Page anomaly detection method and device, equipment and medium

Abstract

The invention discloses a page anomaly detection method and device, equipment and a medium, relates to the technical field of computers, and can be applied to the field of financial science and technology. In response to a starting operation of the interactive interface, entering a target page of the target software; obtaining a page detection behavior of a user, and generating a detection record table based on the page detection behavior and the starting operation; the page detection behavior comprises a click operation and a slide operation; and performing anomaly detection on the target page based on the detection record table and the anomaly weight reference. By adopting the technical scheme, the problems of high page anomaly cost and high false alarm rate in the prior art are solved, the detection record table is generated through the user behaviors, anomaly detection is performed on the user behaviors of the detection record table according to the anomaly weight reference, page lagging detection does not need to be performed by three-party tools, the detection cost of page anomaly detection is reduced, and the detection efficiency is improved. And too high consumption of equipment performance resources during anomaly detection is avoided.

Description

Technical Field

[0001] This invention relates to the field of computer technology and can be applied to the field of financial technology. In particular, it relates to a method, apparatus, device, and medium for detecting page anomalies. Background Technology

[0002] Existing smart devices often have a large number of apps with different functions installed. However, due to limited internal resources, some apps may fail to open or experience abnormal page loading or lag.

[0003] Current methods for detecting software often involve determining the percentage of white dots on the screen pixels to confirm a blank screen, or using third-party tools to detect screen frame rate and refresh status to detect lag.

[0004] However, the white screen detection solution has the risk of false alarms, as the software may display a white-style page; while the frame rate detection solution has the risk of performance consumption, uses third-party tools, has various limitations such as procurement, and will also increase the package size. In addition, the frame rate detection is only used in debugging scenarios, which is too limited in its application scenarios.

[0005] Therefore, there is an urgent need for a page anomaly detection method to ensure that page anomalies can be detected during software use, thereby reducing usage costs and ensuring device performance. Summary of the Invention

[0006] This invention provides a page anomaly detection method, apparatus, device, and medium, which solves the problems of high cost and high false alarm rate of existing page anomaly detection methods. It generates a detection record table through user behavior and performs anomaly detection on the user behavior in the detection record table according to the anomaly weight benchmark. It eliminates the need for third-party tools to detect page lag, reduces the detection cost of page anomaly detection, and avoids excessive consumption of device performance resources during anomaly detection.

[0007] According to one aspect of the present invention, a page anomaly detection method is provided, comprising:

[0008] In response to the startup operation of the interactive interface, enter the target page of the target software;

[0009] The system acquires the user's page detection behavior and generates a detection record table based on the page detection behavior and the launch operation; the page detection behavior includes click operations and swipe operations.

[0010] Anomaly detection is performed on the target page based on the detection record table and the anomaly weight benchmark.

[0011] According to another aspect of the present invention, a page anomaly detection device is provided, comprising:

[0012] The response module is used to respond to the startup operation of the interactive interface and enter the target page of the target software;

[0013] A generation module is used to acquire the user's page detection behavior and generate a detection record table based on the page detection behavior and the launch operation; the page detection behavior includes click operations and swipe operations;

[0014] The detection module is used to perform anomaly detection on the target page based on the detection record table and the anomaly weight benchmark.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the page anomaly detection method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the page anomaly detection method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the page anomaly detection method according to any embodiment of the present invention.

[0021] The technical solution of this invention generates a detection record table based on user behavior and performs anomaly detection on the user behavior in the detection record table according to the anomaly weight benchmark. This solves the problems of high cost and high false alarm rate of existing page anomaly detection. It eliminates the need for third-party tools to detect page lag, reduces the detection cost of page anomaly detection, and avoids excessive consumption of device performance resources during anomaly detection.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a page anomaly detection method provided according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of a method for generating a detection record table according to an embodiment of the present invention;

[0026] Figure 3 This is a flowchart of a page anomaly detection method provided according to an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of a page anomaly detection device according to an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the page anomaly detection method of this invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device 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 not explicitly listed or inherent to such process, method, product or device.

[0031] Furthermore, it should be noted that the information collected in the technical solution of this invention is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data all comply with the relevant laws, regulations and standards of relevant countries and regions, necessary confidentiality measures have been taken, and public order and good morals are not violated. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0032] Figure 1 This invention provides a flowchart of a page anomaly detection method according to an embodiment of the present invention. This embodiment is applicable to situations requiring page anomaly detection. The method can be executed by a transaction anomaly detection device, which can be implemented in hardware and / or software and configured in a server. Figure 1 As shown, the method includes:

[0033] S110, In response to the startup operation of the interactive interface, enter the target page of the target software.

