Service system monitoring method and system based on service dimension, and medium

Abstract

The invention discloses a business system monitoring method and system based on business dimensions and a medium. The method comprises the following steps: acquiring hardware and software information in a business system and running state data of hardware and software; constructing a service monitoring topological structure according to the incidence relation between the hardware and the software; mapping the running state data of the hardware and the software into the service monitoring topological structure to form monitoring data of a service dimension; and analyzing the monitoring data based on the service dimension to obtain a monitoring result of the service system, and outputting and displaying the monitoring result. According to the method and the system disclosed by the invention, the service system is monitored based on the service dimension, so that the service exception can be found in time, the stable operation of the service system is ensured, and the monitoring result is visually displayed by constructing the service system topological structure, so that the service system is integrally monitored, and meanwhile, the service exception can be timely found. And a more intuitive operation state of the service system is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise information monitoring, and particularly to a business system monitoring method, system and medium based on business dimensions. Background Art

[0002] With the development of information technology, business systems have become increasingly complex, and there are more and more businesses supported by business systems. Therefore, in order to ensure the normal operation of various businesses, the stability and security of business systems are becoming increasingly important.

[0003] Traditional business system monitoring methods are often limited to the hardware or software aspects of a single dimension, and it is difficult to comprehensively and truly reflect the actual operating status of the business system. For example: most monitoring systems are still limited to indicators such as CPU usage, memory occupancy, hard disk space, and network traffic of hardware resources; at the same time, the monitoring of software is also limited to parameters such as the number of client connections, data request frequency, and processing speed. This single-dimensional monitoring mode cannot effectively integrate software and hardware resources to form an overall monitoring of the business system.

[0004] Therefore, the prediction methods in the prior art need to be further improved. Summary of the Invention

[0005] In view of the deficiencies in the above-mentioned related technologies, the purpose of the present invention is to provide a business system monitoring method, system and medium based on business dimensions, which overcome the defect that the monitoring system in the prior art is limited to single-dimensional monitoring and cannot comprehensively and accurately monitor the business system.

[0006] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0007] In a first aspect, the present application discloses a business system monitoring method based on business dimensions, which includes:

[0008] Obtain the hardware and software information in the business system, as well as the operation status data of the hardware and software;

[0009] Construct a business monitoring topology result according to the association relationship between each piece of hardware and software;

[0010] Map the operation status data of the hardware and software into the business monitoring topology result to form monitoring data in business dimensions;

[0011] Analyze the monitoring data based on business dimensions to obtain the monitoring result of the business system, and output and display the monitoring result.

[0012] Optionally, the step of obtaining the hardware and software information in the business system, as well as the operation status data of the hardware and software includes:

[0013] Obtain the device information of each hardware in the business system, as well as the software information installed in each hardware device; among them, the device information includes: device name information, IP address information, and subordinate relationship information between devices.

[0014] Obtain the operating status information of the hardware according to the physical performance and status of the hardware, and obtain the operating status information of the software according to the performance of each application program and the device resource usage information.

[0015] Optionally, the step of constructing the business monitoring topology result according to the association relationship between each hardware and software includes:

[0016] Determine the relationship between the software and the physical machine or virtual machine according to whether the IP address of the monitored software is the same as the IP address of the physical machine or virtual machine.

[0017] Determine the physical machine to which each virtual machine belongs according to the detailed information of the physical machine and virtual machine scanned by the hyper-convergence protocol.

[0018] Obtain the connection relationship between the network device and the physical machine or virtual machine according to the SNMP protocol of the network device.

[0019] Construct a business monitoring topology structure according to the relationship between the software and the physical machine or virtual machine, the physical machine to which each virtual machine belongs, and the connection relationship between the network device and the physical machine or virtual machine.

[0020] Optionally, the step of obtaining the connection relationship between the network device and the physical machine or virtual machine according to the SNMP protocol of the network device includes:

[0021] Obtain the network MAC address and IP address of each physical machine and each virtual machine respectively through the SNMP protocol.

[0022] Obtain the MIB table information of all network devices respectively, and determine the first end network device according to the MIB table information corresponding to each network device.

[0023] Determine the device information connected to the first end network device according to the port information corresponding to the first end network device.

[0024] Mask the first end network device, use the device connected to the first end network device as the second end device, and search for the device information connected to the second end device according to the port information corresponding to the second end network device.

