Container-based service data monitoring method, device and equipment and storage medium

Abstract

The application provides a data monitoring method and device based on containerization service, equipment and storage medium. The method can be used in the financial field or other fields, and the method comprises the following steps: collecting data of levels in a container according to a current data monitoring strategy of the levels in the container to obtain current data of the levels in the container; if it is determined that the current data of the levels in the container is abnormal data according to a preset abnormal data determination condition, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library; collecting data of levels corresponding to the abnormal data according to the target data monitoring strategy to obtain target data of the levels corresponding to the abnormal data. The method of the application realizes dynamic monitoring of data of each level in the container and improves the flexibility of data monitoring.

Description

Technical Field

[0001] This application relates to big data technology, and in particular to a data monitoring method, apparatus, device and storage medium based on containerized services. Background Technology

[0002] As containerization technology continues to advance, data at each layer—including the business layer, service layer, and device layer—is becoming increasingly complex. Anomalies at any layer can lead to service errors. To improve data management efficiency and ensure normal service operation, monitoring data at each layer is essential.

[0003] In existing technologies, monitoring solutions for each layer differ. For example, business layer monitoring is usually established by developers and monitors business service calls, latency, and operational status; service layer monitoring is usually established by system service providers and monitors the operational status of system services, supporting services, resource scheduling, and usage.

[0004] However, in existing technologies, data monitoring at each layer relies on pre-set monitoring rules to determine the presence of anomalies. If an anomaly is found, a report is generated directly, informing staff of the anomaly for verification, and the specific level of the anomaly data is manually determined. This method of data monitoring is fixed, wastes manpower and time, has poor flexibility, and affects the efficiency and accuracy of data monitoring. Summary of the Invention

[0005] This application provides a data monitoring method, apparatus, device, and storage medium based on containerized services to improve the flexibility and efficiency of data monitoring.

[0006] On the one hand, this application provides a data monitoring method based on containerized services, including:

[0007] Based on the current data monitoring strategy of the layers in the container, data is collected from the layers in the container to obtain the current data of the layers in the container;

[0008] If the current data at the level in the container is determined to be abnormal data based on the preset abnormal data determination conditions, then the target data monitoring strategy associated with the abnormal data is determined based on the preset expert experience base.

[0009] According to the target data monitoring strategy, data is collected from the layers corresponding to the abnormal data to obtain the target data of the layers corresponding to the abnormal data.

[0010] On the other hand, this application provides a data monitoring device based on containerized services, including:

[0011] The current data acquisition module is used to collect data from the layers in the container according to the current data monitoring strategy in the container, and obtain the current data of the layers in the container.

[0012] The target data monitoring strategy determination module is used to determine the target data monitoring strategy associated with the abnormal data if the current data at the level in the container is determined to be abnormal data according to the preset abnormal data determination conditions.

[0013] The target data acquisition module is used to collect data from the layers corresponding to the abnormal data according to the target data monitoring strategy, and obtain the target data of the layers corresponding to the abnormal data.

[0014] On the other hand, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory to implement the data monitoring method based on containerized services as described in any embodiment of this application.

[0017] On the other hand, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the data monitoring method based on containerized services as described in any embodiment of this application.

[0018] On the other hand, this application provides a computer program product, including a computer program that, when executed by a processor, implements the data monitoring method based on containerized services as described in any embodiment of this application.

[0019] The technical solution provided in this application sets a current data monitoring strategy for each level, collects data from each level, and obtains the current data for each level. This allows for the separate acquisition of data at each level, facilitating unified monitoring and analysis. Pre-set abnormal data identification conditions determine whether the current data is abnormal. If so, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The target data monitoring strategy replaces the current data monitoring strategy, and data from the levels with abnormal data is collected according to the target data monitoring strategy to obtain the target data. This enables adjustments to the monitoring strategies for each level when abnormal data occurs, allowing for more accurate identification of anomalies and automatic updates to the data monitoring strategies at each level. This solves the problem in existing technologies where monitoring strategies cannot be automatically adjusted based on anomalies, avoiding errors in anomaly identification caused by fixed monitoring rules. Furthermore, the monitoring strategies for each level are independent and updated separately, improving the flexibility of data monitoring and thus enhancing its efficiency and accuracy. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 A flowchart illustrating a data monitoring method based on containerized services provided in this application embodiment;

[0022] Figure 2 A flowchart illustrating a data monitoring method based on containerized services provided in this application embodiment;

[0023] Figure 3 A flowchart illustrating a data monitoring method based on containerized services provided in this application embodiment;

[0024] Figure 4 This is a schematic diagram of the structure of the container multi-dimensional real-time monitoring and analysis tool provided in the embodiments of this application;

[0025] Figure 5 A schematic diagram of the structure of a data monitoring device based on containerized services provided in this application embodiment;

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0029] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0030] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0032] It should be noted that, due to space limitations, this application specification does not exhaustively list all possible implementation methods. Those skilled in the art, after reading this application specification, should be able to deduce that, as long as the technical features do not contradict each other, any combination of technical features can constitute an optional implementation method. The following provides a detailed description of each embodiment.