[0034] The interactive interface is the touch screen of the target device; the launch operation can be a user's click operation on the target software to open the target software; the target software is the software installed on the target device; and the target page is the homepage of the target software after it is opened.

[0035] Specifically, in response to the user's launch operation on the target device's interactive interface, the target software is opened and the target page of the target software is entered.

[0036] S120. Obtain the user's page detection behavior and generate a detection record table based on the page detection behavior and startup operation; the page detection behavior includes click operation and swipe operation.

[0037] Among them, the page detection behavior is to obtain user operations through the interactive interface, including click operations and swipe operations on the touchable screen; the detection record table records user behavior; click operation is when the user's initial click position and final click position are the same; swipe operation is when the user's initial click position and final click position are not the same.

[0038] Specifically, in response to click and swipe operations on the interactive interface, a detection record table is generated to show the user's behavior based on the detected page actions and launch operations.

[0039] Optional, such as Figure 2 The method for generating a detection record table, as shown, involves acquiring the user's page detection behavior and generating a detection record table based on the page detection behavior and startup operations, including:

[0040] S121. In response to the startup operation of the interactive interface, start the timer and determine the startup time corresponding to the startup operation.

[0041] Among them, the timer is the interval for recording user behavior and the execution time; the start time is the interval between the start operation and the next operation.

[0042] Specifically, in response to the user's startup operation obtained from the interactive interface, that is, when the user clicks on the target software on the target device, a timer is started to record the time variable, and the startup time corresponding to the startup operation is determined in response to the next operation.

[0043] S122. Generate the detection time corresponding to the page detection behavior based on the page detection behavior and the timer.

[0044] The detection time can include the click time for a click operation and the swipe time for a swipe operation.

[0045] Specifically, based on the click and swipe actions detected on the page, and the timer generates the click time corresponding to the click action and the swipe time corresponding to the swipe action.

[0046] Optionally, the detection time corresponding to the page detection behavior is generated based on the page detection behavior and the timer, including:

[0047] In response to user behavior on the current page, close or clear the timer.

[0048] Start a timer to record the detection time of the current page's detection behavior until the next page's detection behavior occurs.

[0049] Specifically, upon receiving the startup operation of the target software, a timer is started. When the user's current page detection behavior is obtained, the timer is closed and stopped. The first timer is used as the startup time corresponding to the startup operation. The timer is started while the timer is being cleared, and it continues until the next page detection behavior occurs, at which point the timer is closed. The time from when the timer starts to when the timer closes in this round is used as the detection time of the current page detection behavior.

[0050] Understandably, by responding to page detection behavior, the timer can be turned on and off, and the timer can record the page detection behavior in a loop, without the need for external tools, thus further reducing the internal resource consumption of the target device.

[0051] S123. Associate the page detection behavior with the detection time and the startup operation with the startup time to generate a detection record table.

[0052] The detection record table records the startup operation, click operation, and swipe operation corresponding to the target software; and each operation is associated with its corresponding time, such as the startup time of the startup operation, the click time of the click operation, and the swipe time of the swipe operation.

[0053] Optionally, the page detection behavior can be stored in association with the detection time, including:

[0054] If the page detection behavior is a swipe operation, then determine the starting position and swipe direction of the swipe operation;

[0055] The sliding time is correlated with the starting position, sliding direction, and detection time and stored as sliding information;

[0056] If the page detection behavior is a click operation, then determine the click location;

[0057] The click location is associated with the click time in the detection time and stored as click information.

[0058] The starting position is the initial click position of the swipe operation; the swipe direction is the direction of the swipe operation from the initial position to the end position, which can include swiping to the left, right, up, or down; the click position is the click of a non-swipe operation, where the initial position and the starting position are the same.

[0059] Specifically, if the page detection behavior is a swipe operation, the corresponding starting position and swipe direction are determined based on the swipe operation, and the starting position, swipe direction, and swipe time in the detection time are associated and stored as swipe information; if the page detection behavior is a click operation, the corresponding click position is determined based on the click operation, and the click position is associated with the click time in the detection time and stored as click information.

