A data analysis method and system for mobile applications

Abstract

The present invention proposes a method and system for using data analysis of mobile applications, which relates to the technical field of data analysis. It obtains user operation behavior information, calculates behavior weight coefficients and event weight coefficients, obtains event storage nodes, sub-event behavior paths and key event behavior paths, aggregates the paths to obtain aggregated paths; calculates the key event anomaly coefficients of the key event nodes of the application program to obtain abnormal key event nodes, and then calculates the sub-event anomaly coefficients to obtain abnormal sub-event nodes; adjusts the node information of the abnormal sub-event nodes, obtains the number of adjusted abnormal sub-event nodes and the number of abnormal key event nodes, determines whether to continue the adjustment, and obtains the adjustment result. The present invention realizes refined, real-time and intelligent analysis of user behavior data, and provides more accurate and efficient data support for the optimization and operation decision-making of mobile applications.

Description

Technical Field

[0001] The present invention proposes a data analysis method and system for mobile application usage, which relates to the technical field of data analysis, specifically to the technical field of mobile application usage data analysis. Background Art

[0002] With the rapid development of mobile Internet technology, mobile applications (APPs) have become an indispensable part of people's daily life and work. These applications greatly improve the efficiency and satisfaction of users by providing rich functions and convenient operation experiences. However, with the increasing complexity of applications and the continuous increase in functions, how to effectively monitor and analyze users' usage behaviors to optimize the performance of applications, enhance the user experience, and prevent potential problems has become an important challenge for developers and operators.

[0003] Existing mobile application data analysis methods mainly rely on log collection and statistical analysis. By collecting users' operation logs, the behavior patterns and usage habits of users are statistically obtained, and then data support is provided for product optimization and operation decisions. However, this method has many deficiencies in dealing with large-scale user data and complex behavior paths. First, traditional log analysis methods often can only provide relatively coarse-grained statistical information and are difficult to deeply analyze at the level of specific events and sub-events. Second, due to the diversity and complexity of user behavior data, traditional analysis methods are prone to false alarms or missed reports when processing these data, resulting in insufficient accuracy of the analysis results. Finally, traditional analysis methods also have bottlenecks in data storage and anomaly analysis and are difficult to meet the real-time processing requirements of large-scale user data. Summary of the Invention

[0004] The present invention provides a data analysis method and system for mobile application usage to solve the above problems:

[0005] A data analysis method and system for mobile application usage proposed by the present invention, the method includes:

[0006] S1. Obtain user operation behavior information, classify the behavior information and calculate the behavior weight coefficient, and then calculate the event weight coefficient for the corresponding preset time period to obtain key event nodes and sub-event nodes;

[0007] S2. Store the event node information distributively to obtain event storage nodes, obtain sub-event behavior paths and key event behavior paths according to the event storage nodes, and perform aggregation of the paths to obtain an aggregated path;

[0008] S3. Calculate the key event anomaly coefficient of the key event nodes of the application program to obtain the abnormal key event nodes, and then calculate the sub-event anomaly coefficient to obtain the abnormal sub-event nodes;

[0009] S4. Adjust the node information of the abnormal sub-event nodes, obtain the number of adjusted abnormal sub-event nodes and the number of abnormal key event nodes, determine whether to continue the adjustment, and obtain the adjustment result.

[0010] Further, the S1 includes:

[0011] Obtain the user operation behavior information, classify the user operation behavior information according to the preset behavior types to obtain the behavior classification information;

[0012] Perform edge computing on each type of behavior classification information, and calculate the behavior weight coefficient of each type of behavior classification information through the edge computing method;

[0013] Obtain the behavior weight coefficients of each type of behavior classification information in the same preset time period, and calculate the event weight coefficient corresponding to the preset time period;

[0014] Perform event annotation with corresponding weights on the user operation behavior information in the preset time period according to the event weight coefficient to obtain the key event nodes, and perform sub-event annotation on each behavior classification of the key event nodes to obtain the sub-event nodes.

[0015] Further, the S2 includes:

[0016] Obtain the data of multiple sub-event nodes and their key event nodes and upload them to the cloud;

[0017] Perform distributed storage on each sub-event node of the key event nodes through the cloud to obtain the event storage nodes;

[0018] Connect the sub-event nodes of each behavior classification of the event storage nodes to obtain the sub-event behavior paths corresponding to the behavior classifications;

[0019] Connect the key event nodes of each event storage node to obtain the key event behavior paths;

[0020] Aggregate the sub-event behavior paths and the key event behavior paths to obtain the aggregation path.

[0021] Further, the S3 includes:

[0022] Obtain the application performance data on the aggregation path, and calculate the key event anomaly coefficient of the key event nodes of the application program according to the application performance data combined with the event weight coefficient;

[0023] Perform anomaly annotation on each key event node according to the key event anomaly coefficient to obtain anomaly key event nodes;

[0024] Obtain the sub-event node information of the anomaly key event nodes, and calculate the sub-event anomaly coefficient of each sub-event node of the anomaly key event nodes according to the application performance information in combination with the sub-event information;

[0025] Perform anomaly annotation on each sub-event node according to the sub-event anomaly coefficient to obtain anomaly sub-event nodes.

[0026] Further, the S4 includes:

[0027] Obtain the node information of each anomaly sub-event node, adjust the node information, and obtain the adjusted sub-event anomaly coefficient;

[0028] Obtain the number of anomaly sub-event nodes according to the adjusted sub-event anomaly coefficient;

[0029] Judge whether to calculate the corresponding key event anomaly coefficient according to the number of anomaly sub-event nodes;

[0030] Furthermore, obtain the adjusted number of anomaly key event nodes.

[0031] Further, the system includes:

[0032] A weight node acquisition module, configured to acquire user operation behavior information, classify the behavior information and calculate the behavior weight coefficient, and then calculate the event weight coefficient for the corresponding preset time period, and acquire key event nodes and sub-event nodes;

[0033] An event path acquisition module, configured to perform distributed storage on the event node information to obtain event storage nodes, acquire sub-event behavior paths and key event behavior paths according to the event storage nodes, and perform path aggregation to obtain an aggregated path;

[0034] An anomaly calculation and analysis module, configured to calculate the key event anomaly coefficient of the key event nodes of the application program, obtain anomaly key event nodes, and then calculate the sub-event anomaly coefficient to obtain anomaly sub-event nodes;

[0035] An anomaly adjustment and judgment module, configured to adjust the node information of the anomaly sub-event nodes, obtain the adjusted number of anomaly sub-event nodes and the number of anomaly key event nodes, judge whether to continue the adjustment, and obtain the adjustment result.

