Page monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a page monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, in particular to the field of large models and artificial intelligence. According to the specific implementation scheme, sampling data, sent by a client, of an application program are received; determining at least one page belonging to the same service function according to the function mapping information; performing abnormal function analysis according to the aggregated sampling data of the at least one page, and determining an abnormal business function and abnormal information of the abnormal business function; and calling the large generative model, and outputting an exception repair strategy of the exception service function by the large generative model according to the exception service function and the exception information.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, specifically to the field of large models and artificial intelligence, and in particular to a page monitoring method, device, electronic device and storage medium. Background Art

[0002] Currently, the monitoring of mobile terminals is mainly focused on performance monitoring such as crashes and freezes, and functional abnormalities are rarely monitored. It is difficult to comprehensively monitor the display of mobile pages and function clicks. The existing code tracking coverage is difficult to cover the entire page and all click areas, and it is impossible to comprehensively monitor abnormalities. Summary of the invention

[0003] The present disclosure provides a page monitoring method, device, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, a page monitoring method is provided, comprising:

[0005] Receive sampling data of an application sent by a client, wherein the sampling data includes aggregated sampling data of a user on a same page and function mapping information corresponding to the same page;

[0006] Determine, according to the function mapping information, at least one page belonging to the same business function;

[0007] Performing abnormal function analysis based on the aggregated sampling data of the at least one page to determine abnormal business functions and abnormal information of the abnormal business functions;

[0008] The generative big model is called, and the generative big model outputs an abnormal repair strategy for the abnormal business function according to the abnormal business function and the abnormal information.

[0009] According to a second aspect of the present disclosure, another page monitoring method is provided, comprising:

[0010] Global monitoring of application pages;

[0011] Aggregate the user's operations on the same page within a set time unit to obtain aggregated sampling data of the user on the same page;

[0012] Obtaining function mapping information corresponding to the same page;

[0013] generating sampling data of the application according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page;

[0014] Sending the sampled data of the application to the server.

[0015] According to a third aspect of the present disclosure, a page monitoring device is provided, comprising:

[0016] A receiving module, configured to receive sampled data of an application sent by a client, wherein the sampled data includes aggregated sampled data of a user on the same page and function mapping information corresponding to the same page;

[0017] A first acquisition module, configured to determine at least one page belonging to the same business function according to the function mapping information;

[0018] A second acquisition module is used to perform abnormal function analysis based on the aggregated sampling data of the at least one page to determine abnormal business functions and abnormal information of the abnormal business functions;

[0019] The calling module is used to call the generative big model, and the generative big model outputs the abnormal repair strategy of the abnormal business function according to the abnormal business function and the abnormal information.

[0020] According to a fourth aspect of the present disclosure, another page monitoring device is provided, comprising:

[0021] Monitoring module, used to globally monitor the application pages;

[0022] A first acquisition module is used to aggregate the operations of the user on the same page within a set time unit to obtain aggregated sampling data of the user on the same page;

[0023] A second acquisition module is used to acquire function mapping information corresponding to the same page;

[0024] A third acquisition module, configured to generate sampling data of the application program according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page;

[0025] The sending module is used to send the sampled data of the application to the server.

[0026] According to the fifth aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect or the second aspect embodiment.

[0027] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the first aspect or the second aspect embodiment.

[0028] According to a seventh aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the method described in the first aspect or the second aspect when executed by a processor.

[0029] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0031] Figure 1 It is a flow chart of a page monitoring method provided by an embodiment of the present disclosure;

[0032] Figure 2 It is a flowchart of another page monitoring method provided by an embodiment of the present disclosure;

[0033] Figure 3 It is a flowchart of another page monitoring method provided by an embodiment of the present disclosure;

[0034] Figure 4 It is a flowchart of another page monitoring method provided by an embodiment of the present disclosure;

[0035] Figure 5 It is a flowchart of another page monitoring method provided by an embodiment of the present disclosure;

[0036] Figure 6 It is a logical schematic diagram of a page monitoring method provided by an embodiment of the present disclosure;

[0037] Figure 7 is a structural block diagram of a page monitoring device provided by an embodiment of the present disclosure;

[0038] Figure 8 is a structural block diagram of another page monitoring device provided by an embodiment of the present disclosure;

[0039] Fig. 9 A schematic block diagram of an electronic device for implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0040] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0041] Data processing is the collection, storage, retrieval, processing, transformation and transmission of data. Its basic purpose is to extract and derive valuable and meaningful data for certain specific people from a large amount of data that may be disorganized and difficult to understand.