[0025] Block the second end network device, use the device connected to the second end network device as the third end network device, and find the device information connected to the third end device according to the port information corresponding to the third end device;

[0026] Block the third end network device, repeat the above process of using the device connected to the found Nth end device as the (N + 1)th end device, and find the device information connected to the (N + 1)th end device according to the port information corresponding to the (N + 1)th end device until the network device is found, so as to obtain the connection relationship between all network devices and physical machines or virtual machines.

[0027] Optionally, the steps of constructing the service monitoring topology structure according to the relationship between the software and the physical machine or virtual machine, the physical machines to which each virtual machine belongs, and the connection relationship between the network device and the physical machine or virtual machine include:

[0028] Integrate the connection relationship between the network device and the physical machine or virtual machine, the physical machines to which each virtual machine belongs, and the relationship between each software and the physical machine or virtual machine to construct the service monitoring topology structure.

[0029] Optionally, the steps of determining the first end network device according to the MIB table information corresponding to each network device include:

[0030] Obtain the iproute table information of each device, and find the MAC address of the next connected device according to the iproute table information of the device;

[0031] Judge whether the MAC address of the next connected device is the same as the MAC addresses of other devices. If the MAC address of the next connected device is different from the MAC addresses of other devices, the next connected device is the end network device.

[0032] Optionally, the steps of analyzing the monitoring data based on the service dimension to obtain the monitoring result of the service system include:

[0033] Input the monitoring data into the trained prediction model to obtain the predicted values of the running state data corresponding to different service entity types in different service dimensions;

[0034] Input the predicted values of the running state data corresponding to different service entity types in different service dimensions and the true values of the currently obtained running state data into the trained anomaly detection model to obtain the monitoring result output by the anomaly detection model, where the anomaly detection model is trained based on the service monitoring indicators and monitoring rules respectively configured for different service entity types in different service dimensions.

[0035] Optionally, before the step of analyzing the monitoring data based on the service dimension to obtain the monitoring result of the service system, the method further includes:

[0036] Obtain matching service monitoring metrics and monitoring rules according to the corresponding service entity types in the monitoring data;

[0037] Based on the service monitoring metrics and monitoring rules respectively configured for different service entity types in different service dimensions, and the historical monitoring data corresponding to each service entity type obtained, train a prediction model and an anomaly detection model.

[0038] In a second aspect, the present application provides a service system monitoring system based on service dimensions, which includes:

[0039] An information acquisition module, configured to acquire the hardware and software information in the service system, as well as the operation status data of the hardware and software;

[0040] A topology construction module, configured to construct a service monitoring topology result according to the association relationship between each piece of hardware and software;

[0041] A monitoring data generation module, configured to map the operation status data of the hardware and software into the service monitoring topology result to form monitoring data in service dimensions;

[0042] A monitoring data analysis module, configured to analyze the monitoring data based on service dimensions to obtain the monitoring result of the service system, and output and display the monitoring result.

[0043] In a third aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores one or more computer-readable programs, and the one or more computer-readable programs can be executed by one or more processors. When the computer-readable program is executed by the processor, the service system monitoring method based on service dimensions as described above is implemented.

[0044] Beneficial effects:

[0045] This embodiment discloses a business system monitoring method, system and medium based on business dimensions. By obtaining the hardware and software information in the business system, as well as the operation status data of the hardware and software; constructing a business monitoring topology result according to the association relationship between each piece of hardware and software; mapping the operation status data of the hardware and software into the business monitoring topology result to form monitoring data in business dimensions; analyzing the monitoring data based on business dimensions to obtain the monitoring result of the business system, and outputting and displaying the monitoring result. The method and system disclosed in this application monitor the business system based on business dimensions to timely detect business anomalies and ensure the stable operation of the business system. Moreover, the method of this application combines hardware and software information, constructs a topology result of the business system, visually displays the monitoring result, monitors the business system as a whole, obtains an accurate monitoring result, and at the same time provides a more intuitive operation status of the business system, providing technical support for users to more quickly locate abnormal problems and more efficiently troubleshoot faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the steps of the business system monitoring method based on business dimensions according to an embodiment of the present invention;

[0047] Figure 2 It is an information instance diagram of new business monitoring in the business system according to an embodiment of the present invention;

[0048] Figure 3 It is a display diagram of the attribution information of physical machines and virtual machines in the business system according to an embodiment of the present invention;

[0049] Figure 4 It is a flowchart of the steps for finding the association relationship between network devices and physical machines / virtual machines in the business system according to an embodiment of the present invention;

[0050] Figure 5 It is a topology relationship table between devices in the business system according to an embodiment of the present invention;

