A Real-time Performance Monitoring and Optimization Method for Cross-border E-commerce APP

Abstract

The present invention discloses a method for real-time performance monitoring and optimization of a cross-border e-commerce APP, which relates to the technical field of APP performance monitoring and analysis. The specific steps of this optimization method include: Data collection and preprocessing: Collect behavioral data of user operations, interface interaction data, and user voice and expression data during the use of the APP. Through the construction of a multi-dimensional user experience index system, the present invention realizes the comprehensive monitoring and analysis of the performance of the cross-border e-commerce APP. It not only pays attention to user operation behaviors and interface interaction data, but also takes into account user emotional state data, thus more accurately reflecting the true experience of users. Through in-depth mining of algorithm formulas, the present invention can comprehensively evaluate the performance of the cross-border e-commerce APP in different dimensions, providing users with more detailed and comprehensive performance feedback. This multi-dimensional monitoring method enables APP developers to more accurately locate performance bottlenecks and timely optimize the user experience, thereby gaining an advantage in the highly competitive market.

Description

Technical Field

[0001] The present invention relates to the technical field of APP performance monitoring and analysis, and particularly to a real-time performance monitoring and optimization method for cross-border e-commerce APPs. Background Art

[0002] With the rapid development of global e-commerce, cross-border e-commerce APPs, as a key bridge connecting merchants and consumers, have become increasingly important in terms of their performance and user experience. Cross-border e-commerce APPs face complex and changing network environments, user behaviors, and business requirements. Therefore, how to ensure the efficient and stable operation of APPs and provide a high-quality user experience has become an important issue that cross-border e-commerce enterprises urgently need to solve. Real-time monitoring and optimization of APP performance can effectively improve user satisfaction and thus promote business growth. Currently, performance monitoring and optimization technologies have become a research hotspot in the field of cross-border e-commerce APP development and operation.

[0003] However, there are many deficiencies in traditional cross-border e-commerce APP performance monitoring and optimization technologies. On the one hand, traditional methods often focus on the monitoring of single technical indicators, such as response time and loading speed, and ignore the actual experience and feelings of users during use. This monitoring method centered on technical indicators is difficult to comprehensively reflect the performance status of APPs and cannot accurately capture the key factors affecting user experience. On the other hand, traditional optimization means are mostly passive repairs after problems occur, lacking foresight. This lagging optimization method is not only inefficient but also likely to lead to a decline in user experience and business losses. In addition, traditional methods often have performance bottlenecks when dealing with large-scale data and high-concurrency scenarios, and it is difficult to meet the high requirements of cross-border e-commerce APPs for real-time and accuracy.

[0004] Therefore, developing a real-time performance monitoring and optimization method for cross-border e-commerce APPs, which can meet the high requirements of cross-border e-commerce APPs for real-time and accuracy, provides an efficient performance monitoring and optimization solution for cross-border e-commerce enterprises. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and provide a real-time performance monitoring and optimization method for cross-border e-commerce APPs. This method comprehensively collects and analyzes multi-dimensional data such as user operation behaviors, interface interactions, and emotional states, constructs a comprehensive user experience index system, and uses the Monte Carlo simulation algorithm for behavior simulation and performance analysis to accurately identify the key factors affecting APP performance. At the same time, through a real-time resource demand prediction and performance anomaly warning mechanism, the present invention realizes the proactive optimization and fault prevention of APP performance, significantly improving the stability and user experience of APPs.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A real-time performance monitoring and optimization method for a cross-border e-commerce APP. The specific steps of the optimization method are as follows:

[0007] S100, Data collection and preprocessing: Under legal circumstances, collect the behavioral data of user operations, interface interaction data, and user voice and expression data during APP use. Clean the original data, remove noise and outliers, convert it into a unified format, and mark the time stamp;

[0008] S200, Construction of multi-dimensional experience indicators: Use algorithms to deeply mine the preprocessed data, comprehensively consider user operation behaviors, interface interactions, and emotional state data, and combine historical data to construct a multi-dimensional user experience indicator system;