[0042] A large model refers to a machine learning model with a large number of parameters and complex structure. It is pre-trained with massive data, has strong expressive and generalization capabilities, and can process massive data and complete various complex tasks.

[0043] Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. It aims to understand the essence of intelligence and produce an intelligent machine that can respond in a similar way to human intelligence.

[0044] Figure 1 FIG. 1 is a flow chart of a page monitoring method provided by an embodiment of the present disclosure. Figure 1 As shown, the method includes:

[0045] S101, receiving sample data of an application sent by a client.

[0046] Optionally, the client may be a program such as software or a web browser, and the application is installed on the client. The sampling data includes aggregated sampling data of the user on the same page and function mapping information corresponding to the same page.

[0047] In some implementations, the aggregated sampling data of users on the same page refers to the aggregation of sampling data of one or more users on the page, where the sampling data of users on the page can be the number of times the users perform operations such as clicks or page display on the page.

[0048] In some implementations, the function mapping information corresponding to the same page refers to the function information of the current page, and the corresponding function mapping information is determined based on the function corresponding to the page.

[0049] S102: Determine at least one page belonging to the same business function according to the function mapping information.

[0050] Optionally, business functions may include different functions such as search, view, marketing and display; determine the function corresponding to each page according to the function mapping information of the page, and determine at least one page belonging to the same business function according to the function corresponding to each page, that is, classify all pages according to their corresponding business functions, and group pages with the same business function together.

[0051] S103: Perform abnormal function analysis based on aggregated sampling data of at least one page to determine abnormal business functions and abnormal information of the abnormal business functions.

[0052] In some implementations, feature value extraction can be performed based on aggregated sampling data of at least one page, and the feature value can be compared with an abnormality threshold to determine whether the current page has an abnormality; for example, the variance of the aggregated sampling data is obtained, the variance is compared with an abnormal variance threshold, and when the variance is greater than the abnormal variance threshold, it is determined that the page has an abnormality.

[0053] Furthermore, the business function of the abnormal page is determined as the abnormal business function, and the abnormal information of the abnormal business is used as the abnormal information of the abnormal business function. The abnormal information may include information such as the abnormal degree of the aggregated sampling data compared with the abnormal threshold and the abnormal time.

[0054] S104, calling the generative big model, and the generative big model outputs an abnormal repair strategy for the abnormal business function according to the abnormal business function and abnormal information.

[0055] It can be understood that the generative big model refers to a generative model based on a large-scale corpus, which can be trained and generated in an end-to-end manner, and can provide question answers and service support based on the input content; in this embodiment, the generative big model can be trained based on abnormal business functions, abnormal information and corresponding abnormal repair strategies to obtain a trained generative big model.

[0056] Furthermore, the pre-trained generative big model is called, and the generative big model outputs the abnormal repair strategy of the abnormal business function according to the abnormal business function and abnormal information. Through the rich expression ability of the generative big model, a more complete abnormal repair strategy is obtained, thereby improving the efficiency of obtaining the abnormal repair strategy.

[0057] In this embodiment, sampling data of an application sent by a client is received, and at least one page corresponding to each business function is determined according to the function mapping information in the sampling data; abnormal function analysis is performed according to the aggregated sampling data of at least one page, abnormal business functions and abnormal information of the abnormal business functions are determined, and the generative big model is called to obtain the abnormal repair strategy of the abnormal business function. Through the rich expression ability of the generative big model, a more complete abnormal repair strategy is derived, an effective solution is provided for user interaction anomalies, and the user experience is improved.

[0058] Figure 2 FIG. 1 is a flow chart of another page monitoring method provided by an embodiment of the present disclosure. Figure 2 As shown, the method includes:

[0059] S201, receiving sample data of an application sent by a client.

[0060] In the embodiments of the present disclosure, the implementation method of step S201 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0061] S202: Determine at least one page belonging to the same business function according to the function mapping information.

[0062] In the embodiments of the present disclosure, the implementation method of step S202 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0063] S203: Count the aggregated sampling data of different users of the page to obtain a second statistical parameter set of the page.

[0064] Optionally, the aggregated sampling data may be the number of page clicks or the number of page displays; the second statistical parameter set of the page may include at least one of the standard deviation, variance, mean and minimum value corresponding to the page, that is, obtaining at least one of the standard deviation, variance, mean and minimum value of all aggregated sampling data of the page within a preset time period.

[0065] In some implementations, the preset time period may be 1 day, that is, the sum value, variance or standard deviation of the aggregated sampling data of the page within 1 day is obtained as the second statistical parameter set.

[0066] S204: Determine whether there are abnormal pages among the pages associated with the business function according to the second statistical parameter and the first statistical parameter set.

