Stress testing index analysis method, device, equipment and medium based on multi-curve fitting

Abstract

The present invention relates to artificial intelligence technology, and discloses a stress test indicator analysis method based on multi-curve fitting, including: performing normal fitting on the business exposure data of the target system according to a first preset time; selecting data with an exposure greater than a preset first threshold from the business exposure data according to the fitting curve; performing Poisson fitting on the first data set according to a second preset time, and selecting data with an exposure greater than a preset second threshold from the first data set according to the fitting curve; analyzing the stress estimation index of the selected data; obtaining the stress mean of similar systems of the target system, and numerically correcting the stress estimation value according to the stress mean to obtain the stress test index of the target system. In addition, the present invention also relates to blockchain technology, and the business exposure data can be stored in the nodes of the blockchain. The present invention also proposes a stress test indicator analysis device, equipment and medium based on multi-curve fitting. The present invention can improve the degree of refinement when performing product stress testing.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a stress testing indicator analysis method, device, electronic device and computer-readable storage medium based on multi-curve fitting. Background Art

[0002] With the rapid development of Internet products, in order to realize the function of providing diversified services to users by using individual products, the architecture of each product will become very large. Furthermore, in order to improve the efficiency of product operation and maintenance, more and more product suppliers need to conduct strict operation and maintenance tests on their products before releasing them.

[0003] At present, the main product testing method is the test method based on the increase of fixed index. For example, when testing the access of a product, the product is first exposed to a fixed number of visits within a preset time based on experience, and the number of exposure visits is gradually increased according to the fixed multiple parameters to test the performance of the product under the pressure of different access exposures. However, this method increases according to a fixed multiple, and can only test the performance of the product under the pressure of different access exposures within a certain multiple range, and cannot refine the tested performance indicators, that is, the degree of refinement of the tested performance indicators is low. Summary of the invention

[0004] The present invention provides a stress testing indicator analysis method, device and computer-readable storage medium based on multi-curve fitting, the main purpose of which is to solve the problem of low refinement level when performing product stress testing.

[0005] To achieve the above object, the present invention provides a stress testing indicator analysis method based on multi-curve fitting, comprising:

[0006] Acquire business exposure data of the target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve;

[0007] Selecting data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve;

[0008] Performing Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and selecting data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve;

[0009] Using a pre-trained time series analysis model to perform a mixed time series analysis on the first data set and the second data set to obtain a pressure estimation index of the target system;

[0010] The pressure average of the same type of system as the target system is obtained, and the pressure estimation index is numerically corrected according to the pressure average to obtain the pressure test index of the target system.

[0011] Optionally, performing normal fitting on the business exposure data according to the first preset time to obtain a first fitting curve includes:

[0012] Mapping the business exposure data to a pre-constructed coordinate system according to each first preset time and the exposure amount corresponding to each first preset time, to obtain the change coordinates of the exposure amount of the business exposure data at different first preset times;

[0013] Calculating the fitting coordinate of each of the variable coordinates using a preset standard normal function;

[0014] Calculating the difference between the fitting coordinates and the changing coordinates;

[0015] Determine whether the difference value is less than a preset difference threshold;

[0016] When the difference value is greater than or equal to the preset difference threshold, the parameters of the initial function are adjusted according to the difference value, and the process returns to the step of calculating the fitting coordinates of each of the change coordinates using a preset standard normal function;

[0017] When the difference value is less than the preset difference threshold, it is determined that the initial function at this time is the first fitting curve.

[0018] Optionally, the calculating the difference between the fitting coordinates and the change coordinates includes:

[0019] The difference between the fitted coordinates and the changed coordinates is calculated using the following difference algorithm:

[0020]

[0021] in, is the difference value, is the number of the changing coordinates, For the The horizontal coordinate of the variable coordinate, For the The horizontal coordinate of the fitted coordinate, For the The ordinate of the changing coordinate, For the The ordinate of the fitted coordinate.

[0022] Optionally, performing Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve includes:

[0023] Selecting business exposure data at different first preset times from the first data set one by one as target data, dividing the target data according to a second preset time, and obtaining a plurality of second preset time data;

[0024] Calculating the average occurrence probability corresponding to each of the second preset time data using a preset Poisson distribution probability mass function;

[0025] The business exposure data within each second preset time in the target data is fitted into a second fitting curve according to the average occurrence probability.