[0060] Understandably, by analyzing different page detection behaviors and setting different logging information, a refined analysis of user behavior can be achieved, ensuring the accuracy of subsequent anomaly detection.

[0061] S130. Perform anomaly detection on the target page based on the detection record table and anomaly weight benchmark.

[0062] The anomaly weight benchmark is the weight calculation rule corresponding to different user behaviors in the detection record table.

[0063] Specifically, by detecting different user behaviors in the record table, the corresponding weight calculation rules are selected from the abnormal weight benchmark to calculate the weight value, and the target page is then subjected to anomaly detection based on the weight value.

[0064] This invention, in response to a startup operation on the interactive interface, enters the target page of the target software; acquires the user's page detection behavior, and generates a detection record table based on the page detection behavior and the startup operation; the page detection behavior includes click operations and swipe operations; and performs anomaly detection on the target page based on the detection record table and anomaly weight benchmark. The above technical solution generates a detection record table based on user behavior and performs anomaly detection on the user behavior in the detection record table according to anomaly weight benchmark, solving the problems of high cost and high false alarm rate of existing page anomaly detection methods. It eliminates the need for third-party tools for page lag detection, reduces the detection cost of page anomaly detection, and avoids excessive consumption of device performance resources during anomaly detection.

[0065] Figure 3 This is a flowchart of a page anomaly detection method according to an embodiment of the present invention. The embodiments of the present invention supplement the anomaly detection method for the target page based on the above embodiments. It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the relevant descriptions in other embodiments. For example... Figure 3 As shown, the method includes:

[0066] S210, In response to the startup operation of the interactive interface, enter the target page of the target software.

[0067] S220. Obtain the user's page detection behavior and generate a detection record table based on the page detection behavior and startup operation; the page detection behavior includes click operation and swipe operation.

[0068] S230. Calculate the abnormal startup weight value based on the startup operation, startup time and the startup weight benchmark in the abnormal weight benchmark in the detection record table.

[0069] Among them, the start weight benchmark in the abnormal weight benchmark is the weight calculation rule corresponding to the start operation; the abnormal start weight value is the weight value corresponding to each start operation.

[0070] Specifically, the startup operation and startup time are obtained from the detection record table. If the startup time exceeds the startup weight benchmark in the abnormal weight benchmark by 5 seconds, the corresponding abnormal startup weight value is 1. If it does not exceed the startup weight benchmark, the abnormal startup weight value is 0.

[0071] S240. Determine the return information in the detection record table; wherein, the return information includes sliding information and click information.

[0072] Specifically, the returned information in the detection record table is determined sequentially, including the starting position, sliding direction, and sliding time information during the detection time, as well as the click information of the click position and click time during the detection time.

[0073] S250. Calculate the abnormal return weight value based on the returned information and the abnormal weight benchmark.

[0074] The returned weight benchmarks are the sliding weight benchmark and the click weight benchmark; the abnormal returned weight values ​​can include abnormal sliding weight values ​​and abnormal click weight values.

[0075] Specifically, the sliding and clicking information in the returned information are weighted and calculated with the sliding weight benchmark and clicking weight benchmark of the returned weight benchmark, respectively, to obtain the abnormal sliding weight value and abnormal clicking weight value.

[0076] Optionally, the abnormal return weight value is calculated based on the returned information and the abnormal weight benchmark, including:

[0077] The abnormal sliding and the number of abnormal slidings are determined based on the sliding information and abnormal sliding rules in the detection record table;

[0078] The abnormal clicks and the number of abnormal clicks are determined based on the click information and abnormal click rules in the detection record table;

[0079] The abnormal swipe count and abnormal click count are calculated by comparing them with the abnormal return weight benchmark in the abnormal weight benchmark to obtain the abnormal return weight value; the abnormal return weight value includes the abnormal swipe weight value and the abnormal click weight value.

[0080] Among them, the abnormal swipe rule refers to the abnormal swipe position and abnormal swipe direction of the swipe operation; it can be at one-third to two-thirds of the screen height, and the swipe direction is to the right; abnormal swipe is a swipe operation that meets the abnormal swipe rule; the number of abnormal swipes is the sum of the number of swipes corresponding to the abnormal swipe; the abnormal click rule refers to the abnormal click position of the click operation, such as the back button area in the upper left corner; abnormal click is a click operation that meets the abnormal click rule; the number of abnormal clicks is the sum of the number of clicks corresponding to the abnormal click; the abnormal return weight benchmark includes the abnormal swipe weight benchmark and the abnormal click weight benchmark; the abnormal return weight benchmark is a rule for weight calculation when the number of abnormal swipes or abnormal clicks reaches the abnormal number threshold.

