System operation state monitoring method and device, electronic equipment and storage medium

Abstract

The application discloses a system operation state monitoring method and device, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: in response to a monitoring instruction, sending an acquisition request of a target message to a message collection unit; receiving the target message of the system operation time returned by the message collection unit; analyzing index data and system operation state data based on the target message; and generating operation state monitoring data according to the index data and the system operation state data. The technical scheme is beneficial to realizing the interaction between an external service and a message collection unit, improving the flexibility of data collection, and avoiding collecting too much useless data.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and specifically relates to a system operation status monitoring method, device, electronic device and storage medium. Background Technology

[0002] With the popularization of information technology, in order to ensure the normal operation of applications, server administrators need to keep abreast of information such as the performance, operating status and execution status of the monitored system.

[0003] Traditional system performance and operational status monitoring solutions involve first developing monitoring scripts, then pushing these scripts to the monitored system to collect data. The scripts then periodically upload the collected data to a server for processing and generate reports that are displayed on the front end.

[0004] However, it lacks flexibility as it cannot achieve interaction between the server and the system's data collection end. During script execution, data collection is a continuous process of gathering and reporting, which increases the overhead of the monitored system itself. It cannot collect data only when needed and allow it to hibernate when not needed, and it collects too much useless data. In addition, while current monitoring methods can visually display the system's running status and performance over a certain period, their depth and breadth of process-level tracing are insufficient. Therefore, how to flexibly monitor the monitored system and collect the necessary information is a technical challenge that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a system operation status monitoring method, device, electronic device, and storage medium. The purpose is to provide tracking means and data support for troubleshooting performance problems throughout the entire process. Furthermore, during the monitoring process, the data to be collected can be flexibly controlled to avoid the energy consumption impact of monitoring operations on the monitored system.

[0006] In a first aspect, embodiments of this application provide a method for monitoring the operational status of a system, the method comprising:

[0007] In response to the start monitoring command, a request to acquire the target message is sent to the message collection unit; wherein the message type of the target message is determined based on the start monitoring command;

[0008] Receive the target message returned by the message collection unit during system runtime;

[0009] Based on the target message, the indicator data and system operation status data are parsed out;

[0010] Operational status monitoring data is generated based on the aforementioned indicator data and system operation status data; wherein, the operational status monitoring data includes: dynamic correlation between process operation and operational status, and / or, dynamic correlation between process increase / decrease and performance changes.

[0011] Furthermore, after generating operational status monitoring data based on the aforementioned indicator data and system operational status data, the method further includes:

[0012] In response to the operation status anomaly analysis command, the target process associated with the operation status anomaly event is determined based on the operation status monitoring data.

[0013] Furthermore, based on the operational status monitoring data, the target process associated with the operational status anomaly event is determined, including:

[0014] Obtain the occurrence time of abnormal events in the runtime status;

[0015] Identify process running data and / or process increase / decrease changes within a preset time period before and after the occurrence time;

[0016] Based on the process running data and / or process increase / decrease change data, determine the relevance of the preset running state abnormal events of each process;

[0017] The process that achieves a preset relevance is identified as the target process.

[0018] Furthermore, based on the aforementioned indicator data and system operating status data, operating status monitoring data is generated, including:

[0019] The first time process set and the second time process set are determined based on the aforementioned indicator data.

[0020] The first time-based process set and the second time-based process set are processed using a process increment algorithm to obtain newly added processes and destroyed processes;

[0021] Based on the newly added processes, the destroyed processes, and the system running status data, running status monitoring data is generated.

[0022] Furthermore, based on the aforementioned indicator data and system operating status data, operating status monitoring data is generated, including:

[0023] The process data for each process at each time point is determined based on the aforementioned indicator data; wherein, the process data includes the current process ID and the parent process ID of the current process;

[0024] Based on the parent process ID in the process data, construct a process ownership chain that changes over time;

[0025] Operational status monitoring data is generated based on the process ownership chain and the system operation status data.