[0026] Optionally, calculating the average occurrence probability corresponding to each of the second preset time data using a preset Poisson distribution probability mass function includes:

[0027] The average occurrence probability corresponding to each of the second preset time data is calculated using the following Poisson distribution probability mass function:

[0028]

[0029] in, The first data set The average occurrence probability corresponding to the second preset time data, The first data set A second preset time data, is the natural base, is the preset parameter, is the factorial symbol.

[0030] Optionally, the using a pre-trained time series analysis model to perform mixed time series analysis on the first data set and the second data set to obtain a pressure estimation index of the target system includes:

[0031] Aggregating the first data set and the second data set into feature data;

[0032] Mapping the feature data to a preset feature space using a pre-built time series detection model, and counting the spatial coordinates of each feature data in the preset feature space;

[0033] Performing curve fitting on the spatial coordinates to obtain a fitting function;

[0034] Substituting a future preset time period as a parameter into the fitting function, obtaining a maximum estimated exposure amount in the future preset time period, and using the maximum estimated exposure amount as a stress estimation indicator of the target system.

[0035] Optionally, the performing numerical correction on the pressure estimation index according to the pressure mean value to obtain the pressure test index of the target system includes:

[0036] Calculating a pressure difference between the pressure mean and the pressure estimation index;

[0037] The reciprocal absolute value of the pressure difference is used as a correction coefficient of the pressure test index;

[0038] The product of the correction coefficient and the pressure estimation index is calculated, and the product is added to the pressure estimation index to obtain the pressure test index of the target system.

[0039] In order to solve the above problems, the present invention also provides a stress test index analysis device based on multi-curve fitting, the device comprising:

[0040] A first fitting module, used to obtain business exposure data of a target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve;

[0041] A data screening module, configured to select data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve;

[0042] A second fitting module is used to perform Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and select data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve;

[0043] A timing analysis module, configured to perform a mixed timing analysis on the first data set and the second data set using a pre-trained timing analysis model to obtain a pressure estimation index of the target system;

[0044] The indicator calculation module is used to obtain the pressure average of the same system as the target system, perform numerical correction on the pressure estimation indicator according to the pressure average, and obtain the pressure test indicator of the target system.

[0045] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0046] at least one processor; and,

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned stress testing indicator analysis method based on multi-curve fitting.

[0049] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned stress testing indicator analysis method based on multi-curve fitting.

[0050] The embodiment of the present invention performs fitting analysis on the business exposure data of the target system according to the granularity of the first preset time and the second preset time respectively, and uses a pre-trained timing analysis model to perform timing analysis on the analysis results of the two granularities to obtain the stress estimation index of the target system, which can improve the accuracy of the stress test index analysis of the target system; at the same time, the pressure estimation index obtained by the timing analysis is numerically corrected in combination with the stress mean of the same system of the target system, which is conducive to further improving the accuracy of the stress test index analysis of the target system. Therefore, the stress test index analysis method, device, electronic device and computer-readable storage medium based on multi-curve fitting proposed by the present invention can solve the problem of low degree of refinement when performing product stress testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a flow chart of a stress testing indicator analysis method based on multi-curve fitting provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a Poisson fitting process according to an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of a hybrid timing analysis process according to an embodiment of the present invention;

[0054] Figure 4 A functional module diagram of a stress measurement indicator analysis device based on multi-curve fitting provided by an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of the structure of an electronic device for implementing the stress testing indicator analysis method based on multi-curve fitting provided by an embodiment of the present invention.

[0056] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0058] The embodiment of the present application provides a stress test index analysis method based on multi-curve fitting. The execution subject of the stress test index analysis method based on multi-curve fitting includes but is not limited to at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server, a terminal, etc. In other words, the stress test index analysis method based on multi-curve fitting can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0059] Reference Figure 1 FIG. 1 is a flow chart of a stress test indicator analysis method based on multiple curve fitting provided by an embodiment of the present invention. In this embodiment, the stress test indicator analysis method based on multiple curve fitting includes:

[0060] S1. Obtain business exposure data of a target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve.

[0061] In the embodiment of the present invention, the target system may be any system with a data response function, which can be used by users to perform operations such as data storage, data transmission, and data display.