[0067] Optionally, sampling information of the sampled data can be determined, and historical sampling data can be determined based on the sampling information of the sampled data; the sampling information can be information such as sampling position or sampling frequency, and historical sampling data can be determined based on the same sampling information. The historical sampling data can be sampling data of pages from the previous day and previous consecutive days.

[0068] Furthermore, based on the historical sampling data, the historical aggregated sampling data of the pages associated with each business function is determined, that is, the historical usage data of all users of the page corresponding to each business function is aggregated to obtain the historical aggregated sampling data; the historical aggregated sampling data of the pages associated with the business functions are statistically analyzed to determine the first statistical parameter set for anomaly evaluation of the business functions, and the first statistical parameter set may include at least one of the standard deviation, variance, mean and minimum value corresponding to the page; the first statistical parameter set is calculated using the historical sampling data as a reference parameter to more accurately determine the data fluctuation situation and ensure the accuracy of anomaly identification.

[0069] In some implementations, for each page, the abnormality judgment condition of each type of statistical parameter can be obtained; for example, assuming that the statistical parameter is the mean, standard deviation or variance, the abnormality judgment condition can be to determine whether the second statistical parameter of the page of the day exceeds the first statistical parameter by a certain multiple, and the multiple is a set value, for example, 2, then satisfying the abnormality judgment condition means that the mean, standard deviation or variance of the page of the day exceeds 2 times the mean, standard deviation or variance of the historical aggregated sampling data; assuming that the statistical parameter is the minimum value, the abnormality judgment condition can be to determine whether the deviation between the minimum value of the second statistical parameter set and the minimum value of the first statistical parameter set is greater than a threshold value, for example, the threshold value is 10, then satisfying the abnormality judgment condition means that the deviation between the minimum value of the second statistical parameter set and the minimum value of the first statistical parameter set is greater than 10.

[0070] In response to the existence of at least one type of statistical parameter in the first statistical parameter set that meets the corresponding abnormality judgment condition, the page is determined to be an abnormal page; that is, any one of the characteristic values ​​​​of the mean, standard deviation, variance or minimum value meets the abnormality judgment condition, and the page is an abnormal page with large click or display fluctuations, thereby improving the accuracy of abnormality analysis.

[0071] S205 , in response to the existence of an abnormal page, determining that the business function is an abnormal business function, and determining abnormal information of the abnormal business function according to abnormal information of the abnormal page.

[0072] In some implementations, abnormal alarm information of the application can be generated based on the abnormal business function and the corresponding abnormal information. The abnormal alarm information includes at least the abnormal business function and the abnormal information, and can be sent to the corresponding user device via email for reminder, so as to obtain the abnormal situation in time for processing.

[0073] In the embodiments of the present disclosure, the implementation method of step S205 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0074] S206, calling the generative big model, and the generative big model outputs an abnormal repair strategy for the abnormal business function according to the abnormal business function and the abnormal information.

[0075] In the embodiments of the present disclosure, the implementation method of step S206 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0076] In this embodiment, sampling data of an application sent by a client is received, and at least one page belonging to the same business function is determined based on the function mapping information of the sampling data, and the aggregated sampling data of different users of the page are counted to obtain a second statistical parameter set of the page, and the second statistical parameter set is used as a characteristic parameter of the user's aggregated sampling data for abnormality analysis, and the first statistical parameter set is obtained through historical aggregated sampling data, and the first statistical parameter set is used as a reference characteristic parameter. Through comparative analysis between the current characteristic parameter and the reference characteristic parameter, it is determined whether the abnormality judgment condition is met, thereby improving the accuracy of the abnormality analysis; the abnormal page is determined based on whether the abnormality judgment condition is met, and the abnormal business function and the abnormal information of the abnormal business function are determined based on the business function of the abnormal page, providing an accurate basis for generating an abnormality repair strategy, and ensuring the stability and accuracy of subsequent abnormality repair strategy generation.

[0077] Figure 3 FIG. 1 is a flow chart of another page monitoring method provided by an embodiment of the present disclosure. Figure 3 As shown, the method includes:

[0078] S301, receiving sample data of an application sent by a client.

[0079] In the embodiments of the present disclosure, the implementation method of step S301 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0080] S302: Determine at least one page belonging to the same business function according to the function mapping information.

[0081] In the embodiments of the present disclosure, the implementation method of step S302 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0082] S303: Count the aggregated sampling data of different users of the page to obtain a second statistical parameter set of the page.

[0083] In the embodiments of the present disclosure, the implementation method of step S303 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0084] S304: Determine whether there are abnormal pages among the pages associated with the business function according to the second statistical parameter and the first statistical parameter set.