[0036] Further, the weight node acquisition module includes:

[0037] A behavior classification module, which is used to obtain user operation behavior information, classify the user operation behavior information according to preset behavior categories, and obtain behavior classification information;

[0038] A classification weight calculation module, which is used to perform edge computing on each behavior classification information, and calculate the behavior weight coefficient of each behavior classification information through an edge computing method;

[0039] An event weight calculation module, which is used to obtain the behavior weight coefficient of each behavior classification information in the same preset time period and calculate the event weight coefficient of the corresponding preset time period;

[0040] An event node acquisition module, which is used to perform event annotation with corresponding weights on the user operation behavior information in a preset time period according to the event weight coefficient, obtain key event nodes, and perform sub-event annotation on each behavior classification of the key event nodes to obtain sub-event nodes.

[0041] Further, the event path acquisition module includes:

[0042] A distributed storage module, which is used to obtain data of multiple sub-event nodes and their key event nodes and upload them to the cloud;

[0043] Through the cloud, each sub-event node of the key event node is stored distributively to obtain event storage nodes;

[0044] A path generation module, which is used to connect the sub-event nodes of each behavior classification of the event storage nodes to obtain a sub-event behavior path corresponding to the behavior classification;

[0045] Connect the key event nodes of each event storage node to obtain a key event behavior path;

[0046] A path aggregation module, which is used to aggregate the sub-event behavior path and the key event behavior path to obtain an aggregated path.

[0047] Further, the abnormal calculation and analysis module includes:

[0048] A key abnormal calculation module, which is used to obtain application performance data on the aggregated path, and calculate the key event abnormal coefficient of the key event node of the application program according to the application performance data combined with the event weight coefficient;

[0049] A key node abnormal acquisition module, which is used to perform abnormal annotation on each key event node according to the key event abnormal coefficient to obtain abnormal key event nodes;

[0050] The sub - anomaly calculation module is used to obtain the sub - event node information of the anomaly - critical event node, and calculate the sub - event anomaly coefficient of each sub - event node of the anomaly - critical event node according to the application performance information in combination with the sub - event information;

[0051] The sub - node anomaly acquisition module is used to perform anomaly annotation on each sub - event node according to the sub - event anomaly coefficient to obtain the anomaly sub - event nodes.

[0052] Further, the anomaly adjustment judgment module includes:

[0053] The adjusted sub - node acquisition module is used to obtain the node information of each anomaly sub - event node, adjust the node information, and obtain the adjusted sub - event anomaly coefficient;

[0054] Obtain the number of anomaly sub - event nodes according to the adjusted sub - event anomaly coefficient;

[0055] The adjusted critical - node acquisition module is used to judge whether to calculate the corresponding critical - event anomaly coefficient according to the number of anomaly sub - event nodes;

[0056] Furthermore, obtain the number of adjusted anomaly - critical event nodes.

[0057] The beneficial effects of the present invention: By accurately identifying the abnormal points in user behavior and making targeted adjustments, the satisfaction and efficiency of users during the use of the application can be significantly improved. Based on the results of big - data analysis, application developers can formulate product strategies more scientifically, such as adjusting the function layout, optimizing the recommendation algorithm, etc., to better meet user needs. By real - time monitoring and timely handling of abnormal events in user behavior, the risk of application crashes or performance degradation can be effectively reduced, and the stability and reliability of the application can be enhanced. The distributed storage and efficient data - processing mechanism enable the system to quickly respond to data changes. Based on the in - depth analysis of user behavior data, the application can provide a more personalized service experience, such as personalized recommendations, customized content, etc., thereby enhancing user stickiness and loyalty. Brief Description of the Drawings

[0058] Figure 1 It is a schematic diagram of a data - analysis method for a mobile - terminal application;

[0059] Figure 2 It is a schematic diagram of an aggregation path. Detailed Embodiments

[0060] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0061] An embodiment of the present invention provides a method and system for using data analysis of a mobile application program. The method includes:

[0062] S1. Obtain user operation behavior information, classify the behavior information, calculate the behavior weight coefficient, and then calculate the event weight coefficient for a corresponding preset time period, and obtain the key event nodes and sub-event nodes;

[0063] S2. Store the event node information distributively to obtain event storage nodes, obtain the sub-event behavior paths and key event behavior paths based on the event storage nodes, and perform path aggregation to obtain aggregated paths;

[0064] S3. Calculate the key event anomaly coefficient of the key event nodes of the application program to obtain the abnormal key event nodes, and then calculate the sub-event anomaly coefficient to obtain the abnormal sub-event nodes;

[0065] S4. Adjust the node information of the abnormal sub-event nodes, obtain the number of adjusted abnormal sub-event nodes and the number of abnormal key event nodes, determine whether to continue the adjustment, and obtain the adjustment result, as Figure 1 shown.

[0066] The working principle of the above technical solution is as follows: The system first collects various operation behavior information of users in the application, such as clicks, swipes, and residence times. Classify this behavior information to identify different types of user activities, such as browsing, searching, purchasing, etc. Calculate the weight coefficient of each behavior type to reflect its importance to the overall user behavior. Based on the behavior weight coefficient, further calculate the weight coefficient of each event within a preset time period, which helps to identify which application usage events have a greater impact on the overall performance or user experience of the application. Identify the key event nodes and their sub-event nodes, which represent the key paths or important turning points of user behavior within the application. Store the event node information distributively to improve the efficiency and scalability of data processing. Based on the stored event nodes, construct and aggregate the sub-event behavior paths and key event behavior paths to form a complete user behavior path map. Calculate the anomaly coefficient of the key event nodes, and identify the abnormal key event nodes by comparing the actual behavior with the expected or historical average behavior. Calculate the sub-event anomaly coefficient to further identify the specific abnormal sub-event nodes. Adjust the information of the identified abnormal sub-event nodes, which may be to refresh the operation data or the running data, etc. Monitor the effect after adjustment, and evaluate whether the adjustment is effective by comparing the number of abnormal sub-event nodes and key event nodes before and after the adjustment, and decide whether to continue the adjustment.

