Page monitoring method and device, computer equipment and storage medium

Abstract

The invention provides a page monitoring method and device, computer equipment and a storage medium. The method comprises the steps of obtaining page snapshots and timestamps corresponding to the page snapshots; obtaining a feature vector corresponding to each page snapshot; sorting each feature vector according to a plurality of timestamps; obtaining a page monitoring model, wherein the model comprises a plurality of neuron nodes which are connected in series to form a neuron queue; and inputting each feature vector into a neuron node of a neuron queue, analyzing the received feature vector by each neuron node, outputting a page monitoring result corresponding to the feature vector, and inputting the page monitoring result into a next neuron node in the neuron queue for updating, the last neuron node in the neuron queue outputs a final monitoring result; if it is determined that the application program has the page exception, monitoring is completed according to the exception type corresponding to the page exception; the exception type comprises any one of continuous blank of the page and an exceptional block structure of the page.

Description

Technical Field

[0001] The present application relates to the field of software monitoring, which is applied to the financial field, and in particular to a page monitoring method, device, computer equipment and storage medium. Background Art

[0002] In the financial field, the pages of financial applications are generally sensitive and have zero tolerance for error messages. The applications corresponding to the current major financial systems all contain banners, diamond areas, and various classified sub-pages, which makes the pages of financial applications very heavy and complicated. Furthermore, if the developers and operators are not careful, they may write some wrong information or configuration, resulting in more obscure error messages on the page. In addition, due to negligence, the production line is released, allowing users of financial system applications to see error messages when opening the page, affecting the user experience.

[0003] Therefore, there is an urgent need for a page monitoring method for financial systems, focusing on abnormal information monitoring of financial application pages to improve and optimize the user experience. Summary of the invention

[0004] This application provides a page monitoring method, device, computer equipment and storage medium, which aims to solve the current application programs corresponding to major financial systems, which all contain banner positions, diamond areas and various classified subordinate pages, resulting in the pages of financial application programs being very heavy and complicated. Furthermore, if the developers and operators are not careful in operation, they may write some wrong information or configuration, resulting in more obscure error messages on the page, and due to negligence, the release production line causes users of financial system applications to see error messages when opening the page, affecting the user experience.

[0005] In a first aspect, the present application provides a page monitoring method, comprising:

[0006] In the application to be monitored, obtain page snapshots and the timestamp corresponding to each page snapshot;

[0007] Perform feature extraction on each page snapshot to obtain the feature vector corresponding to each page snapshot;

[0008] Sort the feature vectors corresponding to each page snapshot according to multiple timestamps;

[0009] Obtain a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue;

[0010] Each feature vector is input into a neuron node in the neuron queue in sequence. Each neuron node parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating. The last neuron node in the neuron queue outputs the final monitoring result.

[0011] If it is determined according to the final monitoring result that the application has a page anomaly, the monitoring of the application is completed according to the anomaly type corresponding to the page anomaly; wherein the anomaly type includes at least one of a continuous blank page and an abnormal block structure of the page.

[0012] In some embodiments, before obtaining a page snapshot and a timestamp corresponding to each page snapshot in an application to be monitored, it also includes: obtaining the program type and format type of the application; determining multiple sensitive behavior types based on the program type; generating a behavior tracking point corresponding to each sensitive behavior type based on the format type; and adding each behavior tracking point to the application to obtain a page snapshot and a timestamp when the behavior tracking point is triggered.

[0013] In some embodiments, before obtaining a pre-built page monitoring model, it also includes: obtaining multiple historical page snapshots and the timestamp corresponding to each historical page snapshot; wherein the historical page snapshots include normal page snapshots and abnormal page snapshots; determining the first sample number and the second sample number according to a preset ratio; constituting training samples based on the first sample number of normal page snapshots and the second sample number of abnormal page snapshots; completing the training of the page monitoring model to be trained according to the historical anomaly types corresponding to the training samples and the abnormal page snapshots, and obtaining the constructed page monitoring model.

[0014] In some embodiments, based on the exception type corresponding to the page exception, it includes: determining the monitoring time corresponding to the final monitoring result based on multiple timestamps corresponding to the final monitoring result; if it is determined based on the normalized final monitoring result and the monitoring time that the page blank time corresponding to the application is greater than the preset time, the exception type is determined to be that the page is continuously blank; if the value corresponding to the normalized final monitoring result is not within the preset range, the exception type is determined to be that the page has an abnormal block structure.

[0015] In some embodiments, before determining the abnormality type corresponding to the page abnormality, the method further includes: obtaining multiple final monitoring results corresponding to the application; obtaining a result vector corresponding to each final monitoring result; obtaining a weight matrix corresponding to each result vector; normalizing each result vector according to a preset normalization formula and a weight matrix, so as to determine the abnormality type corresponding to each final monitoring result according to the corresponding value after normalization of each final monitoring result; wherein the expression of the normalization formula includes:

[0016]

[0017] Among them, Y is the corresponding value of the normalized final monitoring result, n is the number of final monitoring results, U is the weight matrix, and X is the result vector.