[0085] In the embodiments of the present disclosure, the implementation method of step S304 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0086] S305 , in response to the existence of an abnormal page, determining that the business function is an abnormal business function, and determining abnormal information of the abnormal business function according to abnormal information of the abnormal page.

[0087] In the embodiments of the present disclosure, the implementation method of step S305 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0088] S306, calling the generative large model to obtain the repair prompt word prompt.

[0089] Optionally, the repair prompt may include an abnormal business function and abnormal information of the abnormal business function, for example, the repair prompt is a decrease in search result points, and a repair idea is given; wherein the abnormal business function is a search function, and the abnormal information is a decrease in search result points.

[0090] S307, input the repair prompt, abnormal business function and abnormal information into the large model.

[0091] It is understandable that the abnormal business functions and abnormal information include more detailed and specific abnormal situations, so that the large model can output more accurate abnormal repair strategies based on the more detailed and specific abnormal information.

[0092] S308, the big model generates an exception repair strategy based on the abnormal business function and abnormal information combined with the pre-configured output reference example.

[0093] In some implementations, the preconfigured output reference example refers to a reasonable anomaly repair strategy output template, that is, a more comprehensive and detailed anomaly repair strategy is output based on the output reference example as a reference template.

[0094] In some implementations, after determining the exception repair strategy, the abnormal page in the abnormal business function can be determined; the abnormal page exception repair information can be extracted from the exception repair strategy; and a page repair task for the abnormal page can be generated based on the abnormal page and the corresponding exception repair information; that is, the exception repair information related to the abnormal page is determined from the exception repair strategy, and then a page repair task is generated based on the abnormal page and the corresponding exception repair information, and the page repair task is used to perform exception repair on the abnormal page based on the exception repair information.

[0095] Furthermore, a page repair task is sent to the first associated object corresponding to the abnormal page; the first associated object may be the R&D personnel corresponding to the abnormal page. The page repair task is sent to the first associated object, and the first associated object executes the page repair task to complete the repair of the abnormal page, thereby ensuring the repair effect of the abnormal page and improving the efficiency of abnormal repair.

[0096] In some implementations, a repair task set for abnormal business functions can also be generated based on the abnormal repair strategy; that is, the repair task for the abnormal page corresponding to each abnormal business function is generated based on the abnormal repair strategy, and the repair task set is composed of the repair tasks of all abnormal pages corresponding to the abnormal business function.

[0097] A repair task set is sent to the second associated object corresponding to the abnormal business function, and the tasks in the repair business set are distributed to the first associated object corresponding to the abnormal page through the second associated object; the second associated object may be the management personnel corresponding to the abnormal business function, and the management personnel distribute the repair tasks of the abnormal pages of the abnormal business functions to the corresponding first associated objects, that is, the corresponding R&D personnel to execute the repair tasks, so as to repair the abnormal pages, uniformly allocate the abnormal tasks, more clearly control the page repair tasks, and improve the efficiency of abnormal repair.

[0098] In this embodiment, sampling data of an application sent by a client is received, and at least one page belonging to the same business function is determined based on the function mapping information of the sampling data, and the aggregated sampling data and historical aggregated sampling data of different users of the page are respectively counted to obtain corresponding feature parameters, and whether the abnormality judgment condition is met is determined by comparative analysis between the current feature parameters and the reference feature parameters, thereby improving the accuracy of the abnormality analysis; after determining that an abnormal page exists, a repair prompt is obtained, and the abnormal business function, abnormal information and repair prompt are input into the large model to generate an abnormal repair strategy, thereby improving the accuracy and efficiency of the generation of the abnormal repair strategy; based on the abnormal repair strategy, a corresponding page repair task or a repair task set of the abnormal business function is generated, and sent to the corresponding associated object for execution of the repair task, so as to realize the repair of the abnormal page, thereby ensuring the repair effect of the abnormal page and improving the efficiency of the abnormal repair.

[0099] Figure 4 FIG. 1 is a flow chart of another page monitoring method provided by an embodiment of the present disclosure. Figure 4 As shown, the method includes:

[0100] S401, globally monitoring the application pages.

[0101] Optionally, you can embed data in all pages of the application to achieve global monitoring; embedding means adding data tracking code to every page and interaction of the application to collect user behavior and usage and obtain detailed data.

[0102] S402, aggregate the operations performed by the user on the same page within a set time unit to obtain aggregated sampling data of the user on the same page.

[0103] In some implementations, the set time unit can be 1 day or multiple days. If the set time unit is 1 day, the user's operations on the same page within 1 day are aggregated to obtain aggregated sampling data of the user on the same page; the user operations may include page clicks and page displays, etc.