[0062] In detail, the business exposure data refers to the record of data display generated by the target system in the process of processing system data. For example, the business exposure data includes but is not limited to the number of exposures and exposure duration of each business data when the target system displays different business data to users.

[0063] Specifically, a computer statement with a data capture function (such as a Java statement, a Python statement, etc.) can be used to capture the pre-stored business exposure data from a predetermined data storage area, wherein the data storage includes but is not limited to a database, a blockchain node, and a network cache, etc.

[0064] In one of the practical application scenarios of the present invention, in order to achieve accurate analysis of the business exposure data, the business exposure data can be normally fitted according to the first preset time (24h) as a time unit length to obtain a first fitting curve for the business exposure data, and the first fitting curve is used to represent the change of the business exposure data at each first preset time in multiple first preset times.

[0065] In an embodiment of the present invention, performing normal fitting on the business exposure data according to the first preset time to obtain a first fitting curve includes: mapping the business exposure data according to each first preset time and the exposure amount corresponding to each first preset time to a pre-constructed coordinate system to obtain the changing coordinates of the exposure amount of the business exposure data within different first preset times; calculating the fitting coordinates of each of the changing coordinates using a preset standard normal function; calculating the difference value between the fitting coordinates and the changing coordinates; judging whether the difference value is less than a preset difference threshold; when the difference value is greater than or equal to the preset difference threshold, adjusting the parameters of the initial function according to the difference value, and returning to the step of calculating the fitting coordinates of each of the changing coordinates using the preset standard normal function; when the difference value is less than the preset difference threshold, determining that the initial function at this time is the first fitting curve.

[0066] Specifically, the exposure amount at each first preset time in the business exposure data can be used as a dependent variable, and the change time (first preset time) can be used as an independent variable to be mapped into a pre-constructed coordinate system. For example, the change time is , the exposure is , the change time and exposure can be mapped to the pre-built coordinate system to obtain the change coordinates .

[0067] Exemplarily, in the standard normal function, is the value of the dependent variable (change time) in the coordinate system, is the value of the independent variable (exposure) in the coordinate system, It is the preset parameter to be adjusted.

[0068] Specifically, the horizontal coordinates or vertical coordinates of all the variable coordinates can be substituted into the initial function to calculate the fitting coordinates corresponding to each variable coordinate using the initial function, and then the difference between the coordinates can be calculated based on the variable coordinates and the fitting coordinates.

[0069] In the embodiment of the present invention, the calculating the difference between the fitting coordinates and the change coordinates includes:

[0070] The difference between the fitted coordinates and the changed coordinates is calculated using the following difference algorithm:

[0071]

[0072] in, is the difference value, is the number of the changing coordinates, For the The horizontal coordinate of the variable coordinate, For the The horizontal coordinate of the fitted coordinate, For the The ordinate of the changing coordinate, For the The ordinate of the fitted coordinate.

[0073] When the difference value is greater than or equal to the preset difference threshold, it can be confirmed that the initial function has a poor fitting effect on the changing coordinates. The preset optimization function (such as the Foundation Toolbox function, the Quick Fit function, etc.) can be used to adjust the parameters of the initial function according to the difference value, and return to calculate the fitting coordinates of each of the changing coordinates using the preset initial function, recalculate the difference value until the difference value is less than the preset difference threshold, and determine that the initial function at this time is the first fitting curve.

[0074] S2. Selecting data with exposure greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve.

[0075] In one of the practical application scenarios of the present invention, when the exposure of any business data is greater, it means that the number of times users access the business data is greater. At this time, it can also better reflect the pressure tolerance level of the target system for the user's access request.

[0076] Therefore, in order to accurately test the stress resistance performance of the target system, the business exposure data can be screened according to the first fitting curve to obtain data with an exposure greater than a preset first threshold, and the screened data can be collected as a first data set.

[0077] In an embodiment of the present invention, data with an exposure amount greater than a preset first threshold value is selected from the business exposure data as a first data set according to the first fitting curve, and data with a smaller exposure amount in the business exposure data can be removed, and data with a larger exposure amount in the business exposure data is retained, which is beneficial to improving the accuracy of subsequent analysis of the stress testing performance of the target system using the first data set.

[0078] S3. Perform Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and select data with exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve.

