APP performance analysis system, analysis method and server

Abstract

The invention discloses an APP performance analysis system and method and a server, and the system comprises a data collection unit which is used for collecting multiple groups of performance index data of a target APP in different mobile terminals, and corresponding multiple groups of equipment parameter data; the data processing unit is used for performing specific value calculation on the performance index data and the equipment parameter number to obtain a performance coefficient and a corresponding performance analysis coefficient; the comparative analysis unit is used for carrying out comparative analysis on the performance analysis coefficients of all the mobile terminals and judging whether an APP acquisition signal is generated or not; and the acquisition management unit is used for acquiring product information of each mobile terminal corresponding to the corresponding APP according to the APP acquisition signal, and performing code optimization on the APP according to the product information. According to the method, the influence of the equipment parameters on the APP performance is considered, large-scale performance evaluation can be realized, the test efficiency and accuracy are improved, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of application development, and in particular to an APP performance analysis system, analysis method and server. Background Art

[0002] With the rapid development of mobile Internet, the use of smartphones and other mobile terminal devices is becoming more and more popular. Users have higher and higher performance requirements for applications (APPs), especially performance indicators such as APP startup speed and fluency. However, due to the large performance differences among mobile terminal devices, such as processor speed, RAM size, storage speed, etc., the performance of the same APP on different devices may vary significantly. Therefore, how to accurately evaluate the performance of an APP on various mobile terminals so that developers can perform targeted code optimization and improve user experience has become an urgent problem to be solved.

[0003] At present, there are mainly the following methods for evaluating APP performance:

[0004] The first is through manual testing, that is, testers manually operate the APP on different devices and record its performance data. This method is inefficient and is greatly affected by subjective factors, making it difficult to achieve large-scale, high-precision performance evaluation;

[0005] The second method is to use automated testing tools, such as Appi um and MonkeyRunner, to simulate user operations for performance testing. Although this method improves testing efficiency, it still cannot fully consider the impact of device parameters on APP performance;

[0006] The third method is to collect a large amount of performance data on real devices for analysis through cloud testing platforms such as Firebase and Test in. This method can obtain rich performance data, but the data processing and analysis process is complicated and it is difficult to achieve real-time feedback.

[0007] Therefore, there is an urgent need for a technical solution for APP performance analysis that can solve the above problems. Summary of the invention

[0008] In order to solve the deficiencies of the prior art, the present application provides an APP performance analysis system and analysis method, which can comprehensively collect the performance index data and device parameter data of the target APP on each mobile terminal, obtain the performance analysis coefficient of each mobile terminal through preprocessing and analysis, and then conduct comparative analysis to provide targeted product information for APP developers for code optimization. This method not only takes into account the impact of device parameters on APP performance, but also realizes large-scale performance evaluation, improves test efficiency and accuracy, and helps to improve user experience.

[0009] The technical effects to be achieved by this application are achieved through the following solutions:

[0010] According to a first aspect of the present application, an APP performance analysis system is provided, including a data collection unit, a data processing unit, a comparison and analysis unit, and an acquisition management unit, wherein:

[0011] The data collection unit is used to collect multiple sets of performance indicator data of the target APP on different mobile terminals, and corresponding multiple sets of device parameter data;

[0012] The data processing unit is used to calculate the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient;

[0013] The comparison and analysis unit is used to compare and analyze the performance analysis coefficients of each mobile terminal to determine whether the APP acquisition signal is generated;

[0014] The acquisition management unit is used to acquire product information of each mobile terminal corresponding to the corresponding APP according to the APP acquisition signal, and perform code optimization on the APP according to the product information.

[0015] Preferably, in a mobile terminal, based on the acquisition time nodes of the performance indicator data, multiple groups of device parameter data of corresponding time nodes are acquired, and the number of groups of device parameter data corresponds one-to-one to the performance indicator data, wherein the performance indicator data includes the startup time of the APP, and the device parameter data includes the processor speed, RAM size, storage speed and available running memory size.