[0018] In some embodiments, feature extraction is performed on each page snapshot to obtain a feature vector corresponding to each page snapshot, including: decomposing the page snapshot according to a preset page element type, obtaining multiple page element information and the position information corresponding to each page element information in the page snapshot; wherein the page element type includes at least images, texts and buttons; if the page element type corresponding to the page element information is an image, the visual features corresponding to the page element information are obtained, and a feature vector is generated according to the visual features and the corresponding position information; if the page element type corresponding to the page element information is text, the text features corresponding to the page element information are obtained, and a feature vector is generated according to the text features and the corresponding position information; if the page element type corresponding to the page element information is a button, the interactive features corresponding to the page element information are obtained, and a feature vector is generated according to the interactive features and the corresponding position information.

[0019] In some embodiments, after completing the monitoring of the application according to the exception type corresponding to the page exception, it also includes: parsing the final monitoring result to obtain the exception timestamp corresponding to the page exception; matching the timestamps of multiple page snapshots corresponding to the exception timestamp and the final monitoring result, and determining the page snapshot corresponding to the successfully matched timestamp as the target page snapshot; inputting the exception type and the target page snapshot into a preset page repair model, the page repair model parses the exception type and the target page snapshot, and outputs a repair plan corresponding to the page exception; and completing the page exception repair of the application according to the repair plan.

[0020] In a second aspect, the present application provides a page monitoring device, comprising:

[0021] A snapshot acquisition unit, used to acquire page snapshots and a timestamp corresponding to each page snapshot in the application to be monitored;

[0022] A feature extraction unit, used to extract features from each page snapshot and obtain a feature vector corresponding to each page snapshot;

[0023] A vector sorting unit, used to sort the feature vectors corresponding to each page snapshot according to multiple timestamps;

[0024] A model acquisition unit is used to acquire a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue;

[0025] A result output unit is used to sequentially input each feature vector into a neuron node in the neuron queue, each neuron node parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating, and the last neuron node in the neuron queue outputs the final monitoring result;

[0026] The monitoring completion unit is used to complete the monitoring of the application program according to the exception type corresponding to the page exception if it is determined according to the final monitoring result that the application program has a page exception; wherein the exception type includes at least one of a continuous blank page and an abnormal block structure of the page.

[0027] In a third aspect, the present application further provides a computer device, including:

[0028] Memory and processor;

[0029] The memory is used to store computer programs;

[0030] The processor is used to execute the computer program and implement the steps of the page monitoring method described in the first aspect when executing the computer program.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the steps of the page monitoring method described in the first aspect above.

[0032] The present application provides a page monitoring method, device, computer equipment and storage medium. The method first obtains snapshots of pages in financial applications regularly or when necessary, and records corresponding timestamps for each snapshot. This helps to monitor changes in pages at different time points. Then, feature extraction is performed on each page snapshot to generate feature vectors. These vectors capture the structural and content features of the page by analyzing the visual and textual content of the page. Then, the feature vectors are sorted according to the timestamp to ensure that the page changes are analyzed in the order in which they occur. A pre-built page monitoring model is further used for analysis. The model consists of a plurality of serially connected neuron nodes to form a neuron queue. Each feature vector is sequentially input into the neuron queue, and each neuron node parses and outputs the page monitoring result of the feature vector, which is then input into the next node for updating. The last node of the neuron queue outputs the final monitoring result. Finally, the final monitoring result is used to determine whether the page is abnormal. If there is an abnormality, the application is monitored and processed according to the abnormality type (such as continuous blankness of the page or abnormal block structure) to improve the reliability of the page. The method ensures that the financial system application maintains a high-quality user experience in complex pages through structured monitoring and analysis.

[0033] The provided method has the following beneficial effects:

[0034] 1. Improve page stability and reliability: Avoid poor user experience caused by unintentional errors by developers or operators by timely detecting potential problems in the page.

[0035] 2. Automated monitoring and real-time response: The automated monitoring process can greatly reduce the burden of manual inspection, improve detection efficiency and accuracy, and enable any abnormal situation to be responded to and repaired quickly.

[0036] 3. Reduce user impact: Detect and fix problems before they affect users' normal use, reducing user churn and complaints caused by incorrect information.

[0037] 4. Accurate problem location and resolution: Based on the detailed classification of exception types, the problem can be identified more accurately, helping the team to quickly locate and resolve the problem. This approach ensures that financial system applications maintain a high-quality user experience in complex pages through structured monitoring and analysis.

[0038] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is a schematic flow chart of the steps of a page monitoring method provided by an embodiment of the present application;

[0041] Figure 2 is a schematic flow chart of the steps of a method for obtaining an exception type provided by an embodiment of the present application;

[0042] Figure 3 is a schematic flow chart of the steps of a method for obtaining a feature vector provided in an embodiment of the present application;

[0043] Figure 4 is a structural diagram of a page monitoring device provided in an embodiment of the present application;

[0044] Figure 5 It is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0048] It should be understood that, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the difference.

[0049] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0050] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0052] In the financial field, the pages of financial applications are generally sensitive and have zero tolerance for error messages. The applications corresponding to the current major financial systems all contain banners, diamond areas, and various classified sub-pages, which makes the pages of financial applications very heavy and complicated. Furthermore, if the developers and operators are not careful, they may write some wrong information or configuration, resulting in more obscure error messages on the page. In addition, due to negligence, the production line is released, allowing users of financial system applications to see error messages when opening the page, affecting the user experience.

[0053] Therefore, there is an urgent need for a page monitoring method for financial systems, focusing on abnormal information monitoring of financial application pages to improve and optimize the user experience.