[0051] Figure 6 It is a schematic diagram of the topology result between devices in the business system according to an embodiment of the present invention;

[0052] Figure 7 It is a display diagram of alarm information in the topology result of the business system according to an embodiment of the present invention;

[0053] Figure 8 It is a classified display diagram of monitoring information in the business system according to an embodiment of the present invention;

[0054] Figure 9 It is a display diagram of the alarm content list in the business system according to an embodiment of the present invention;

[0055] Figure 10It is a diagram showing the information display of the corresponding IA help queried for the alarm content in the embodiment of the present invention;

[0056] Figure 11 It is a flow chart of the example steps of the adaptive monitoring rule in the embodiment of the present invention;

[0057] Figure 12 It is a schematic diagram of the analysis principle of the service dimension data in the embodiment of the present invention;

[0058] Figure 13 It is a principle block diagram of the service system monitoring system based on the service dimension in the embodiment of the invention. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] A business system is a system within an enterprise or organization for processing daily business operations, management, and integration of information flow. It is usually composed of multiple modules or components, and each module is designed with specific business functions or processes. The business system can cover multiple modules with different functions, so it is a comprehensive system. With the improvement of the automation level of the business system, the system structure has become more and more complex, so the operation safety of the business system is becoming more and more important.

[0061] Traditional monitoring methods are often limited to a single hardware or software level and cannot comprehensively reflect the actual operating conditions of the business system. When a problem occurs in the business system, it is impossible to determine whether it is a problem with the business system software itself or the hardware it depends on. Therefore, accurate monitoring judgments cannot be made, and furthermore, there is a lack of overall alarm analysis for all the software / hardware involved in the business itself, and overall monitoring index analysis and statistics cannot be given.

[0062] To solve the above problems in the prior art, this embodiment provides a business system monitoring method, system and medium based on business dimensions. By obtaining the hardware and software information in the business system, as well as the operation status data of the hardware and software; constructing a business monitoring topology result according to the association relationship between each hardware and software; mapping the operation status data of the hardware and software into the business monitoring topology result to form monitoring data in business dimensions; analyzing the monitoring data based on business dimensions to obtain the monitoring result of the business system, and outputting and displaying the monitoring result. The method and system disclosed in this embodiment comprehensively analyze software and hardware to quickly determine problems in the business system, and visually display the monitoring information based on the topology result, which provides convenience for users to comprehensively understand the business system monitoring information.

[0063] The following further describes a business system monitoring method, system and medium based on business dimensions disclosed in this embodiment with reference to the accompanying drawings.

[0064] This application discloses a business system monitoring method based on business dimensions, as Figure 1 shown, including:

[0065] Step S1, obtain the hardware and software information in the business system, as well as the operation status data of the hardware and software.

[0066] When it is necessary to monitor the business system to determine whether there is an abnormality in the business system, first, it is necessary to obtain various data in the business system, analyze based on the obtained various data, and then determine whether there is an abnormality. In this step, in order to achieve comprehensive monitoring of the business system, not only the data of the hardware in the business system is collected, but also the data of the software in the business system is collected. These data not only come from the business system itself, but also include various data generated during the operation of the business system.

[0067] Further, the hardware in the business system includes: servers, storage devices, network devices, terminal devices or other auxiliary devices (such as printers, scanners, etc.), and the software in the business system is various management systems or office automation systems, or other software that supports the daily operation and management activities of the enterprise.

[0068] Specifically, the step of obtaining the hardware and software information in the business system, as well as the operation status data of the hardware and software includes:

[0069] Obtain the device information of each hardware in the business system, as well as the software information installed in each hardware device; among them, the device information includes: device name information, IP address information, and subordinate relationship information between devices; obtain the operating status information of the hardware according to the physical performance and status of the hardware, and obtain the operating status information of the software according to the performance of each application program and the device resource usage information. The collected operating status data includes key performance indicators such as CPU, memory, disk, network, number of requests, number of responses, online time, number of operations, and response time.

[0070] There are different types of services in the business system. In order to comprehensively monitor the business system, different business monitoring modules can be established according to different business requirements to divide business monitoring based on business types, thereby improving the accuracy of monitoring. Combining Figure 2 As shown, taking the new business monitoring as an example of the OA system, it is possible to select how many hardware and software resources are included in this OA system, and group the added business monitoring for systematic monitoring of the business system.

[0071] Step S2: Construct a business monitoring topology result according to the association relationship between each hardware and software.