[0016] Preferably, the data processing unit calculates the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient, specifically:

[0017] Calculate the ratio B1 between the startup time of the APP and the processor speed, the ratio B2 between the startup time of the APP and the RAM size, the ratio B3 between the startup time of the APP and the storage speed, and the ratio B4 between the startup time of the APP and the available running memory size;

[0018] Through the following formula:

[0019]

[0020] The performance coefficient X corresponding to the performance index data of the group of equipment parameter data is calculated; wherein λ1 and λ2 are preset proportional coefficients, and μ is a fixed value, which is 0.54781;

[0021] The performance coefficients of the performance index data corresponding to the multiple sets of equipment parameter data are calculated in turn, and the performance analysis coefficients are obtained by averaging the performance coefficients.

[0022] Preferably, before calculating the performance analysis coefficient, the performance coefficient corresponding to the mobile terminal is first screened using the interquartile range method, and based on the screening result, the average value of the performance coefficient is obtained to obtain the performance analysis coefficient.

[0023] Preferably, the specific steps of screening out the performance coefficients corresponding to the mobile terminals using the interquartile range method are as follows:

[0024] The performance coefficients are sorted in ascending order to form a sequence table, and the first quartile Q1 and the third quartile Q3 are selected in the sequence table;

[0025] The interquartile range IQR is calculated by the formula: IQR = Q3-Q1;

[0026] The performance judgment coefficients K1 and K2 are obtained by the formula: K1 = Q1-t*IQR and K2 = Q3+t*IQR, where: t is a fixed value, and the value of t is 1.35;

[0027] The performance coefficients between K1 and K2 are obtained and averaged, and the average is marked as the performance analysis coefficient.

[0028] Preferably, the comparison and analysis unit compares and analyzes the performance analysis coefficients of each mobile terminal, and the specific method for determining whether the APP acquisition signal is generated is:

[0029] Obtain all performance analysis coefficients and select a performance analysis coefficient as a benchmark parameter;

[0030] Calculate the absolute value XCj of the difference between other performance analysis coefficients and the benchmark parameters, using the variance calculation formula:

[0031]

[0032] Calculate the deviation value XL1 of all the absolute values ​​of the difference XCj;

[0033] Compare the deviation value XL1 with the corresponding preset discrete threshold L1y. If XL1≤L1y, proceed to the next step; if XL1>L1y, delete the corresponding XCj values ​​in descending order of |XCj-XCp| and calculate the remaining deviation values ​​XL1 accordingly until XL1≤L1y, where XCp is the average value of XCj used to calculate the corresponding discrete value;

[0034] Obtain the absolute value XCj of the difference involved in calculating the corresponding deviation value XL1, and calculate the average value of all the corresponding absolute values ​​XCj to obtain the performance difference factor CY;

[0035] The performance difference factor CY is compared with a preset performance difference threshold CY0. If CY>CY0, an APP acquisition signal is generated; otherwise, no APP acquisition signal is generated.

[0036] Preferably, the product information includes the device model, operating system version and screen resolution of the mobile terminal.

[0037] Preferably, the code is optimized by improving the structure and logic of the code to adapt to different operating system versions and screen resolutions of mobile terminals.

[0038] According to a second aspect of the present application, there is provided an APP performance analysis method using the above APP performance analysis system, comprising the following steps:

[0039] Collect multiple sets of performance indicator data of the target APP on different mobile terminals, as well as multiple sets of corresponding device parameter data;

[0040] Calculate the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient;

[0041] Compare and analyze the performance analysis coefficients of each mobile terminal to determine whether the APP acquisition signal is generated;

[0042] The product information of each mobile terminal corresponding to the corresponding APP is obtained according to the APP acquisition signal, and the code of the APP is optimized according to the product information.

[0043] According to a third aspect of the present application, there is provided a server, comprising: a memory and at least one processor;

[0044] The memory stores a computer program, and the at least one processor executes the computer program stored in the memory to implement the above-mentioned APP performance analysis method.

[0045] According to an embodiment of the present application, the beneficial effects of using the present APP performance analysis system are: being able to fully understand the performance of the APP on different devices, providing rich data support for subsequent analysis and optimization; analyzing the performance coefficient from different angles, improving the accuracy and reliability of the analysis; being able to intuitively reflect the performance differences of the APP on different devices, making it easier for developers to find performance bottlenecks; clarifying whether the APP needs to be optimized in code, which helps to improve the performance, readability and maintainability of the APP; being suitable for different types of mobile terminals and APPs, having strong versatility and adaptability, and being able to provide APP developers with targeted optimization suggestions to improve the market competitiveness of their products. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 This is a structural diagram of an APP performance analysis system in one embodiment of the present application;