[0079] In one of the practical application scenarios of the present invention, since the above-mentioned first data set is obtained after analyzing the business exposure data according to the dimension of the first preset time, the normal distribution can display the exposure amount of the data at different first preset times in the business exposure data set, but in order to further analyze the stress testing performance of the target system, the first data set can be fitted from the dimension of the second preset time.

[0080] In detail, the first data set may be fitted by Poisson fitting to obtain a second fitting curve, that is, exposure events of different data are regarded as random events, and the number of occurrences of exposure at different second preset times is displayed using Poisson distribution.

[0081] In the embodiment of the present invention, Figure 2 As shown, performing Poisson fitting on the first data set according to the second preset time to obtain a second fitting curve includes:

[0082] S21, selecting business exposure data of different first preset times from the first data set one by one as target data, and dividing the target data according to a second preset time to obtain a plurality of second preset time data;

[0083] S22, using a preset Poisson distribution probability mass function to calculate the average occurrence probability corresponding to each of the second preset time data;

[0084] S23: Fit the business exposure data within each second preset time in the target data into a second fitting curve according to the average occurrence probability.

[0085] In detail, the second preset time is 1 hour (60 minutes).

[0086] Specifically, the calculating the average occurrence probability corresponding to each second preset time data by using a preset Poisson distribution probability mass function includes:

[0087] The average occurrence probability corresponding to each of the second preset time data is calculated using the following Poisson distribution probability mass function:

[0088]

[0089] in, The first data set The average occurrence probability corresponding to the second preset time data, The first data set A second preset time data, is the natural base, is the preset parameter, is the factorial symbol.

[0090] In the embodiment of the present invention, the step of selecting data with exposure greater than a preset second threshold from the first data set as the second data set according to the second fitting curve is consistent with the step of selecting data with exposure greater than a preset first threshold from the business exposure data as the first data set according to the first fitting curve in S2, and will not be repeated here.

[0091] S4. Use a pre-trained timing analysis model to perform mixed timing analysis on the first data set and the second data set to obtain a pressure estimation index for the target system.

[0092] In the embodiment of the present invention, the time series analysis model includes but is not limited to a LightGBM model and a LSTM (Long-short-term memory) model.

[0093] In the embodiment of the present invention, Figure 3 As shown, the use of the pre-trained time series analysis model to perform mixed time series analysis on the first data set and the second data set to obtain the pressure estimation index of the target system includes:

[0094] S31, combining the first data set and the second data set into feature data;

[0095] S32, mapping the feature data to a preset feature space using a pre-built time series detection model, and counting the spatial coordinates of each feature data in the preset feature space;

[0096] S33, performing curve fitting on the spatial coordinates to obtain a fitting function;

[0097] S34, substituting a future preset time period as a parameter into the fitting function to obtain a maximum estimated exposure amount in the future preset time period, and using the maximum estimated exposure amount as a stress estimation indicator for the target system.

[0098] In detail, the time series detection model can use a preset mapping function to map the feature data to a preset feature space, and then determine the spatial coordinates of each feature data in the preset feature space. The mapping function includes but is not limited to a Gaussian function and a map function.

[0099] Specifically, a smooth curve may be used to connect the spatial coordinates of each feature data in the preset feature space to obtain a fitting function of the spatial coordinates.

[0100] In the embodiment of the present invention, after obtaining the fitting function, the future preset time period may be substituted into the fitting function as a parameter to obtain a maximum estimated exposure for the future preset time period.

[0101] S5. Obtain a pressure average of similar systems of the target system, perform numerical correction on the pressure estimation index according to the pressure average, and obtain a pressure test index of the target system.

[0102] In the embodiment of the present invention, the similar system refers to a system having the same or similar business as the target system. For example, when the target system is a shopping system, the similar system may be other computer systems having shopping functions.

[0103] In detail, the stress test index of the same type of system as the target system may be obtained, and the average of the stress test index may be calculated to obtain the stress average of the same type of system as the target system.

[0104] In one of the practical application scenarios of the present invention, since the pressure prediction index is obtained after time series analysis of the business exposure data of the target system, in order to further improve the accuracy of the pressure prediction index, the pressure prediction index can be numerically corrected by combining the pressure average of similar systems of the target system.