[0033] Figure 1 This is a flowchart illustrating a data monitoring method based on containerized services provided in an embodiment of this application, as shown below. Figure 1 As shown, the method provided in this embodiment is executed by a data monitoring device based on containerized services, which can be configured on a container multi-dimensional real-time monitoring and analysis tool. Figure 1 As shown, the method includes the following steps:

[0034] S101. Based on the current data monitoring strategy of the layers in the container, collect data from the layers in the container to obtain the current data of the layers in the container.

[0035] Containerized services can monitor multi-dimensional data across different layers, including the business layer, service layer, and device layer. Data in the business layer can include business logs, system logs, and business service calls; data in the service layer can include the running status of business containers, system middleware logs, and service framework data; and data in the device layer can include CPU usage and allocation for each component, memory consumption for each process, disk read / write resource usage, and network traffic.

[0036] Each layer within a container can be configured with a data monitoring policy. A data monitoring policy refers to the strategy for acquiring data from each layer for monitoring purposes. A data monitoring policy can collect data from various dimensions within its corresponding layer. For example, if the current data monitoring policy for the device layer is to acquire current data every 30 seconds, then every 30 seconds it can acquire data such as the memory usage of each process, disk read / write resource usage, and network traffic.

[0037] The current data monitoring strategy for each level is the data monitoring strategy for that level at the current time, and the current data monitoring strategies for each level can be different. For example, the current data monitoring strategy for the business layer is to obtain current data once every 1 minute; the current data monitoring strategy for the device layer is to obtain current data once every 30 seconds.

[0038] Determine the current data monitoring strategy for each layer. Based on the current data monitoring strategy for each layer, perform real-time or periodic data collection for each layer in the container. The collected data is the current data for the corresponding layer. For example, based on the current data monitoring strategy for the device layer, the current data for the device layer can be obtained as follows: CPU resource utilization has reached 50%.

[0039] In this embodiment, the data monitoring device based on containerized services can be configured on a dynamic container multi-dimensional real-time monitoring and analysis tool. "Dynamic" means that the data monitoring strategy used by this tool is dynamically variable. The dynamic container multi-dimensional real-time monitoring and analysis tool can be equipped with various data collectors. These data collectors can be used to collect current data at various levels, such as collecting log files at the application layer, collecting container running status and CPU time consumption of each thread at the system layer, and collecting disk storage I / O and network bandwidth usage at the device layer.

[0040] S102. If the current data at the level in the container is determined to be abnormal data based on the preset abnormal data determination conditions, then the target data monitoring strategy associated with the abnormal data is determined based on the preset expert experience base.

[0041] The process involves pre-setting abnormal data identification conditions. These conditions determine whether the collected data is abnormal, i.e., whether anomalies exist at each level. Based on these conditions, the system checks if the current data meets them. If it does, the data is identified as abnormal. For example, the abnormal data identification conditions can include a range of values ​​for abnormal data; data falling within this range is used as a criterion for identifying abnormal data. If the current data falls within the preset range, it is determined that the data meets the abnormal data identification conditions, and therefore, the data is considered abnormal.

[0042] The criteria for identifying abnormal data can differ across different levels. At any given time, data from different levels can all be abnormal, all be normal, or some levels may have abnormal data while others have normal data. Different rules for identifying abnormal data can also be set for different dimensions of current data within the same level. For example, rule one can be set for identifying abnormal data regarding the running status of business containers in the service layer; rule two can be set for identifying abnormal data regarding system middleware logs in the service layer; and rule three can be set for identifying abnormal data regarding network traffic in the device layer.

[0043] After obtaining the current data at each level, determine the criteria for identifying outlier data associated with the current data in each dimension at each level. Based on the outlier data identification rules corresponding to various current data types, determine whether the current data in each dimension at each level is outlier data.

[0044] If the current data is not abnormal, it can be recorded and stored, and the current data collection and abnormal data judgment can continue to be carried out at each level according to the current data monitoring strategy of each level.

[0045] If the current data is abnormal, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The expert experience database can be a strategy database pre-set by the user based on expert experience, storing various data monitoring strategies. After determining that the current data is abnormal, a data monitoring strategy different from the current data monitoring strategy can be found in the expert experience database and used as the target data monitoring strategy. In this embodiment, the target data monitoring strategy at the level where the abnormal data resides can be found. For example, if the current data at the business layer is abnormal, while the current data at the service layer and device layer is normal, then the target data monitoring strategy related to the business layer can be found.

[0046] The expert experience base stores the relationships between various levels and data monitoring strategies. After identifying current data anomalies, the level at which the anomaly data resides is determined. Based on the preset relationships between levels and data monitoring strategies, the target data monitoring strategy is then determined. The expert experience base can also store various relationships between anomaly data and data monitoring strategies; different values ​​of anomaly data are associated with different data monitoring strategies. For example, if the anomaly data is CPU resource usage between 50% and 59%, the associated target data monitoring strategy is Strategy One; if the anomaly data is CPU resource usage between 60% and 69%, the associated target data monitoring strategy is Strategy Two. In other words, the target data monitoring strategy associated with the anomaly data can be found from the expert experience base.

[0047] S103. Based on the target data monitoring strategy, collect data from the layers corresponding to the abnormal data to obtain the target data for the layers corresponding to the abnormal data.

