Cross-border e-commerce APP real-time performance monitoring and optimizing method

Abstract

The invention discloses a cross-border e-commerce APP real-time performance monitoring and optimization method, and relates to the technical field of APP performance monitoring and analysis, and the optimization method comprises the specific steps: data collection and preprocessing: collecting behavior data, interface interaction data and user voice and expression data of user operation during the use of an APP, according to the method, comprehensive monitoring and analysis of the cross-border e-commerce APP performance are realized by constructing a multi-dimensional user experience index system, user operation behaviors and interface interaction data are concerned, and user emotional state data are taken into consideration, so that the real experience of the user is reflected more accurately, and through deep mining of an algorithm formula, the real experience of the cross-border e-commerce APP performance is improved. According to the method, the performance of the cross-border e-commerce APP in different dimensions can be comprehensively evaluated, more detailed and comprehensive performance feedback is provided for a user, and the multi-dimensional monitoring mode enables an APP developer to more accurately position the performance bottleneck and optimize the user experience in time, so that the method has advantages in a fierce 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, the importance of their performance and user experience has become increasingly prominent. 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 the APP 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 of APP performance and its optimization 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 and maintenance.

[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, ignoring the actual experience and feelings of users during the use process. This monitoring method centered on technical indicators is difficult to comprehensively reflect the performance status of the APP 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 easily leads 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 that 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 existing technology 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 of 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 the APP.

[0006] To solve the above technical problems, the present invention provides the following technical solution: 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: S100, Data collection and preprocessing: 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 stamp. 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 a simulated user operation sequence, and use the simulated behavior performance correlation formula to analyze the relationship between the simulated behavior 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 with 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 the data from behavior simulation and performance analysis and real-time resource demand prediction, identify the potential patterns and trends in the data, and predict abnormal situations based on the identification results according to the performance anomaly prediction model. When the predicted abnormal probability exceeds the threshold , send an alarm through email, text message, and the APP management background system.

[0007] Furthermore, in the S100, data collection and preprocessing, the collection of user-related data is carried out by embedding event listeners at the APP code level to collect user operation behavior data, recording the time difference at the APP startup and page jump loading by setting marker points in the code to obtain the interface loading time, and collecting user voice and expression data by the APP calling the device microphone and camera.

[0008] Even further, in the S200, construction of multi-dimensional experience indicators, the construction of the multi-dimensional user experience indicator system, the calculation of the user operation behavior fusion indicator The formula is: , 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 The secondary sliding displacement, is the total sliding time, is the total number of page stay times, is at the page stay time, 、 、 are the weight coefficients set according to the importance of each behavior; The interface interaction comprehensive index is calculated by the formula: , where is the nd interface loading time, is the th element response time, and are the corresponding weights; 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 multi-dimensional user experience index is calculated by the formula: , where 、 、 are used to balance the proportion of each part in the overall experience index.

[0009] Furthermore, in the S300, the Monte Carlo simulation algorithm is used in the 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 type of operation, is the th type of operation vector representation, is a subscript index, is a parameter that controls the change range of the operation, and the random function generates random operations according to the given probability distribution.

[0010] Furthermore, in the S300, the relationship between the simulated behavior and the performance index is analyzed using the simulated behavior performance correlation formula, and the formula is: , where is the Performance metric values of the APP during step simulation operations Step simulation operation represents the total number of steps of the simulation operation.

[0011] Furthermore, in S300, for the judgment of key factors affecting the APP performance in behavior simulation and performance analysis, when a certain specific simulation operation occurs significantly increases, and the corresponding also becomes significantly longer, the real user operation corresponding to this simulation operation is a key factor affecting the APP performance.

[0012] Furthermore, in 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 resource demand after future time, 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.

[0013] Furthermore, in S400, for adjusting the resource allocation strategy according to the prediction result 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, release the memory space that is no longer in use, 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.

[0014] Furthermore, in S500, for predicting performance anomalies, identify potential patterns and trends in the data through the comprehensive metric Suppose there are pattern categories obtained through the clustering analysis method, and each pattern category has a characteristic center vector , , the 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 a data point, data point at the eigenvalue on the is the feature center vector at the eigenvalue on the , and the trend index is set as , where is the data value of time f, is the length of the time series, and the comprehensive index is calculated as: , where and are adjustment coefficients, is the weight of the th pattern category,

[0015] Furthermore, for the S500, in the creation of the performance anomaly prediction model in performance anomaly prediction, an anomaly probability prediction model is constructed based on the comprehensive index , and the formula is: , where is the performance anomaly probability, are the parameters of the model, which are determined using historical data by the maximum likelihood estimation method, is the comprehensive index other input variables outside is an index variable.

[0016] Compared with the prior art, the real-time performance monitoring and optimization method for this cross-border e-commerce APP has the following beneficial effects: First, by constructing 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 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 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.