[0026] Furthermore, based on the aforementioned indicator data and system operating status data, operating status monitoring data is generated, including:

[0027] Based on the aforementioned indicator data, a first time-based process set and a second time-based process set are determined; the first time-based process set and the second time-based process set are processed using a process increment algorithm to obtain newly added processes;

[0028] and,

[0029] The process data for each process at each time point is determined based on the aforementioned indicator data; wherein, the process data includes the current process ID and the parent process ID of the current process; and a process ownership chain that changes over time is constructed based on the parent process ID in the process data.

[0030] If there are child processes with the same name belonging to the same parent process, then the influence weight of each child process with the same name on the system running status data is determined based on the newly added process, the process ownership chain, and the system running status data.

[0031] Secondly, embodiments of this application provide a system operation status monitoring device, the device comprising:

[0032] The request module is used to send a request to the message collection unit to obtain the target message in response to the start monitoring command; wherein the message type of the target message is determined based on the start monitoring command.

[0033] The message receiving module is used to receive the target message returned by the message collection unit during system operation;

[0034] The data parsing module is used to parse the target message to extract indicator data and system operation status data.

[0035] The operation status monitoring data generation module is used to generate operation status monitoring data based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance change.

[0036] Furthermore, the device also includes:

[0037] The target process determination module is used to determine the target process associated with the abnormal operation event based on the operation status monitoring data in response to the operation status anomaly analysis command.

[0038] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0039] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0040] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0041] In this embodiment, in response to a monitoring start command, a request to acquire a target message is sent to the message collection unit; the system runtime target message returned by the message collection unit is received; indicator data and system operation status data are parsed from the target message; and operation status monitoring data is generated based on the indicator data and system operation status data. This technical solution facilitates interaction between external services and the message collection unit, improves the flexibility of data collection, and avoids collecting too much useless data. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the system operation status monitoring method provided in Embodiment 1 of this application;

[0043] Figure 2 This is an architecture diagram of the system operation status monitoring method provided in Embodiment 1 of this application;

[0044] Figure 3 This is a schematic diagram of the indicator data provided in Embodiment 1 of this application;

[0045] Figure 4 This is a flowchart illustrating another system operation status monitoring method provided in Embodiment 2 of this application;

[0046] Figure 5 This is a flowchart illustrating another system operation status monitoring method provided in Embodiment 3 of this application;

[0047] Figure 6 This is a flowchart illustrating another system operation status monitoring method provided in Embodiment 4 of this application;

[0048] Figure 7 This is a schematic diagram of the system operation status monitoring device provided in Embodiment 5 of this application;

[0049] Figure 8 This is a schematic diagram of the structure of the electronic device provided in Embodiment Six of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0051] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0052] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0053] The following description, in conjunction with the accompanying drawings, details the system operation status monitoring method, apparatus, electronic device, and storage medium provided in this application embodiment through specific embodiments and application scenarios.

[0054] Example 1

[0055] Figure 1 This is a flowchart illustrating the system operation status monitoring method provided in Embodiment 1 of this application. Figure 1 As shown, the specific steps include the following:

[0056] S101. In response to the start monitoring command, a request to acquire the target message is sent to the message collection unit; wherein the message type of the target message is determined based on the start monitoring command.

[0057] Figure 2 This is an architecture diagram of the system operation status monitoring method provided in Embodiment 1 of this application, as follows: Figure 2 As shown, this solution is used in scenarios where an external service monitors the monitored system, collects its packets, processes the collected packets, and provides the data to the external service. The external service can control the data processing end by calling its interface to enable or disable monitoring and query data. The data processing end can be deployed on local servers, cloud servers, or personal PCs, and is used to obtain performance packets from the packet collector, parse, calculate, cache, and provide query services. The monitored system can be a server, personal PC, or mobile device running operating systems such as Windows, Linux, or Android.