[0048] Specifically, after determining the target data monitoring strategy for the level where the abnormal data resides, the current data monitoring strategy for that level is replaced with the target data monitoring strategy. If the current data for that level is normal, there is no need to replace the current data monitoring strategy for that level.

[0049] A target data monitoring strategy is adopted, where new current data at the level where abnormal data exists is re-collected, and the new current data obtained is the target data for that level. For example, if the current data monitoring strategy collects data every 30 seconds, the target data monitoring strategy could collect data every minute. This allows the monitoring strategies at each level to be independent and updated separately, improving the flexibility and efficiency of data monitoring. In S103, the target data can be collected by the data collector in a dynamic container multi-dimensional real-time monitoring and analysis tool.

[0050] It should be noted that the data monitoring method based on containerized services proposed in this application can be used in the financial field, or in any field other than the financial field. This application does not limit the application field of the data monitoring method based on containerized services.

[0051] This application embodiment sets a current data monitoring strategy for each level, collects data from each level, and obtains the current data for each level. This allows for separate acquisition of data at each level, facilitating unified monitoring and analysis. Pre-set abnormal data determination conditions determine whether the current data is abnormal. If so, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The target data monitoring strategy replaces the current data monitoring strategy, and data at each level is collected according to the target data monitoring strategy to obtain the target data. This allows for adjustments to the monitoring strategies at each level when an anomaly occurs, enabling more accurate identification of anomalies and automatic updates to the data monitoring strategies at each level. This solves the problem in existing technologies where monitoring strategies cannot be automatically adjusted based on anomalies, avoiding errors in anomaly judgment caused by fixed monitoring rules. Furthermore, the monitoring strategies at each level are independent and updated separately, improving the flexibility of data monitoring and thus enhancing its efficiency and accuracy.

[0052] Figure 2 This is a flowchart illustrating a data monitoring method based on containerized services, which is an optional embodiment based on the above embodiments.

[0053] In this embodiment, the current data of the layer in the container is determined to be abnormal data according to the preset abnormal data determination conditions. This can be further refined as follows: the data to be confirmed of the layer is determined from the current data according to the preset abnormal data determination conditions of the layer in the container; if the data to be confirmed meets the abnormal data determination conditions of the corresponding layer, then the data to be confirmed is determined to be abnormal data.

[0054] like Figure 2 As shown, the method includes the following steps:

[0055] S201. Based on the current data monitoring strategy of the container layer, collect data from the container layer to obtain the current data of the container layer.

[0056] S202. Based on the preset abnormal data determination conditions of the levels in the container, determine the data to be confirmed at each level from the current data.

[0057] Each layer within the container can be pre-defined with corresponding abnormal data identification conditions. These conditions can assess abnormal data across all current data at their respective layers, or they can assess abnormal data within a subset of the current data at each layer. For example, the current data in the service layer includes the running status of the current business container, system middleware logs, and the service framework. The abnormal data identification condition for the service layer is to assess abnormal data based on the running status of the business container; that is, the abnormal data identification condition for the service layer is to assess a subset of the current data within its layer.

[0058] The data to be confirmed refers to the current data specified in the anomaly data determination criteria that needs to be determined to be anomaly. For example, the current data might be the running status of the current business container, system middleware logs, or service framework. If the anomaly data determination criteria assess the running status of the current business container, then the data to be confirmed is the running status of the current business container. Based on the anomaly data determination criteria at each level within the container, the data to be confirmed within the current data at each level can be identified. This helps to reduce the scope of anomaly data assessment and improves the accuracy and efficiency of anomaly data determination.

[0059] S203. If the data to be confirmed meets the abnormal data determination conditions of the corresponding level, then the data to be confirmed is determined to be abnormal data.

[0060] Specifically, based on the abnormal data determination criteria, it is determined whether the data to be confirmed at the corresponding level meets the criteria. If so, the data to be confirmed is determined to be abnormal; otherwise, it is determined that the data to be confirmed is not abnormal. For example, the abnormal data determination criteria may include a reception time threshold for the data to be confirmed. If the reception time of the data to be confirmed exceeds the reception time threshold, it is determined that the data to be confirmed has a delay, and the data to be confirmed is abnormal.

[0061] A single data level can contain multiple data points awaiting confirmation. This means a single level can have various rules for identifying anomalous data to assess different dimensions of the data within that level. Each level's data can be judged as anomalous, resulting in a result for each level. A single level can contain multiple data points awaiting confirmation, meaning it can generate multiple anomalous data assessment results. Within a single level's current data, some data may be anomalous, while some may not. By defining the anomalous data assessment conditions for each level, the accuracy of anomalous data assessment is effectively improved, reducing manual judgment and thus increasing the efficiency of data monitoring.

[0062] In this embodiment, if the data to be confirmed meets the abnormal data determination conditions of the corresponding level, the data to be confirmed is determined to be abnormal data, including: if the data to be confirmed exceeds the current monitoring threshold of the corresponding level, the data to be confirmed is determined to be abnormal data, and an alarm prompt message is issued.

