Omnibearing supervision method, system and equipment based on intelligent network ecological system

Abstract

The invention relates to an omnibearing supervision method, system and equipment based on an intelligent network ecological system, and the method comprises the steps: issuing an installation package of a special Agent monitor from a supervision platform to a terminal system to deploy the Agent monitor, collecting and returning the system operation data of a Java virtual machine to the supervision platform in real time through the Agent monitor, the prediction technology based on the time sequence model predicts possible performance fluctuation of the Java virtual machine in the future in real time, comprehensively identifies the performance fluctuation in time and helps to accurately solve potential performance bottlenecks or problems of the Java virtual machine. Compared with the traditional technology, by introducing a time sequence model for prediction, automatically analyzing stack information, automatically tracing problems and other innovative technologies, the scheme not only improves the efficiency and effect of the operation and maintenance work of the Java virtual machine, but also provides a powerful guarantee for realizing high availability, stability and safety of services.

Description

Technical Field

[0001] The present invention belongs to the technical field of network monitoring, and relates to an all-round monitoring method, system and equipment based on an intelligent network ecosystem. Background Art

[0002] With the popularization of Java technology, Java Virtual Machine (JVM) has become the main operating environment for enterprise-level applications. Java Virtual Machine is an abstract computer that is realized by simulating various computer functions on an actual computer. It has its own complete hardware architecture, such as processor, stack and register, and has a corresponding instruction system. Java Virtual Machine mainly consists of the following parts: class loader, runtime data area and execution engine. Class loader is responsible for loading Java classes from the file system, network or other sources and generating corresponding bytecodes. The runtime data area includes program counter, Java virtual machine stack, native method stack, heap and method area, which are used to store data during program execution. The execution engine is responsible for interpreting and executing bytecodes or just-in-time compiling them into native code and executing them.

[0003] The Java Runtime Environment (JRE) is a software package consisting of a Java virtual machine and other libraries, which provides an executable environment for Java programs. JRE contains Java's core class library, native method interface, native library and other components that support the running of Java programs. The functions of JRE include class file parsing, memory management, garbage collection, exception handling and security mechanisms, etc., to ensure the security and stability of Java code. However, due to the complexity and dynamic nature of JVM, monitoring and management of JVM has become a real challenge in practical applications. Existing monitoring tools can monitor JVM, but they can only provide limited monitoring information and require operation and maintenance personnel to manually query and parse logs, which is not only inefficient but may also delay troubleshooting and problem solving. In addition, it can only alarm abnormal indicators and is difficult to automatically trace the source of the problem, which cannot meet the comprehensive and real-time monitoring needs of JVM. Therefore, it is necessary to develop a monitoring technology that can monitor the JVM status comprehensively and in real time. Summary of the invention

[0004] In view of the problems existing in the above-mentioned traditional methods, the present invention proposes a comprehensive supervision method based on an intelligent network ecosystem, a comprehensive supervision system based on an intelligent network ecosystem, and a computer device, which can comprehensively and real-time monitor the status of the JVM.

[0005] In order to achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] On the one hand, a comprehensive supervision method based on an intelligent network ecosystem is provided, comprising the steps of:

[0007] Send the installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system;

[0008] Receive the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time;

[0009] Perform anomaly detection on system operation data based on time series model and obtain detection results;

[0010] When the detection result indicates that the Java virtual machine has abnormal indicators, the heap memory of the Java virtual machine is analyzed to obtain the memory identification result of the Java virtual machine;

[0011] Perform thread stack tracing on the Java virtual machine to obtain the diagnosis results of concurrency problems of the Java virtual machine;

[0012] The root cause of the Java virtual machine problem is located based on the memory identification results and the concurrent problem diagnosis results; the root cause of the problem is used to indicate the code block where the problem occurs.