[0067] The technical effects of the above technical solution are as follows: By accurately identifying abnormal points in user behavior and making targeted adjustments, the satisfaction and efficiency of users during the use of the application can be significantly improved. Based on the results of big data analysis, application developers can formulate product strategies more scientifically, such as adjusting the function layout, optimizing the recommendation algorithm, etc., to better meet user needs. By real-time monitoring of abnormal events in user behavior and timely handling, the risk of application crashes or performance degradation can be effectively reduced, and the stability and reliability of the application can be enhanced. The distributed storage and efficient data processing mechanism enable the system to quickly respond to data changes. Based on the in-depth analysis of user behavior data, the application can provide a more personalized service experience, such as personalized recommendations, customized content, etc., thereby enhancing user stickiness and loyalty.

[0068] In one embodiment of the present invention, S1 includes:

[0069] Obtain user operation behavior information, classify the user operation behavior information according to preset behavior types, and obtain behavior classification information;

[0070] Perform edge computing on each behavior classification information, and calculate the behavior weight coefficient of each behavior classification information through the edge computing method;

[0071] The calculation formula of the behavior weight coefficient is:

[0072]

[0073] Where Q xw is the behavior weight coefficient, c is the number of operation types of the behavior to be calculated, Z ix is the average operation data of the i-th operation type, Z yxi is the preset operation data of the i-th operation type, n is the total number of types of behavior classification information, Z ia is the average operation data of the i-th operation type of the a-th behavior classification information, Z ya is the preset operation data of the i-th operation type of the a-th behavior classification information;

[0074] Obtain the behavior weight coefficient of each behavior classification information in the same preset time period, and calculate the event weight coefficient corresponding to the preset time period;

[0075] The calculation formula of the event weight coefficient is:

[0076]

[0077] Where Q sj is the event weight coefficient, s is the number of types of behavior classification information within the preset time period, Q xwdis the behavior weight coefficient of the d-th type of behavior classification information within a preset time period, Q xwa is the behavior weight coefficient of the a-th type of behavior classification information;

[0078] Perform event annotation with corresponding weights on the user operation behavior information in the preset time period according to the event weight coefficient to obtain key event nodes, and perform sub-event annotation on each behavior classification of the key event nodes to obtain sub-event nodes.

[0079] The working principle of the above technical solution is as follows: The system first collects various operation behavior information of users in the application in real time or periodically, including but not limited to clicks, swipes, inputs, dwell times, etc. According to the preset behavior types (such as browsing, searching, purchasing, sharing, etc.), classify and process the user operation behavior information to form structured behavior classification information. For each type of behavior classification information, use edge computing technology to perform calculations at the source or near the source where it is generated to reduce data transmission latency and relieve the pressure on the cloud server. Calculate the behavior weight coefficient of each type of behavior classification information. This coefficient reflects the importance or influence of this behavior in the overall user behavior. Within the same preset time period (such as one day, one week, or one month), summarize the behavior weight coefficients of various behavior classification information. Calculate the event weight coefficient for this time period. This coefficient is used to measure the overall trend or key nodes of user behavior during the entire time period. According to the event weight coefficient, perform weighted annotation on the user operation behavior information in the preset time period. Behaviors or sequences of behaviors with higher weights are regarded as key event nodes, which represent key turning points or important activities in user behavior. For each key event node, further analyze the internal behavior classification information and perform sub-event annotation. Sub-event nodes are the refinement within key events and are used to more precisely describe the specific behaviors of users in this key event.

[0080] The technical effects of the above technical solution are as follows: Through edge computing technology, real-time processing and analysis of user operation behavior information are achieved, reducing data transmission latency and improving the response speed of the system. Using complex calculation formulas and weighting mechanisms, the importance of user behavior can be evaluated more accurately, and key events and their sub-events can be identified. This provides strong support for subsequent personalized recommendations, user behavior prediction, etc. Edge computing relieves the burden on the cloud server, enabling the system to utilize computing resources more efficiently. At the same time, distributed data storage and processing also improve the scalability and fault tolerance of the system. By deeply analyzing user behavior data, developers can more accurately understand user needs and behavior habits, thereby optimizing aspects such as the functional design and interface layout of the application to enhance the user experience. Based on the analysis results of user behavior data, enterprises can make more scientific and reasonable business decisions, such as product pricing and marketing strategy adjustment, to achieve better business benefits.

[0081] In one embodiment of the present invention, S2 includes:

[0082] Obtain data of multiple sub - event nodes and their key event nodes and upload them to the cloud;

[0083] Through the cloud, perform distributed storage on each sub - event node of the key event node to obtain event storage nodes;

[0084] Connect the sub - event nodes of each behavior classification of the event storage node to obtain a sub - event behavior path corresponding to the behavior classification;

[0085] Connect the key event nodes of each event storage node to obtain a key event behavior path;

[0086] Aggregate the sub - event behavior path and the key event behavior path to obtain an aggregation path, as Figure 2 shown.

[0087] The working principle of the above - mentioned technical solution is as follows: Data of multiple sub - event nodes and their corresponding key event nodes collected by a mobile device or an edge device are packaged and securely transmitted to a cloud server. The cloud server receives this data and performs preliminary verification and cleaning to ensure the integrity and accuracy of the data. The cloud server uses a distributed storage system (such as Hadoop, NoSQL database, etc.) to store the received data. The sub - event nodes of each key event node are independently stored as event storage nodes, and these nodes are distributed in different storage units to improve data reliability and access efficiency. For each event storage node, the system connects the sub - event nodes belonging to the same behavior classification according to the internal behavior classification information therein to form a sub - event behavior path corresponding to the behavior classification. The system also connects the key event nodes of each event storage node to form a key event behavior path. These paths reflect the specific behavior processes of the user under different behavior classifications and the associations between key events. The system aggregates the sub - event behavior path and the key event behavior path to form a complete aggregation path. This aggregation path not only contains the detailed behavior processes of the user under different behavior classifications but also reveals the position and role of key events in the entire user behavior.

[0088] The technical effects of the above technical solution are as follows: Through distributed storage and path construction technologies, the originally scattered user behavior data is integrated into an ordered and visual path graph, enabling developers to intuitively understand the user behavior patterns and key events. The distributed storage system provides efficient data query and analysis capabilities, allowing developers to quickly retrieve user behavior data within a specific time period or behavior classification for in-depth statistical analysis and data mining. The distributed storage system can make full use of the computing resources and storage resources in the cloud to achieve efficient data management and fast access. At the same time, as the number of users and the amount of data increase, the system can easily expand its storage and computing capabilities to meet business needs. Based on the analysis results of the aggregated paths, enterprises can make decisions such as formulating more precise marketing strategies, optimizing product functions, and enhancing user experience to achieve better business benefits and user satisfaction. Since the data is processed in the cloud, the system can receive and process new user behavior data in real time and perform flexible path construction and aggregated analysis according to requirements. This enables the system to quickly respond to market changes and changes in user needs.