[0063] Specifically, the abnormal data determination criteria can include a current monitoring threshold. This threshold represents the maximum, minimum, or range of values ​​that the data to be confirmed is allowed to reach. For example, the criteria could specify that data exceeding the current monitoring threshold is considered abnormal. When determining abnormal data, the data to be confirmed is compared to the current monitoring threshold. If the data exceeds the preset threshold, it is determined to be abnormal; otherwise, it is determined not to be abnormal.

[0064] For example, the current monitoring threshold set in the abnormal data identification criteria is 50%, used to judge the CPU resource utilization. The data to be confirmed is the current CPU resource utilization in the current data. The current CPU resource utilization is 60%, which exceeds 50%. Therefore, it can be determined that the current CPU resource utilization is abnormal data.

[0065] Once abnormal data is identified, an alarm message can be sent to the user, such as a voice alarm or a pop-up notification, to inform the user of the current anomaly. The alarm message can include the hierarchy of the abnormal data for easy viewing by the user. For current data that is not abnormal, no notification may be given, or all current data can be displayed with the abnormal data highlighted, improving the user's viewing efficiency and accuracy.

[0066] The benefits of this setup are that by preset monitoring thresholds, abnormal data can be quickly identified, improving the efficiency of data monitoring. Users can modify the current monitoring thresholds according to actual needs, increasing the flexibility and accuracy of data monitoring. By issuing alarm notifications, users can be promptly alerted, preventing abnormal data from impacting container applications, reducing the professional requirements on users, and improving the efficiency and accuracy of container operation.

[0067] S204. Based on the preset expert experience database, determine the target data monitoring strategy associated with the abnormal data.

[0068] The expert experience database can store various data monitoring strategies, each corresponding to a solution for different abnormal data situations. When abnormal data occurs, the appropriate data monitoring strategy can be found and used as the target data monitoring strategy.

[0069] In this embodiment, the target data monitoring strategy associated with the abnormal data is determined based on a preset expert experience base, including: searching for historical data corresponding to the abnormal data based on the preset expert experience base; and obtaining the target data monitoring strategy associated with the abnormal data based on the historical data monitoring strategy of the historical data.

[0070] Specifically, each data monitoring strategy can include user-inputted strategies or historical data monitoring strategies applied when monitoring historical data. After anomaly data is detected, historical data corresponding to the anomaly data is searched from an expert experience base. For example, historical data consistent with the anomaly data can be searched, or historical data within a preset numerical range of the anomaly data can be searched. The historical data monitoring strategy to be applied in the case of the found historical data is determined; that is, the historical data monitoring strategy applied to resolve the anomaly in the historical data is obtained. The found historical data monitoring strategy is then identified as the target data monitoring strategy associated with the anomaly data.

[0071] Data monitoring strategies can include collection frequency and data types. For example, the current data monitoring strategy collects data every 30 seconds, and the collected data is the current CPU resource utilization rate. If the current CPU resource utilization rate is 60%, it is abnormal data. The strategy then checks if a CPU resource utilization rate of 60% exists in historical data. If it does, the data monitoring strategy for that historical period with a CPU resource utilization rate of 60% is determined, and that data monitoring strategy is set as the target data monitoring strategy.

[0072] If no such strategy exists, a strategy can be selected from manually input strategies as the target data monitoring strategy. For example, each manually input strategy corresponds to a preset abnormal data point. The currently identified abnormal data is compared with the preset abnormal data to determine if the currently identified abnormal data exists in the expert experience base. If so, the strategy corresponding to that abnormal data is determined as the target data monitoring strategy. Alternatively, a data monitoring strategy of the corresponding level can be randomly selected from the expert experience base as the target data monitoring strategy.

[0073] The advantage of this setup is that by searching historical data, solutions can be determined when the same abnormal data appears in the historical data, directly obtaining the target data monitoring strategy, avoiding the need to redefine the target data monitoring strategy, improving the efficiency and accuracy of strategy updates, and thus improving the efficiency and accuracy of data monitoring.

[0074] In this embodiment, after searching for historical data corresponding to the abnormal data based on a preset expert experience base, the method further includes: determining the historical monitoring threshold in the historical data based on the historical data; and setting the historical monitoring threshold as the target monitoring threshold to replace the current monitoring threshold.

[0075] Specifically, the expert experience base can include various data monitoring strategies and monitoring thresholds. These thresholds can be pre-set manually or are historical thresholds applied under various historical data conditions. After identifying the historical data corresponding to the abnormal data, the historical monitoring threshold for that historical data is determined, and this historical threshold is set as the target monitoring threshold, replacing the current monitoring threshold.

[0076] For example, if the current monitoring threshold is 50%, and the abnormal data rate is 60%, and historical data also shows a rate of 60%, with a historical monitoring threshold of 55%, then 55% can be used as the target monitoring threshold instead of 50%. If the expert experience base does not contain historical data or historical monitoring thresholds corresponding to the abnormal data, the current monitoring threshold can be increased or decreased according to a preset monitoring threshold adjustment rule to obtain the target monitoring threshold. For example, the monitoring threshold adjustment rule could be that the current monitoring threshold increases by 5% each time.

[0077] The advantages of this setup are that the expert experience base can store multiple data monitoring strategies and thresholds. By searching historical data, the target monitoring threshold can be quickly determined, providing the criteria for monitoring abnormal data, improving the efficiency of threshold determination, and avoiding errors caused by re-determining the threshold. This allows for flexible changes to monitoring strategies and thresholds, reducing user operations and improving the efficiency and flexibility of data monitoring.