[0048] Figure 2 This is a flow chart of an APP performance analysis method in one embodiment of the present application;

[0049] Figure 3 This is a schematic diagram of the structure of a server in an embodiment of the present application;

[0050] In the figure: 301, memory; 302, processor. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0052] like Figure 1 As shown, the APP performance analysis system in one embodiment of the present application includes a data collection unit, a data processing unit, a comparison and analysis unit, and an acquisition management unit, wherein:

[0053] A data collection unit, used to collect multiple sets of performance indicator data of the target APP on different mobile terminals, and corresponding multiple sets of device parameter data;

[0054] Among them, the performance indicator data includes the startup time of the APP, and the device parameter data includes processor speed, RAM size, storage speed and available running memory size.

[0055] In a data acquisition process of a mobile terminal, multiple groups of device parameter data of corresponding time nodes are acquired according to the acquisition time nodes of the performance indicator data, and the number of groups of the device parameter data corresponds to the performance indicator data one by one.

[0056] By collecting the performance indicator data and device parameter data of the target APP on various mobile terminals, we can fully understand the performance of the APP on different devices and provide sufficient data support for subsequent data analysis and optimization.

[0057] A data processing unit, used to calculate the ratio between the performance index data and the equipment parameter number respectively, to obtain the performance coefficient and its corresponding performance analysis coefficient;

[0058] After obtaining a set of device parameter data and a corresponding set of performance indicator data, the startup time of the APP in the set of performance indicator data, and the processor speed, RAM size, storage speed, and available running memory size corresponding to the set of device parameter data are dimensioned and taken to obtain their values, and the ratio B1 between the startup time of the APP and the processor speed, the ratio B2 between the startup time of the APP and the RAM size, the ratio B3 between the startup time of the APP and the storage speed, and the ratio B4 between the startup time of the APP and the available running memory size are obtained in turn;

[0059] By formula:

[0060]

[0061] The performance coefficient X corresponding to the performance index data of the group of equipment parameter data is calculated; in the formula, λ1 and λ2 are preset proportional coefficients, and μ is a fixed value, which is 0.54781, preset according to the experience of relevant personnel, and is used to expand the proportion of B3;

[0062] Through dimensional processing, we can get the ratio between the app startup time and the processor speed, RAM size, storage speed, and available running memory size, which can more accurately reflect the relationship between app performance and device parameters.

[0063] Similarly, the performance coefficients of the performance index data corresponding to the multiple sets of equipment parameter data are calculated, and the average value of all performance coefficients is obtained, and the average value is marked as the performance analysis coefficient;

[0064] A comparison and analysis unit, used to compare and analyze the performance analysis coefficients of various mobile terminals to determine whether the APP acquisition signal is generated;

[0065] Obtain all performance analysis coefficients and select a performance analysis coefficient as a benchmark parameter;

[0066] Then, the absolute values ​​of the differences between all other performance analysis coefficients and the benchmark parameters are obtained and recorded as XCj, where j = 1, 2, ... m-1, and m represents the number of all mobile terminals;

[0067] Then through the formula:

[0068]

[0069] The average value CY corresponding to all the absolute values ​​of the differences XCj is calculated, and then its value is marked as the performance difference factor.

[0070] Then the performance difference factor CY is compared with the preset performance difference threshold CY0:

[0071] If CY>CY0, an APP acquisition signal is generated;

[0072] If CY≤CY0, no APP acquisition signal is generated;

[0073] By comparing the performance difference factor with the preset performance difference threshold, it is possible to reasonably determine whether it is necessary to generate an APP acquisition signal, thereby deciding whether to optimize the code and avoiding unnecessary optimization work.

[0074] The acquisition management unit is used to acquire product information of each mobile terminal corresponding to the corresponding APP according to the APP acquisition signal, and perform code optimization on the APP according to the product information.

[0075] The product information includes the device model, operating system version, and screen resolution of the mobile terminal. In this embodiment, the purpose of code optimization is for the corresponding APP developers to optimize the product to adapt to different mobile terminals, and code optimization refers to improving the structure and logic of the code by the APP developer to improve the performance, readability and maintainability of the program. Code optimization is an ongoing process that needs to be carried out according to specific application scenarios and needs. It can provide a reference for continuous optimization for APP developers, which helps to improve the overall performance and user experience of the APP.