[0054] To solve the above problems, please refer to Figure 1 , Figure 1 1 is a schematic flow chart of a page monitoring method provided in an embodiment of the present application. The page monitoring method can be implemented by a computer device, which can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0055] It should be noted that the acquisition of relevant information involved in the acquisition process of any information such as page snapshots by the provided method complies with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0056] At the same time, the provided method can also monitor the pages of applications in other fields, such as medical and educational applications where the pages are very heavy and complex, and the embodiments of the present application do not limit this.

[0057] Specifically, Figure 1As shown, the provided page monitoring method includes steps S101 to S106, which are described in detail as follows:

[0058] Step S101. In the application to be monitored, obtain page snapshots and a timestamp corresponding to each page snapshot.

[0059] Specifically, use tools or custom scripts to capture snapshots of application pages at different points in time. You may use a headless browser (such as Puppeteer or Sleen i um) to automatically obtain rendered screenshots or DOM structures of pages. Attach a timestamp accurate to milliseconds to each captured page snapshot to determine when it was obtained. This can help track the moment when changes occurred in subsequent analysis. For example, obtain the complete DOM structure and visual presentation of the login page once an hour, and automatically collect these page snapshots through scripts. This provides a detailed time record and retains the real-time page status at each system update or configuration change, allowing developers and operations personnel to trace back and compare historical status.

[0060] In some embodiments, before obtaining a page snapshot and a timestamp corresponding to each page snapshot in an application to be monitored, it also includes: obtaining the program type and format type of the application; determining multiple sensitive behavior types based on the program type; generating a behavior tracking point corresponding to each sensitive behavior type based on the format type; and adding each behavior tracking point to the application to obtain a page snapshot and a timestamp when the behavior tracking point is triggered.

[0061] Before implementing monitoring, identify the specific type of application (such as web application, mobile application, etc.) and content format (such as HTML5, Native App, etc.). This identification helps to set monitoring strategies more accurately in subsequent steps. According to the type of application, identify the "sensitive behaviors" related to the program, that is, those important interactions or operations that may cause page abnormalities. These behaviors may include login, payment, transfer, data input, etc. According to the identified sensitive behavior types, define and configure corresponding tracking points to track the specific information when users perform these behaviors in the application. These tracking points need to be compatible with the application format to accurately capture the corresponding data. Integrate the above generated tracking points into the application. The tracking points are designed to be automatically triggered when sensitive behaviors occur, thereby generating page snapshots and corresponding timestamps. This design ensures that the monitoring system can capture the real-time status of the page at key interaction points.

[0062] For example, in the Web application scenario, the application type is "Financial Transaction System" and the format type is "HTML5". Sensitive behaviors include user login, fund transfer, investment operations, etc. Set a JavaScript tracking point for each sensitive behavior. When the user clicks the "Login" button or performs a "Transfer", the tracking point is triggered, and the page snapshot and timestamp are captured in real time for subsequent analysis.

[0063] By monitoring key user interactions in real time, we can ensure that possible abnormal conditions are captured immediately and improve the monitoring response speed and quality. By tracking sensitive behavior points, we avoid extensive monitoring of all user operations, thereby reducing system load and focusing on monitoring key areas and key operations. At the same time, by collecting accurate page status when anomalies and abnormal causes occur, detailed background information is provided for fault diagnosis and repair. When the abnormal monitoring mechanism is more accurate and effective, it ultimately reduces problems in the user experience and enhances user trust and satisfaction. The above-mentioned embodiment successfully achieves efficient monitoring of sensitive operations by strategically embedding behavioral tracking points in the application, avoiding the previous cumbersome comprehensive monitoring strategy and improving the value and effectiveness of monitoring.

[0064] Step S102: Extract features from each page snapshot to obtain a feature vector corresponding to each page snapshot.

[0065] Specifically, use OCR, image processing libraries or DOM parsing tools to extract important features from page snapshots. Features may include: text content (such as titles, button labels) UI element position and size (coordinates, pixel size) color and style (CSS rule application) unstructured data (pictures, icons) These feature data need to be converted into feature vectors in numerical form so that they can be input into the machine learning model. For example, by parsing the DOM, the position coordinates of all buttons on the page and their text content are extracted to form vectors such as [Btn1_Text, Btn1_CoordX, Btn1_CoordY,...]. By decomposing visual information and page structure into a processable digital form, the accuracy and applicability of subsequent detection algorithms are enhanced.

[0066] Step S103: Sort the feature vectors corresponding to each page snapshot according to multiple timestamps.

[0067] Specifically, by sorting the captured feature vectors according to their timestamps, it is possible to construct a time series-based dataset that is convenient for detecting trends and sudden changes. For example, by comparing the feature vectors of the same page at different times of the day, we can check whether there are abnormal patterns in the changes of these vectors, such as the disappearance of content or significant changes in layout during a certain period of time. It is easier to identify the specific time points and triggering conditions when the anomaly occurs, so that errors can be corrected quickly.

[0068] Step S104. Obtain a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue.

[0069] Specifically, use a deep learning framework (such as TensorFlow or PyTorch) to build a neural network model. It can be a model that focuses on pattern recognition and classification tasks, such as CNN, or a many-to-one model in RNN. For example, neuron nodes are connected through weighted connections, allowing complex feature mapping and learning of abnormal patterns, and providing adaptive detection capabilities for different pages. For example, build a pre-trained neural network model that focuses on learning page layout and visual features to identify normal and abnormal samples. By improving the automation level of the detection system and reducing manual intervention, the efficiency and accuracy of anomaly detection in large-scale systems can be improved.