[0009] S300, Behavior simulation and performance analysis: According to the constructed indicator system and user operation behavior data, use the Monte Carlo simulation algorithm to generate simulated user operation sequences, and use the simulated behavior performance correlation formula to analyze the relationship between simulated behaviors and performance indicators to determine the key factors affecting APP performance;

[0010] S400, Real-time resource demand prediction: Collect APP historical performance data and current running state data, clean and normalize them, construct a resource demand prediction model through a resource demand prediction algorithm, train the model using historical and current state data, use the model to predict future resource demands, and adjust the resource allocation strategy according to the prediction results;

[0011] S500, Performance anomaly prediction: Receive the data from behavior simulation and performance analysis and real-time resource demand prediction, identify potential patterns and trends in the data, predict abnormal situations based on the identification results according to the performance anomaly prediction model, and construct an abnormal probability prediction model based on the comprehensive index The formula is: , where is the performance anomaly probability, are the parameters of the model, determined using historical data through the maximum likelihood estimation method, is the comprehensive index and other input variables outside is an index variable. When the predicted anomaly probability exceeds the threshold , an alarm is sent via email, text message, or the APP management background system.

[0012] Further, for S100, the collection of user-related data in data collection and preprocessing, user operation behavior data is collected by embedding an event listener at the APP code level, the interface loading time is obtained by setting marker points in the code to record the time difference when the APP starts and page jumps and loads. Under legal circumstances, the APP is used to call the device microphone and camera to collect user voice and expression data.

[0013] Furthermore, for S200, the construction of the multi-dimensional user experience index system in multi-dimensional experience index construction, the calculation of the user operation behavior fusion index is calculated by the formula: , where is the total number of click operations, is the th click operation, is the total monitoring time, is the total number of sliding displacements, is the th sliding displacement, is the total sliding time, is the total number of page stay times, is the stay time on the th page, , , are the weight coefficients set according to the importance of each behavior; the calculation of the interface interaction comprehensive index is calculated by the formula: , where is the total number of interface loading times, is the th interface loading time, is the total number of element response times, is the th element response time, and are the corresponding weights; the calculation of the user emotion quantification index is calculated by the formula: , where is the index variable in the summation formula, represents the th emotion value obtained through voice emotion analysis, represents the th emotion value of facial expression recognition, and are the weights of the two; the calculation formula of the multi-dimensional user experience index is: , where , , Used to balance the proportion of each part in the overall experience index.

[0014] Furthermore, in the S300, the Monte Carlo simulation algorithm is used in behavior simulation and performance analysis to generate a simulated user operation sequence , and the calculation formula is: , where is the th step of the simulated operation, is the probability of the th operation, is the vector representation of the th operation, is a subscript index, is a parameter that controls the change range of the operation. The random function generates a random operation according to a given probability distribution, and x represents the number of operation types.

[0015] Furthermore, in the S300, the relationship between the simulated behavior and the performance index is analyzed using the simulated behavior performance correlation formula in behavior simulation and performance analysis. The formula is: , where is the performance index value of the APP at the th step of the simulated operation, the th step of the simulated operation, represents the total number of steps of the simulated operation.

[0016] Furthermore, in the S300, for the judgment of the key factors affecting the APP performance in behavior simulation and performance analysis, when a specific simulated operation appears, increases significantly, and the corresponding also becomes significantly longer. The real user operation corresponding to this simulated operation is the key factor affecting the APP performance.

[0017] Furthermore, in the S400, for the construction of the resource demand prediction model in real-time resource demand prediction, the formula is: , where is the predicted value of the resource demand after time in the future, are the historical resource usage values at different past times, are the corresponding coefficients, is the comprehensive value of the current running state of the APP, is the state influence coefficient, is an index variable, represents the current moment, represents the time interval, and M represents the time step span of considering historical resource usage data when calculating the predicted value of the future resource demand.