[0013] On the other hand, a comprehensive supervision system based on the intelligent network ecosystem is also provided, including:

[0014] The installation package delivery module is used to deliver the installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system;

[0015] The data receiving module is used to receive the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time;

[0016] Anomaly detection module, used to detect anomalies in system operation data based on a time series model and obtain detection results;

[0017] The heap memory analysis module is used to perform heap memory analysis on the Java virtual machine to obtain memory identification results of the Java virtual machine when the detection result indicates that the Java virtual machine has abnormal indicators;

[0018] A problem diagnosis module is used to trace the thread stack of the Java virtual machine and obtain the concurrent problem diagnosis results of the Java virtual machine;

[0019] The problem location module is used to locate the root cause of the Java virtual machine problem based on the memory identification result and the concurrent problem diagnosis result; the root cause of the problem is used to indicate the code block where the problem occurs.

[0020] On the other hand, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned all-round supervision method based on the intelligent network ecosystem when executing the computer program.

[0021] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned comprehensive supervision method based on the intelligent network ecosystem are implemented.

[0022] One of the above technical solutions has the following advantages and beneficial effects:

[0023] The above-mentioned all-round supervision method, system and device based on the intelligent network ecosystem, by sending the installation package of the dedicated Agent monitor from the supervision platform to the terminal system, deploys the dedicated Agent monitor on the terminal system where the Java virtual machine is located, and the Agent monitor collects and returns the system operation data of the Java virtual machine to the supervision platform in real time, and then the supervision platform uses the prediction technology based on the time series model, with its advantage of being able to analyze the time trend and periodic changes in historical data, to make real-time predictions on the possible future performance fluctuations of the Java virtual machine, so as to timely and comprehensively identify and help accurately solve the potential performance bottlenecks or problems of the Java virtual machine. Compared with traditional technologies, by introducing innovative technologies such as prediction by time series models, automatic analysis of stack information and automatic problem tracing, the above-mentioned solution not only improves the efficiency and effectiveness of Java virtual machine operation and maintenance work, but also provides a strong guarantee for achieving high availability, stability and security of the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 is a flow chart of a comprehensive supervision method based on an intelligent network ecosystem in one embodiment;

[0026] Figure 2 A schematic diagram of an application process of an all-round supervision method based on an intelligent network ecosystem in one embodiment;

[0027] Figure 3 The module structure diagram of an all-round supervision system based on an intelligent network ecosystem in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0029] It should be noted that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The presentation of this phrase at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It will be appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the specification of the present invention and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0030] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0031] In one embodiment, Figure 1 As shown, a comprehensive supervision method based on an intelligent network ecosystem is provided, which may include the following processing steps S10 to S20:

[0032] S10, sending an installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system;

[0033] S12, receiving the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time;

[0034] S14, performing anomaly detection on the system operation data based on the time series model and obtaining the detection result;

[0035] S16, when the detection result indicates that the Java virtual machine has an abnormal indicator, perform heap memory analysis on the Java virtual machine to obtain a memory identification result of the Java virtual machine;

[0036] S18, performing thread stack tracing on the Java virtual machine to obtain a concurrency problem diagnosis result of the Java virtual machine;

[0037] S20, locating the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrent problem diagnosis result; the root cause of the problem is used to indicate the code block where the problem occurs.

[0038] It can be understood that first, the Agent monitor is deployed on the terminal system where the Java virtual machine is located; the supervision platform will customize and issue the corresponding Agent monitor installation package according to the specific monitoring requirements and operating environment of the terminal system. These installation packages have been carefully designed and tested and optimized, and can capture the system operation data of the Java virtual machine in real time to provide global monitoring capabilities. Then run the Agent monitor for real-time data feedback and monitoring; once the Agent monitor is deployed, it will collect the system operation data of the Java virtual machine in real time and send it back to the supervision platform. These system operation data will be processed by the supervision platform for anomaly detection, so as to effectively identify the abnormal behavior of the Java virtual machine. Finally, the root cause analysis of the problem is carried out; once anomalies are detected, the supervision platform will conduct in-depth analysis of these anomalies and use its powerful data mining and traceability capabilities to locate the root cause of the problem. This helps to quickly locate and solve potential system problems of the Java virtual machine and ensure the system stability and reliability of the Java virtual machine.