[0058] The monitoring enable command can be issued by an external server and can be used to instruct the monitoring service to be started. The packet collection unit can collect packets in the monitored system and respond to packet retrieval requests, returning the collected packets to the data processing end. Target packets can include the number of packets and their types. Packet types can include OSPF protocol packets, ICMP packets, etc. In this solution, optionally, the packet type of the target packets is determined based on the monitoring enable command. The specific packet type can be determined according to the monitoring enable command entered by the administrator in the external service, which can increase the flexibility of packet collection, avoid collecting too much useless data, and reduce system performance.

[0059] In this solution, external services, data processing terminals, and the monitored system are connected via a network. Administrators set the required target messages through the external services and send an enable monitoring command to the message request unit of the data processing terminal. After receiving the enable monitoring command, the administrator sends a request to the message collection unit to obtain the target messages.

[0060] S102. Receive the target message returned by the message collection unit during system operation.

[0061] The target message during system operation can be a message extracted during system operation that conforms to the message entered by administrators in external services. The data collection unit, i.e. Figure 2 The message collector in the system acquires the target message during system runtime and then sends the target message to the message request unit. The message request unit receives the target message during system runtime returned by the message collection unit.

[0062] S103. Parse the indicator data and system operation status data based on the target message.

[0063] Figure 3 This is a schematic diagram of the indicator data provided in Embodiment 1 of this application, as shown below. Figure 3 As shown, the indicator data may include the process's unique ID, parent process's unique ID, process name, process auto-command, and process auto-time. System operating status data may include working status, hibernation status, sleep status, and shutdown status, as well as blocked status data, ready status data, and running status data. In this solution, the received target message can be parsed by the message parsing unit to obtain the indicator data and system operating status data, and encapsulated into a specific data format, such as a JSON module unit. The data storage unit can be a module unit that stores performance data and various data derived from calculations for external querying.

[0064] S104. Generate operation status monitoring data based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance change.

[0065] Operational status monitoring data can include dynamic changes in CPU, memory, process count, and logs over a certain period of time, and may also include change trends and tracing chains. Change trends refer to the changes in corresponding data within a certain event. Tracing chains can be used to determine the relationships between processes. Optionally, in this scheme, the operational status monitoring data includes: dynamic correlation between process execution and operational status, and / or dynamic correlation between process increases / decreases and performance changes. A process can be a single execution of a program, or the process of a program running on a data set, and is an independent unit for system resource allocation and scheduling. Process execution can include the running process number, running process name, and process auto-time, etc. The operational status at this time can refer to the running state of the process. Process increases / decreases can refer to the increase or decrease in the number of applications within the monitored system. Process performance can be used to indicate whether the process can run normally.

[0066] In this solution, the data processing unit at the data processing terminal can generate runtime status monitoring data from performance data in a specific format through a series of calculations. The runtime status monitoring data can be used to understand the changes between running processes and their running states within the system, allowing for an overall observation of process operations. It can also be used to understand the relationship between changes in the number of processes added or removed and their performance, and to directly observe changes before and after a specific abnormal state.

[0067] In this embodiment, in response to an enable monitoring command, a request to acquire a target message is sent to the message collection unit; wherein, the message type of the target message is determined based on the enable monitoring command; the system receives the target message returned by the message collection unit during system operation; based on the target message, indicator data and system operation status data are parsed out; and operation status monitoring data is generated based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance changes. This facilitates interaction between external services and the message collection unit, improves the flexibility of data collection, and avoids collecting too much useless data.

[0068] In this embodiment, optionally, after generating operational status monitoring data based on the indicator data and system operational status data, the method further includes:

[0069] In response to the operation status anomaly analysis command, the target process associated with the operation status anomaly event is determined based on the operation status monitoring data.

[0070] Runtime anomaly analysis commands can also be issued by external servers and can be used to instruct the monitored system to perform state anomaly analysis. Runtime anomaly events can include data storage anomalies, type conversion anomalies, and attempts to illegally change thread states, etc.