[0018] Furthermore, in the S400, the resource allocation strategy is adjusted according to the prediction result in the real-time resource demand prediction:

[0019] If it is predicted that the CPU demand will increase significantly: Apply to the cloud service provider in advance to increase the number of CPU cores, or allocate some computing tasks to other idle server nodes;

[0020] For the increase in memory demand: Optimize the memory management algorithm of the APP to release the memory space that is no longer used, or increase the physical memory capacity of the server;

[0021] For the increase in network bandwidth demand: Negotiate with the network service provider to temporarily increase the bandwidth, or optimize the network request strategy of the APP to reduce unnecessary network data transmission.

[0022] Furthermore, in the S500, potential patterns and trends in the data are identified through comprehensive metrics in the performance anomaly prediction. Suppose pattern categories are obtained through the clustering optimization method, and each pattern category has a characteristic center vector , , , the Euclidean distance from the data point to the clustering center vector is , and the calculation formula is: , where is the dimension of the feature vector, is the data point, the data point at the -th dimension of the eigenvalue, is the eigenvalue of the characteristic center vector at the -th dimension. Suppose the trend index is , and the calculation formula is: , where is the data value at time f, is the length of the time series. The calculation formula of the comprehensive metric is: , where and are adjustment coefficients, is the weight of the -th pattern category, is an index variable.

[0023] Compared with the prior art, the real-time performance monitoring and optimization method of this cross-border e-commerce APP has the following beneficial effects:

[0024] 1. The present invention realizes comprehensive monitoring and analysis of the performance of cross-border e-commerce APPs by constructing a multi-dimensional user experience index system. It not only focuses on user operation behaviors and interface interaction data but also takes into account user emotional state data, thus more accurately reflecting the real experience of users. Through in-depth mining of algorithm formulas, the present invention can comprehensively evaluate the performance of cross-border e-commerce APPs in different dimensions, providing users with more detailed and comprehensive performance feedback. This multi-dimensional monitoring method enables APP developers to more accurately locate performance bottlenecks and timely optimize the user experience, thereby gaining an advantage in the highly competitive market.

[0025] 2. The present invention realizes accurate prediction of future resource requirements by cleaning and normalizing historical performance data and current running state data and constructing a resource demand prediction model using prediction algorithms. When it predicts that resource requirements will change, the present invention can automatically adjust resource allocation strategies, such as increasing the number of CPU cores, optimizing memory management algorithms, and temporarily increasing network bandwidth, to ensure the stable operation of the APP under different loads. This dynamic adjustment strategy not only improves the response speed and stability of cross-border e-commerce APPs but also greatly reduces operating costs, providing a strong guarantee for the long-term stable development of cross-border e-commerce APPs.

[0026] Other advantages, objectives, and features of the present invention will be described to some extent in the following specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0028] Figure 1 It is a flowchart of a real-time performance monitoring and optimization method for a cross-border e-commerce APP;

[0029] Figure 2 It is a framework diagram of a real-time performance monitoring and optimization method for a cross-border e-commerce APP. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and their effects according to the present invention as follows.

[0031] Example 1:

[0032] Performance Monitoring and Optimization during Peak Shopping Periods of Cross - border E - commerce APPs

[0033] Data collection and pre - processing: During peak shopping periods, the cross - border e - commerce APP enters the peak traffic period. To comprehensively understand the user experience and APP performance, data is collected from multiple dimensions. At the APP code level, event listeners are pre - set to collect user operation behavior data. When the user browses the product details page, every click on the product picture, view of product parameters, addition to the shopping cart operation, and swipe operation on the product list page will be captured and recorded by the event listener. At the same time, marker points are set at key nodes of the APP, such as APP startup and page - to - page jump and loading. By recording the time difference between these marker points, the interface loading time is obtained. In addition, the APP also calls the device's microphone and camera to collect the user's voice and facial expression data during the process of the user browsing products or placing orders.