[0104] S403, obtaining function mapping information corresponding to the same page.

[0105] Optionally, a page identifier or page code may be obtained, and function mapping information corresponding to the page may be determined based on a mapping relationship between the page identifier or page code and the function, where the function may include marketing, display or search functions, and the function mapping information of the page includes at least the function corresponding to the page.

[0106] In some implementations, the importance and urgency of exception identification can also be determined based on the function mapping information. For example, in related function pages such as advertising marketing or cash registers, when an exception occurs on the page, it will directly affect business transactions. Therefore, the corresponding exception identification is more urgent and requires urgent processing.

[0107] S404: Generate sampling data of the application program according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page.

[0108] It is understandable that the sampling data of the application includes aggregated sampling data and function mapping information, so as to analyze page anomalies with more comprehensive sampling data and improve the reliability of anomaly identification.

[0109] S405: Send the sampled data of the application to the server.

[0110] In this embodiment, the pages of the application are fully monitored by a full-embedded point method to obtain more comprehensive aggregated sampling data, and the function mapping information of the page is further obtained. The function mapping information and the aggregated sampling data are combined into the sampling data of the application, and the sampling data is sent to the server, so that the server can analyze the page anomalies with more comprehensive sampling data, thereby improving the reliability of anomaly identification and analysis.

[0111] Figure 5 FIG. 1 is a flow chart of another page monitoring method provided by an embodiment of the present disclosure. Figure 5 As shown, the method includes:

[0112] S501, globally monitoring the application pages.

[0113] In the embodiments of the present disclosure, the implementation method of step S501 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0114] S502, aggregate the operations performed by the user on the same page within a set time unit to obtain aggregated sampling data of the user on the same page.

[0115] In the embodiments of the present disclosure, the implementation method of step S502 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0116] S503, determining the code class of the same page, and determining the target business component to which the code class belongs.

[0117] In some implementations, the code class of a page can reflect basic attribute information of the page, and the target business component to which it belongs is determined according to the code information in the code class. The business component is a component corresponding to different business functions.

[0118] S504: Generate function mapping information corresponding to the same page according to the code class and the target business component.

[0119] Optionally, a function query can be performed based on the code class and the target business component, for example, the function mapping information corresponding to the code class and the target business component can be queried from a mapping file, where the mapping file can be a file that automatically maps the class and function generated by placing the code class into its corresponding target business component and performing binary packaging.

[0120] S505 , generating sampling data of the application program according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page.

[0121] In the embodiments of the present disclosure, the implementation method of step S505 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0122] S506: Send the sampled data of the application to the server.

[0123] Optionally, the aggregated sampling data of some users can be sampled from the aggregated sampling data corresponding to the same page as the target aggregated sampling data; in some implementations, the aggregated sampling data of some users can be randomly sampled or sampled according to pre-configured rules as the target sampling data.

[0124] According to the target aggregated sampling data and the function mapping information corresponding to the same page, the sampling data of the application is generated and sent to the server, so as to avoid the sampling data occupying a large amount of memory and increasing the storage burden, and reduce the storage cost of the server.

[0125] In this embodiment, the pages of the application are fully monitored by means of full embedding points, so as to obtain more comprehensive aggregated sampling data, and the accurate function mapping information of the page is determined according to the code class and the target business component to which the code class belongs. The function mapping information reflects the urgency of abnormal identification and improves the recognition effect of abnormal identification. The aggregated sampling data of some users can also be randomly sampled from the aggregated sampling data as the target aggregated sampling data, and the sampling data of the application is generated and sent with the target aggregated sampling data and the function mapping information, so as to avoid the sampling data occupying a large amount of memory and increasing the storage burden, and reduce the storage cost of the server.

[0126] Based on the above embodiments, Figure 6 FIG. 1 is a logic diagram of a page monitoring method provided by an embodiment of the present disclosure. Figure 6 As shown, the client collects the user's page display and click operations to obtain the user's aggregated sampling data, randomly samples the user to obtain the target aggregated sampling data, generates sampling data in combination with the function mapping information and sends it to the server. The server stores the sampling data and performs an exception analysis. By calculating the statistical parameter set (minimum value, mean, variance or standard deviation), it analyzes whether the exception judgment conditions are met, thereby determining whether there is an abnormal page, and obtaining the abnormal business function and abnormal information corresponding to the abnormal page. The generative large model is called based on the abnormal business function and abnormal information to output the exception repair strategy and abnormal alarm, providing effective solutions to user interaction anomalies and improving user experience.