[0089] In one embodiment of the present invention, the S3 includes:

[0090] Obtain the application performance data on the aggregated path, and calculate the key event anomaly coefficient of the key event node of the application program according to the application performance data combined with the event weight coefficient;

[0091] The calculation formula of the key event anomaly coefficient is:

[0092]

[0093] where, T gj is the key event anomaly coefficient of the key event node, G is the total number of types of application performance data within the preset time period of the key event node, r se is the actual performance data of the e-th type of application performance data, r yes is the preset performance data of the e-th type of application performance data;

[0094] Perform anomaly annotation on each key event node according to the key event anomaly coefficient to obtain abnormal key event nodes;

[0095] Obtain the sub-event node information of the abnormal key event node, and calculate the sub-event anomaly coefficient of each sub-event node of the abnormal key event node according to the application performance information combined with the sub-event information;

[0096] The calculation formula of the sub-event anomaly coefficient is:

[0097]

[0098] Among them, T z is the sub - event exception coefficient of the sub - event node, m is the total number of types of application performance data within the preset time period of the sub - event node, r so is the actual performance data of the o - th type of application performance data, r yos is the preset performance data of the o - th type of application performance data; the calculation data corresponds one by one according to the types.

[0099] Perform anomaly annotation on each sub - event node according to the sub - event exception coefficient to obtain anomaly sub - event nodes.

[0100] The working principle of the above - mentioned technical solution is as follows: The system needs to collect performance data of the application program on the aggregation path from various monitoring points or log sources. These data may include key metrics such as response time, throughput, error rate, etc. For each key event node, the system calculates its weight coefficient, which reflects the degree of influence of this event node on the overall application performance. Using the collected application performance data and event weight coefficients, calculate the key - event exception coefficient of each key event node according to the given formula. This coefficient is a quantitative indicator used to evaluate the current anomaly degree of the event node. According to the calculated key - event exception coefficient, the system performs anomaly annotation on each key event node. When the exception coefficient exceeds the preset threshold, the event node is marked as abnormal. For the key event nodes marked as abnormal, the system further analyzes their sub - event nodes. By obtaining the detailed information of the sub - event nodes (such as sub - event type, occurrence time, performance data, etc.), combined with the application performance data of the parent node, calculate the sub - event exception coefficient of each sub - event node. According to the sub - event exception coefficient, perform anomaly annotation on each sub - event node. This helps to accurately locate the source of the problem and provides a basis for subsequent fault troubleshooting and optimization.

[0101] The technical effects of the above - mentioned technical solution are as follows: By analyzing layer by layer (from key event nodes to sub - event nodes), it is possible to more accurately locate the performance bottleneck or the source of anomalies in the application program, improving the efficiency of problem - solving. The system can collect and analyze application performance data in real - time, timely discover potential performance problems, and avoid the expansion of problems. Based on quantitative data analysis and weight coefficients, the system can more scientifically evaluate the anomaly degree of event nodes, providing strong support for decision - making. By identifying key event nodes and anomaly sub - event nodes, enterprises can more targeted optimize resource allocation and improve resource utilization efficiency.

[0102] Timely discovery and solution of performance problems in the application program can significantly improve the user experience and enhance user satisfaction and loyalty.

[0103] In one embodiment of the present invention, the S4 includes:

[0104] Obtain the node information of each abnormal sub - event node, adjust the node information, and obtain the adjusted sub - event abnormality coefficient;

[0105] Obtain the number of abnormal sub - event nodes according to the adjusted sub - event abnormality coefficient;

[0106] Judge whether to calculate the corresponding key - event abnormality coefficient according to the number of abnormal sub - event nodes;

[0107] Compare the number of abnormal sub - event nodes with a preset number threshold. When the number of abnormal sub - event nodes is greater than the preset number threshold, calculate the corresponding key - event abnormality coefficient;

[0108] Furthermore, obtain the adjusted number of abnormal key - event nodes. When there are no abnormal key - event nodes, stop the calculation and adjustment of the abnormality coefficient.

[0109] The working principle of the above - mentioned technical solution is as follows: For each abnormal sub - event node, the system first obtains its node information, which may include occurrence time, duration, influence range, relevant resource usage, etc. The system adjusts the node information according to preset rules. After adjustment, recalculate the abnormality coefficient of the sub - event node to obtain the adjusted sub - event abnormality coefficient. According to the adjusted sub - event abnormality coefficient, the system counts the number of abnormal sub - event nodes under the current key event. The system compares the number of abnormal sub - event nodes with a preset number threshold. This threshold is used to judge whether the current key event is "abnormal" enough to require further calculation of its key - event abnormality coefficient. If the number of abnormal sub - event nodes is greater than the preset number threshold, it indicates that the key event is likely to have significant performance problems or abnormal behaviors and requires deeper analysis. When the condition is met, the system will calculate the corresponding key - event abnormality coefficient according to the adjusted sub - event abnormality coefficient and other relevant information (such as key - event weight, historical data, etc.). This calculation process may involve complex mathematical models or machine - learning algorithms to ensure the accuracy and reliability of the abnormality coefficient. After calculating the key - event abnormality coefficient, the system may further evaluate or adjust the key event itself according to this coefficient (for example, adjust the weight, re - classify, adjust the operation data, etc.). If necessary, the system will also update the number of abnormal key - event nodes according to the adjustment result. If no abnormal key - event nodes are found during the inspection process, or the predetermined analysis depth has been reached, the system will stop the calculation and adjustment process of the abnormality coefficient.

[0110] The technical effects of the above technical solution are as follows: Through the adjustment of node information and multiple iterative calculations, the system can more accurately evaluate the abnormality levels of sub-events and key events, reducing false alarms and missed alarms. By setting a quantity threshold, the system can avoid in-depth analysis of each minor abnormality, thus saving computing resources and time. This process can be adjusted and optimized according to different application scenarios and requirements, such as by modifying the threshold, adjusting the node information adjustment rules, or improving the abnormal coefficient calculation algorithm, etc. The system can collect and analyze data in real time, and dynamically adjust the calculation and annotation results of the abnormal coefficient according to the changes in the data, ensuring real-time monitoring and response to the performance of the application program. By accurately locating abnormal events and sub-events, the system can provide more targeted fault troubleshooting and optimization suggestions for developers and operation and maintenance personnel, thereby improving the efficiency of problem-solving.