[0071] In this solution, anomaly analysis commands can be set in an external service to analyze system anomalies, based on the administrator's needs. These commands are then sent to the data processing terminal. Upon receiving the commands, the data processing terminal parses the packets returned from the packet collection unit and obtains operational status monitoring data based on the parsed indicator data and system operational status data. This operational status monitoring data is then used to observe the target process associated with the anomaly. Specifically, the unique ID or process name of the target process associated with the anomaly can be determined through the operational status monitoring data, thus identifying which target process the operational status anomaly occurred in.

[0072] In this embodiment, in response to an anomaly analysis command, the target process associated with the anomaly event is determined based on the operational status monitoring data. When a system anomaly occurs, the process to which the anomaly event belongs can be quickly identified, and data support can be provided.

[0073] In this solution, optionally, determining the target process associated with the abnormal operation status event based on the operation status monitoring data includes:

[0074] Obtain the occurrence time of abnormal events in the runtime status;

[0075] Identify process running data and / or process increase / decrease changes within a preset time period before and after the occurrence time;

[0076] Based on the process running data and / or process increase / decrease change data, determine the relevance of the preset running state abnormal events of each process;

[0077] The process that achieves a preset relevance is identified as the target process.

[0078] In this solution, the occurrence time of an abnormal event can be the time when the abnormal message is collected. This solution can obtain the occurrence time of the abnormal event via the network. The preset duration before and after the occurrence time can be set by the administrator according to monitoring needs. Process running data can include process running status data. Process increase / decrease data can represent changes in the number of normally running processes. After determining the preset duration before and after the occurrence time, this solution can use a data identification unit to identify the process running data and / or process increase / decrease data within that preset duration. The preset abnormal running status events for each process can be events predetermined by the administrator based on historical monitoring data. The correlation of the preset abnormal running status events for each process can be used to determine whether each process is a target process or its similarity to a target process. The preset correlation can be determined based on the correlation between historical abnormal running status data and historical target processes.

[0079] In this scheme, the relevance of preset abnormal running state events for each process is determined based on the process running data and / or process increase / decrease data. Specifically, the process running data and process increase / decrease data can be compared with the running data and process increase / decrease data of preset abnormal running state events, and the relevance is determined based on the comparison result. Processes with a relevance reaching a preset relevance are identified as target processes. For example, the preset relevance can be set to 85%. If the relevance of preset abnormal running state events for each process is greater than or equal to 85%, then the process can be identified as a target process. Conversely, if the preset relevance is less than 85%, then the process can be identified as not a target process.

[0080] In this embodiment, the correlation between preset abnormal running state events and process running data and / or process increase / decrease data within a preset time period before and after the occurrence of an abnormal running state event can be determined. Processes with a correlation reaching a preset correlation are identified as target processes. This facilitates quickly determining whether the abnormal running state event occurred in the target process.

[0081] Example 2

[0082] Figure 4 This is a flowchart illustrating another system operation status monitoring method provided in Embodiment 2 of this application; as shown Figure 4 As shown, it includes the following steps:

[0083] S401. Determine the first time process set and the second time process set based on the indicator data.

[0084] The first time point process set can be the set of system processes monitored at a specific point in time. The second time point process set can be the set of system processes monitored at a different point in time. Both the first and second time points refer to the time of monitoring system processes, but they are different.

[0085] In this scheme, the first-time process set and the second-time process set can be determined based on the process numbers in the indicator data. For example, the first-time process set can be named process set A, and the second-time process set can be named process set B. Process set A, monitored at the first time, includes processes 1, 2, 3, 4, and 5. Process set B, monitored at the second time, includes processes 3, 4, 5, and 6.

[0086] S402. The first time process set and the second time process set are processed using the process increment algorithm to obtain the newly added process and the destroyed process.

[0087] The process increment algorithm can be used to calculate the number of processes added or removed from the system at different times. A newly added process is one that increases the process set at a second time relative to the process set at a first time. A destroyed process is one that decreases the process set at a second time relative to the process set at a first time.