[0039] Specifically, Figure 2 As shown in the figure, the supervision platform sends the Agent monitor installation package; the supervision platform will customize and distribute the special Agent monitor installation package according to the monitoring requirements and environment configuration of the user's terminal system. This installation package contains the necessary software and configuration so that users can successfully deploy and run the Agent monitor on their systems.

[0040] The user installs, starts and runs the Agent monitor on the terminal system; after receiving the installation package of the Agent monitor, the user can install and configure the Agent monitor according to the instructions of the supervision platform. After the installation is complete, start the Agent monitor to start running and automatically monitor the specified Java virtual machine.

[0041] The Agent monitor continuously monitors the Java virtual machine and returns its system operation data in real time. After the Agent monitor is started and running, it will continuously collect the system operation data of the Java virtual machine in real time and transmit it back to the supervision platform in real time through the preset communication protocol. These system operation data include various indicators and status information during the system operation of the Java virtual machine.

[0042] Next, the supervision platform uses a time series model to analyze and predict the system operation data of the Java virtual machine. After receiving the system operation data of the Java virtual machine, the supervision platform uses the time series model to conduct in-depth analysis and prediction of the system operation data of the Java virtual machine. This time series model can predict the future operation trend of the Java virtual machine based on its historical data, so as to timely discover abnormal behavior or performance degradation of the Java virtual machine. Through this analysis, the supervision platform can effectively monitor the system health of the Java virtual machine and issue alarms or take corresponding management measures in a timely manner.

[0043] The supervision platform can analyze the root causes of the analyzed problems, which can include the following processes: First, perform heap memory analysis, such as in-depth analysis of the heap usage pattern, including the size, usage, allocation and recycling of heap memory. By performing heap memory analysis, the supervision platform can identify problems such as memory leaks and over-allocation of the Java virtual machine, thereby helping to optimize the dynamic memory management of the Java virtual machine and improve its performance and stability. Then, thread stack tracing is performed. The supervision platform helps developers understand the execution status and interaction of the threads of the Java virtual machine by capturing the execution path and call stack information of the threads of the Java virtual machine. Through thread stack tracing, complex concurrency problems in the Java virtual machine, such as deadlocks and race conditions, can be diagnosed, thereby accurately locating the root cause of the problem and optimizing the concurrent performance of Java applications. Finally, problem location is performed. Combining the processing results of heap memory analysis and thread stack tracing, the root cause of the Java virtual machine problem can be accurately located and the code block where the problem occurs can be parsed, so that users can quickly solve the exceptions of the Java virtual machine and tune the performance.

[0044] The above-mentioned all-round supervision method based on the intelligent network ecosystem deploys a dedicated Agent monitor on the terminal system where the Java virtual machine is located by sending a dedicated Agent monitor installation package from the supervision platform to the terminal system. The Agent monitor collects and returns the system operation data of the Java virtual machine to the supervision platform in real time. Then, the supervision platform uses the prediction technology based on the time series model, with its advantage of being able to analyze the time trend and periodic changes in historical data, to make real-time predictions on the possible future performance fluctuations of the Java virtual machine, so as to timely and comprehensively identify and help accurately solve the potential performance bottlenecks or problems of the Java virtual machine. Compared with traditional technologies, by introducing innovative technologies such as time series model prediction, automatic analysis of stack information, and automatic problem tracing, the above-mentioned solution not only improves the efficiency and effectiveness of Java virtual machine operation and maintenance work, but also provides a strong guarantee for achieving high availability, stability and security of the business.

[0045] In one embodiment, Figure 2As shown, regarding the above step S14, when the detection result indicates that the Java virtual machine has normal indicators, jump to step S12, so that when the Java virtual machine is not predicted to be abnormal, real-time monitoring is continuously performed.

[0046] In order to better understand the above scheme, this specification takes the deadlock problem of the user locating the Java virtual machine as an example. The user can first configure the server ip (such as 192.168.xx.xx) of the installation package of the Agent monitor on the supervision platform, login username xxx and login password xxxx and other information, and start the operation of the Agent monitor after the test connection is successful. Then the user can view the system operation data fed back in real time by the Agent monitor through the supervision platform, and find that multiple threads of the Java virtual machine are in a waiting state (such as thread blocking or long-term non-response). The supervision platform can help users view the current memory allocation of the Java virtual machine by analyzing the heap memory of the Java virtual machine, especially whether there is a memory leak, frequent garbage collection or the phenomenon of objects occupying memory for a long time. The user can use the supervision platform to generate a heap dump file, and use the tools of the supervision platform to analyze the specific memory usage mode of the Java virtual machine to eliminate the indirect impact caused by memory problems.