[0111] In an embodiment of the present invention, the system includes:

[0112] A weight node acquisition module, configured to acquire user operation behavior information, classify the behavior information, calculate a behavior weight coefficient, and further calculate an event weight coefficient for a corresponding preset time period, and acquire key event nodes and sub-event nodes;

[0113] An event path acquisition module, configured to perform distributed storage on event node information to obtain event storage nodes, acquire sub-event behavior paths and key event behavior paths according to the event storage nodes, and perform aggregation of the paths to obtain an aggregated path;

[0114] An abnormal calculation and analysis module, configured to calculate a key event abnormal coefficient of a key event node of an application program to obtain an abnormal key event node, and further calculate a sub-event abnormal coefficient to obtain an abnormal sub-event node;

[0115] An abnormal adjustment and judgment module, configured to adjust the node information of the abnormal sub-event nodes, obtain the number of adjusted abnormal sub-event nodes and the number of abnormal key event nodes, judge whether to continue the adjustment, and obtain an adjustment result.

[0116] The working principle of the above technical solution is as follows: The system first collects various operation behavior information of users within the application, such as clicks, swipes, and dwell times. Classify this behavior information to identify different types of user activities, such as browsing, searching, purchasing, etc. Calculate the weight coefficients of each behavior type to reflect its importance to the overall user behavior. Based on the behavior weight coefficients, further calculate the weight coefficients of each event within a preset time period, which helps to identify which application usage events have a greater impact on the overall performance or user experience of the application. Identify the key event nodes and their sub-event nodes, which represent the key paths or important turning points of user behavior within the application. Store the event node information distributively to improve the efficiency and scalability of data processing. Based on the stored event nodes, construct and aggregate the sub-event behavior paths and key event behavior paths to form a complete user behavior path map. Calculate the anomaly coefficients of the key event nodes, and identify the abnormal key event nodes by comparing the differences between the actual behavior and the expected or historical average behavior. Calculate the sub-event anomaly coefficients to further identify specific abnormal sub-event nodes. Adjust the information of the identified abnormal sub-event nodes, which may involve refreshing operation data or running data, etc. Monitor the effect after adjustment, and evaluate whether the adjustment is effective by comparing the number of abnormal sub-event nodes and key event nodes before and after adjustment, and decide whether to continue the adjustment.

[0117] The technical effects of the above technical solution are as follows: By accurately identifying the abnormal points in user behavior and making targeted adjustments, the satisfaction and efficiency of users during the use of the application can be significantly improved. Based on the results of big data analysis, application developers can formulate product strategies more scientifically, such as adjusting the function layout, optimizing the recommendation algorithm, etc., to better meet user needs. By real-time monitoring and timely handling of abnormal events in user behavior, the risk of application crashes or performance degradation can be effectively reduced, and the stability and reliability of the application can be improved. The distributed storage and efficient data processing mechanism enable the system to quickly respond to data changes. Based on the in-depth analysis of user behavior data, the application can provide a more personalized service experience, such as personalized recommendations, customized content, etc., thereby enhancing user stickiness and loyalty.

[0118] In an embodiment of the present invention, the weight node acquisition module includes:

[0119] A behavior classification module, configured to obtain user operation behavior information, classify the user operation behavior information according to preset behavior categories, and obtain behavior classification information;

[0120] A classification weight calculation module, configured to perform edge computing on each behavior classification information, and calculate the behavior weight coefficient of each behavior classification information through an edge computing method;

[0121] The calculation formula for the behavior weight coefficient is as follows:

[0122]

[0123] Among them, Q xw is the behavior weight coefficient, c is the number of types of operation behaviors to be calculated, Z ix is the average operation data of the i-th type of operation behavior, Z yxi is the preset operation data of the i-th type of operation behavior, n is the total number of behavior classification information, Z ia is the average operation data of the i-th type of operation behavior of the a-th behavior classification information, Z ya is the preset operation data of the i-th type of operation behavior of the a-th behavior classification information;

[0124] An event weight calculation module, configured to obtain the behavior weight coefficient of each behavior classification information in the same preset time period and calculate the event weight coefficient corresponding to the preset time period;

[0125] The calculation formula for the event weight coefficient is as follows:

[0126]

[0127] Among them, Q sj is the event weight coefficient, s is the number of types of behavior classification information in the preset time period, Q xwd is the behavior weight coefficient of the d-th type of behavior classification information in the preset time period, Q xwa is the behavior weight coefficient of the a-th type of behavior classification information;

[0128] An event node acquisition module, configured to perform event annotation with corresponding weights on the user operation behavior information in the preset time period according to the event weight coefficient, obtain key event nodes, and perform sub-event annotation on each behavior classification of the key event nodes to obtain sub-event nodes.

[0129] The working principle of the above technical solution is as follows: The system first collects various operation behavior information of users within the application in real time or regularly, including but not limited to clicks, swipes, inputs, dwell time, etc. According to preset behavior categories (such as browsing, searching, purchasing, sharing, etc.), the user operation behavior information is classified to form structured behavior classification information. For each behavior classification information, edge computing technology is used to perform calculations at the source or near the source where it is generated, so as to reduce data transmission latency and relieve the pressure on the cloud server. Calculate the behavior weight coefficient of each behavior classification information. This coefficient reflects the importance or influence of this behavior in the overall behavior of the user. Within the same preset time period (such as one day, one week, or one month), summarize the behavior weight coefficients of various behavior classification information. Calculate the event weight coefficient for this time period. This coefficient is used to measure the overall trend or key nodes of the user behavior during the entire time period. According to the event weight coefficient, weight annotation is performed on the user operation behavior information in the preset time period. Behaviors or behavior sequences with higher weights are regarded as key event nodes, which represent the key turning points or important activities in the user behavior. For each key event node, further analyze the internal behavior classification information and perform sub-event annotation. Sub-event nodes are the refinement within the key event and are used to more precisely describe the specific behavior of the user in this key event.