[0081] Specifically, based on the sliding information and abnormal sliding rules in the detection record table, abnormal sliding that conforms to the abnormal sliding rules is identified, and the number of abnormal slidings is obtained through the abnormal sliding in the detection record table; based on the click information and abnormal click rules in the record table, abnormal clicks that conform to the abnormal click rules are identified, and the number of abnormal clicks is obtained through the abnormal click operations in the detection record table; the number of abnormal slidings and the number of abnormal clicks are compared with the abnormal sliding number threshold and the abnormal click number threshold in the abnormal weight benchmark, respectively. If at least one of them is higher than the abnormal number threshold, the abnormal sliding number and the number of abnormal clicks are weighted according to the abnormal return benchmark rules to obtain the abnormal sliding weight value and the abnormal click weight value.

[0082] The calculation method for the exception return baseline rule is as follows:

[0083]

[0084] Where 'a' can be the number of abnormal swipes or abnormal clicks; and 'W' is the abnormal return weight value, which can be the abnormal swipe weight value or the abnormal click weight value.

[0085] Understandably, by performing refined weight calculations on abnormal behaviors, precise targeting of user behavior can be achieved, and page anomalies can be detected based on different behavior algorithms to avoid false alarms and further reduce resource consumption and computational load.

[0086] It should be noted that, in this embodiment of the invention, the threshold for abnormal sliding times and the threshold for abnormal clicking times are preferably 3 times. Relevant technicians can set them according to actual experience, and this embodiment of the invention does not impose specific limitations on them.

[0087] In an optional embodiment of the present invention, if click and swipe operations occur consecutively, the abnormal return weight value is incremented by 1. Furthermore, the startup operations and page detection behaviors in the detection record table are calculated sequentially until the detection record table stops recording. By weighting the consecutive occurrences of click and swipe operations, it can be indirectly proven that the problem is not due to a particular return method being unsuitable, but rather a page lag or inability to use the return function. In such cases, the entire page will experience problems, preventing the user from returning to the homepage, and the overall operation of the app will be interrupted, thus improving the accuracy and applicability of the abnormal weight value.

[0088] S260. Traverse the startup operations in the detection record table to determine the number of restarts and the restart interval. Calculate the abnormal restart weight value based on the number of restarts, the restart interval, and the restart weight benchmark in the abnormal weight benchmark.

[0089] The restart count is the number of startup operations recorded in the detection record table; the restart interval is the time interval between adjacent startup operations; and the restart weight benchmark is the number of restarts that meet the page exception criteria and the corresponding restart interval.

[0090] Specifically, the startup operations in the detection record table are traversed to identify at least two startup operations. The restart interval between two adjacent startup operations is determined based on their times, and a restart count is determined based on the two adjacent startup operations. The restart count, restart interval, and abnormal weight benchmark are used to determine whether the operation is abnormal. If the restart count is greater than the restart threshold, the weight value is calculated based on the restart count and restart weight benchmark to obtain the abnormal restart weight value. If the restart interval is greater than the interval threshold, it indicates that the current page is unresponsive and there is a possibility of lag, which is considered an abnormal state. At this time, the abnormal restart weight value is incremented by 1.

[0091] The formula for calculating the weight of abnormal restart is as follows:

[0092]

[0093] Where Q is the abnormal restart weight value; b is the number of restarts.

[0094] It should be noted that, in this embodiment of the invention, the restart threshold is preferably 3 times, and the interval threshold can be 5 seconds. Relevant technicians can set it according to actual experience, and this embodiment of the invention does not impose any specific limitations on it.

[0095] S270. Determine the abnormal weight value based on the abnormal startup weight value, abnormal return weight value, and abnormal restart weight value.

[0096] The anomaly weight value is the weight value calculated sequentially from the detection record table.

[0097] Specifically, based on the abnormal weight calculation order in the detection record table, the abnormal start weight value, abnormal return weight value, and abnormal restart weight value of at least one abnormal start weight value are superimposed to obtain the abnormal weight value.