[0047] Next, thread dump analysis is performed. Through thread dump analysis, users can deeply view the status of each thread on the server of the Java virtual machine, including which threads are in running, waiting or blocked state. If the thread is in a long-term blocked or waiting state, the supervision platform will focus on identifying possible deadlock situations, that is, multiple threads are waiting for each other to release resources. Then locate the deadlock thread. If the supervision platform detects a deadlock, users can use thread dumps to analyze the root cause of the deadlock in detail. The supervision platform will display information about interdependent threads, including which threads hold lock resources and which threads are waiting for resources. Users can see the resource contention chain of the deadlock and determine the thread ID (identity identifier) ​​and related resource locking information that specifically caused the deadlock. Finally, to solve the deadlock problem, users can quickly locate the code block where the problem occurs through the supervision platform and modify the system code (or configuration) of the Java virtual machine. For example, the order of acquiring locks in the code can be adjusted, unnecessary synchronization blocks can be reduced, or the access method to shared resources can be optimized to avoid deadlock again.

[0048] In one embodiment, the time series model includes an ARIMA model or an LSTM model.

[0049] It is understandable that existing monitoring tools often rely on post-event data analysis, making it difficult to predict Java virtual machine problems before they occur, and their ability to trace the source of problems is relatively weak. However, prediction technologies based on time series models, such as ARIMA (AutoRegressive Integrated Moving Average) or LSTM (Long Short-Term Memory Network), can analyze the time trends and periodic changes in historical data, thereby predicting possible future performance fluctuations of Java virtual machines.

[0050] ARIMA model or LSTM model mainly analyzes time series data. Time series data is a set of observations arranged in chronological order, which records the state of a variable (such as performance indicators in system operation data: CPU usage, memory occupancy, network throughput, etc.) at different time points. Time series data usually contains characteristics such as time trend, periodic change, and random fluctuation. Therefore, ARIMA model or LSTM model can be used to predict the performance indicator changes of Java virtual machine system in the future, such as possible performance increase or decrease trend, periodic fluctuation, etc. When it is predicted that performance bottleneck or abnormal problem is about to occur, the above model can automatically issue a warning signal to remind operation and maintenance personnel to take measures to intervene in advance. The prediction results can provide data support for operation and maintenance decisions, such as resource allocation, expansion plan and troubleshooting. This forward-looking capability enables the system to issue warnings before performance bottlenecks or abnormal problems actually occur, thereby greatly improving the efficiency and effectiveness of operation and maintenance. Through continuous monitoring and prediction, operation and maintenance personnel can more actively manage the Java virtual machine system to ensure the stability and reliability of its system.

[0051] For example, the specific implementation of model prediction analysis is as follows: first, data preprocessing is performed, including removing missing values, outliers and other data cleaning operations in the time series data of the system operation data, smoothing processing (for data with large noise, smoothing technology can be used to reduce fluctuations) and data standardization / normalization (converting data of different dimensions to the same scale for better processing by the model), and then model selection and training are carried out. For the ARIMA model, the appropriate parameters (p, d, q) can be selected to build the model according to the autocorrelation and partial autocorrelation of the time series data. The parameter p is the autoregressive (AR) order, the parameter d is the difference order, and the parameter q is the moving average (MA) order. Then, the time series data in the historical system operation data is used for model training to find the best fitting parameters. For the LSTM model, a neural network structure can be constructed, including an input layer, an LSTM layer (possibly multiple layers), a fully connected layer, and an output layer. The model is trained using the time series data in the historical system operation data, and the weights are adjusted through the back propagation algorithm so that the model can accurately predict future time series data.

