset comparison rules and compares the first and second specified data according to these rules to obtain a first evaluation value and a second evaluation value. In essence, this calculates the overall operational effort of the business process based on the user's repeated operations, resulting in the first evaluation value. Specifically, it counts the number of steps the user takes from entering to exiting the health check process, the number of operations at each node, checks for repetitions and rollbacks, identifies which user actions were repeated, and identifies which nodes were repeatedly performed. The second evaluation value is obtained by scoring the user's operation time. In other words, by specifically examining the time consumption of relevant indicators, the rationality of the health check business process is assessed. In addition, during the comparison process, warning values ​​can be set for process nodes with a high concentration of negative reviews. If the operation of a process node is laborious or time-consuming, triggering the corresponding warning value, an automatic warning notification will be sent to the relevant product. Finally, the first and second evaluation values ​​are weighted according to preset weights to obtain the total score corresponding to the data to be synchronized. The data analysis module 202 determines multiple total scores corresponding to multiple data to be synchronized, and sorts the multiple data to be synchronized according to the multiple total scores to obtain the ranking corresponding to each data to be synchronized. In order to select the most valuable user data to send to the scene optimization module 203, this method also sets user record boundaries, and synchronizes the top-ranked data to be synchronized, that is, the data whose ranking falls within the preset range, as the target data to the scene optimization module 203.

[0075] The scene optimization module 203 is used to intelligently analyze target data using NLP (Natural Language Processing) technology. It categorizes the target data synchronized to this module 203 into scenes to obtain the target scenes corresponding to the target data. Specifically, the scene optimization module 203 calculates the vectors of the target data and each use case library by accumulating word vectors positionally. Using cosine similarity, it calculates the similarity between the target data vector and the use case library vectors to determine the similarity between the target data and each use case library, obtaining multiple similarity scores. The target similarity score, which is greater than other similarity scores, is determined, and the scene corresponding to the target similarity score is taken as the target scene corresponding to the target data. Furthermore, for more difficult text matching tasks, it considers introducing supervised signals and using more complex neural networks such as "DSSM" or "CLSM" to calculate semantic relevance.

[0076] By continuously collecting user data and observing user feedback, and using NLP technology (recommendation algorithm as follows) to intelligently recommend relevant test scenarios, we continuously improve the test case set for physical examination services and automatically improve the test scenarios for physical examination services, forming a mature implementation solution for improving test scenarios based on user experience.

[0077] The scenario optimization module 203 obtains test execution results by triggering the test case set corresponding to the target scenario, and updates the test case set based on the test execution results to complete the optimization of the target scenario. In actual operation, the scenario optimization module 203 can also first identify the target scenarios where problems are concentrated, then trigger the test case sets corresponding to these target scenarios, and finally expand or refine the test case sets based on the execution results, thereby improving the physical examination business test scenario.

[0078] Furthermore, the data visualization module 205 enables the visualization of synchronized data. Specifically, taking the physical examination business as an example, through the analysis and processing of raw user physical examination data, multi-dimensional analytical directions, flexible filtering conditions, and rich visualization reports can be provided. Specifically, by displaying the distribution of user data from multiple dimensions, such as by physical examination business process, by business operation time, and by operation cost, user data can be displayed to help product, operations, and testing departments identify problems and find solutions, thereby improving user experience and refining the physical examination business testing scenarios. Relevant personnel can use the data visualization module 205 to regularly view the distribution of user feedback, using high-quality user data to understand consumer behavior and improve personalized user interaction experiences.

[0079] In an exemplary embodiment, see Figure 3 Furthermore, a device is provided, comprising a communication bus, a processor, a memory, and a communication interface. It may also include input / output interfaces and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the test scenario optimization method described in the above embodiments.