[0088] For example, process set A is collected at 00:00:01, process set B is collected at 00:00:02, process set C = A ∩ B, process set D = AC, and system process set E = BC. A: The set of process data collected at time 00:00:01; B: The set of system process data collected at time 00:00:02; C: The set of process data that remained unchanged in the system from time 00:00:01 to 00:00:02; D: The set of process data that was destroyed in the system from time 00:00:01 to 00:00:02; E: The set of process data newly created in the system from time 00:00:01 to 00:00:02.

[0089] S403. Generate operating status monitoring data based on the newly added process, the destroyed process, and the system operating status data.

[0090] Specifically, this solution can generate runtime monitoring data based on the addition and destruction of processes, as well as the system runtime, showing the increase and decrease of processes over time.

[0091] In this embodiment, a process increment algorithm is used to process the first time process set and the second time process set to obtain newly added processes and destroyed processes. Based on the newly added processes and destroyed processes, as well as system running status data, running status monitoring data is generated. By calculating and saving the data, when a user needs to investigate performance problems that occur at a certain time, the changes in process additions and subtractions and the abnormal performance indicators of the corresponding processes can be quickly identified.

[0092] Example 3

[0093] Figure 5 This is a flowchart illustrating another system operation status monitoring method provided in Embodiment 3 of this application; as shown Figure 5 As shown, it includes the following steps:

[0094] S501. Determine the process data of each process at each time point based on the indicator data; wherein the process data includes the current process ID and the parent process ID of the current process.

[0095] Process data may include process names, which can be used to distinguish different processes. Optionally, in this scheme, the process data includes the current process ID and the parent process ID of the current process; the current process ID can be a unique identifier for the current process, and the parent process ID can be a unique identifier for the parent process of the current process. All resources of a child process are inherited from its parent process, receiving a copy of the parent process's resources. Therefore, in this scheme, when the current process experiences a runtime abnormality event, its parent process may also experience a runtime abnormality event. In this scheme, the current process ID and the parent process ID of the current process are determined based on indicator data.

[0096] S502. Based on the parent process ID in the process data, construct a process ownership chain that changes over time.

[0097] A process ownership chain can be a connection relationship between processes within a system determined based on the relationship between child processes and parent processes. For example, if process ID 110 is in process data 0 and parent process ID 120; process ID 120 is in process data 1 and parent process ID 130; and process ID 130 is in process data 2 and parent process ID 140, it can be understood that the parent process in process data 0 is a child process of the process in process data 1, and the parent process in process data 1 is a child process of the process in process data 2. Therefore, a process ownership chain can be generated in real time by calculating and tracking the parent process ID.

[0098] S503. Generate operating status monitoring data based on the process ownership chain and the system operating status data.

[0099] Specifically, this solution can generate operational status monitoring data by performing real-time calculations and tracking based on the process ownership chain and the system's operational status data.

[0100] In this embodiment, a process ownership chain can be generated by real-time calculation and tracking of the parent process's unique ID, allowing the tracking of the relationships between a specified process and other processes. Combined with the results of the process increment algorithm, the entire lifecycle of the specified process can be tracked.

[0101] Example 4

[0102] S601. Determine the first time process set and the second time process set based on the indicator data; process the first time process set and the second time process set using the process increment algorithm to obtain the newly added process.

[0103] In this scheme, a first time process set and a second time process set are determined based on the aforementioned indicator data; the first time process set and the second time process set are processed using a process increment algorithm to obtain new processes. For the specific implementation process, please refer to Embodiment 2.

[0104] S602. Determine the process data of each process at each time point based on the indicator data; wherein the process data includes the current process ID and the parent process ID of the current process; construct a process ownership chain that changes over time based on the parent process ID in the process data.

[0105] Based on Example 2, this solution determines the process ownership; for the specific implementation process, please refer to Example 3.