[0052] Next, feature extraction is performed. For the ARIMA model, the focus is on the autocorrelation and partial autocorrelation of the data. For the LSTM model, useful features need to be extracted from the time series data, such as the average, maximum, minimum, and standard deviation within the time window, as the input of the model.

[0053] Finally, the model is validated and optimized. The constructed validation set can be used to evaluate the performance of the model, such as mean square error (MSE) or root mean square error (RMSE). The model parameters or structure are adjusted according to the validation results to improve the prediction accuracy of the model.

[0054] By introducing innovative technologies such as timing model prediction, automatic analysis of stack information, and automatic problem tracing, the above solution not only improves the efficiency and effectiveness of Java virtual machine operation and maintenance, but also provides a strong guarantee for achieving high availability, stability, and security of the business. After actual use, the above solution can automatically trace the abnormal problems in the JVM, making it convenient for operation and maintenance personnel to quickly solve problems.

[0055] It should be understood that although Figure 1 and Figure 2 The steps in the method are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps can be executed in other orders. Figure 1 and Figure 2At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequentially, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0056] In one embodiment, Figure 3 As shown, a comprehensive supervision system 100 based on an intelligent network ecosystem is provided, which may include an installation package sending module 11, a data receiving module 13, an anomaly detection module 15, a heap memory analysis module 17, a problem diagnosis module 19 and a problem location module 21. Among them, the installation package sending module 11 is used to send the installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system. The data receiving module 13 is used to receive the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time. The anomaly detection module 15 is used to perform anomaly detection on the system operation data based on the time series model and obtain the detection result. The heap memory analysis module 17 is used to perform heap memory analysis on the Java virtual machine when the detection result indicates that the Java virtual machine has an indicator abnormality, and obtain the memory identification result of the Java virtual machine. The problem diagnosis module 19 is used to trace the thread stack of the Java virtual machine and obtain the concurrent problem diagnosis result of the Java virtual machine. The problem locating module 21 is used to locate the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrent problem diagnosis result; the root cause of the problem is used to indicate the code block where the problem occurs.

[0057] The above-mentioned all-round supervision system 100 based on the intelligent network ecosystem, by sending the installation package of the dedicated Agent monitor from the supervision platform to the terminal system, deploys the dedicated Agent monitor on the terminal system where the Java virtual machine is located, and the Agent monitor collects and returns the system operation data of the Java virtual machine to the supervision platform in real time, and then the supervision platform uses the prediction technology based on the time series model, with its advantage of being able to analyze the time trend and periodic changes in historical data, to make real-time predictions on the possible future performance fluctuations of the Java virtual machine, so as to timely and comprehensively identify and help accurately solve the potential performance bottlenecks or problems of the Java virtual machine. Compared with traditional technologies, by introducing innovative technologies such as prediction by time series models, automatic analysis of stack information and automatic problem tracing, the above-mentioned solution not only improves the efficiency and effect of Java virtual machine operation and maintenance work, but also provides a strong guarantee for achieving high availability, stability and security of the business.

[0058] In one embodiment, when the detection result indicates that the Java virtual machine has normal indicators, the anomaly detection module 15 jumps to the data receiving module 13.

[0059] In one embodiment, the time series model includes an ARIMA model or an LSTM model.

[0060] It can be understood that for the specific limitations of the comprehensive supervision system 100 based on the intelligent network ecosystem, please refer to the corresponding limitations of the comprehensive supervision method based on the intelligent network ecosystem above, which will not be repeated here.

[0061] Each module in the above-mentioned all-round supervision system 100 based on the intelligent network ecosystem can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of a device with data processing functions in the form of hardware, or can be stored in the memory of the aforementioned device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules. The aforementioned device can be, but is not limited to, various types of network monitoring devices already available in the art.

[0062] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following processing steps when executing the computer program: sending an installation package of an Agent monitor to a terminal system where a Java virtual machine is located; the installation package of the Agent monitor is customized according to monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system; receiving system operating data of the Java virtual machine collected and returned by the Agent monitor in real time; performing anomaly detection on the system operating data based on a time series model and obtaining a detection result; when the detection result indicates that an indicator of the Java virtual machine is abnormal, performing heap memory analysis on the Java virtual machine to obtain a memory identification result of the Java virtual machine; performing thread stack tracing on the Java virtual machine to obtain a concurrent problem diagnosis result of the Java virtual machine; locating the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrent problem diagnosis result; the root cause of the problem is used to indicate a code block where the problem occurs.