[0130] The technical effects of the above technical solution are as follows: Through edge computing technology, the instant processing and analysis of user operation behavior information are realized, the data transmission latency is reduced, and the response speed of the system is improved. By using complex calculation formulas and weighting mechanisms, the importance of user behavior can be evaluated more accurately, and key events and their sub-events can be identified. This provides strong support for subsequent personalized recommendations, user behavior prediction, etc. Edge computing relieves the burden on the cloud server, enabling the system to utilize computing resources more efficiently. At the same time, distributed data storage and processing also improve the scalability and fault tolerance of the system. By deeply analyzing user behavior data, developers can more accurately understand user needs and behavior habits, thereby optimizing aspects such as the function design and interface layout of the application to enhance the user experience. Based on the analysis results of user behavior data, enterprises can make more scientific and reasonable business decisions, such as product pricing, marketing strategy adjustment, etc., to achieve better business benefits.

[0131] In one embodiment of the present invention, the event path acquisition module includes:

[0132] A distributed storage module, which is used to obtain the data of multiple sub-event nodes and their key event nodes and upload them to the cloud;

[0133] Through the cloud, each sub-event node of the key event node is distributedly stored to obtain event storage nodes;

[0134] A path generation module, which is used to connect the sub - event nodes of each behavior classification of the event storage node to obtain the sub - event behavior path corresponding to the behavior classification;

[0135] Connect the key - event nodes of each event storage node to obtain the key - event behavior path;

[0136] A path aggregation module, which is used to aggregate the sub - event behavior path and the key - event behavior path to obtain the aggregated path.

[0137] The working principle of the above - mentioned technical solution is as follows: The data of multiple sub - event nodes and their corresponding key - event nodes collected by the mobile device or edge device are packaged and securely transmitted to the cloud server. The cloud server receives these data and performs preliminary verification and cleaning to ensure the integrity and accuracy of the data. The cloud server uses a distributed storage system (such as Hadoop, NoSQL database, etc.) to store the received data. The sub - event nodes of each key - event node are independently stored as event storage nodes, and these nodes are distributed in different storage units to improve the reliability and access efficiency of the data. For each event storage node, the system connects the sub - event nodes belonging to the same behavior classification according to the internal behavior classification information to form the sub - event behavior path corresponding to the behavior classification. The system also connects the key - event nodes of each event storage node to form the key - event behavior path. These paths reflect the specific behavior processes of the user under different behavior classifications and the associations between key events. The system aggregates the sub - event behavior path and the key - event behavior path to form a complete aggregated path. This aggregated path not only contains the detailed behavior processes of the user under different behavior classifications, but also reveals the position and role of key events in the entire user behavior.

[0138] The technical effects of the above technical solution are as follows: Through distributed storage and path construction technologies, the originally scattered user behavior data is integrated into an ordered and visual path graph, enabling developers to intuitively understand the user behavior patterns and key events. The distributed storage system provides efficient data query and analysis capabilities, allowing developers to quickly retrieve user behavior data within a specific time period or behavior classification for in-depth statistical analysis and data mining. The distributed storage system can make full use of the computing resources and storage resources in the cloud to achieve efficient data management and fast access. At the same time, as the number of users and the amount of data increase, the system can easily expand its storage and computing capabilities to meet business needs. Based on the analysis results of the aggregated paths, enterprises can make decisions such as formulating more precise marketing strategies, optimizing product functions, and enhancing user experience to achieve better business benefits and user satisfaction. Since the data is processed in the cloud, the system can receive and process new user behavior data in real time and perform flexible path construction and aggregated analysis according to requirements. This enables the system to quickly respond to market changes and changes in user needs.

[0139] In one embodiment of the present invention, the abnormal calculation and analysis module includes:

[0140] A key abnormal calculation module, configured to obtain application performance data on the aggregated path and calculate the key event abnormal coefficient of the key event node of the application program according to the application performance data in combination with the event weight coefficient;

[0141] The calculation formula for the key event abnormal coefficient is:

[0142]

[0143] Where, T gj is the key event abnormal coefficient of the key event node, G is the total number of types of application performance data within the preset time period of the key event node, r se is the actual performance data of the e-th type of application performance data, and r yes is the preset performance data of the e-th type of application performance data;

[0144] A key node abnormal acquisition module, configured to perform abnormal annotation on each key event node according to the key event abnormal coefficient to obtain abnormal key event nodes;

[0145] A sub-abnormal calculation module, configured to obtain the sub-event node information of the abnormal key event node and calculate the sub-event abnormal coefficient of each sub-event node of the abnormal key event node according to the application performance information in combination with the sub-event information;

[0146] The calculation formula for the sub-event abnormal coefficient is:

[0147]

[0148] Among them, T z is the sub-event exception coefficient of the sub-event node, m is the total number of types of application performance data within the preset time period of the sub-event node, r so is the actual performance data of the o-th type of application performance data, r yos is the preset performance data of the o-th type of application performance data; the calculation data corresponds one by one according to the types.

[0149] The sub-node exception acquisition module is used to perform exception annotation on each sub-event node according to the sub-event exception coefficient to obtain exception sub-event nodes.

[0150] The working principle of the above technical solution is as follows: The system needs to collect performance data of the application program on the aggregation path from various monitoring points or log sources. These data may include key metrics such as response time, throughput, error rate, etc. For each key event node, the system calculates its weight coefficient, which reflects the impact degree of the event node on the overall application performance. Using the collected application performance data and event weight coefficients, the key event exception coefficient of each key event node is calculated according to the given formula. This coefficient is a quantitative indicator used to evaluate the current exception degree of the event node. According to the calculated key event exception coefficient, the system performs exception annotation on each key event node. When the exception coefficient exceeds the preset threshold, the event node is marked as abnormal. For the key event nodes marked as abnormal, the system further analyzes their sub-event nodes. By obtaining the detailed information of the sub-event nodes (such as sub-event type, occurrence time, performance data, etc.), combined with the application performance data of the parent node, the sub-event exception coefficient of each sub-event node is calculated. According to the sub-event exception coefficient, exception annotation is performed on each sub-event node. This helps to accurately locate the source of the problem and provides a basis for subsequent fault troubleshooting and optimization.

[0151] The technical effect of the above technical solution is as follows: Through layer-by-layer analysis (from key event nodes to sub-event nodes), it is possible to more accurately locate the performance bottleneck or the source of exceptions in the application program, improving the efficiency of problem-solving. The system can collect and analyze application performance data in real time, timely discover potential performance problems, and prevent problems from expanding. Based on quantitative data analysis and weight coefficients, the system can more scientifically evaluate the exception degree of event nodes, providing strong support for decision-making. By identifying key event nodes and exception sub-event nodes, enterprises can more targeted optimize resource allocation and improve resource utilization efficiency.