[0127] Figure 7 is a structural block diagram of a page monitoring device provided by an embodiment of the present disclosure. Figure 7 As shown, the page monitoring device 700 includes:

[0128] The receiving module 701 is used to receive the sampled data of the application sent by the client, where the sampled data includes the aggregated sampled data of the user on the same page and the function mapping information corresponding to the same page;

[0129] A first acquisition module 702 is used to determine at least one page belonging to the same business function according to the function mapping information;

[0130] The second acquisition module 703 is used to perform abnormal function analysis based on the aggregated sampling data of at least one page to determine the abnormal business function and abnormal information of the abnormal business function;

[0131] The calling module 704 is used to call the generative big model, and the generative big model outputs the abnormal repair strategy of the abnormal business function according to the abnormal business function and the abnormal information.

[0132] In some implementations, calling module 704 includes:

[0133] Get the repair prompt word prompt;

[0134] Input the repair prompt, abnormal business function and abnormal information into the big model;

[0135] The big model generates an exception repair strategy based on the abnormal business functions and abnormal information combined with pre-configured output reference examples.

[0136] In some implementations, calling module 704 further includes:

[0137] Identify abnormal pages in abnormal business functions;

[0138] Extract abnormal page abnormal repair information from the abnormal repair strategy;

[0139] A page repair task for the abnormal page is generated according to the abnormal page and the corresponding abnormal repair information, and the page repair task is sent to the first associated object corresponding to the abnormal page.

[0140] In some implementations, the calling means 704 further includes:

[0141] According to the abnormal repair strategy, a repair task set of the abnormal business function is generated; the repair task set is sent to the second associated object corresponding to the abnormal business function, and the tasks in the repair business set are distributed to the first associated object corresponding to the abnormal page through the second associated object.

[0142] In some implementations, the second acquisition module 703 further includes:

[0143] Determine sampling information of the sampling data, and determine historical sampling data according to the sampling information of the sampling data;

[0144] Based on the historical sampling data, determine the historical aggregate sampling data of the page associated with each business function;

[0145] Statistical analysis is performed on historical aggregated sampling data of pages associated with the business function to determine a first statistical parameter set for abnormality assessment of the business function.

[0146] In some implementations, the second acquisition module 703 includes:

[0147] Counting the aggregated sampling data of different users of the page to obtain a second statistical parameter set of the page;

[0148] According to the second statistical parameter and the first statistical parameter set, determining whether there are abnormal pages among the pages associated with the business function;

[0149] In response to the existence of the abnormal page, the business function is determined to be an abnormal business function, and abnormal information of the abnormal business function is determined according to abnormal information of the abnormal page.

[0150] In some implementations, after determining the abnormal business function and the abnormal information of the abnormal business function, the apparatus 700 further includes:

[0151] Generate application exception alarm information based on abnormal business functions and corresponding abnormal information.

[0152] In some implementations, the statistical parameter set includes at least one of a standard deviation, a variance, a mean, and a minimum value corresponding to the page, and the apparatus 700 further includes:

[0153] For each page, obtain the abnormality judgment conditions for each type of statistical parameter;

[0154] In response to at least one type of statistical parameter in the first statistical parameter set satisfying a corresponding abnormality determination condition, the page is determined to be an abnormal page.

[0155] In this embodiment, sampling data of an application sent by a client is received, and at least one page corresponding to each business function is determined according to the function mapping information in the sampling data; abnormal function analysis is performed according to the aggregated sampling data of at least one page, abnormal business functions and abnormal information of the abnormal business functions are determined, and the generative big model is called to obtain the abnormal repair strategy of the abnormal business function. Through the rich expression ability of the generative big model, a more complete abnormal repair strategy is derived, an effective solution is provided for user interaction anomalies, and the user experience is improved.

[0156] Figure 8 is a structural block diagram of another page monitoring device provided by an embodiment of the present disclosure. Figure 8 As shown, the page monitoring device 800 includes:

[0157] Monitoring module 801, used for global monitoring of application pages;

[0158] The first acquisition module 802 is used to aggregate the operations performed by the user on the same page within a set time unit to obtain aggregated sampling data of the user on the same page;

[0159] The second acquisition module 803 is used to acquire function mapping information corresponding to the same page;

[0160] The third acquisition module 804 is used to generate sampling data of the application program according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page;

[0161] The sending module 805 is used to send the sampled data of the application to the server.

[0162] In some implementations, the second acquisition module 803 includes:

[0163] Determine the code class of the same page and the target business component to which the code class belongs;

[0164] Generate the function mapping information corresponding to the same page based on the code class and target business component.