[0070] In some embodiments, before obtaining a pre-built page monitoring model, it also includes: obtaining multiple historical page snapshots and the timestamp corresponding to each historical page snapshot; wherein the historical page snapshots include normal page snapshots and abnormal page snapshots; determining the first sample number and the second sample number according to a preset ratio; constituting training samples based on the first sample number of normal page snapshots and the second sample number of abnormal page snapshots; completing the training of the page monitoring model to be trained according to the historical anomaly types corresponding to the training samples and the abnormal page snapshots, and obtaining the constructed page monitoring model.

[0071] Collect multiple historical page snapshots and attach a timestamp to each snapshot. These snapshots include pages captured during normal operation, as well as pages recorded when anomalies occurred in the past.

[0072] The collected historical page snapshots are classified into two categories, "normal" and "abnormal", for the subsequent training process. According to the preset ratio (for example, 80% normal samples, 20% abnormal samples), the first number of samples (normal page snapshots) and the second number of samples (abnormal page snapshots) for training are calculated. According to the determined ratio, normal and abnormal pages are selected from the collected historical snapshots to form a model training sample set. This step ensures the representativeness and balance of the training data, especially when there is less abnormal data. Through appropriate proportion design, the model is prevented from being too biased towards the normal state. The model is trained using the selected training samples and the corresponding historical anomaly types. The model learns the feature differences between normal and abnormal pages. The anomaly type information is used to help the model refine its recognition capabilities for different abnormal conditions, such as distinguishing different abnormal block structures or page blank types.

[0073] For example, there are 1,000 normal pages and 100 abnormal pages in the historical data set. Set the sample ratio used in the training set to 80% normal and 20% abnormal, and select 800 normal pages and 100 abnormal pages for model training. Use deep learning technology, such as convolutional neural network (CNN), input the above training samples for multiple rounds of training. Adjust parameters according to training indicators (such as accuracy and recall) to optimize model performance.

[0074] By using rich historical data for training, the model can more accurately identify and distinguish between normal and abnormal page states. By using the specific types of past anomalies for training, the model can not only detect whether it is abnormal, but also identify the specific type of anomaly, thereby improving fault diagnosis capabilities. By setting a reasonable sample ratio and diversified abnormal samples, the model reduces the misjudgment of normal samples, reduces the false alarm rate, and improves the reliability of the monitoring system. New abnormal events can be added to the historical data after they occur, and the model can be continuously updated with the latest data to maintain the cutting-edge and dynamic adaptability of the model's detection capabilities. Through the above embodiments, the page monitoring model is fully trained with historical data before construction, which improves the adaptability and detection effect to different application scenarios.

[0075] Step S105. Input each feature vector into a neuron node in the neuron queue in sequence. Each neuron node parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating. The last neuron node in the neuron queue outputs the final monitoring result.

[0076] Specifically, by inputting each feature vector into the neuron queue in sequence, the intermediate results of each layer are calculated layer by layer, and finally the monitoring results are given by the output layer. Each intermediate node performs local feature analysis, which can highlight the changes in deeper features and refine the final judgment. For example, for each input feature vector, the model outputs "normal" or "abnormal", and can also output more fine-grained abnormal types, such as "blank module" or "misplaced element". Through layer-by-layer analysis, the learning ability of normal behavior and abnormal features of complex pages is enhanced, and the accuracy of system abnormality recognition is improved.

[0077] Step S106. If it is determined according to the final monitoring result that the application has a page anomaly, the application is monitored according to the anomaly type corresponding to the page anomaly; wherein the anomaly type includes at least one of a continuous blank page and an abnormal block structure on the page.

[0078] Specifically, based on the output of abnormal results, execute automated scripts or prompt operation and maintenance personnel to perform further manual analysis. Take appropriate repair and optimization measures for the identified abnormal types. Log abnormal events for future model training and adjustment. For example, when a page is detected to be continuously blank, an alarm is automatically triggered and the background log or network request link is checked. By reducing manual processing time, the system can ensure timely response and reduce user loss and trust crisis caused by page errors.

[0079] Steps S101 to S106 work together to identify and handle anomalies in a timely manner based on the continuous acquisition and analysis of page data, thereby improving the stability of financial applications and user experience.

[0080] In some embodiments, Figure 2 As shown, according to the exception type corresponding to the page exception, steps S106a to S106b are included.

[0081] Step S106a. Determine the monitoring duration corresponding to the final monitoring result according to multiple timestamps corresponding to the final monitoring result;

[0082] Step S106b. If the blank time of the page corresponding to the application is greater than the preset time according to the normalized final monitoring result and the monitoring time, the abnormality type is determined to be continuous blank page; if the value corresponding to the normalized final monitoring result is not within the preset range, the abnormality type is determined to be the presence of abnormal block structure on the page.

[0083] By collecting multiple timestamps corresponding to the final monitoring results, the length of time the monitoring activity lasts, i.e. the monitoring duration, is calculated. The final monitoring results are normalized. These normalized results can standardize the monitoring data of different indicators and time periods, making it easier to compare and analyze. By comparing the normalized monitoring results and monitoring duration, if the cumulative blank time of the page (the time when the page is loaded or rendered without content) exceeds the preset threshold during the monitoring duration, it is judged as a "continuous blank page" anomaly. This detection focuses on identifying the problem of a page displaying no content for a long time, which is a serious problem for user experience.