[0078] S205. Based on the target data monitoring strategy, collect data from the layers corresponding to the abnormal data to obtain the target data for the layers corresponding to the abnormal data.

[0079] This application embodiment sets a current data monitoring strategy for each level, collects data from each level, and obtains the current data for each level. This allows for separate acquisition of data at each level, facilitating unified monitoring and analysis. Pre-set abnormal data determination conditions determine whether the current data is abnormal. If so, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The target data monitoring strategy replaces the current data monitoring strategy, and data at each level is collected according to the target data monitoring strategy to obtain the target data. This allows for adjustments to the monitoring strategies at each level when an anomaly occurs, enabling more accurate identification of anomalies and automatic updates to the data monitoring strategies at each level. This solves the problem in existing technologies where monitoring strategies cannot be automatically adjusted based on anomalies, avoiding errors in anomaly judgment caused by fixed monitoring rules. Furthermore, the monitoring strategies at each level are independent and updated separately, improving the flexibility of data monitoring and thus enhancing its efficiency and accuracy.

[0080] Figure 3 This is a flowchart illustrating a data monitoring method based on containerized services, which is an optional embodiment based on the above embodiments.

[0081] In this embodiment, the data monitoring strategy includes the collection frequency and the collection data; accordingly, according to the current data monitoring strategy of the layer in the container, data is collected at the layer in the container to obtain the current data of the layer in the container, which can be further refined as follows: according to the collection frequency in the current data monitoring strategy, the collection data of the layer in the container is collected to obtain the current data of the layer.

[0082] like Figure 3 As shown, the method includes the following steps:

[0083] S301. Based on the collection frequency in the current data monitoring strategy, collect the collection data of the layer in the container to obtain the current data of the layer.

[0084] The data monitoring strategy can include the collection frequency and the collected data. The collection frequency refers to the collection cycle for acquiring data, while the collected data refers to the data from each level that needs to be acquired. The collected data can be identified using identifiers such as data names or numbers. For example, for the business layer, if the current data monitoring strategy is to collect business logs every ten minutes, then the collection frequency is ten minutes, and the collected data is the business logs.

[0085] The data collection frequency and data set for each level of the current data monitoring strategy are determined. Data is then collected at the corresponding level according to the collection frequency to obtain the current data for that level. By setting the collection frequency and data set, real-time or scheduled data collection can be achieved, reducing user operations, preventing data omissions, and improving the efficiency and accuracy of data monitoring.

[0086] In this embodiment, after determining that the current data at the level in the container is abnormal data, the method further includes: determining the target abnormal data operation and maintenance rule associated with the abnormal data according to the preset candidate abnormal data operation and maintenance rule; and investigating the level corresponding to the abnormal data according to the target abnormal data operation and maintenance rule to obtain the investigation results.

[0087] Specifically, if abnormal data exists in any level of the container, the abnormal data can be recorded, and the abnormal data can be automatically maintained and the cause of the abnormality can be investigated.

[0088] Multiple abnormal data operation and maintenance rules can be pre-set as candidate abnormal data operation and maintenance rules. The association between candidate abnormal data operation and maintenance rules and preset abnormal data is pre-stored; that is, different operation and maintenance methods are associated with different preset abnormal data. After abnormal data is confirmed to exist, the abnormal data operation and maintenance rule associated with the abnormal data is determined from the candidate abnormal data operation and maintenance rules and becomes the target abnormal data operation and maintenance rule. For example, if the abnormal data is excessive CPU resource utilization, and the associated candidate abnormal data operation and maintenance rule is Rule 1, then Rule 1 is determined as the target abnormal data operation and maintenance rule.

[0089] According to the target abnormal data operation and maintenance rules, the corresponding layer of abnormal data is investigated to determine the cause of the abnormal data and obtain the investigation results. The investigation results can be the cause of the abnormal data or new current data collected after the abnormal data operation and maintenance. For example, for abnormal data that leads to a decrease in business success rate, the investigation method set in the target abnormal data operation and maintenance rules can be to simultaneously complete service call checks to check whether the service is normal and whether the service latency has increased, and obtain the investigation results. Alternatively, for service abnormal scenarios, the system-level container running status can be queried. If the container is not ready due to abnormal node status, business traffic can be switched to a backup container. For the problem of normal service but increased latency, the system-level connection usage data is queried, and the database connection is monitored to check whether it is normal. If the system-side connections are normal, the usage of device-level CPU, memory, storage, and network resources is further investigated and analyzed, and the corresponding data is snapshotted and saved for development analysis. If the investigation of device-level CPU, memory, storage, and network resources reveals resource bottlenecks, the collection frequency of monitoring components can be reduced or collection can be turned off. Once resources are sufficient, the original data monitoring strategy will be restored. In this embodiment, the specific abnormal data operation and maintenance rules can be referenced based on the operation and maintenance experience library, and can be adjusted by the user.