[0152] Timely discovering and solving performance problems in the application program can significantly improve the user experience and enhance user satisfaction and loyalty.

[0153] In one embodiment of the present invention, the abnormal adjustment judgment module includes:

[0154] An adjustment sub-node acquisition module, configured to acquire the node information of each abnormal sub-event node, adjust the node information, and acquire the adjusted sub-event abnormal coefficient;

[0155] Acquire the number of abnormal sub-event nodes according to the adjusted sub-event abnormal coefficient;

[0156] An adjustment key-node acquisition module, configured to determine whether to calculate the corresponding key-event abnormal coefficient according to the number of abnormal sub-event nodes;

[0157] Compare the number of abnormal sub-event nodes with a preset number threshold. When the number of abnormal sub-event nodes is greater than the preset number threshold, calculate the corresponding key-event abnormal coefficient;

[0158] Furthermore, acquire the adjusted number of abnormal key-event nodes. When there is no abnormal key-event node, stop the calculation and adjustment of the abnormal coefficient.

[0159] The working principle of the above technical solution is as follows: For each abnormal sub-event node, the system first acquires its node information, which may include occurrence time, duration, influence range, relevant resource usage, etc. The system adjusts the node information according to preset rules. After adjustment, recalculate the abnormal coefficient of the sub-event node to obtain the adjusted sub-event abnormal coefficient. According to the adjusted sub-event abnormal coefficient, the system counts the number of abnormal sub-event nodes under the current key event. The system compares the number of abnormal sub-event nodes with a preset number threshold. This threshold is used to determine whether the current key event is "abnormal" enough to require further calculation of its key-event abnormal coefficient. If the number of abnormal sub-event nodes is greater than the preset number threshold, it indicates that the key event is likely to have significant performance problems or abnormal behaviors and requires deeper analysis. When the condition is met, the system will calculate the corresponding key-event abnormal coefficient according to the adjusted sub-event abnormal coefficient and other relevant information (such as key-event weight, historical data, etc.). This calculation process may involve complex mathematical models or machine learning algorithms to ensure the accuracy and reliability of the abnormal coefficient. After calculating the key-event abnormal coefficient, the system may further evaluate or adjust the key event itself according to this coefficient (for example, adjust the weight, reclassify, adjust the running data, etc.). If necessary, the system will also update the number of abnormal key-event nodes according to the adjustment result. If no abnormal key-event node is found during the inspection, or the predetermined analysis depth has been reached, the system will stop the calculation and adjustment process of the abnormal coefficient.

[0160] The technical effects of the above technical solution are as follows: Through the adjustment of node information and multiple iterative calculations, the system can more accurately evaluate the abnormal degrees of sub-events and key events, reducing false alarms and missed alarms. By setting a number threshold, the system can avoid in-depth analysis of each minor abnormality, thus saving computing resources and time. This process can be adjusted and optimized according to different application scenarios and requirements, such as by modifying the threshold, adjusting the node information adjustment rules, or improving the abnormal coefficient calculation algorithm. The system can collect and analyze data in real time and dynamically adjust the calculation and annotation results of the abnormal coefficient according to the changes in the data to ensure real-time monitoring and response to the performance of the application program. By accurately locating abnormal events and sub-events, the system can provide more targeted troubleshooting and optimization suggestions for developers and operators, thereby improving the efficiency of problem-solving.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A data analysis method for a mobile application, characterized in that, The method includes: S1. Obtain user operation behavior information, classify the behavior information, calculate the behavior weight coefficient, then calculate the event weight coefficient for a corresponding preset time period, and obtain the key event nodes and sub-event nodes; The calculation formula for the behavior weight coefficient is: Among them, Q xw is the behavior weight coefficient, c is the number of operation types for which the behavior score needs to be calculated, Z ix is the average operation data of the i-th operation type, Z yxi is the preset operation data of the i-th operation type, n is the total number of types of behavior classification information, Z ia is the average operation data of the i-th operation type of the a-th behavior classification information, Z ya is the preset operation data of the i-th operation type of the a-th behavior classification information; The calculation formula for the event weight coefficient is: Among them, Q sj is the event weight coefficient, s is the number of types of behavior classification information within a preset time period, and Q xwd is the behavior weight coefficient of the d-th type of behavior classification information within a preset time period, and Q xwa is the behavior weight coefficient of the a-th type of behavior classification information; S2. Store the event node information in a distributed manner to obtain event storage nodes, obtain the sub-event behavior paths and key event behavior paths according to the event storage nodes, and perform path aggregation to obtain an aggregated path; S3. Calculate the key event anomaly coefficient of the key event nodes of the application program to obtain abnormal key event nodes, and then calculate the sub-event anomaly coefficient to obtain abnormal sub-event nodes; The calculation formula for the key event anomaly coefficient is: Among them, T gj is the key event anomaly coefficient of the key event node, G is the total number of types of application performance data within the preset time period of the key event node, r se is the actual performance data of the e-th type of application performance data, r yes is the preset performance data of the e-th type of application performance data; The calculation formula for the sub-event anomaly coefficient is: Among them, T z is the sub-event exception coefficient of the sub-event node, m is the total number of types of application performance data within the preset time period of the sub-event node, r so is the actual performance data of the o-th type of application performance data, r yos is the preset performance data of the o-th type of application performance data; S4. Adjust the node information of the abnormal sub-event nodes, obtain the number of adjusted abnormal sub-event nodes and the number of abnormal key event nodes, determine whether to continue the adjustment, and obtain the adjustment result.

2. The data analysis method for using a mobile application according to claim 1, characterized in that The S1 includes: Obtain user operation behavior information, classify the user operation behavior information according to preset behavior types to obtain behavior classification information; Perform edge computing on each behavior classification information, and calculate the behavior weight coefficient of each behavior classification information through the edge computing method; Obtain the behavior weight coefficients of each behavior classification information in the same preset time period and calculate the event weight coefficient for the corresponding preset time period; Perform event annotation with corresponding weights on the user operation behavior information in the preset time period according to the event weight coefficient to obtain key event nodes, and perform sub-event annotation on each behavior classification of the key event nodes to obtain sub-event nodes.

3. The data analysis method for using a mobile application according to claim 1, wherein The S2 includes: Obtain the data of multiple sub-event nodes and their key event nodes and upload them to the cloud; Perform distributed storage on each sub-event node of the key event nodes through the cloud to obtain event storage nodes; Connect the sub-event nodes of each behavior classification of the event storage nodes to obtain the sub-event behavior paths corresponding to the behavior classifications; Connect the key event nodes of each event storage node to obtain the key event behavior paths; Aggregate the sub-event behavior paths and the key event behavior paths to obtain an aggregated path.