[0165] In some implementations, the sending module 805 includes:

[0166] From the aggregate sampling data corresponding to the same page, sample the aggregate sampling data of some users as the target aggregate sampling data;

[0167] The sampling data of the application is generated according to the target aggregated sampling data and the function mapping information corresponding to the same page.

[0168] In this embodiment, the pages of the application are fully monitored by a full-embedded point method to obtain more comprehensive sampling data, and the user's operations on the same page are aggregated to obtain aggregated sampling data, and the function mapping information of the page is further obtained. The function mapping information and the aggregated sampling data are combined to form the sampling data of the application, and the sampling data is sent to the server, so that the server can analyze the page anomalies with more comprehensive sampling data, thereby improving the reliability of anomaly identification and analysis.

[0169] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0170] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0171] Fig. 9 A schematic block diagram of an electronic device for implementing an embodiment of the present disclosure 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 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0172] like Fig. 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0173] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0174] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as the page monitoring method. For example, in some embodiments, the page monitoring method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the page monitoring method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the page monitoring method in any other appropriate manner (e.g., by means of firmware).

[0175] 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0176] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0177] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0178] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer 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 can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0179] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0180] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0181] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0182] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A page monitoring method, wherein: The method comprises: Receive sampling data of an application sent by a client, wherein the sampling data includes aggregated sampling data of a user on a same page and function mapping information corresponding to the same page; Determine, according to the function mapping information, at least one page belonging to the same business function; Performing abnormal function analysis based on the aggregated sampling data of the at least one page to determine abnormal business functions and abnormal information of the abnormal business functions; The generative big model is called, and the generative big model outputs an abnormal repair strategy for the abnormal business function according to the abnormal business function and the abnormal information.

2. The method according to claim 1, wherein: The generative big model outputs an abnormal repair strategy for the abnormal business function according to the abnormal business function and the abnormal information, including: Get the repair prompt word prompt; Inputting the repair prompt, the abnormal business function and the abnormal information into the large model; The big model generates the exception repair strategy according to the abnormal business function and the abnormal information in combination with a pre-configured output reference example.

3. The method according to claim 2, wherein: After outputting the abnormal repair strategy of the abnormal business function, the method further includes: Determine an abnormal page in the abnormal business function; Extracting the abnormal page abnormality repair information from the abnormality repair strategy; A page repair task of the abnormal page is generated according to the abnormal page and the corresponding abnormal repair information, and the page repair task is sent to a first associated object corresponding to the abnormal page.

4. The method according to claim 2, wherein: After outputting the abnormal repair strategy of the abnormal business function, the method further includes: According to the abnormal repair strategy, a repair task set for the abnormal business function is generated; the repair task set is sent to the second associated object corresponding to the abnormal business function, and the tasks in the repair business set are distributed to the first associated object corresponding to the abnormal page through the second associated object.

5. The method according to any one of claims 1 to 4, wherein: Before performing abnormal function analysis according to the aggregated sampling data of the at least one page to determine the abnormal business function and abnormal information of the abnormal business function, the method further includes: Determine sampling information of the sampling data, and determine historical sampling data according to the sampling information of the sampling data; Determine the historical aggregated sampling data of the page associated with each business function according to the historical sampling data; Statistical analysis is performed on historical aggregated sampling data of the pages associated with the business function to determine a first statistical parameter set for abnormality assessment of the business function.

6. The method according to claim 5, wherein: The performing abnormal function analysis according to the aggregated sampling data of the at least one page to determine the abnormal business function and abnormal information of the abnormal business function includes: Performing statistics on the aggregated sampling data of different users of the page to obtain a second statistical parameter set of the page; According to the second statistical parameter and the first statistical parameter set, determining whether there is an abnormal page among the pages associated with the business function; In response to the existence of the abnormal page, the business function is determined to be an abnormal business function, and abnormal information of the abnormal business function is determined according to abnormal information of the abnormal page.

7. The method according to any one of claims 4 to 6, wherein: After determining the abnormal business function and the abnormal information of the abnormal business function, the method further includes: Generate abnormal alarm information of the application program according to the abnormal business function and the corresponding abnormal information.

8. The method according to claim 5 or 6, wherein: The statistical parameter set includes at least one of a standard deviation, a variance, a mean, and a minimum value corresponding to the page, and the method further includes: For each page, obtain the abnormality judgment conditions for each type of statistical parameter; In response to at least one type of statistical parameter in the first statistical parameter set satisfying a corresponding abnormality determination condition, the page is determined to be an abnormal page.

9. A page monitoring method, wherein: The method comprises: Global monitoring of application pages; Aggregate the user's operations on the same page within a set time unit to obtain aggregated sampling data of the user on the same page; Obtaining function mapping information corresponding to the same page; generating sampling data of the application according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page; Sending the sampled data of the application to the server.