[0017] Second, through cleaning and normalizing historical performance data and current operating status data, the present invention constructs a resource demand prediction model using a prediction algorithm, achieving accurate prediction of future resource demands. When it predicts that the resource demands will change, the present invention can automatically adjust the resource allocation strategy, such as increasing the number of CPU cores, optimizing the memory management algorithm, and temporarily increasing the 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 the cross-border e-commerce APP but also greatly reduces the operating costs, providing a strong guarantee for the long-term stable development of the cross-border e-commerce APP.

[0018] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent 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 learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] Figure 1 It is a flowchart of a real-time performance monitoring and optimization method for a cross-border e-commerce APP; 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

[0021] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in combination with the accompanying drawings and preferred embodiments, detail the specific embodiments, structures, features, and their effects of the present invention as follows.

[0022] Embodiment 1: Performance monitoring and optimization of the cross-border e-commerce APP during the peak shopping period.

[0023] Data collection and preprocessing. During the peak shopping period, 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, at key nodes of the APP, such as APP startup and page jump and loading, marking points are set. By recording the time difference between these marking 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 an order.

[0024] There is a lot of noise and outliers in the collected raw data. For example, due to network fluctuations, duplicate operation records occur, 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, an accurate timestamp is marked for each piece of data to facilitate the analysis of data characteristics in different time periods.

[0025] The preprocessed data can be used to construct multi-dimensional user experience indicators. First, construct the user operation behavior integration 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 of various operation behaviors and the degree of influence on the user experience are statistically analyzed to determine the weight value.

[0026] Next, construct the interface interaction comprehensive indicator , and the calculation formula is: , where represents the response time of the th interface interaction, which is obtained by recording the time difference between the user operation and the interface feedback. m represents the number of indicators related to interface interaction. Similarly, combining historical data, analyze the normal response time range of different interface interaction operations to judge whether the current indicator is within a reasonable range.

[0027] Then, construct the user emotion quantification indicator , using speech recognition and facial expression analysis technologies, 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 numerical values. The calculation formula is: , where represents the number of emotion-related factors or indicators involved in calculating the emotion index , represents the th quantified value of the emotion state, is the corresponding weight.

[0028] Finally, obtain the overall experience index through the multi-dimensional user experience index calculation formula. The formula is: , where , , are the corresponding weights, which are determined according to the comprehensive influence degree of different indicators on the user experience. Through this index system, the user experience during the shopping peak period can be comprehensively evaluated.

[0029] Behavior simulation and performance analysis. According to the constructed index 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 by means of random sampling. During the simulation process, according to the occurrence probabilities of various operation behaviors in the historical data, different operation sequences are randomly generated. Analyze the relationship between the simulated behavior and the performance indicators 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 indicator value corresponding to the simulated operation, such as the interface loading time, CPU usage rate, represents the weight of this simulated operation. During the simulation process, if it is found that when a certain simulated operation appears, the performance correlation index increases significantly, and the corresponding APP performance indicator value (such as the interface loading time becomes significantly longer) is also significantly abnormal, then it is determined that the real user operation corresponding to this simulated operation (such as a large number of users clicking on the same promotional product link at the same time) is the key factor affecting the APP performance.

[0030] Real-time resource demand prediction. Collect the historical performance data and current running state data of the APP during the shopping peak period, including CPU usage rate, memory occupancy, network bandwidth usage. Clean and normalize these data, remove the noise and outliers in the data, and unify the data in different ranges to the same scale. Use the resource demand prediction algorithm to construct a resource demand prediction model. The formula is: , where is the predicted value of resource requirements after a 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, It means that when calculating the predicted value of future resource requirements, the input of the model is the processed historical performance data and the current running state data, and the output is the predicted value of the resource requirements of the APP within a future period. For example, the prediction results show that as the number of user visits increases, the CPU requirements will increase significantly, the memory requirements will also rise, and the network bandwidth requirements will also increase accordingly.

[0031] Performance anomaly prediction, receiving data of behavior simulation, performance analysis, and real-time resource requirement prediction, identifying potential patterns and trends in the data through comprehensive indicators. The formula is: , where represents data indicators in different analysis dimensions, such as performance correlation indicators and the predicted value of resource requirements, are the corresponding weights. Based on the comprehensive indicators construct 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 a pre-set threshold, the system sends an alarm to the operation and maintenance personnel in multiple ways, such as by email, text message, and the APP management background system, so as to take optimization measures in a timely manner.

[0032] In summary, the performance monitoring and optimization plan implemented during the peak shopping period of the cross-border e-commerce APP has achieved remarkable results. Through data collection and preprocessing, comprehensive and accurate data is obtained, 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 requirement prediction plans resources in advance to ensure the stable operation of the APP. Performance anomaly prediction issues alarms in a timely manner to prevent performance problems.

[0033] Example 2: Performance monitoring and optimization during the launch of new functions of the cross-border e-commerce APP.