[0090] The advantage of this setup is that it allows for the determination of target abnormal data operation and maintenance rules for abnormal data, enabling automated operation and maintenance and troubleshooting, timely correction of abnormalities, reduction of the impact of abnormal data, and automated processing of container monitoring.

[0091] In this embodiment, after obtaining the current data of the hierarchy in the container, the method further includes: associating the current data with the corresponding hierarchy and storing it in a preset database, and displaying the data on a visual interface.

[0092] Specifically, after obtaining the current data at each level, the current data at each level can be stored and displayed on a visualization interface. For example, a data storage device can be set up in a container's multi-dimensional real-time monitoring and analysis tool to store collected log data. A data storage device is an abstract storage device that can employ different storage structures, such as databases, Kafka, and HDFS (Hadoop Distributed File System). A monitoring display device can be set up in the container's multi-dimensional real-time monitoring and analysis tool to visualize and query the current data stored in the data storage device.

[0093] The benefits of this setup are that it enables unified storage and processing of current data, unified data display based on the stored data, facilitating subsequent viewing and improvement by users, and unified analysis of data across various dimensions.

[0094] In this embodiment, before collecting data from the layers in the container according to the current data monitoring strategy of the layers in the container to obtain the current data of the layers in the container, the method further includes: determining the data monitoring strategy associated with the layers in the container as the current data monitoring strategy of the layers.

[0095] Specifically, each layer corresponds to its own current data monitoring strategy, and initial current data monitoring strategies can be pre-associated to each layer. Determining the current data monitoring strategy associated with each layer in the container facilitates data collection at each layer according to the current data monitoring strategy, thus obtaining the current data for each layer in the container. By defining the current data monitoring rules for each layer, data collection can be performed on a case-by-case basis, avoiding data clutter and improving the accuracy and efficiency of data monitoring.

[0096] S302. If the current data at the level in the container is determined to be abnormal data based on the preset abnormal data determination conditions, then the target data monitoring strategy associated with the abnormal data is determined based on the preset expert experience base.

[0097] S303. Based on the target data monitoring strategy, collect data from the layers corresponding to the abnormal data to obtain the target data for the layers corresponding to the abnormal data.

[0098] This application embodiment sets a current data monitoring strategy for each level, collects data from each level, and obtains the current data for each level. This allows for separate acquisition of data at each level, facilitating unified monitoring and analysis. Pre-set abnormal data determination conditions determine whether the current data is abnormal. If so, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The target data monitoring strategy replaces the current data monitoring strategy, and data at each level is collected according to the target data monitoring strategy to obtain the target data. This allows for adjustments to the monitoring strategies at each level when an anomaly occurs, enabling more accurate identification of anomalies and automatic updates to the data monitoring strategies at each level. This solves the problem in existing technologies where monitoring strategies cannot be automatically adjusted based on anomalies, avoiding errors in anomaly judgment caused by fixed monitoring rules. Furthermore, the monitoring strategies at each level are independent and updated separately, improving the flexibility of data monitoring and thus enhancing its efficiency and accuracy.

[0099] Figure 4 This is a schematic diagram of the structure of the container multi-dimensional real-time monitoring and analysis tool provided in this embodiment. The tool provided in this embodiment can execute a data monitoring method based on containerized services. Figure 4 As shown, the container multi-dimensional real-time monitoring and analysis tool 41 includes a data collector 410, a policy configurator 411, an expert experience base 412, a data storage device 413, and a monitoring display device 414.

[0100] The data collector 410 collects data from each level according to the current data monitoring strategies at each level in the strategy configurator 411, obtaining the current data for each level. After obtaining the current data, the data collector 410 can determine whether the current data is abnormal based on preset abnormal data determination conditions. If so, it determines the target data monitoring strategy associated with the abnormal data according to the preset expert experience base 412 and sends the target data monitoring strategy to the strategy configurator 411. This facilitates the acquisition of new current data based on the target data monitoring strategy in the strategy configurator 411. The current data acquired by the data collector 410 can also be sent to the data storage device 413 for storage and to the monitoring display device 414 for display.

[0101] Figure 5 This is a schematic diagram of a data monitoring device based on containerized services provided in an embodiment of this application. The device can be configured on a container multi-dimensional real-time monitoring and analysis tool, and can be implemented through software, hardware, or a combination of both. Figure 5As shown, the device includes: a current data acquisition module 501, a target data monitoring strategy determination module 502, and a target data acquisition module 503.

[0102] The current data acquisition module 501 is used to collect data from the layers in the container according to the current data monitoring strategy in the container, and obtain the current data of the layers in the container.

[0103] The target data monitoring strategy determination module 502 is used to determine the target data monitoring strategy associated with the abnormal data if the current data at the level in the container is determined to be abnormal data according to the preset abnormal data determination conditions.

[0104] The target data acquisition module 503 is used to collect data from the layer corresponding to the abnormal data according to the target data monitoring strategy, and obtain the target data of the layer corresponding to the abnormal data.

[0105] Optionally, the target data monitoring strategy determination module 502 includes:

[0106] The data to be confirmed determination unit is used to determine the data to be confirmed at the level from the current data according to the preset abnormal data determination conditions at the level in the container.

[0107] An abnormal data determination unit is used to determine that the data to be confirmed is abnormal data if the data to be confirmed meets the abnormal data determination conditions of the corresponding level.