10. The method according to claim 9, characterized in that The obtaining the function mapping information corresponding to the same page includes: Determine the code class of the same page, and determine the target business component to which the code class belongs; Function mapping information corresponding to the same page is generated according to the code class and the target business component.

11. The method according to claim 9 or 10, characterized in that: The sending the sampled data of the application to the server includes: From the aggregated sampling data corresponding to the same page, sampling the aggregated sampling data of some users as target aggregated sampling data; The sampling data of the application is generated according to the target aggregated sampling data and the function mapping information corresponding to the same page.

12. A page monitoring device, comprising: A receiving module, configured to receive sampled data of an application sent by a client, wherein the sampled data includes aggregated sampled data of a user on a same page and function mapping information corresponding to the same page; A first acquisition module, configured to determine at least one page belonging to the same business function according to the function mapping information; A second acquisition module is used to perform abnormal function analysis based on the aggregated sampling data of the at least one page to determine abnormal business functions and abnormal information of the abnormal business functions; The calling module is used to call the generative big model, and the generative big model outputs the abnormal repair strategy of the abnormal business function according to the abnormal business function and the abnormal information.

13. The device according to claim 12, wherein: The calling module comprises: Get the repair prompt word prompt; Inputting the repair prompt, the abnormal business function and the abnormal information into the large model; The big model generates the exception repair strategy according to the abnormal business function and the abnormal information in combination with a pre-configured output reference example.

14. The device according to claim 13, wherein: The calling module further includes: Determine an abnormal page in the abnormal business function; Extracting the abnormal page abnormality repair information from the abnormality repair strategy; A page repair task of the abnormal page is generated according to the abnormal page and the corresponding abnormal repair information, and the page repair task is sent to a first associated object corresponding to the abnormal page.

15. The device according to claim 13, wherein: The calling device further includes: According to the abnormal repair strategy, a repair task set for the abnormal business function is generated; the repair task set is sent to the second associated object corresponding to the abnormal business function, and the tasks in the repair business set are distributed to the first associated object corresponding to the abnormal page through the second associated object.

16. The device according to any one of claims 12 to 15, wherein: The second acquisition module further includes: Determine sampling information of the sampling data, and determine historical sampling data according to the sampling information of the sampling data; Determine the historical aggregated sampling data of the page associated with each business function according to the historical sampling data; Statistical analysis is performed on historical aggregated sampling data of the pages associated with the business function to determine a first statistical parameter set for abnormality assessment of the business function.

17. The device according to claim 16, wherein: The second acquisition module includes: Performing statistics on the aggregated sampling data of different users of the page to obtain a second statistical parameter set of the page; According to the second statistical parameter and the first statistical parameter set, determining whether there is an abnormal page among the pages associated with the business function; In response to the existence of the abnormal page, the business function is determined to be an abnormal business function, and abnormal information of the abnormal business function is determined according to abnormal information of the abnormal page.

18. The device according to any one of claims 15 to 17, wherein: After determining the abnormal service function and the abnormal information of the abnormal service function, the device further includes: Generate abnormal alarm information of the application program according to the abnormal business function and the corresponding abnormal information.

19. The method according to claim 16 or 17, wherein: The statistical parameter set includes at least one of a standard deviation, a variance, a mean, and a minimum value corresponding to the page, and the device further includes: For each page, obtain the abnormality judgment conditions for each type of statistical parameter; In response to at least one type of statistical parameter in the first statistical parameter set satisfying a corresponding abnormality determination condition, the page is determined to be an abnormal page.

20. A page monitoring device, comprising: Monitoring module, used to globally monitor the application pages; A first acquisition module is used to aggregate the operations of the user on the same page within a set time unit to obtain aggregated sampling data of the user on the same page; A second acquisition module is used to acquire function mapping information corresponding to the same page; A third acquisition module, configured to generate sampling data of the application program according to the aggregated sampling data of the user on the same page and the function mapping information corresponding to the same page; The sending module is used to send the sampled data of the application to the server.

21. The device according to claim 20, characterized in that The second acquisition module includes: Determine the code class of the same page, and determine the target business component to which the code class belongs; Function mapping information corresponding to the same page is generated according to the code class and the target business component.

22. The device according to claim 20 or 21, characterized in that The sending module comprises: From the aggregated sampling data corresponding to the same page, sampling the aggregated sampling data of some users as target aggregated sampling data; The sampling data of the application is generated according to the target aggregated sampling data and the function mapping information corresponding to the same page.

23. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8 or 9-11.

24. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8 or 9-11.

25. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-8 or 9-11.

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