[0034] Data Collection and Preprocessing After the new function is launched, the operating environment of the cross-border e-commerce APP has changed. To evaluate the impact of the new function on user experience and APP performance, comprehensive data collection is required. In the APP code, dedicated event listeners are 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 filtering conditions, the event listener records these operation behaviors. For the new payment method selection function, it records the operations of the user selecting different payment methods.

[0035] 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 leveraging the APP's call to the device's microphone and camera, voice and facial expression data of users during the use of the new function are collected. When the user is confused about the operation of the new function, they may emit questioning voices or show frowning expressions, and all this data is collected.

[0036] The collected raw data has various problems, such as inconsistent data formats and noise. Therefore, the data needs to be cleaned. First, remove the invalid data caused by device failures or network problems. Then, uniformly convert the data from different sources into the same format for subsequent processing. Finally, label each piece of data with an accurate timestamp to facilitate the analysis of data characteristics in different time periods.

[0037] Construction of Multidimensional Experience Metrics Combining the historical data before the launch of the new function and the data collected after the launch, construct multidimensional user experience metrics. First, construct the integrated user operation behavior metric , and the calculation formula is: . Next, construct the comprehensive interface interaction metric , and the calculation formula is: . By recording the time difference between the user's operation of the new function interface and the interface feedback, obtain these response time data. Combining with historical data, analyze the normal response time range of the new function interface interaction to judge whether the current metric is reasonable. Then, construct the user emotion quantification metric . Using speech recognition and facial expression analysis technologies, process the collected user voice and facial expression data. According to the intonation, speech rate of the voice 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: . Finally, obtain the overall experience metric through the multidimensional user experience metric calculation formula. The formula is: . Through this metric system, evaluate the impact of the new function on user experience.

[0038] Behavior simulation and performance analysis. Using the Monte Carlo simulation algorithm, generate simulated user operation sequences based on the user operation behavior data related to the new function. The Monte Carlo simulation algorithm simulates a large number of new function usage scenarios through random sampling. During the simulation, according to the occurrence probabilities of various new function operation behaviors in the 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 indicator 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.

[0039] Real-time resource demand prediction. Collect the 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 demand because the new function may need to cache more data, and at the same time, the network bandwidth demand will also increase because the new function may need to interact with the server more.

[0040] 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 the 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 the 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.

[0041] 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 quantifies the impact of new functions on user experience, facilitating the understanding of user feedback. Behavior simulation and performance analysis identify key operations affecting performance, clarifying the focus of optimization. Real-time resource demand prediction anticipates resource changes brought about by new functions in advance, avoiding resource shortages. Performance anomaly prediction promptly detects potential problems for rapid optimization. This strategy helps new functions to be successfully launched, enhances user acceptance of new functions, promotes the continuous improvement of cross-border e-commerce APP functions, and strengthens the platform's competitiveness.

[0042] The above is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or refinements to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as the content of the technical solution of the present invention is not departed from, any brief modifications, equivalent changes, and refinements 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 a cross-border e-commerce APP, characterized in that, The specific steps of this optimization method are as follows: S100, Data collection and preprocessing: 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, 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 behavior 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 the resource demand prediction algorithm, train the model with 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 issue an alert via email, SMS, or the APP management background system when the predicted anomaly probability exceeds the threshold , and issue an alert via email, SMS, or the APP management background system 2. The 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 to obtain the interface loading time by setting marker points in the code, and collect the user voice and expression data by the APP calling the device microphone and camera.

3. The real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, The S200 is for constructing a multi - dimensional user experience indicator system in the construction of multi - dimensional experience indicators, and the user operation is a fusion indicator The calculation formula is: 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 comprehensive interface interaction indicator The calculation formula is: where is the th interface loading time, is the th element response time, and are the corresponding weights; The user emotion quantification indicator The calculation formula is: 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 The calculation formula is: where , , are used to balance the proportion of each part in the overall experience indicator.

4. The 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 used 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 that controls the variation range of the operation, and the random function generates random operations according to a given probability distribution.

5. The 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 as follows: , 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. The real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, 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. The 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.

8. The real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, In S400, for adjusting 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, 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. The 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 in performance anomaly prediction. Suppose pattern categories are obtained through clustering analysis, and each pattern category has a characteristic central vector , . For a data point to the clustering central 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 central 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 for the comprehensive metric is: , where and are adjustment coefficients, is the weight of the -th pattern category, is an index variable.

10. The real-time performance monitoring and optimization method for a cross-border e-commerce APP according to claim 1, characterized in that, For the S500, the creation of the performance anomaly prediction model in performance anomaly prediction is based on comprehensive metrics Construct an anomaly probability prediction model, the formula is: , where is the performance anomaly probability, are the parameters of the model, determined by the maximum likelihood estimation method using historical data, is the comprehensive metric are other input variables other than is an index variable.

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