[0072] When obtaining various different data in the business system in the above step S2, a business monitoring topology result can be constructed according to the association relationship between the hardware and software in the business system.

[0073] The association relationship between hardware and software includes: the association relationship between a physical machine and software, the subordinate relationship between a physical machine and a virtual machine, and the association relationship between each network device and a physical machine / virtual machine. The monitoring topology result of the business system is constructed through the above association relationship between hardware and software.

[0074] Specifically, the step of constructing a business monitoring topology result according to the association relationship between each hardware and software includes:

[0075] Step S221: Determine the relationship between the software and the physical machine or virtual machine according to whether the IP address of the monitored software is the same as the IP address of the physical machine or virtual machine.

[0076] When determining the association relationship between software and a physical machine / virtual machine, the relationship between the software and the physical machine or virtual machine is determined by judging whether the IP address of the monitored software is the same as the IP address of the physical machine or virtual machine. If the IP address corresponding to the software is the same as the IP address of the physical machine or virtual machine, then the software belongs to that physical machine or virtual machine. For example: the IP address of the monitored software nginx is 192.168.31.23, and the IP address of the virtual machine rocky8.9-QA is also 192.168.31.23, then it is identified that nginx belongs to the virtual machine rocky8.9-QA.

[0077] Step S222: Determine the physical machine to which each virtual machine belongs according to the detailed information of the physical machines and virtual machines scanned by the hyper-converged protocol.

[0078] The construction of the hyper-converged protocol means that through software-defined means on a physical machine, computing resources, storage resources, and network resources are highly integrated and uniformly managed. In this architecture, the physical machine hosts the operation of all virtualization components, including virtual machines. A virtual machine is a logical computer created on a physical machine through virtualization technology. They share the hardware resources of the physical machine but are logically isolated from each other.

[0079] In a hyper-converged or virtualized environment, the physical machine information where a virtual machine is located can be obtained by viewing the detailed information of the virtual machine, so as to determine the physical machine associated with the virtual machine. It is also possible to find the name of the physical machine associated with the virtual machine by viewing the summary or configuration information of the virtual machine, so as to determine the physical machine to which the virtual machine belongs. As shown in Figure 3 Through the Nutanix protocol, it is scanned that there are virtual machines such as Docker-31.47 and Dev-02-31.24 under the physical machine ESXi-A, so as to determine that virtual machines such as Docker-31.47 and Dev-02-31.24 belong to the physical machine ESXi-A.

[0080] Step S223: Obtain the connection relationship between the network device and the physical machine or virtual machine according to the SNMP protocol of the network device.

[0081] In the business system, in addition to physical machines or virtual machines, many network devices are also set up. In this step, according to the SNMP protocol of the network devices, the topological relationships between each network device and the physical machine, or between each network device and the virtual machine in the business system can be discovered. Specifically, the topological relationships between each network device and the physical machine, or between each network device and the virtual machine include: discovery of the network MAC address / IP address of the physical machine / virtual machine, discovery of the network layer topology, discovery of the link layer topology, and discovery of the host layer topology. The discovery of the network layer topology mainly refers to the routers, subnets in the network, and the connection relationships between them. The discovery of the link layer topology mainly refers to the physical connection relationships between devices such as switches and routers in the network, as well as their connections to the hosts. The discovery of the host layer topology mainly refers to the host devices in the network and the connection relationships between the host devices. This includes identifying the host devices in the network, determining the network connections between them, and the services running on the host devices.

[0082] In one implementation, combined with Figure 4 As shown, the steps of obtaining the connection relationship between the network device and the physical machine or virtual machine according to the SNMP protocol of the network device include:

[0083] Step S231: Obtain the network MAC address and IP address of each physical machine and each virtual machine through the SNMP protocol respectively. In specific implementation, obtain the corresponding network MAC address / IP address of each physical machine / virtual machine through protocols such as the SNMP protocol, Agent protocol, and Nutanix.

[0084] Step S232: Obtain the MIB table information of all network devices respectively, and determine the first end network device according to the MIB table information corresponding to each network device.

[0085] The MIB table information includes: dot1dTpFdbTable information, dot1dBasePortTable information, ifTable information, ipRouteTable information, and ipNetToMediaTable information. After obtaining the above MIB table information, the connection relationship between each network device and the physical machine or virtual machine can be judged according to the corresponding field values in the table information.