[0106] S603. If there are child processes with the same name belonging to the same parent process, then the influence weight of each child process with the same name on the system running status data is determined based on the newly added process, the process affiliation chain, and the system running status data.

[0107] Child processes with the same name can refer to processes belonging to the same parent process and having the same process name. In this scheme, it is not convenient to trace the relationship between child processes with the same name. The influence weight of system running status data can be used to represent the magnitude of the influence of each child process with the same name on the system running status data. The larger the weight coefficient, the greater the influence, and vice versa.

[0108] For example, from 00:00:00 to 00:00:01: System.exe creates LastContainer.exe-A; from 00:00:01 to 00:00:02: LastContainer.exe-A creates LastContainer-B; from 00:00:02 to 00:00:03: LastContainer.exe-A creates LastContainer.exe-C. Suppose that during the time period from 00:00:02 to 00:00:03, the LastContainer.exe application experiences performance issues and exhibits abnormal metrics. Based on this tracing chain, it can be quickly located and inferred that the creation of LastContainer.exe-C caused the problem, narrowing the investigation scope to the creation process of C.

[0109] In this embodiment, based on the newly added process, the process ownership chain, and the system running status data, the influence weight of each child process with the same name on the system running status data is determined. This allows for quick identification of the causes of application performance problems and abnormal indicators, narrowing the scope of investigation. It provides effective troubleshooting methods and data support for performance issues occurring within a certain time period and performance issues caused by processes with the same name.

[0110] Example 5

[0111] Figure 7 This is a schematic diagram of the system operation status monitoring device provided in Embodiment 5 of this application; as shown Figure 7 As shown, the device includes:

[0112] The request module 71 is used to send a request to the message collection unit to obtain the target message in response to the start monitoring command; wherein the message type of the target message is determined based on the start monitoring command;

[0113] The message receiving module 72 is used to receive the target message returned by the message collection unit during system operation;

[0114] Data parsing module 73 is used to parse indicator data and system operation status data based on the target message;

[0115] The operation status monitoring data generation module 74 is used to generate operation status monitoring data based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance change.

[0116] Furthermore, the device also includes:

[0117] The target process determination module is used to determine the target process associated with the abnormal operation event based on the operation status monitoring data in response to the operation status anomaly analysis command.

[0118] In this embodiment, in response to an enable monitoring command, a request to acquire a target message is sent to the message collection unit; wherein, the message type of the target message is determined based on the enable monitoring command; the system receives the target message returned by the message collection unit during system operation; based on the target message, indicator data and system operation status data are parsed out; and operation status monitoring data is generated based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance changes. This facilitates interaction between external services and the message collection unit, improves the flexibility of data collection, and avoids collecting too much useless data.

[0119] Example 6

[0120] like Figure 8 As shown in the figure, this application embodiment also provides an electronic device that can integrate the calibration device of the four-eye imaging system provided in this application embodiment. Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (Reference) Figure 8 The computer device includes: an input device 83, an output device 84, a memory 82, and one or more processors 81; the memory 82 is used to store one or more programs; when one or more programs are executed by one or more processors 81, the one or more processors 81 implement the calibration method of the four-lens imaging system as described in the above embodiment. The input device 83, output device 84, memory 82, and processor 81 can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0121] Example 7

[0122] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described system operation status monitoring method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0123] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0124] Example 8