[0105] In the embodiment of the present invention, the step of numerically correcting the pressure estimation index according to the pressure mean value to obtain the pressure test index of the target system includes:

[0106] Calculating a pressure difference between the pressure mean and the pressure estimation index;

[0107] The reciprocal absolute value of the pressure difference is used as a correction coefficient of the pressure test index;

[0108] The product of the correction coefficient and the pressure estimation index is calculated, and the product is added to the pressure estimation index to obtain the pressure test index of the target system.

[0109] In the embodiment of the present invention, by combining the pressure average of the same system as the target system to perform numerical correction on the pressure estimation index of the target system, it is beneficial to improve the accuracy of the pressure test index finally calculated.

[0110] The embodiment of the present invention performs fitting analysis on the business exposure data of the target system according to the granularity of the first preset time and the second preset time respectively, and uses the pre-trained time series analysis model to perform time series analysis on the analysis results of the two granularities to obtain the stress estimation index of the target system, which can improve the accuracy of the stress test index analysis of the target system; at the same time, the pressure estimation index obtained by the time series analysis is numerically corrected in combination with the pressure mean of the same system of the target system, which is conducive to further improving the accuracy of the stress test index analysis of the target system. Therefore, the stress test index analysis method based on multi-curve fitting proposed by the present invention can solve the problem of low degree of refinement when performing product stress testing.

[0111] like Figure 4 , which is a functional module diagram of a stress testing indicator analysis device based on multi-curve fitting provided by an embodiment of the present invention.

[0112] The stress test index analysis device 100 based on multi-curve fitting of the present invention can be installed in an electronic device. According to the functions implemented, the stress test index analysis device 100 based on multi-curve fitting can include a first fitting module 101, a data screening module 102, a second fitting module 103, a timing analysis module 104 and an index calculation module 105. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0113] In this embodiment, the functions of each module / unit are as follows:

[0114] The first fitting module 101 is used to obtain business exposure data of the target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve;

[0115] The data screening module 102 is used to select data with exposure amounts greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve;

[0116] The second fitting module 103 is used to perform Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and select data with exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve;

[0117] The timing analysis module 104 is used to perform mixed timing analysis on the first data set and the second data set using a pre-trained timing analysis model to obtain a pressure estimation index of the target system;

[0118] The indicator calculation module 105 is used to obtain the pressure average of the same system as the target system, and perform numerical correction on the pressure estimation indicator according to the pressure average to obtain the pressure test indicator of the target system.

[0119] In detail, each module described in the stress measurement indicator analysis device 100 based on multi-curve fitting in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The stress testing indicator analysis method based on multi-curve fitting is the same technical means as described in the previous section and can produce the same technical effects, so it will not be repeated here.

[0120] like Figure 5 , which is a schematic diagram of the structure of an electronic device for implementing a stress testing indicator analysis method based on multi-curve fitting provided by an embodiment of the present invention.

[0121] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a stress test indicator analysis program based on multi-curve fitting.

[0122] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a stress test indicator analysis program based on multi-curve fitting, etc.), and calling data stored in the memory 11.

[0123] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a stress test indicator analysis program based on multi-curve fitting, but also can be used to temporarily store data that has been output or is to be output.

[0124] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0125] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0126] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0127] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0128] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0129] The stress test indicator analysis program based on multi-curve fitting stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0130] Acquire business exposure data of the target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve;

[0131] Selecting data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve;

[0132] Performing Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and selecting data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve;

[0133] Using a pre-trained time series analysis model to perform a mixed time series analysis on the first data set and the second data set to obtain a pressure estimation index of the target system;

[0134] The pressure average of the same type of system as the target system is obtained, and the pressure estimation index is numerically corrected according to the pressure average to obtain the pressure test index of the target system.

[0135] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0136] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0137] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0138] Acquire business exposure data of the target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve;

[0139] Selecting data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve;

[0140] Performing Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and selecting data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve;

[0141] Using a pre-trained time series analysis model to perform a mixed time series analysis on the first data set and the second data set to obtain a pressure estimation index of the target system;