[0084] Observe the normalized monitoring results. If the result value deviates from the preset normal range, it indicates that the page has an abnormal block structure (such as unexpected blanks, misaligned or repeated modules).

[0085] For example, if the monitoring results show that the page has no content (blank) for 6 minutes in the past 10 minutes, which exceeds the preset 5-minute threshold, the abnormal type is determined to be "page continuously blank". At the same time, in the normalized results, the values ​​of certain structural indicators, such as layout integrity and module display ratio, are far away from the preset range (for example, [0.8, 1.2]), thus confirming the existence of "abnormal block structure" in the page, which may be due to failed ad loading or misplaced modules.

[0086] The above-mentioned embodiments improve the analysis of abnormal states of complex pages by subdividing specific abnormal types, making troubleshooting and resolution more targeted and efficient. Specifically, the detection of continuous blank pages ensures that users will not wait for a long time or leave due to the lack of content on the page, thereby enhancing the consistency of user experience. Through an intelligent mechanism based on monitoring time and normalized data, the false alarm phenomenon of normal pages is reduced, and the reliability and accuracy of the overall monitoring system are improved. At the same time, detailed analysis of abnormal types provides effective data support for subsequent automated debugging, helping the system to respond and adjust more quickly. Furthermore, through multi-layer data analysis, the detailed identification of different types of page anomalies is enhanced, which improves the practicality and diagnostic effect of page monitoring.

[0087] In some embodiments, before determining the abnormality type corresponding to the page abnormality, the method further includes: obtaining multiple final monitoring results corresponding to the application; obtaining a result vector corresponding to each final monitoring result; obtaining a weight matrix corresponding to each result vector; normalizing each result vector according to a preset normalization formula and a weight matrix, so as to determine the abnormality type corresponding to each final monitoring result according to the corresponding value after normalization of each final monitoring result; wherein the expression of the normalization formula includes:

[0088]

[0089] Among them, Y is the corresponding value of the normalized final monitoring result, n is the number of final monitoring results, U is the weight matrix, and X is the result vector.

[0090] First, multiple final monitoring results corresponding to the application are obtained, and a result vector is generated for each result. At the same time, for each result vector, the corresponding weight matrix is ​​obtained. The design of the weight matrix is ​​usually associated with the features contained in the result vector, so that different features can be given different influences during the normalization process. Each result vector is processed with a preset normalization formula. According to the value of each final monitoring result after normalization, the corresponding abnormal type is determined. The normalized results can help to see more clearly which values ​​deviate from the normal range and support accurate abnormality classification. Since the complex relationship and influence between multiple features are considered, the normalization method mentioned above can more accurately and fine-grainedly evaluate whether the monitoring results are normal or not. At the same time, by using adjustable weight matrices and formulas, this method is suitable for different application scenarios and monitoring indicators, and can maintain the accuracy of monitoring in a changing environment. Through fine normalization processing, the results can not only determine whether they are abnormal, but also clearly classify them into specific abnormal types, supporting developers to perform precise debugging and rapid repairs. This method has learning ability, and can gradually improve its own recognition ability through repeated training and adjustment of the weight matrix, showing higher adaptability to changing applications. The above examples greatly improve the analysis depth and accuracy of page monitoring results through complex mathematical processing and machine learning algorithms, providing support for more efficient exception management.

[0091] In some embodiments, Figure 3 The process of performing feature extraction on each page snapshot to obtain a feature vector corresponding to each page snapshot includes steps S102a to S102b.

[0092] Step S102a. Decomposing the page snapshot according to the preset page element type, obtaining a plurality of page element information and the corresponding position information of each page element information in the page snapshot; wherein the page element type includes at least an image, text and a button;

[0093] Step S102b. If the page element type corresponding to the page element information is an image, obtain the visual features corresponding to the page element information, and generate a feature vector based on the visual features and the corresponding position information; if the page element type corresponding to the page element information is text, obtain the text features corresponding to the page element information, and generate a feature vector based on the text features and the corresponding position information; if the page element type corresponding to the page element information is a button, obtain the interactive features corresponding to the page element information, and generate a feature vector based on the interactive features and the corresponding position information.

[0094] Each page snapshot is decomposed to identify and extract multiple elements in the page and their location information. These page elements include images, text, and buttons. Visual features of the image are extracted, such as color histogram, shape, texture, etc. Combined with the location information of the image in the page, a comprehensive feature vector is generated. Text features are extracted, such as word frequency, keywords, semantic information, etc. Combined with the location information of the text, a feature vector is generated. The interactive features of the button are extracted, including clickability, function description, user behavior data, etc. Feature vectors are also generated in combination with the location information. The feature vector generated for each type of page element contains a combination of the specific features of the element (visual, textual or interactive features) and location information, ensuring that the features of specific elements and their impact can be accurately located and identified in the analysis.

[0095] For example, for an image on a news website, extract its main color, edge detection contour information, and record its position on the page (such as at the top or bottom). Extract the most frequent words and sentiment analysis results (such as positive, negative) from the article part of the page, and combine them with the page area where the text is located. For several buttons such as "Submit" on the page, record their position, default state (clickable or disabled), and historical data of click events.

[0096] Through detailed feature extraction, the method can more accurately distinguish between normal and abnormal page states, especially the detection of subtle changes in complex pages. Including extensive analysis of visual, textual, and interactive features, it can cover the characteristics of most page elements and provide a more comprehensive portrait of page status. At the same time, it supports deeper user behavior analysis. For example, based on the interactive features of buttons, users' usage patterns and preferences can be inferred, which helps improve user interface design. This fine-grained feature vector generation method is applicable to a variety of business scenarios, from advertising monitoring to content management, and can provide strong support and optimization suggestions.