[0076] In another embodiment of the present application, after the data processing unit calculates the performance coefficients of the performance indicator data corresponding to the multiple sets of device parameter data, the performance coefficient corresponding to the mobile terminal is screened out using the interquartile range method. The specific method is as follows:

[0077] The performance coefficients are sorted in ascending order to form a sequence table, and in the sequence table, the first quartile Q1 and the third quartile Q3 are selected;

[0078] Then, the interquartile range IQR is calculated by IQR=Q3-Q1;

[0079] The performance judgment coefficients K1 and K2 are obtained by the formula: K1 = Q1-t*IQR and K2 = Q3+t*IQR. In the formula, t is a fixed value, and the value of t is 1.35. This value is based on the experience of relevant personnel. The specific value can also be adjusted according to the actual situation. If this value cannot meet the needs in a specific application, a more appropriate t value can be determined through further data analysis and experiments.

[0080] The performance coefficients between K1 and K2 are obtained and averaged, and then the average is marked as the performance analysis coefficient.

[0081] In another embodiment of the present application, the method for the comparative analysis unit to perform comparative analysis is as follows:

[0082] Obtain all performance analysis coefficients and select a performance analysis coefficient as a benchmark parameter;

[0083] Then, the absolute values ​​of the differences between all other performance analysis coefficients and the benchmark parameters are obtained and recorded as XCj, where j = 1, 2, ... m-1, and m represents the number of all mobile terminals;

[0084] Then use the variance calculation formula:

[0085] Calculate XL1 for all XCj;

[0086] Then XL1 is compared with the corresponding preset discrete threshold L1y:

[0087] If XL1>L1y, it means that the degree of dispersion of all performance coefficients is large. Then, the corresponding XCj values ​​are deleted in the order of |XCj-XCp| from large to small, and the remaining deviation values ​​XL1 are calculated accordingly until XL1≤L1y, where XCp represents the average value of XCj used to calculate the corresponding discrete value;

[0088] When XL1≤L1y is obtained, XCj of the corresponding XL1 is calculated, and the average value of all corresponding XCj is obtained, and then its value is marked as the performance difference factor CY;

[0089] Then the performance difference factor CY is compared with the preset performance difference threshold CY0:

[0090] If CY>CY0, an APP acquisition signal is generated;

[0091] If CY≤CY0, no APP acquisition signal is generated

[0092] The performance coefficients are screened and analyzed using a variety of methods such as the interquartile range method and variance calculation formula, which can analyze APP performance from different angles and levels, thus improving the accuracy and reliability of the analysis;

[0093] By calculating the absolute value of the difference between all performance analysis coefficients and benchmark parameters, and using the variance calculation formula to calculate the degree of dispersion, we can carefully compare and analyze the performance differences of apps on various mobile terminals, and provide targeted guidance for subsequent optimization.

[0094] like Figure 2 As shown, the APP performance analysis method using the above APP performance analysis system includes the following steps:

[0095] Step 1: Collect multiple sets of performance indicator data of the target APP on different mobile terminals, as well as multiple sets of corresponding device parameter data;

[0096] In this step, the performance indicator data of the target APP on each mobile terminal is collected, such as the startup time; the device parameter data of each mobile terminal is collected, such as the processor speed, RAM size and storage speed;

[0097] Step 2: Calculate the ratio between the performance index data and the equipment parameter number to obtain the performance coefficient and its corresponding performance analysis coefficient;

[0098] In this step, multiple groups of performance indicator data are obtained, and according to the time nodes of obtaining the performance indicator data, the device parameter data of the corresponding time nodes are obtained; the number of groups of the device parameter data is the same as the number of groups of the performance indicator data, and they correspond one to one;

[0099] For each set of device parameter data, the startup time in the corresponding set of performance indicator data is obtained and dimensionalized to obtain the ratio between the APP startup time and the processor speed, the ratio between the APP startup time and the RAM size, the ratio between the APP startup time and the storage speed, and the ratio between the APP startup time and the available running memory size;

[0100] Calculate the performance coefficient corresponding to each set of equipment parameter data through a preset formula;

[0101] The performance coefficient is screened and processed using the interquartile range method to obtain the performance analysis coefficient;

[0102] Step 3: Compare and analyze the performance analysis coefficients of each mobile terminal to determine whether the APP acquisition signal is generated;

[0103] In this step, the performance analysis coefficients of all mobile terminals are obtained and one is selected as a benchmark parameter;

[0104] Calculate the absolute value of the difference between all other performance analysis coefficients and the benchmark parameters, denoted as XCj;

[0105] Calculate the degree of dispersion XL1 of all XCj using the variance calculation formula;

[0106] Compare XL1 with the preset discrete threshold L1y. If XL1 is greater than L1y, delete the corresponding XCj in descending order of difference until XL1 is less than or equal to L1y.