[0142] The pressure average of the same type of system as the target system is obtained, and the pressure estimation index is numerically corrected according to the pressure average to obtain the pressure test index of the target system.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0144] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0146] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0147] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0148] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0149] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0150] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A stress test indicator analysis method based on multi-curve fitting, characterized in that: The method comprises: Acquire business exposure data of the target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve; Selecting data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve; Performing Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and selecting data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve; Aggregating the first data set and the second data set into feature data; Mapping the feature data to a preset feature space using a pre-built time series detection model, and counting the spatial coordinates of each feature data in the preset feature space; Performing curve fitting on the spatial coordinates to obtain a fitting function; Substituting a future preset time period as a parameter into the fitting function to obtain a maximum estimated exposure amount in the future preset time period, and using the maximum estimated exposure amount as a stress estimation indicator of the target system; The pressure average of the same type of system as the target system is obtained, and the pressure estimation index is numerically corrected according to the pressure average to obtain the pressure test index of the target system, including: calculating the pressure difference between the pressure average and the pressure estimation index, using the reciprocal absolute value of the pressure difference as the correction coefficient of the pressure test index, calculating the product of the correction coefficient and the pressure estimation index, and adding the product to the pressure estimation index to obtain the pressure test index of the target system.

2. The stress testing indicator analysis method based on multi-curve fitting according to claim 1, characterized in that: The performing normal fitting on the business exposure data according to the first preset time to obtain a first fitting curve includes: Mapping the business exposure data to a pre-constructed coordinate system according to each first preset time and the exposure amount corresponding to each first preset time, to obtain the change coordinates of the exposure amount of the business exposure data at different first preset times; Calculating the fitting coordinate of each of the variable coordinates using a preset standard normal function; Calculating the difference between the fitting coordinates and the changing coordinates; Determine whether the difference value is less than a preset difference threshold; When the difference value is greater than or equal to the preset difference threshold, the parameters of the preset initial function are adjusted according to the difference value, and the process returns to the step of calculating the fitting coordinates of each of the change coordinates using the preset standard normal function; When the difference value is less than the preset difference threshold, it is determined that the initial function at this time is the first fitting curve.

3. The stress testing indicator analysis method based on multi-curve fitting according to claim 2, characterized in that: The calculating the difference between the fitting coordinates and the change coordinates includes: The difference between the fitted coordinates and the changed coordinates is calculated using the following difference algorithm: in, is the difference value, is the number of the changing coordinates, For the The horizontal coordinate of the variable coordinate, For the The horizontal coordinate of the fitted coordinate, For the The ordinate of the changing coordinate, For the The ordinate of the fitted coordinate.

4. The stress testing indicator analysis method based on multi-curve fitting according to claim 1, characterized in that: The performing Poisson fitting on the first data set according to the second preset time to obtain a second fitting curve includes: Selecting business exposure data of different first preset time periods from the first data set one by one as target data, and dividing the target data according to second preset time periods to obtain a plurality of second preset time period data; Calculating the average occurrence probability corresponding to each of the second preset time data using a preset Poisson distribution probability mass function; The business exposure data within each second preset time in the target data is fitted into a second fitting curve according to the average occurrence probability.

5. The stress testing indicator analysis method based on multi-curve fitting according to claim 4, characterized in that: Calculating the average occurrence probability corresponding to each of the second preset time data using a preset Poisson distribution probability mass function, including: The average occurrence probability corresponding to each of the second preset time data is calculated using the following Poisson distribution probability mass function: in, The first data set The average occurrence probability corresponding to the second preset time data, The first data set A second preset time data, is the natural base, is the preset parameter, is the factorial symbol.

6. A stress test indicator analysis device based on multiple curve fitting, used to implement the stress test indicator analysis method based on multiple curve fitting as described in any one of claims 1 to 5, characterized in that: The device comprises: A first fitting module, used to obtain business exposure data of a target system, and perform normal fitting on the business exposure data according to a first preset time to obtain a first fitting curve; A data screening module, configured to select data with an exposure amount greater than a preset first threshold from the business exposure data as a first data set according to the first fitting curve; A second fitting module is used to perform Poisson fitting on the first data set according to a second preset time to obtain a second fitting curve, and select data with an exposure greater than a preset second threshold from the first data set as a second data set according to the second fitting curve; A timing analysis module, configured to perform a mixed timing analysis on the first data set and the second data set using a pre-trained timing analysis model to obtain a pressure estimation index of the target system; The indicator calculation module is used to obtain the pressure average of the same system as the target system, perform numerical correction on the pressure estimation indicator according to the pressure average, and obtain the pressure test indicator of the target system.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the stress testing indicator analysis method based on multi-curve fitting as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the stress testing indicator analysis method based on multi-curve fitting as described in any one of claims 1 to 5 is implemented.

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