[0097] Through the above embodiments, the method can extract multi-level information from the page snapshot, providing a solid foundation for subsequent page anomaly identification and processing.

[0098] In some embodiments, after completing the monitoring of the application according to the exception type corresponding to the page exception, it also includes: parsing the final monitoring result to obtain the exception timestamp corresponding to the page exception; matching the timestamps of multiple page snapshots corresponding to the exception timestamp and the final monitoring result, and determining the page snapshot corresponding to the successfully matched timestamp as the target page snapshot; inputting the exception type and the target page snapshot into a preset page repair model, the page repair model parses the exception type and the target page snapshot, and outputs a repair plan corresponding to the page exception; and completing the page exception repair of the application according to the repair plan.

[0099] After completing the monitoring of page anomalies, parse the final monitoring results to obtain the anomaly timestamp corresponding to the page anomaly. Match the anomaly timestamp with the timestamps of multiple page snapshots recorded in the final monitoring results, and identify and determine the page snapshots corresponding to those successfully matched timestamps as the target page snapshots. Input the identified anomaly type and target page snapshot into the preset page repair model. The model is trained to parse the input anomaly type and page snapshot.

[0100] The page repair model outputs a repair plan for page anomalies according to the input content. The method automatically or assists in completing the repair operation of the application page anomaly based on the repair plan to ensure that the page recovers normal function and display.

[0101] For example, if the application detects a page loading anomaly at a specific time (e.g., 10:30:45), the specific page snapshot where the anomaly occurred is locked by analyzing the timestamp. The snapshot and the anomaly type (e.g., "page continues to be blank") are input into the page repair model. The model outputs suggestions based on the trained knowledge, such as adjusting the page loading logic or updating a specific resource file. And by obtaining repair measures, such as updating the resource file and redeploying, the page status is restored.

[0102] Through timestamp matching, it is possible to accurately locate which page snapshot caused the exception, supporting the rapid location and tracing of the problem. The use of repair models to automatically generate and implement repair plans greatly improves the efficiency of exception handling and reduces the need for manual intervention. Since exceptions can be quickly matched and repaired, user experience is guaranteed, and the availability and reliability of applications are improved. The page repair model can accumulate experience from past exceptions and repair plans through continuous "learning", gradually optimizing repair capabilities and the accuracy of suggestions. The workload of operation and maintenance personnel is reduced, allowing them to focus on more complex development issues rather than daily exception repairs. The described embodiment demonstrates an intelligent page exception repair mechanism, which greatly improves the timeliness and effectiveness of exception handling through automation and model-driven methods.

[0103] The proposed method first obtains snapshots of pages in financial applications at regular intervals or when necessary, and records the corresponding timestamp for each snapshot. This helps monitor page changes at different points in time. Then, feature extraction is performed on each page snapshot to generate feature vectors. These vectors capture the structural and content characteristics of the page by analyzing the visual and textual content of the page. The feature vectors are then sorted according to the timestamp to ensure that page changes are analyzed in the order in which they occur. Further analysis is performed using a pre-built page monitoring model. The model consists of multiple serially connected neuron nodes to form a neuron queue. Each feature vector is input into the neuron queue in turn, and each neuron node parses and outputs the page monitoring result of the feature vector, which is then input into the next node for update. The last node of the neuron queue outputs the final monitoring result. Finally, the final monitoring result is used to determine whether the page is abnormal. If there is an abnormality, the application is monitored and processed according to the type of abnormality (such as continuous blankness of the page or abnormal block structure) to improve the reliability of the page. Through structured monitoring and analysis, the method ensures that the financial system application maintains a high-quality user experience in complex page changes.

[0104] The provided method has the following beneficial effects:

[0105] 1. Improve page stability and reliability: Avoid poor user experience caused by unintentional errors by developers or operators by timely detecting potential problems in the page.

[0106] 2. Automated monitoring and real-time response: The automated monitoring process can greatly reduce the burden of manual inspection, improve detection efficiency and accuracy, and enable any abnormal situation to be responded to and repaired quickly.

[0107] 3. Reduce user impact: Detect and fix problems before they affect users' normal use, reducing user churn and complaints caused by incorrect information.

[0108] 4. Accurate problem location and resolution: Based on the detailed classification of exception types, the problem can be identified more accurately, helping the team to quickly locate and resolve the problem. This approach ensures that financial system applications maintain a high-quality user experience in complex pages through structured monitoring and analysis.

[0109] See also Figure 4 As shown, Figure 41 is a schematic diagram of the structure of the page monitoring device 200 provided in an embodiment of the present application. The page monitoring device 200 is used to execute the steps of the page monitoring method shown in the above embodiments. The page monitoring device 200 can be a single server or a server cluster, or the page monitoring device 200 can be a terminal, which can be a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0110] like Figure 4 As shown, the page monitoring device 200 includes:

[0111] The snapshot acquisition unit 201 is used to acquire page snapshots and a timestamp corresponding to each page snapshot in the application to be monitored;

[0112] A feature extraction unit 202 is used to extract features from each page snapshot and obtain a feature vector corresponding to each page snapshot;

[0113] A vector sorting unit 203, used to sort the feature vectors corresponding to each page snapshot according to multiple timestamps;

[0114] The model acquisition unit 204 is used to acquire a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue;

[0115] The result output unit 205 is used to sequentially input each feature vector into a neuron node in the neuron queue, each neuron node parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating, and the last neuron node in the neuron queue outputs the final monitoring result;

[0116] The monitoring completion unit 206 is used to complete the monitoring of the application program according to the exception type corresponding to the page exception if it is determined according to the final monitoring result that the application program has a page exception; wherein the exception type includes at least one of a page being continuously blank and a page having an abnormal block structure.