[0108] Optional, an abnormal data determination unit, specifically used for:

[0109] If the data to be confirmed exceeds the current monitoring threshold of the corresponding preset level, the data to be confirmed is determined to be abnormal data, and an alarm message is issued.

[0110] Optionally, the target data monitoring strategy determination module 502 includes:

[0111] The historical data search unit is used to search for historical data corresponding to the abnormal data based on a preset expert experience database.

[0112] The target strategy acquisition unit is used to obtain the target data monitoring strategy associated with the abnormal data based on the historical data monitoring strategy of the historical data.

[0113] Optionally, the device may also include:

[0114] The historical monitoring threshold determination module is used to determine the historical monitoring threshold in the historical data based on the historical data after searching for historical data corresponding to the abnormal data according to a preset expert experience database.

[0115] The target monitoring threshold determination module is used to determine the historical monitoring threshold as the target monitoring threshold and replace the current monitoring threshold.

[0116] Optionally, the device may also include:

[0117] The target abnormal data operation and maintenance rule determination module is used to determine the target abnormal data operation and maintenance rule associated with the abnormal data after determining that the current data at the level in the container is abnormal data, based on the preset candidate abnormal data operation and maintenance rules.

[0118] The investigation result acquisition module is used to investigate the corresponding level of the abnormal data according to the operation and maintenance rules of the target abnormal data, and obtain the investigation results.

[0119] Optionally, the device may also include:

[0120] The data storage module is used to, after obtaining the current data of the hierarchy in the container, associate the current data with the corresponding hierarchy and store it in a preset database, and display the data on a visual interface.

[0121] Optionally, the device may also include:

[0122] The current data monitoring strategy determination module is used to determine the data monitoring strategy associated with the layer in the container before collecting data from the layer in the container according to the current data monitoring strategy in the container to obtain the current data of the layer in the container, and to use the current data monitoring strategy of the layer in the container as the current data monitoring strategy of the layer in the container.

[0123] Optionally, the data monitoring strategy includes the collection frequency and the data collected;

[0124] Accordingly, the current data acquisition module 501 is specifically used for:

[0125] Based on the collection frequency in the current data monitoring strategy, the collection data of the layer in the container is collected to obtain the current data of the layer.

[0126] Optionally, the container layers include a business layer, a service layer, and a device layer.

[0127] This application embodiment sets a current data monitoring strategy for each level, collects data from each level, and obtains the current data for each level. This allows for separate acquisition of data at each level, facilitating unified monitoring and analysis. Pre-set abnormal data determination conditions determine whether the current data is abnormal. If so, a target data monitoring strategy associated with the abnormal data is determined based on a pre-set expert experience database. The target data monitoring strategy replaces the current data monitoring strategy, and data at each level is collected according to the target data monitoring strategy to obtain the target data. This allows for adjustments to the monitoring strategies at each level when an anomaly occurs, enabling more accurate identification of anomalies and automatic updates to the data monitoring strategies at each level. This solves the problem in existing technologies where monitoring strategies cannot be automatically adjusted based on anomalies, avoiding errors in anomaly judgment caused by fixed monitoring rules. Furthermore, the monitoring strategies at each level are independent and updated separately, improving the flexibility of data monitoring and thus enhancing its efficiency and accuracy.

[0128] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0129] The device 600 may include one or more of the following components: a processing component 602, a memory 604, a power supply component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616.

[0130] Processing component 602 typically controls the overall operation of device 600, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 602 may include one or more modules to facilitate interaction between processing component 602 and other components. For example, processing component 602 may include a multimedia module to facilitate interaction between multimedia component 608 and processing component 602.

[0131] Memory 604 is configured to store various types of data to support the operation of device 600. Examples of such data include instructions for any application or method operating on device 600, contact data, phonebook data, messages, pictures, videos, etc. Memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0132] Power supply component 606 provides power to the various components of device 600. Power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 600.

[0133] Multimedia component 608 includes a screen that provides an output interface between the device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When the device 600 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0134] Audio component 610 is configured to output and / or input audio signals. For example, audio component 610 includes a microphone (MIC) configured to receive external audio signals when device 600 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 604 or transmitted via communication component 616. In some embodiments, audio component 610 also includes a speaker for outputting audio signals.

[0135] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0136] Sensor assembly 614 includes one or more sensors for providing status assessments of various aspects of device 600. For example, sensor assembly 614 may detect the on / off state of device 600, the relative positioning of components such as the display and keypad of device 600, changes in the position of device 600 or a component of device 600, the presence or absence of user contact with device 600, the orientation or acceleration / deceleration of device 600, and temperature changes of device 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0137] Communication component 616 is configured to facilitate wired or wireless communication between device 600 and other devices. Device 600 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0138] In an exemplary embodiment, the apparatus 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0139] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, which can be executed by a processor 620 of the device 600 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0140] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of a terminal device, enables the terminal device to execute the aforementioned data monitoring method based on containerized services.

[0141] This application also discloses a computer program product, including a computer program that, when executed by a processor, implements the method described in this embodiment.

[0142] Various embodiments of the systems and technologies described above in this application can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0143] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or electronic device.