4. A method for analyzing data used in a mobile application according to claim 1, characterized in that The S3 includes: Obtain the application performance data on the aggregated path, and calculate the key event anomaly coefficient of the key event nodes of the application program in combination with the event weight coefficient according to the application performance data; Perform anomaly annotation on each key event node according to the key event anomaly coefficient to obtain abnormal key event nodes; Obtain the sub-event node information of the abnormal key event nodes, and calculate the sub-event anomaly coefficient of each sub-event node of the abnormal key event nodes in combination with the application performance information according to the sub-event node information; Perform anomaly annotation on each sub-event node according to the sub-event anomaly coefficient to obtain abnormal sub-event nodes.

5. The method for analyzing data used by a mobile application according to claim 1, characterized in that The S4 includes: Obtain the node information of each abnormal sub-event node, adjust the node information, and obtain the adjusted sub-event anomaly coefficient; Obtain the number of abnormal sub-event nodes according to the adjusted sub-event anomaly coefficient; Judge whether to calculate the corresponding key event anomaly coefficient according to the number of abnormal sub - event nodes; Furthermore, obtain the adjusted number of abnormal key event nodes.

6. A mobile application usage data analysis system, characterized in that, The system includes: A weight node acquisition module, configured to acquire user operation behavior information, classify the behavior information, calculate the behavior weight coefficient, and then calculate the event weight coefficient for a corresponding preset time period, and obtain key event nodes and sub - event nodes; The calculation formula for the behavior weight coefficient is: Among them, Q xw is the behavior weight coefficient, c is the number of operation types for which the behavior score needs to be calculated, Z ix is the average operation data of the i-th operation type, Z yxi is the preset operation data of the i-th operation type, n is the total number of types of behavior classification information, Z ia is the average operation data of the i-th operation type of the a-th behavior classification information, Z ya is the preset operation data of the i-th operation type of the a-th behavior classification information; The calculation formula for the event weight coefficient is: Among them, Q sj is the event weight coefficient, s is the number of types of behavior classification information within a preset time period, and Q xwd is the behavior weight coefficient of the d-th type of behavior classification information within the preset time period, and Q xwa is the behavior weight coefficient of the a-th type of behavior classification information; An event path acquisition module, configured to perform distributed storage on event node information to obtain event storage nodes, acquire sub - event behavior paths and key event behavior paths according to the event storage nodes, and perform path aggregation to obtain an aggregated path; An anomaly calculation and analysis module, configured to calculate the key event anomaly coefficient of the key event nodes of the application program to obtain abnormal key event nodes, and then calculate the sub - event anomaly coefficient to obtain abnormal sub - event nodes; The calculation formula for the key event anomaly coefficient is: Among them, T gj is the key event anomaly coefficient of the key event node, G is the total number of types of application performance data within the preset time period of the key event node, r se is the actual performance data of the e-th type of application performance data, r yes is the preset performance data of the e-th type of application performance data; The calculation formula for the sub - event anomaly coefficient is: Among them, T z is the sub-event anomaly coefficient of the sub-event node, m is the total number of types of application performance data within the preset time period of the sub-event node, r so is the actual performance data of the o-th type of application performance data, r yos is the preset performance data of the o-th type of application performance data; An anomaly adjustment and judgment module, configured to adjust the node information of the abnormal sub - event nodes, obtain the adjusted number of abnormal sub - event nodes and the number of abnormal key event nodes, judge whether to continue the adjustment, and obtain an adjustment result.

7. The data analysis system for mobile application usage according to claim 6, wherein The weight node acquisition module includes: A behavior classification module, configured to acquire user operation behavior information, classify the user operation behavior information according to preset behavior types, and obtain behavior classification information; A classification weight calculation module, configured to perform edge calculation on each behavior classification information, and calculate the behavior weight coefficient of each behavior classification information through the edge calculation method; An event weight calculation module, configured to obtain the behavior weight coefficient of each behavior classification information in the same preset time period and calculate the event weight coefficient for the corresponding preset time period; An event node acquisition module, configured to perform event annotation with corresponding weights on the user operation behavior information in the preset time period according to the event weight coefficient to obtain key event nodes, and perform sub - event annotation on each behavior classification of the key event nodes to obtain sub - event nodes.

8. The data analysis system for using a mobile application according to claim 6, wherein The event path acquisition module includes: A distributed storage module, configured to upload data of multiple sub - event nodes and their key event nodes to the cloud; Perform distributed storage on each sub - event node of the key event node through the cloud to obtain event storage nodes; A path generation module, configured to connect the sub - event nodes of each behavior classification of the event storage nodes to obtain a sub - event behavior path corresponding to the behavior classification; Connect the key event nodes of each event storage node to obtain a key event behavior path; A path aggregation module, configured to aggregate the sub - event behavior path and the key event behavior path to obtain an aggregated path.

9. The data analysis system for using a mobile application according to claim 6, wherein, The anomaly calculation and analysis module includes: A key anomaly calculation module, configured to acquire application performance data on the aggregated path, and calculate the key event anomaly coefficient of the key event nodes of the application program according to the application performance data in combination with the event weight coefficient; The key node anomaly acquisition module is used to perform anomaly annotation on each key event node according to the key event anomaly coefficient to obtain the anomalous key event nodes; The sub-anomaly calculation module is used to obtain the sub-event node information of the anomalous key event nodes, and calculate the sub-event anomaly coefficient of each sub-event node of the anomalous key event node in combination with the application performance information; The sub-node anomaly acquisition module is used to perform anomaly annotation on each sub-event node according to the sub-event anomaly coefficient to obtain the anomalous sub-event nodes.

10. The data analysis system for use in a mobile application according to claim 6, wherein The anomaly adjustment judgment module includes: The adjustment sub-node acquisition module is used to obtain the node information of each anomalous sub-event node, adjust the node information, and obtain the adjusted sub-event anomaly coefficient; Obtain the number of anomalous sub-event nodes according to the adjusted sub-event anomaly coefficient; The adjustment key node acquisition module is used to judge whether to calculate the corresponding key event anomaly coefficient according to the number of anomalous sub-event nodes; Furthermore, obtain the number of adjusted anomalous key event nodes.

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