[0034] There is a lot of noise and outliers in the collected raw data. For example, due to network fluctuations, there are duplicate operation records, and the interface loading time is abnormally extended. Therefore, the data is cleaned. First, according to the timestamp and operation logic of the data, the data that obviously does not conform to the normal operation process is removed. Then, the data from different sources and in different formats is converted into a unified format for subsequent processing and analysis. Finally, each piece of data is marked with an accurate timestamp to facilitate the analysis of data characteristics in different time periods.

[0035] The pre - processed data can be used to construct multi - dimensional user experience indicators. First, construct the integrated user operation behavior indicator , and the calculation formula is: , where represents the quantification value of the th operation behavior, such as the number of click operations and the distance of swipe displacement. n represents the number of types of user operation behaviors, is the corresponding weight, which is determined according to the importance of different operation behaviors to the user experience. Combining the data of historical peak shopping periods, the occurrence frequency and the impact degree on the user experience of various operation behaviors are statistically analyzed to determine the value of the weight .

[0036] Next, construct the comprehensive interface interaction indicator , and the calculation formula is: , where represents the The response time of the secondary interface interaction is obtained by recording the time difference between the user operation and the interface feedback. Let m represent the number of metrics related to the interface interaction. Similarly, by combining historical data, analyze the normal response time range of different interface interaction operations to determine whether the current metric is within a reasonable range.

[0037] Then, construct a user emotion quantification metric , and use speech recognition and facial expression analysis technologies to process the collected user speech and facial expression data. According to the intonation, speech rate of the speech and the changes in facial expressions, classify the user's emotions into different levels of positive, neutral, and negative, and quantify them into specific values. The calculation formula is: , where represents the number of emotion-related factors or metrics involved in calculating the emotion metric , represents the th quantified value of the emotion state, is the corresponding weight.

[0038] Finally, obtain the overall experience metric through the multi-dimensional user experience metric calculation formula. The formula is: , where , , are the corresponding weights, which are determined according to the comprehensive influence degree of different metrics on the user experience. Through this metric system, the user experience during the shopping peak period can be comprehensively evaluated.

[0039] Behavior simulation and performance analysis. According to the constructed metric system and the collected user operation behavior data, use the Monte Carlo simulation algorithm to generate simulated user operation sequences. The Monte Carlo simulation algorithm simulates a large number of user operation scenarios through random sampling. During the simulation process, according to the occurrence probabilities of various operation behaviors in historical data, randomly generate different operation sequences, and analyze the relationship between the simulated behavior and the performance metrics through the simulated behavior performance correlation formula. The formula is: , where represents the number of terms participating in the summation operation when calculating , represents the th performance metric value corresponding to the simulation operation, such as the interface loading time, CPU usage rate, represents the weight of this simulation operation. During the simulation process, if it is found that when a certain simulation operation occurs, the performance correlation metric significantly increases, and the corresponding APP performance metric value (such as the interface loading time becomes significantly longer) is also significantly abnormal, then determine that the real user operation corresponding to this simulation operation (such as a large number of users clicking on the same promotional product link at the same time) is a key factor affecting the APP performance. ​

[0040] Real-time resource demand prediction, collect the historical performance data and current running status data of the APP during the shopping peak period, including CPU usage, memory occupancy, and network bandwidth usage. Clean and normalize these data to remove noise and outliers in the data and unify data in different ranges to the same scale. Use the resource demand prediction algorithm to build a resource demand prediction model, and the formula is: , where is the predicted value of resource demand after time in the future, are the historical values of resource usage at different past times, are the corresponding coefficients, is the comprehensive value of the current running status of the APP, is the status influence coefficient, It means that when calculating the predicted value of future resource demand, the input of the model is the processed historical performance data and current running status data, and the output is the predicted value of the APP's resource demand within a certain period in the future. For example, the prediction results show that as the user access volume increases, the CPU demand will increase significantly, the memory demand will also rise, and the network bandwidth demand will also increase accordingly.