[0125] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described system operation status monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0126] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0127] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0129] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0130] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for monitoring the operational status of a system, characterized in that, The method includes: In response to the start monitoring command, a request to acquire the target message is sent to the message collection unit; wherein the message type of the target message is determined based on the start monitoring command; Receive the target message returned by the message collection unit during system runtime; Based on the target message, the indicator data and system operation status data are parsed out; Operational status monitoring data is generated based on the aforementioned indicator data and system operation status data; wherein, the operational status monitoring data includes: dynamic correlation between process operation and operational status, and / or, dynamic correlation between process increase / decrease and performance changes; The step of generating operational status monitoring data based on the indicator data and system operational status data includes: determining a first time process set and a second time process set based on the indicator data; processing the first time process set and the second time process set using a process increment algorithm to obtain newly added processes and destroyed processes; and generating operational status monitoring data based on the newly added processes, the destroyed processes, and the system operational status data; or, Based on the aforementioned indicator data, process data for each process at each time point is determined. This process data includes the current process ID and the parent process ID of the current process. Based on the parent process ID in the process data, a process attribution chain that changes over time is constructed. Based on the process attribution chain and the system running status data, running status monitoring data is generated; or... Based on the indicator data, a first time-based process set and a second time-based process set are determined. A process increment algorithm is then used to process the first time-based process set and the second time-based process set to obtain newly added processes. Furthermore, based on the indicator data, process data for each process at each time point is determined. The process data includes the current process ID and the current process's parent process ID. Based on the parent process ID in the process data, a process ownership chain that changes over time is constructed. If there are child processes with the same name belonging to the same parent process, then based on the newly added processes, the process ownership chain, and the system running status data, the influence weight of each child process with the same name on the system running status data is determined.

2. The method according to claim 1, characterized in that, After generating operational status monitoring data based on the aforementioned indicator data and system operational status data, the method further includes: In response to the operation status anomaly analysis command, the target process associated with the operation status anomaly event is determined based on the operation status monitoring data.

3. The method according to claim 2, characterized in that, Based on the operational status monitoring data, the target process associated with the operational status anomaly event is determined, including: Obtain the occurrence time of abnormal events in the runtime status; Identify process running data and / or process increase / decrease changes within a preset time period before and after the occurrence time; Based on the process running data and / or process increase / decrease change data, determine the relevance of the preset running state abnormal events of each process; The process that achieves a preset relevance is identified as the target process.

4. A system operation status monitoring device, characterized in that, The device includes: The request module is used to send a request to the message collection unit to obtain the target message in response to the start monitoring command; wherein the message type of the target message is determined based on the start monitoring command. The message receiving module is used to receive the target message returned by the message collection unit during system operation; The data parsing module is used to parse the target message to extract indicator data and system operation status data. The operation status monitoring data generation module is used to generate operation status monitoring data based on the indicator data and system operation status data; wherein, the operation status monitoring data includes: dynamic correlation between process operation and operation status, and / or, dynamic correlation between process increase / decrease and performance change; The step of generating operational status monitoring data based on the indicator data and system operational status data includes: determining a first time process set and a second time process set based on the indicator data; processing the first time process set and the second time process set using a process increment algorithm to obtain newly added processes and destroyed processes; and generating operational status monitoring data based on the newly added processes, the destroyed processes, and the system operational status data; or, Based on the aforementioned indicator data, process data for each process at each time point is determined. This process data includes the current process ID and the parent process ID of the current process. Based on the parent process ID in the process data, a process attribution chain that changes over time is constructed. Based on the process attribution chain and the system running status data, running status monitoring data is generated; or... Based on the indicator data, a first time-based process set and a second time-based process set are determined. A process increment algorithm is then used to process the first time-based process set and the second time-based process set to obtain newly added processes. Furthermore, based on the indicator data, process data for each process at each time point is determined. The process data includes the current process ID and the current process's parent process ID. Based on the parent process ID in the process data, a process ownership chain that changes over time is constructed. If there are child processes with the same name belonging to the same parent process, then based on the newly added processes, the process ownership chain, and the system running status data, the influence weight of each child process with the same name on the system running status data is determined.

5. The apparatus according to claim 4, characterized in that, The device further includes: The target process determination module is used to determine the target process associated with the abnormal operation event based on the operation status monitoring data in response to the operation status anomaly analysis command.

6. An electronic device, characterized in that, The system includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the system operation status monitoring method as described in any one of claims 1-3.

7. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the system operation status monitoring method as described in any one of claims 1-3.

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