[0107] Calculate the average value of the remaining XCj and mark it as the performance difference factor CY;

[0108] The performance difference factor CY is compared with a preset performance difference threshold CY0. If CY is greater than CY0, an APP acquisition signal is generated; otherwise, no APP acquisition signal is generated.

[0109] Step 4: Obtain product information of each mobile terminal corresponding to the corresponding APP according to the APP acquisition signal, and optimize the code of the APP according to the product information.

[0110] In this step, according to the APP acquisition signal, the product information of each mobile terminal corresponding to the corresponding APP is obtained, including the device model, operating system version and screen resolution;

[0111] APP developers optimize the APP code based on the product information of the mobile terminal to improve the performance, readability and maintainability of the program.

[0112] like Figure 3 As shown, the server in an embodiment of the present application includes: a memory 301 and at least one processor 302;

[0113] The memory 301 stores a computer program, and the at least one processor 302 executes the computer program stored in the memory 301 to implement the above-mentioned APP performance analysis method.

[0114] According to the above embodiments, the beneficial effects of adopting the present application are:

[0115] Comprehensive data: By collecting the performance indicator data and device parameter data of the target APP on various mobile terminals, we can fully understand the performance of the APP on different devices, providing rich data support for subsequent analysis and optimization.

[0116] Fine data processing: By preprocessing performance indicator data and device parameter data, we can obtain the ratios between the app startup time and the processor speed, RAM size, storage speed, and available running memory size. These ratios can more accurately reflect the performance of the app on different devices.

[0117] Various analysis methods: The present invention adopts a variety of analysis methods, including the interquartile range method and variance calculation, etc. These methods can analyze the performance coefficient from different angles, thereby improving the accuracy and reliability of the analysis.

[0118] Intuitive comparative analysis: By calculating the performance analysis coefficients of all mobile terminals and comparing them with the benchmark parameters, it can intuitively reflect the performance differences of the APP on different devices, making it easier for developers to find performance bottlenecks.

[0119] Clear optimization goals: Based on the comparison results of the performance difference factor and the preset performance difference threshold, it can be determined whether the APP needs to be optimized, which helps to improve the performance, readability and maintainability of the APP.

[0120] Strong adaptability: The present invention is applicable to different types of mobile terminals and APPs, has strong versatility and adaptability, and can provide targeted optimization suggestions for APP developers to improve the market competitiveness of products.

[0121] It should be noted that the above detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs.

[0122] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0123] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0124] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0125] For ease of description, spatially relative terms, such as "above", "above", "on the upper surface of", "above", etc., may be used herein to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" may include both "above" and "below". The device may also be positioned in other different ways, such as rotated 90 degrees or in other orientations, and the spatially relative descriptions used herein are interpreted accordingly.

[0126] In the above detailed description, reference is made to the accompanying drawings, which form a part of this document. In the accompanying drawings, similar symbols typically identify similar components unless the context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

[0127] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An APP performance analysis system, characterized in that: It includes a data collection unit, a data processing unit, a comparison and analysis unit, and an acquisition management unit, wherein: The data collection unit is used to collect multiple sets of performance indicator data of the target APP on different mobile terminals, and corresponding multiple sets of device parameter data; The data processing unit is used to calculate the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient; The comparison and analysis unit is used to compare and analyze the performance analysis coefficients of each mobile terminal to determine whether the APP acquisition signal is generated; The acquisition management unit is used to acquire product information of each mobile terminal corresponding to the corresponding APP according to the APP acquisition signal, and perform code optimization on the APP according to the product information.