[0098] In one optional embodiment of the present invention, abnormal location judgment is performed on the user's click operation. Based on the click location and the corresponding page function, an abnormal weight value is accumulated. For example, if the page function of the click location is "Check if the current version is available," it is assumed that the user is checking if the target software is the latest version, and the abnormal weight value is incremented by 1. If the page function of the click location is "Update target software," the abnormal weight value is incremented by 1. If the page function of the click location is "Contact customer service" or "Feedback," the abnormal weight value is incremented by 1. By accumulating the weight values ​​of abnormal page functions, comprehensive page anomaly detection is achieved, improving the accuracy of page anomaly detection.

[0099] S280. Perform anomaly detection on the target page based on anomaly weight values ​​and anomaly thresholds.

[0100] The preferred abnormal threshold is 8.

[0101] Specifically, when performing real-time calculations on user behaviors in the detection record table in sequence, the abnormal threshold can be compared with the continuously updated abnormal weight values ​​so as to detect anomalies on the target page based on the comparison results.

[0102] Optionally, anomaly detection of the target page can be performed based on anomaly weight values ​​and anomaly thresholds, including:

[0103] When the abnormal weight value is greater than the abnormal threshold, determine whether the target page is the homepage of the target software;

[0104] If the target page is not the homepage, then obtain the page information of the target page;

[0105] The page information and page detection behavior in the detection record table are used as early warning information, and the early warning information is displayed through the interactive interface to provide early warning prompts.

[0106] If the target page is the homepage, the target page will be marked as having a startup error, and the target software will be repaired. The target software will then be accessed via a backup route; the backup route is the initial startup process of the target software.

[0107] The homepage is the first page when entering the target software; the target page is the page corresponding to the anomaly detection of the detection record table; the page information can be the page path, such as the general function page under the settings page in WeChat software; the warning information can include the page information and the page detection behavior corresponding to the page; the warning prompt can be a pop-up prompt; the initial version can be the initial version or a stable version.

[0108] Specifically, when the anomaly weight value exceeds the anomaly threshold, the system first determines whether the target page is the homepage of the target software. If the target page is not the homepage, user click data can be stored through event tracking. Based on the uploaded click data, the page information of the problematic page, such as the user's click path and page name, can be obtained. The page information and page detection behavior in the detection record table are used as early warning information, which is displayed in a pop-up window on the interactive interface for early warning. If the target page is the homepage, it is considered that the startup process cannot enter the page normally, and the target software startup process is determined to be stuck. The target page is then marked as a startup anomaly, and the target software is repaired. The system then enters the target software through a backup route. The backup route is the initial version startup process of the target software.

[0109] Understandably, by analyzing the target page, the homepage can be automatically repaired, thus ensuring that users can quickly access the target software and improving the user experience.

[0110] In one optional embodiment of the present invention, the self-repair of the target software can be achieved by reporting the page function information of the homepage and storing the startup exception flag as 1. Upon the next startup, the value of the startup exception flag is checked. If it is 1, it is considered that the previous startup failed, and the startup process is directly repaired via the backup route. If the target software can be accessed, the startup exception flag is changed to 0. Furthermore, since each version update may modify the startup process code, and since each version update does not modify any code for this route, the code logic of this route is simple, maintaining only the most basic original function. Therefore, the startup process code of the most stable or initial version of the target software is obtained, and it does not participate in code iteration, ensuring that the app enters the main page and displays the homepage smoothly without performing any other unnecessary operations; this route is used as the backup route. This ensures that regardless of how many versions of the target software iterate, if the target software encounters a problem, the user can at least access the homepage, preventing the target software from becoming unusable and affecting the user experience.

[0111] This invention analyzes different user behaviors in the detection record table and uses user behavior analysis algorithms corresponding to different user behaviors to calculate abnormal weight values. It can provide personalized and timely early warning notifications without installing third-party tools or adding excessive size to the target software. It uses a weight benchmark to calculate abnormal weight values, reducing the computational load of anomaly detection. It also provides an intuitive and comprehensive analysis of the page's anomaly detection accuracy through user behavior and further automatically repairs page anomalies with minimal impact.