[0063] In one embodiment, when the processor executes the computer program, it can also implement the steps or sub-steps added in each embodiment of the above-mentioned all-round supervision method based on the intelligent network ecosystem.

[0064] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following processing steps are implemented: sending an installation package of an Agent monitor to a terminal system where a Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system; receiving system operating data of the Java virtual machine collected and returned by the Agent monitor in real time; performing anomaly detection on the system operating data based on a time series model and obtaining a detection result; when the detection result indicates that an indicator of the Java virtual machine is abnormal, performing heap memory analysis on the Java virtual machine to obtain a memory identification result of the Java virtual machine; performing thread stack tracing on the Java virtual machine to obtain a concurrency problem diagnosis result of the Java virtual machine; locating the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrency problem diagnosis result; the root cause of the problem is used to indicate a code block where the problem occurs.

[0065] In one embodiment, when the computer program is executed by a processor, it can also implement the steps or sub-steps added in each embodiment of the above-mentioned all-round supervision method based on the intelligent network ecosystem.

[0066] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (RambusDRAM, referred to as RDRAM) and interface dynamic random access memory (DRDRAM).

[0067] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A comprehensive supervision method based on an intelligent network ecosystem, characterized in that: Includes steps: Sending an installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system; Receiving the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time; Performing anomaly detection on the system operation data based on a time series model and obtaining a detection result; When the detection result indicates that an indicator of the Java virtual machine is abnormal, performing a heap memory analysis on the Java virtual machine to obtain a memory identification result of the Java virtual machine; Perform thread stack tracing on the Java virtual machine to obtain a concurrency problem diagnosis result of the Java virtual machine; Locate the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrent problem diagnosis result; The problem root is used to indicate the code block where the problem occurred.

2. The all-round supervision method based on the intelligent network ecosystem according to claim 1 is characterized in that: When the detection result indicates that the Java virtual machine has normal indicators, the process jumps to the step of receiving the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time.

3. The all-round supervision method based on the intelligent network ecosystem according to claim 1 or 2, characterized in that: The time series model includes an ARIMA model or an LSTM model.

4. A comprehensive supervision system based on an intelligent network ecosystem, characterized in that: include: An installation package delivery module is used to deliver an installation package of the Agent monitor to the terminal system where the Java virtual machine is located; the installation package of the Agent monitor is customized according to the monitoring requirements and operating environment of the terminal system, and is used to install and deploy the Agent monitor to the terminal system; A data receiving module, used for receiving the system operation data of the Java virtual machine collected and returned by the Agent monitor in real time; An anomaly detection module, used to perform anomaly detection on the system operation data based on a time series model and obtain a detection result; A heap memory analysis module, configured to perform heap memory analysis on the Java virtual machine to obtain a memory identification result of the Java virtual machine when the detection result indicates that an indicator of the Java virtual machine is abnormal; A problem diagnosis module, used for performing thread stack tracing on the Java virtual machine to obtain a concurrent problem diagnosis result of the Java virtual machine; A problem locating module is used to locate the root cause of the problem of the Java virtual machine according to the memory identification result and the concurrent problem diagnosis result; the root cause of the problem is used to indicate the code block where the problem occurs.

5. The all-round supervision system based on the intelligent network ecosystem according to claim 4 is characterized in that: The abnormality detection module jumps to the data receiving module when the detection result indicates that the Java virtual machine has normal indicators.

6. The all-round supervision system based on the intelligent network ecosystem according to claim 4 or 5, characterized in that: The time series model includes an ARIMA model or an LSTM model.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the comprehensive supervision method based on the intelligent network ecosystem described in any one of claims 1 to 3 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the comprehensive supervision method based on the intelligent network ecosystem described in any one of claims 1 to 3 are implemented.

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