[0080] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the test scenario optimization method.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0082] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0083] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0084] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0085] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A test scenario optimization method, characterized in that, include: The data collection module acquires multiple raw data sets and sends them to the data analysis module. The raw data sets include operation data, feedback data, and complaint data generated by users operating the application software. The data analysis module receives the multiple raw data, performs data cleaning to obtain multiple data to be synchronized, and determines the first evaluation value and the second evaluation value corresponding to the multiple data to be synchronized according to the preset comparison rules. Based on the first evaluation value and the second evaluation value, the corresponding ranking is determined, and the data to be synchronized that whose ranking falls within the preset range is synchronized to the scene optimization module as target data. The scene optimization module uses artificial intelligence natural language processing technology to classify the target data into scenes and obtain the target scenes corresponding to the target data. The scenario optimization module triggers the test cases corresponding to the target scenario, obtains the test execution results, and updates the test cases based on the test execution results to complete the optimization of the target scenario; The step of determining the first evaluation value and the second evaluation value corresponding to the plurality of data to be synchronized according to a preset comparison rule, and determining the corresponding ranking based on the first evaluation value and the second evaluation value, includes: The indicator module sends preset comparison rules to the data analysis module; For each piece of data to be synchronized, the data analysis module reads a first specified data and a second specified data from the data to be synchronized. The first specified data includes at least the number of operations, the number of repetitions, and the number of rollbacks of the process node. The second specified data is used to indicate the operation duration of the process node. The data analysis module receives the preset comparison rules and compares the first specified data and the second specified data according to the preset comparison rules to obtain the first evaluation value and the second evaluation value. The data analysis module calculates the first evaluation value and the second evaluation value according to preset weights to obtain the total score corresponding to the data to be synchronized. The data analysis module determines multiple total scores corresponding to the multiple data to be synchronized, sorts the multiple data to be synchronized according to the multiple total scores, and obtains the ranking corresponding to each data to be synchronized. The first evaluation value is used to measure the operational effort of the application software, and the second evaluation value is used to measure the rationality of the application software's business process.

2. The method according to claim 1, characterized in that, The data analysis module receives the multiple raw data sets, performs data cleaning, and obtains multiple data sets to be synchronized, including: The data analysis module sets multiple granularities to clean the sample data and obtain multiple screening results. The multiple granularities are arranged in order from coarse granularity to fine granularity. The data analysis module determines a target granularity among the multiple granularities based on the multiple screening results, and performs data cleaning on the original data according to the target granularity to obtain the multiple data to be synchronized.

3. The method according to claim 1, characterized in that, The scene optimization module utilizes artificial intelligence's natural language processing technology to classify the target data into scenes, including: The scenario optimization module calculates the vector between the target data and each use case library by accumulating word vectors bit by bit; The scenario optimization module uses cosine similarity to calculate the similarity between the target data vector and the use case library vector, thereby determining the similarity between the target data and each use case library and obtaining multiple similarity scores. The scene optimization module determines the target similarity with a similarity greater than other similarities, and uses the scene corresponding to the target similarity as the target scene corresponding to the target data.

4. A test scenario optimization system, characterized in that, include: Data collection module, data analysis module, and scenario optimization module; The data collection module is used to acquire multiple raw data and send the multiple raw data to the data analysis module. The raw data includes operation data, feedback data and complaint data generated by users operating the application software. The data analysis module is used to receive the multiple raw data, perform data cleaning to obtain multiple data to be synchronized, and determine the first evaluation value and the second evaluation value corresponding to the multiple data to be synchronized according to the preset comparison rules, and determine the corresponding ranking based on the first evaluation value and the second evaluation value, and synchronize the data to be synchronized that whose ranking falls within the preset range to the scene optimization module as target data. The scene optimization module is used to classify the target data into scenes using artificial intelligence natural language processing technology, obtain the target scene corresponding to the target data, trigger the test cases corresponding to the target scene, obtain the test execution results, and update the test cases according to the test execution results to complete the optimization of the target scene. The system also includes: an indicator module; The indicator module is used to send preset comparison rules to the data analysis module; The data analysis module is configured to, for each piece of data to be synchronized, read first specified data and second specified data from the data to be synchronized, and receive the preset comparison rules, compare the first specified data and the second specified data according to the preset comparison rules to obtain a first evaluation value and a second evaluation value, calculate the first evaluation value and the second evaluation value according to preset weights to obtain the total score corresponding to the data to be synchronized, determine multiple total scores corresponding to the multiple pieces of data to be synchronized, and sort the multiple pieces of data to be synchronized according to the multiple total scores to obtain the ranking corresponding to each piece of data to be synchronized. The first specified data includes at least the number of operations, the number of repetitions, and the number of rollbacks of the process node, and the second specified data is used to indicate the operation time of the process node. The first evaluation value is used to measure the operational effort of the application software, and the second evaluation value is used to measure the rationality of the application software's business process.

5. The system according to claim 4, characterized in that, The data analysis module is used to set multiple granularities to clean sample data, obtain multiple filtering results, determine a target granularity among the multiple granularities based on the multiple filtering results, and clean the original data according to the target granularity to obtain the multiple data to be synchronized, wherein the multiple granularities are arranged in order from coarse granularity to fine granularity.

6. The system according to claim 4, characterized in that, The scenario optimization module is used to calculate the vectors of the target data and each use case library by accumulating word vectors bit by bit, and to calculate the similarity between the vectors of the target data and the vectors of the use case library using cosine similarity to obtain multiple similarities. The module then determines the target similarity that is greater than the other similarities and uses the scenario corresponding to the target similarity as the target scenario corresponding to the target data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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