2. The APP performance analysis system according to claim 1, characterized in that: In a mobile terminal, based on the acquisition time nodes of the performance indicator data, multiple groups of device parameter data of corresponding time nodes are acquired, and the number of groups of device parameter data corresponds one-to-one to the performance indicator data, wherein the performance indicator data includes the startup time of the APP, and the device parameter data includes the processor speed, RAM size, storage speed and available running memory size.

3. The APP performance analysis system according to claim 2, characterized in that: The data processing unit calculates the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient, which is specifically: Calculate the ratio B1 between the startup time of the APP and the processor speed, the ratio B2 between the startup time of the APP and the RAM size, the ratio B3 between the startup time of the APP and the storage speed, and the ratio B4 between the startup time of the APP and the available running memory size; Through the following formula: The performance coefficient X corresponding to the performance index data of the group of equipment parameter data is calculated; wherein λ1 and λ2 are preset proportional coefficients, and μ is a fixed value, which is 0.54781; The performance coefficients of the performance index data corresponding to the multiple sets of equipment parameter data are calculated in turn, and the performance analysis coefficients are obtained by averaging the performance coefficients.

4. The APP performance analysis system according to claim 3, characterized in that: Before calculating the performance analysis coefficient, the performance coefficient corresponding to the mobile terminal is first screened using the interquartile range method, and based on the screening result, the average value of the performance coefficient is obtained to obtain the performance analysis coefficient.

5. The APP performance analysis system according to claim 4, characterized in that: The specific steps of using the interquartile range method to screen out the performance coefficient corresponding to the mobile terminal are as follows: The performance coefficients are sorted in ascending order to form a sequence table, and the first quartile Q1 and the third quartile Q3 are selected in the sequence table; The interquartile range IQR is calculated by the formula: IQR = Q3-Q1; The performance judgment coefficients K1 and K2 are obtained by the formula: K1 = Q1-t*IQR and K2 = Q3+t*IQR, where: t is a fixed value, and the value of t is 1.35; The performance coefficients between K1 and K2 are obtained and averaged, and the average is marked as the performance analysis coefficient.

6. The APP performance analysis system according to claim 1, characterized in that: The comparison and analysis unit compares and analyzes the performance analysis coefficients of each mobile terminal, and the specific method for determining whether the APP acquisition signal is generated is: Obtain all performance analysis coefficients and select a performance analysis coefficient as a benchmark parameter; Calculate the absolute value XCj of the difference between other performance analysis coefficients and the benchmark parameters, using the variance calculation formula: Calculate the deviation value XL1 of all the absolute values ​​of the difference XCj; Compare the deviation value XL1 with the corresponding preset discrete threshold value L1y. If XL1≤L1y, proceed to the next step; if XL1>L1y, delete the corresponding XCj values ​​in descending order of |XCj-XCp| and calculate the remaining deviation values ​​XL1 accordingly, until XL1≤L1y, where XCp is the average value of XCj used to calculate the corresponding discrete value; Obtain the absolute value XCj of the difference involved in calculating the corresponding deviation value XL1, and calculate the average value of all the corresponding absolute values ​​XCj to obtain the performance difference factor CY; The performance difference factor CY is compared with a preset performance difference threshold CY0. If CY>CY0, an APP acquisition signal is generated; otherwise, no APP acquisition signal is generated.

7. The APP performance analysis system according to claim 1, characterized in that: The product information includes the device model, operating system version and screen resolution of the mobile terminal.

8. The APP performance analysis system according to claim 7, characterized in that: Code optimization includes: improving the structure and logic of the code to adapt to different operating system versions and screen resolutions of mobile terminals.

9. An APP performance analysis method using the APP performance analysis system according to any one of claims 1 to 8, characterized in that: The steps include: Collect multiple sets of performance indicator data of the target APP on different mobile terminals, as well as multiple sets of corresponding device parameter data; Calculate the ratio between the performance index data and the equipment parameter number respectively to obtain the performance coefficient and its corresponding performance analysis coefficient; Compare and analyze the performance analysis coefficients of each mobile terminal to determine whether the APP acquisition signal is generated; The product information of each mobile terminal corresponding to the corresponding APP is obtained according to the APP acquisition signal, and the code of the APP is optimized according to the product information.

10. A server, characterized in that: include: memory and at least one processor; The memory stores a computer program, and the at least one processor executes the computer program stored in the memory to implement the APP performance analysis method described in claim 9.

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