[0144] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0145] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0146] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data electronic devices), or computing systems that include middleware components (e.g., application electronic devices), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0147] Computer systems can include client and electronic devices. Clients and electronic devices are generally geographically separated and typically interact via communication networks. The client-electronic device relationship is created by computer programs running on the respective computers and having a client-electronic device relationship with each other. The electronic device can be a cloud electronic device, also known as a cloud computing electronic device or cloud host, a host product within the cloud computing service system, addressing the shortcomings of traditional physical hosts and VPS services ("Virtual Private Server," or simply "VPS") in terms of management difficulty and weak business scalability. The electronic device can also be an electronic device in a distributed system or an electronic device incorporating blockchain technology. It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application is achieved, and this is not limited herein.

[0148] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0149] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data monitoring method based on containerized services, characterized in that, include: According to the current data monitoring strategy of the container's layers, data is collected from the layers in the container to obtain the current data of the layers in the container; wherein, the data monitoring strategy includes the collection frequency and the collection data; the layers of the container include the business layer, the service layer, and the device layer; The step of collecting data from the layers in the container according to the current data monitoring strategy in the container to obtain the current data of the layers in the container includes: collecting the data of the layers in the container according to the collection frequency in the current data monitoring strategy to obtain the current data of the layers. If the current data at the level in the container is determined to be abnormal data based on the preset abnormal data determination conditions, then the historical data corresponding to the abnormal data is searched according to the preset expert experience base; a target data monitoring strategy associated with the abnormal data is obtained according to the historical data monitoring strategy of the historical data; and the target data monitoring strategy is used to replace the current data monitoring strategy. According to the target data monitoring strategy, data is collected from the level corresponding to the abnormal data to obtain the target data of the level corresponding to the abnormal data. After determining that the current data at the level in the container is abnormal data, the method further includes: determining the target abnormal data operation and maintenance rule associated with the abnormal data according to the preset candidate abnormal data operation and maintenance rule; and investigating the level corresponding to the abnormal data according to the target abnormal data operation and maintenance rule to obtain the investigation result.

2. The method according to claim 1, characterized in that, Based on preset abnormal data determination conditions, the current data at each level in the container is determined to be abnormal data, including: Based on the preset abnormal data determination conditions of the levels in the container, determine the data to be confirmed at the level from the current data; If the data to be confirmed meets the abnormal data determination conditions of the corresponding level, then the data to be confirmed is determined to be abnormal data.

3. The method according to claim 2, characterized in that, If the data to be confirmed meets the abnormal data determination conditions of the corresponding level, then the data to be confirmed is determined to be abnormal data, including: If the data to be confirmed exceeds the current monitoring threshold of the corresponding preset level, the data to be confirmed is determined to be abnormal data, and an alarm message is issued.

4. The method according to claim 1, characterized in that, After searching for historical data corresponding to the abnormal data based on a pre-set expert experience database, the process also includes: Based on the historical data, determine the historical monitoring threshold in the historical data; The historical monitoring threshold is determined as the target monitoring threshold and replaced with the current monitoring threshold.

5. The method according to claim 1, characterized in that, After obtaining the current data of the hierarchy in the container, the process also includes: The current data is associated with the corresponding level and stored in a preset database, and then displayed on a visual interface.

6. The method according to any one of claims 1-5, characterized in that, Before collecting data from the container's layers according to the current data monitoring strategy within the container, and obtaining the current data for the container's layers, the process further includes: Determine the data monitoring strategy associated with the hierarchy in the container, and use it as the current data monitoring strategy for that hierarchy.

7. A data monitoring device based on containerized services, characterized in that, include: The current data acquisition module is used to collect data from the layers within the container according to the current data monitoring strategy of the layers within the container, and obtain the current data of the layers within the container; wherein, the data monitoring strategy includes the collection frequency and the collection data; the layers of the container include the business layer, the service layer and the device layer; The target data monitoring strategy determination module is used to determine a target data monitoring strategy associated with the abnormal data if the current data at the level in the container is determined to be abnormal data according to a preset abnormal data determination condition; and to replace the current data monitoring strategy with the target data monitoring strategy. The target data acquisition module is used to collect data from the level corresponding to the abnormal data according to the target data monitoring strategy, and obtain the target data of the level corresponding to the abnormal data. The current data acquisition module is specifically used to collect the data at the layer in the container according to the collection frequency in the current data monitoring strategy, so as to obtain the current data at the layer. The target data monitoring strategy determination module includes: The historical data search unit is used to search for historical data corresponding to the abnormal data based on a preset expert experience database. The target strategy acquisition unit is used to obtain the target data monitoring strategy associated with the abnormal data based on the historical data monitoring strategy of the historical data. The device further includes: The target abnormal data operation and maintenance rule determination module is used to determine the target abnormal data operation and maintenance rule associated with the abnormal data after determining that the current data at the level in the container is abnormal data, based on the preset candidate abnormal data operation and maintenance rules. The investigation result acquisition module is used to investigate the corresponding level of the abnormal data according to the operation and maintenance rules of the target abnormal data, and obtain the investigation results.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the data monitoring method based on containerized services as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the data monitoring method based on containerized services as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the data monitoring method based on containerized services as described in any one of claims 1-6.

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