[0117] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the page monitoring device and each module described above can refer to the corresponding processes in the page monitoring method embodiments described in the above embodiments, and will not be repeated here.

[0118] The above-mentioned page monitoring method can be implemented in the form of a computer program. The computer program can be used in Figure 4 Run on the device shown.

[0119] See also Figure 5 , Figure 5 1 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0120] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any page monitoring method.

[0121] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0122] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any page monitoring method.

[0123] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0125] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0126] In the application to be monitored, obtain page snapshots and the timestamp corresponding to each page snapshot;

[0127] Perform feature extraction on each page snapshot to obtain the feature vector corresponding to each page snapshot;

[0128] Sort the feature vectors corresponding to each page snapshot according to multiple timestamps;

[0129] Obtain a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue;

[0130] Each feature vector is input into a neuron node in the neuron queue in sequence. Each neuron node parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating. The last neuron node in the neuron queue outputs the final monitoring result.

[0131] If it is determined according to the final monitoring result that the application has a page anomaly, the monitoring of the application is completed according to the anomaly type corresponding to the page anomaly; wherein the anomaly type includes at least one of a continuous blank page and an abnormal block structure of the page.

[0132] In some embodiments, before obtaining a page snapshot and a timestamp corresponding to each page snapshot in an application to be monitored, it also includes: obtaining the program type and format type of the application; determining multiple sensitive behavior types based on the program type; generating a behavior tracking point corresponding to each sensitive behavior type based on the format type; and adding each behavior tracking point to the application to obtain a page snapshot and a timestamp when the behavior tracking point is triggered.

[0133] In some embodiments, before obtaining a pre-built page monitoring model, it also includes: obtaining multiple historical page snapshots and the timestamp corresponding to each historical page snapshot; wherein the historical page snapshots include normal page snapshots and abnormal page snapshots; determining the first sample number and the second sample number according to a preset ratio; constituting training samples based on the first sample number of normal page snapshots and the second sample number of abnormal page snapshots; completing the training of the page monitoring model to be trained according to the historical anomaly types corresponding to the training samples and the abnormal page snapshots, and obtaining the constructed page monitoring model.

[0134] In some embodiments, based on the exception type corresponding to the page exception, it includes: determining the monitoring time corresponding to the final monitoring result based on multiple timestamps corresponding to the final monitoring result; if it is determined based on the normalized final monitoring result and the monitoring time that the page blank time corresponding to the application is greater than the preset time, the exception type is determined to be that the page is continuously blank; if the value corresponding to the normalized final monitoring result is not within the preset range, the exception type is determined to be that the page has an abnormal block structure.

[0135] In some embodiments, before determining the abnormality type corresponding to the page abnormality, the method further includes: obtaining multiple final monitoring results corresponding to the application; obtaining a result vector corresponding to each final monitoring result; obtaining a weight matrix corresponding to each result vector; normalizing each result vector according to a preset normalization formula and a weight matrix, so as to determine the abnormality type corresponding to each final monitoring result according to the corresponding value after normalization of each final monitoring result; wherein the expression of the normalization formula includes:

[0136]

[0137] Among them, Y is the corresponding value of the normalized final monitoring result, n is the number of final monitoring results, U is the weight matrix, and X is the result vector.

[0138] In some embodiments, feature extraction is performed on each page snapshot to obtain a feature vector corresponding to each page snapshot, including: decomposing the page snapshot according to a preset page element type, obtaining multiple page element information and the position information corresponding to each page element information in the page snapshot; wherein the page element type includes at least images, texts and buttons; if the page element type corresponding to the page element information is an image, the visual features corresponding to the page element information are obtained, and a feature vector is generated according to the visual features and the corresponding position information; if the page element type corresponding to the page element information is text, the text features corresponding to the page element information are obtained, and a feature vector is generated according to the text features and the corresponding position information; if the page element type corresponding to the page element information is a button, the interactive features corresponding to the page element information are obtained, and a feature vector is generated according to the interactive features and the corresponding position information.

[0139] In some embodiments, after completing the monitoring of the application according to the exception type corresponding to the page exception, it also includes: parsing the final monitoring result to obtain the exception timestamp corresponding to the page exception; matching the timestamps of multiple page snapshots corresponding to the exception timestamp and the final monitoring result, and determining the page snapshot corresponding to the successfully matched timestamp as the target page snapshot; inputting the exception type and the target page snapshot into a preset page repair model, the page repair model parses the exception type and the target page snapshot, and outputs a repair plan corresponding to the page exception; and completing the page exception repair of the application according to the repair plan.

[0140] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the page monitoring method provided in the above embodiments of the present application.