[0112] Figure 4 This invention provides a schematic diagram of a page anomaly detection device according to an embodiment of the present invention. This embodiment is applicable to situations requiring page anomaly detection. The page anomaly detection device can be implemented in hardware and / or software and can be configured in a server. Figure 4 As shown, the page anomaly detection device 300 includes a response module 310, a generation module 320, and a detection module 330:

[0113] The response module 310 is used to respond to the startup operation of the interactive interface and enter the target page of the target software;

[0114] The generation module 320 is used to acquire the user's page detection behavior and generate a detection record table based on the page detection behavior and the start operation; the page detection behavior includes click operation and swipe operation;

[0115] The detection module 330 is used to perform anomaly detection on the target page based on the detection record table and the anomaly weight benchmark.

[0116] This invention, in response to a startup operation on the interactive interface, enters the target page of the target software; acquires the user's page detection behavior, and generates a detection record table based on the page detection behavior and the startup operation; the page detection behavior includes click operations and swipe operations; and performs anomaly detection on the target page based on the detection record table and anomaly weight benchmark. The above technical solution generates a detection record table based on user behavior and performs anomaly detection on the user behavior in the detection record table according to anomaly weight benchmark, solving the problems of high cost and high false alarm rate of existing page anomaly detection methods. It eliminates the need for third-party tools for page lag detection, reduces the detection cost of page anomaly detection, and avoids excessive consumption of device performance resources during anomaly detection.

[0117] Optionally, the generation module 320 includes a startup time determination unit, a detection time generation unit, and an associated storage unit;

[0118] The startup time determination unit is used to respond to the startup operation of the interactive interface, start the timer, and determine the startup time corresponding to the startup operation;

[0119] The detection time generation unit is used to generate the detection time corresponding to the page detection behavior based on the page detection behavior and the timer.

[0120] The associated storage unit is used to associate page detection behavior with detection time and startup operation with startup time to generate a detection record table.

[0121] Optionally, the detection time generation unit is also used to respond to the user's current page detection behavior by turning off the timer or clearing the timer.

[0122] Start a timer to record the detection time of the current page's detection behavior until the next page's detection behavior occurs.

[0123] Optionally, the associated storage unit is also used to determine the starting position and direction of the sliding operation if the page detection behavior is a sliding operation;

[0124] The sliding time is correlated with the starting position, sliding direction, and detection time and stored as sliding information;

[0125] If the page detection behavior is a click operation, then determine the click location;

[0126] The click location is associated with the click time in the detection time and stored as click information.

[0127] Optionally, the detection module 330 is also used to calculate the abnormal startup weight value based on the startup operation, startup time and the startup weight benchmark in the abnormal weight benchmark in the detection record table;

[0128] Determine the returned information in the detection record table; the returned information includes swipe information and click information;

[0129] The abnormal return weight value is calculated based on the returned information and the return weight benchmark in the abnormal weight benchmark.

[0130] The startup operations in the detection record table are traversed to determine the number of restarts and the restart interval. Based on the number of restarts, the restart interval, and the restart weight benchmark in the abnormal weight benchmark, the abnormal restart weight value is calculated.

[0131] The abnormal weight value is determined based on the abnormal startup weight value, abnormal return weight value, and abnormal restart weight value.

[0132] Anomaly detection is performed on the target page based on anomaly weight values ​​and anomaly thresholds.

[0133] Optionally, the detection module 330 is also used to determine abnormal sliding and the number of abnormal sliding based on the sliding information and abnormal sliding rules in the detection record table;

[0134] The abnormal clicks and the number of abnormal clicks are determined based on the click information and abnormal click rules in the detection record table;

[0135] The abnormal swipe count and abnormal click count are calculated by comparing them with the abnormal return weight benchmark in the abnormal weight benchmark to obtain the abnormal return weight value; the abnormal return weight value includes the abnormal swipe weight value and the abnormal click weight value.

[0136] Optionally, the detection module 330 is also used to determine whether the target page is the homepage of the target software when the abnormal weight value is greater than the abnormal threshold.

[0137] If the target page is not the homepage, then obtain the page information of the target page;

[0138] The page information and page detection behavior in the detection record table are used as early warning information, and the early warning information is displayed through the interactive interface to provide early warning prompts.

[0139] If the target page is the homepage, the target page will be marked as having a startup error, and the target software will be repaired. The target software will then be accessed via a backup route; the backup route is the initial startup process of the target software.

[0140] The page anomaly detection device provided in this embodiment of the invention can execute the page anomaly detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0141] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0142] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0143] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0144] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0145] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as page anomaly detection methods.

[0146] In some embodiments, the page anomaly detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the page anomaly detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the page anomaly detection method by any other suitable means (e.g., by means of firmware).