[0041] Performance anomaly prediction, receive the data of behavior simulation and performance analysis as well as real-time resource demand prediction, identify potential patterns and trends in the data through comprehensive indicators, and the formula is: , where represents the data indicators in different analysis dimensions, such as performance correlation indicators and the predicted value of resource demand, are the corresponding weights. Based on the comprehensive indicator build an anomaly probability prediction model, use the normal and abnormal situations in the historical data to determine the parameters of the model. When the predicted anomaly probability exceeds the pre-set threshold, the system sends alerts to the operation and maintenance personnel in multiple ways, such as email, SMS, and the APP management background system, so as to take optimization measures in time.

[0042] In summary, the performance monitoring and optimization plan implemented during the shopping peak period of the cross-border e-commerce APP has achieved remarkable results. Comprehensive and accurate data are obtained through data collection and preprocessing, laying a foundation for subsequent analysis. The construction of multi-dimensional experience indicators evaluates the user experience from multiple dimensions and reflects the user's feelings. Behavior simulation and performance analysis determine the key influencing factors and point the way for optimization. Real-time resource demand prediction plans resources in advance to ensure the stable operation of the APP. Performance anomaly prediction issues alerts in time to prevent performance problems.

[0043] Example 2:

[0044] Performance monitoring and optimization during the new function launch period of the cross-border e-commerce APP.

[0045] Data collection and preprocessing: After the new function was launched, the operating environment of the cross-border e-commerce APP changed. To evaluate the impact of the new function on user experience and APP performance, comprehensive data collection was required. In the APP code, special event listeners were set for the usage scenarios of the new function. For example, for the new search and filtering function, when the user enters a search keyword or selects a filtering condition, the event listener records these operation behaviors. For the new payment method selection function, the operations of the user selecting different payment methods are recorded.

[0046] At the same time, marker points are set at the page loading of the new function to record the loading time of the new function page. By means of the APP's call to the device's microphone and camera, voice and expression data of the user when using the new function are collected. When the user is confused about the operation of the new function, they may make a questioning voice or show a frowning expression, and all these data are collected.

[0047] The collected raw data has various problems, such as inconsistent data formats and noise. Therefore, the data needs to be cleaned. First, the invalid data caused by device failures or network problems is removed. Then, the data from different sources is uniformly converted into the same format for subsequent processing. Finally, an accurate timestamp is marked for each piece of data to facilitate the analysis of data characteristics in different time periods.

[0048] Construction of multi-dimensional experience indicators: Combining the historical data before the launch of the new function and the data collected after the launch, multi-dimensional user experience indicators are constructed. First, a user operation behavior integration indicator is constructed , and the calculation formula is: . Next, an interface interaction comprehensive indicator is constructed , and the calculation formula is: . By recording the time difference between the user's operation of the new function interface and the interface feedback, these response time data are obtained. Combining with historical data, the normal response time range of the new function interface interaction is analyzed to judge whether the current indicator is reasonable. Then, a user emotion quantification indicator is constructed . Using speech recognition and expression analysis technologies, the collected user voice and expression data are processed. According to the intonation, speech rate of the voice and the changes in expressions, the user's emotions are divided into different levels of positive, neutral, and negative, and quantified into specific values. The calculation formula is: . Finally, the overall experience indicator is obtained through the multi-dimensional user experience indicator calculation formula. The formula is: . Through this indicator system, the impact of the new function on user experience is evaluated.

[0049] Behavior simulation and performance analysis. Using the Monte Carlo simulation algorithm, generate simulated user operation sequences based on user operation behavior data related to new functions. The Monte Carlo simulation algorithm simulates a large number of new function usage scenarios through random sampling. During the simulation process, according to the occurrence probabilities of various new function operation behaviors in historical data, different operation sequences are randomly generated. Analyze the relationship between the simulated behavior and performance indicators through the simulated behavior performance correlation formula. The formula is: , if it is found that some simulated operations (such as frequently using new complex search and filtering conditions) cause the performance correlation index to change abnormally, and the corresponding APP performance index value (such as the interface loading time becomes significantly longer) also shows obvious abnormalities, determine that the corresponding real user operation is the key factor affecting the APP performance.