[0141] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Medi a Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card), etc., equipped on the computer device.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A page monitoring method, characterized in that: include: In the application to be monitored, obtaining page snapshots and a timestamp corresponding to each of the page snapshots; Perform feature extraction on each of the page snapshots to obtain a feature vector corresponding to each of the page snapshots; sorting the feature vectors corresponding to each of the page snapshots according to the multiple timestamps; Obtaining a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, wherein the plurality of neuron nodes are serially connected to form a neuron queue; Input each of the feature vectors into a neuron node in the neuron queue in sequence, each neuron node parses the received feature vector, outputs a page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating, and the last neuron node in the neuron queue outputs a final monitoring result; If it is determined according to the final monitoring result that the application has a page anomaly, the monitoring of the application is completed according to the anomaly type corresponding to the page anomaly; wherein the anomaly type includes at least one of a continuous blank page and an abnormal block structure on the page.

2. The method according to claim 1, characterized in that Before obtaining the page snapshots and the timestamp corresponding to each of the page snapshots in the application to be monitored, the method further includes: Obtaining a program type and a format type of the application; determining a plurality of sensitive behavior types according to the program type; Generate a behavior tracking point corresponding to each of the sensitive behavior types according to the format type; Each of the behavior embedding points is added to the application to obtain the page snapshot and the timestamp when the behavior embedding point is triggered.

3. The method according to claim 1, characterized in that: Before obtaining the pre-built page monitoring model, the method further includes: Acquire multiple historical page snapshots and a timestamp corresponding to each of the historical page snapshots; wherein the historical page snapshots include normal page snapshots and abnormal page snapshots; Determine the first sample quantity and the second sample quantity according to a preset ratio; Forming a training sample according to the normal page snapshots of the first sample number and the abnormal page snapshots of the second sample number; The training of the page monitoring model to be trained is completed according to the historical anomaly types corresponding to the training samples and the abnormal page snapshots, and the constructed page monitoring model is obtained.

4. The method according to claim 1, characterized in that: The exception type corresponding to the page exception includes: Determine the monitoring duration corresponding to the final monitoring result according to the multiple timestamps corresponding to the final monitoring result; If it is determined according to the normalized final monitoring result and the monitoring duration that the page blank duration corresponding to the application is greater than the preset duration, determining that the abnormality type is that the page is continuously blank; If the value corresponding to the normalized final monitoring result is not within a preset range, it is determined that the abnormal type is that an abnormal block structure exists on the page.

5. The method according to claim 1, characterized in that Before the abnormal type corresponding to the page abnormality, the method further includes: Obtaining a plurality of the final monitoring results corresponding to the application; Obtaining a result vector corresponding to each of the final monitoring results; Obtaining a weight matrix corresponding to each of the result vectors; Each of the result vectors is normalized according to a preset normalization formula and the weight matrix, so as to determine the abnormality type corresponding to each of the final monitoring results according to the corresponding value after normalization of each of the final monitoring results; wherein the expression of the normalization formula includes: Among them, Y is the value corresponding to the normalized final monitoring result, n is the number of the final monitoring results, U is the weight matrix, and X is the result vector.

6. The method according to claim 1, characterized in that The extracting features from each of the page snapshots to obtain a feature vector corresponding to each of the page snapshots includes: Decomposing the page snapshot according to preset page element types, obtaining a plurality of page element information and location information corresponding to each of the page element information in the page snapshot; wherein the page element types include at least images, texts and buttons; If the page element type corresponding to the page element information is an image, obtaining a visual feature corresponding to the page element information, and generating the feature vector according to the visual feature and the corresponding position information; If the page element type corresponding to the page element information is the text, obtaining text features corresponding to the page element information, and generating the feature vector according to the text features and the corresponding position information; If the page element type corresponding to the page element information is the button, an interaction feature corresponding to the page element information is acquired, and the feature vector is generated according to the interaction feature and the corresponding position information.

7. The method according to claim 1, characterized in that After completing the monitoring of the application program according to the exception type corresponding to the page exception, the method further includes: Analyze the final monitoring result and obtain the abnormal timestamp corresponding to the page abnormality; Matching the timestamps of the multiple page snapshots corresponding to the abnormal timestamp and the final monitoring result, and determining the page snapshot corresponding to the timestamp that successfully matches as the target page snapshot; Inputting the exception type and the target page snapshot into a preset page repair model, the page repair model parses the exception type and the target page snapshot, and outputs a repair solution corresponding to the page exception; The page exception of the application program is repaired according to the repair solution.

8. A page monitoring device, characterized in that: include: A snapshot acquisition unit, used to acquire page snapshots and a timestamp corresponding to each page snapshot in the application to be monitored; A feature extraction unit, used to extract features from each of the page snapshots to obtain a feature vector corresponding to each of the page snapshots; A vector sorting unit, used for sorting the feature vectors corresponding to each of the page snapshots according to the multiple timestamps; A model acquisition unit, used to acquire a pre-built page monitoring model; the page monitoring model includes a plurality of neuron nodes, and the plurality of neuron nodes are serially connected to form a neuron queue; A result output unit is used to sequentially input each of the feature vectors into a neuron node of the neuron queue, each of the neuron nodes parses the received feature vector, outputs the page monitoring result corresponding to the feature vector, and inputs the page monitoring result into the next neuron node in the neuron queue for updating, and the last neuron node in the neuron queue outputs the final monitoring result; The monitoring completion unit is used to complete the monitoring of the application according to the exception type corresponding to the page exception if it is determined that the application has a page exception according to the final monitoring result; wherein the exception type includes at least one of a page being continuously blank and a page having an abnormal block structure.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 7.

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