[0147] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0149] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0150] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0151] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0152] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system. This addresses the shortcomings of traditional physical hosts and dedicated virtual services, such as high management difficulty and weak business scalability.

[0153] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0154] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting page anomalies, characterized in that, include: In response to the startup operation of the interactive interface, enter the target page of the target software; Acquire the user's page detection behavior, and generate a detection record table based on the page detection behavior and the startup operation; The page detection behaviors include click operations and swipe operations; Anomaly detection is performed on the target page based on the detection record table and the anomaly weight benchmark.

2. The method according to claim 1, characterized in that, The step of acquiring the user's page detection behavior and generating a detection record table based on the page detection behavior and the launch operation includes: In response to a startup operation on the interactive interface, a timer is started, and the startup time corresponding to the startup operation is determined; Generate the detection time corresponding to the page detection behavior based on the page detection behavior and the timer; The page detection behavior is associated with the detection time and stored, and the startup operation is associated with the startup time and stored, to generate a detection record table.

3. The method according to claim 2, characterized in that, The step of generating the detection time corresponding to the page detection behavior based on the page detection behavior and the timer includes: In response to the user's current page detection behavior, the timer is closed and cleared. The timer is started to record the detection time of the current page detection behavior until the next page detection behavior occurs.

4. The method according to claim 2, characterized in that, The page detection behavior is associated with and stored in relation to the detection time, including: If the page detection behavior is a swipe operation, then determine the starting position and swipe direction of the swipe operation; The sliding information is stored by associating the starting position, the sliding direction, and the sliding time within the detection time. If the page detection behavior is a click operation, then the click location of the click operation is determined; The click location is associated with the click time in the detection time and stored as click information.

5. The method according to claim 1, characterized in that, The anomaly detection of the target page based on the detection record table and the anomaly weight benchmark includes: The abnormal startup weight value is calculated based on the startup operation, startup time and abnormal weight benchmark in the detection record table. Determine the return information in the detection record table; wherein, the return information includes swipe information and click information; The abnormal return weight value is calculated based on the returned information and the return weight benchmark in the abnormal weight benchmark. The number of restarts and the restart interval are determined by traversing the startup operations in the detection record table. Based on the number of restarts, the restart interval, and the restart weight benchmark in the abnormal weight benchmark, the abnormal restart weight value is calculated. An abnormal weight value is determined based on the abnormal startup weight value, the abnormal return weight value, and the abnormal restart weight value; Anomaly detection is performed on the target page based on the anomaly weight value and anomaly threshold.

6. The method according to claim 5, characterized in that, The calculation of the abnormal return weight value based on the returned information and the return weight benchmark in the abnormal weight benchmark includes: The abnormal sliding and the number of abnormal slidings are determined based on the sliding information and abnormal sliding rules in the detection record table. Based on the click information and abnormal click rules in the detection record table, the abnormal clicks and the number of abnormal clicks are determined; The abnormal swipe count and the abnormal click count are respectively calculated with the abnormal return weight benchmark in the abnormal weight benchmark to obtain the abnormal return weight value; the abnormal return weight value includes the abnormal swipe weight value and the abnormal click weight value.

7. The method according to claim 5, characterized in that, The anomaly detection of the target page based on the anomaly weight value and the anomaly threshold includes: When the abnormal weight value is greater than the abnormal threshold, it is determined whether the target page is the homepage of the target software; If the target page is not the homepage, then obtain the page information of the target page; The page information and the page detection behavior in the detection record table are used as early warning information, and the early warning information is displayed through the interactive interface to provide early warning prompts. If the target page is the homepage, then the target page is marked as having a startup error, and the target software is repaired. The target software is then accessed via a fallback route; the fallback route is the initial version startup process of the target software.

8. A page anomaly detection device, characterized in that, include: The response module is used to respond to the startup operation of the interactive interface and enter the target page of the target software; The generation module is used to acquire the user's page detection behavior and generate a detection record table based on the page detection behavior and the startup operation; The page detection behaviors include click operations and swipe operations; The detection module is used to perform anomaly detection on the target page based on the detection record table and the anomaly weight benchmark.

9. An electronic device, characterized in that, The electronic device includes: 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, the computer program being executed by the at least one processor to enable the at least one processor to perform the page anomaly detection method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the page anomaly detection method according to any one of claims 1-7.

Images