[0050] Real-time resource demand prediction. Collect historical performance data and current running state data after the new function of the APP is launched, including CPU usage rate, memory occupancy, and network bandwidth usage. Clean and normalize these data to remove noise and outliers in the data and unify data in different ranges to the same scale. Use the resource demand prediction algorithm to build a resource demand prediction model. The formula is: , the input of the model is the processed historical performance data and current running state data, and the output is the predicted value of the APP's resource demand in the future period. For example, it is predicted that the use of the new function will cause an increase in memory requirements because the new function may need to cache more data, and at the same time, the network bandwidth requirement will also increase because the new function may need to interact with the server more.

[0051] Performance anomaly prediction. Process the data of behavior simulation and performance analysis, and real-time resource demand prediction. Identify potential patterns and trends through comprehensive indicators. The formula is: , based on the comprehensive indicator Build an anomaly probability prediction model. Use the normal and abnormal situations in historical data to determine the parameters of the model. When it is predicted that the probability of performance anomalies caused by the use of the new function exceeds a pre-set threshold, the system sends alerts to the operation and maintenance personnel in multiple ways, such as by email, text message, and the APP management background system, so as to optimize the performance of the new function in a timely manner.

[0052] In summary, the performance monitoring and optimization strategy during the launch of new functions for cross-border e-commerce APPs has shown good results. Data collection and preprocessing comprehensively collect data on the use of new functions to ensure data quality. The construction of multi-dimensional experience indicators quantitatively evaluates the impact of new functions on user experience, facilitating the understanding of user feedback. Behavior simulation and performance analysis identify key operations affecting performance and clarify the focus of optimization. Real-time resource demand prediction anticipates resource changes brought about by new functions in advance to avoid resource shortages. Performance anomaly prediction promptly detects potential problems for rapid optimization. This strategy helps new functions go live smoothly, improves user acceptance of new functions, promotes the continuous improvement of cross-border e-commerce APP functions, and enhances the competitiveness of the platform.

[0053] The above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or variations equivalent to the equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent variations, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A real-time performance monitoring and optimization method for cross-border e-commerce APPs, characterized in that, The specific steps of this optimization method are as follows: S100, Data collection and preprocessing: Under legal circumstances, collect the behavioral data of user operations, interface interaction data, and user voice and expression data during the use of the APP. Clean the original data to remove noise and outliers, convert it into a unified format, and mark the time stamps; S200, Construction of multi-dimensional experience indicators: Use algorithms to deeply mine the preprocessed data, comprehensively consider the user operation behavior, interface interaction, and emotional state data, and combine historical data to construct a multi-dimensional user experience indicator system; S300, Behavior simulation and performance analysis: According to the constructed indicator system and user operation behavior data, use the Monte Carlo simulation algorithm to generate simulated user operation sequences, and use the simulated behavior performance correlation formula to analyze the relationship between simulated behaviors and performance indicators to determine the key factors affecting the APP performance; S400, Real-time resource demand prediction: Collect the historical performance data and current running state data of the APP, clean and normalize them, construct a resource demand prediction model through a resource demand prediction algorithm, train the model using historical and current state data, use the model to predict future resource demands, and adjust the resource allocation strategy according to the prediction results; S500, Performance Anomaly Prediction: Receive data on behavior simulation and performance analysis as well as real-time resource demand prediction, identify potential patterns and trends in the data, predict anomalies based on the identification results according to the performance anomaly prediction model, and based on the comprehensive metrics Construct an anomaly probability prediction model, the formula is: , where is the performance anomaly probability, are the parameters of the model, determined using historical data by the maximum likelihood estimation method, is the comprehensive metric and other input variables other than is an index variable. When the predicted anomaly probability exceeds the threshold , an alarm is sent via email, text message or the APP management background system.

2. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, In S100, for the collection of user-related data in data collection and preprocessing, collect the user operation behavior data by embedding event listeners at the APP code level, record the time difference when the APP starts and page jumps and loads by setting marker points in the code to obtain the interface loading time, and under legal circumstances, collect the user voice and expression data by means of the APP calling the device microphone and camera.

3. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, The S200, which is the construction of a multi-dimensional user experience indicator system in the construction of multi-dimensional experience indicators, and the calculation of the user operation behavior integration indicator is calculated by the formula: , where is the total number of click operations, is the th click operation, is the total monitoring time, is the total number of sliding displacements, is the th sliding displacement, is the total sliding time, is the total number of page residence times, is the residence time on the th page, , , are the weight coefficients set according to the importance of each behavior; The comprehensive interface interaction indicator is calculated by the formula: , where is the total number of interface loading times, is the th interface loading time, is the total number of element response times, is the th element response time, and are the corresponding weights; The user emotion quantification indicator is calculated by the formula: , where is the index variable in the summation formula, represents the th emotion value obtained through voice emotion analysis, represents the th emotion value of facial expression recognition, and are the weights of the two; The multi-dimensional user experience indicator is calculated by the formula: , where , , are used to balance the proportion of each part in the overall experience indicator.

4. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, In the S300, the Monte Carlo simulation algorithm is adopted in behavior simulation and performance analysis to generate a simulated user operation sequence , and the calculation formula is: , where is the th simulated operation, is the probability of the th operation, is the vector representation of the th operation, is a subscript index, is a parameter controlling the change range of the operation, the random function generates a random operation according to a given probability distribution, and x represents the number of operation types.

5. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, In the S300, the relationship between the simulated behavior and the performance index is analyzed by using the simulated behavior performance correlation formula in the behavior simulation and performance analysis. The formula is: , where is the performance index value of the APP during the th step of the simulation operation, the th step of the simulation operation, represents the total number of steps of the simulation operation.

6. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 5, characterized in that In the S300, for the judgment of the key factors affecting the APP performance in behavior simulation and performance analysis, when a specific simulation operation occurs, significantly increases, and the corresponding also becomes significantly longer, then the real user operation corresponding to this simulation operation is the key factor affecting the APP performance.

7. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, For the S400, the construction of the resource demand prediction model in real-time resource demand prediction has the formula: , where is the predicted value of resource demand after future time, are the historical values of resource usage at different past times, are the corresponding coefficients, is the comprehensive value of the current running state of the APP, is the state influence coefficient, is an index variable, represents the current moment, represents the time interval, and M represents the time step span for considering historical resource usage data when calculating the predicted value of future resource demand.

8. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, In S400, adjust the resource allocation strategy according to the prediction results in real-time resource demand prediction: If it is predicted that the CPU demand will increase significantly: Apply to the cloud service provider in advance to increase the number of CPU cores, or allocate some computing tasks to other idle server nodes; For the increase in memory demand: Optimize the memory management algorithm of the APP to release the memory space that is no longer used, or increase the physical memory capacity of the server; For the increase in network bandwidth demand: Negotiate with the network service provider to temporarily increase the bandwidth, or optimize the network request strategy of the APP to reduce unnecessary network data transmission.

9. A real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, The S500 identifies potential patterns and trends in data through comprehensive metrics during performance anomaly prediction. Suppose there are pattern categories obtained through a clustering optimization method, and each pattern category has a characteristic center vector . For a data point to the clustering center vector , the Euclidean distance is , and the calculation formula is: , where is the dimension of the feature vector, is the data point, the data point at the -th dimension is the eigenvalue, is the eigenvalue of the characteristic center vector at the -th dimension. Suppose the trend indicator is , and the calculation formula is: , where is the data value at time f, is the length of the time series. The calculation formula for the comprehensive metric is: , where and are adjustment coefficients, is the weight of the -th pattern category, is an index variable.

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