[0086] In this step, first, determine the first end network device in the current connection relationship according to the MIB table information corresponding to each network device. Then, remove the determined first end network device from the current connection result, find the end network device in the connection result after removing the first end network device, and define the found end network device as the second end network device. Remove the found end network device in turn, repeat the process of finding the end network device in the network after removing the found end network device, and then establish a connection relationship for the end network devices found in each step, so as to obtain the connection relationship between each device in the entire network result, that is, the connection relationship between the network device and the physical machine or virtual machine.

[0087] The step of determining the first end network device according to the MIB table information corresponding to each network device in this step specifically includes:

[0088] Obtain the iproute table information of each device, and find the MAC address of the next connected device according to the iproute table information of the device; determine whether the MAC address of the next connected device is the same as the MAC addresses of other devices. If the MAC address of the next connected device is different from the MAC addresses of other devices, the currently found next connected device is the end network device.

[0089] After obtaining the iproute table information of each device, the MAC address of the next device can be found in the ARP table of the current device according to the ip address of the next device (nexthop) in the iproute table information. If the found MAC address of the next device matches the MAC address of other device ports, it means that the device corresponding to the matching device port is connected to the next device. Therefore, the next device found in this step is not the end network device. Otherwise, if the MAC address of the next device does not match the MAC addresses of other device ports, the next device found in this step is the end network device.

[0090] After finding the end network device, check the MAC addresses contained in the ARP table of the current device to determine whether other devices are the next connected devices. If the MAC addresses contained in the ARP table match the MAC addresses of other devices, find the port number and infindex of the local baseport according to the client MAC address, find the port information of the network device according to the infindex of the baseport, and obtain the association relationship between the end network device and other physical machines or virtual machines according to the obtained port information.

[0091] Step S233. Determine the device information connected to the first end network device according to the port information corresponding to the first end network device;

[0092] Step S233. Shield the first end network device, use the device connected to the first end network device as the second end device, and search for the device information connected to the second end device according to the port information corresponding to the second end network device;

[0093] Step S234. Shield the second end network device, use the device connected to the second end network device as the third end network device, and search for the device information connected to the third end device according to the port information corresponding to the third end device;

[0094] Step S234. Shield the third end network device, repeat the above process of using the device connected to the found Nth end device as the (N + 1)th end device, and search for the device information connected to the (N + 1)th end device according to the port information corresponding to the (N + 1)th end device until the network device is found, so as to obtain the connection relationship between all network devices and physical machines or virtual machines.

[0095] Specifically, when each end network device is found in the above steps S233 - S234, by checking the MAC addresses contained in the ARP table of the found end network device, determine whether other devices are the next connected devices. If the MAC addresses contained in the ARP table match the MAC addresses of other devices, then find the port number and infindex of the local baseport according to the client MAC address, find the port information of the network device according to the infindex of the baseport, and obtain the association relationship between the end network device and other physical machines or virtual machines according to the obtained port information.

[0096] Step S24. Construct a service monitoring topology structure according to the relationship between the software and the physical machine or virtual machine, the physical machines to which each virtual machine belongs, and the connection relationship between the network device and the physical machine or virtual machine.

[0097] When the relationship between the software and the physical machine or virtual machine, the belonging relationship between each virtual machine and the physical machine, and the connection relationship between all network devices and the physical machine or virtual machine are obtained respectively, the connection relationship between the network device and the physical machine or virtual machine, the physical machines to which each virtual machine belongs, and the relationship between each software and the physical machine or virtual machine can be integrated to construct a service monitoring topology structure.

[0098] As Figure 5 shown, according to the relationship between each device, first obtain the topology relationship table of the service system, and then generate the final service relationship topology diagram based on the topology relationship table. AsFigure 6 The figure shows the business topology diagram of the OA system, which includes multiple physical machines, various software installed on the physical machines, and switches connected to the physical machines.

[0099] Step S3: Map the operation status data of the hardware and software to the business monitoring topology result to form monitoring data in the business dimension.

[0100] After constructing the business monitoring topology result, the detailed information of the hardware, the detailed information of the software, and the operation status data of the hardware and software obtained in Step S1 are mapped to the business monitoring topology result to achieve the visual display of the entire business system.

[0101] Step S4: Analyze the monitoring data based on the business dimension to obtain the monitoring result of the business system, and output and display the monitoring result.

[0102] The visual display of the business monitoring topology result in the above Step S3 not only includes the topology structure between various devices in the business system, but also can include monitoring data information (monitoring overview), alarm list, and statistical reports, etc., which is convenient for users to understand the overall operation status of the business system from the business monitoring topology result.

[0103] In one implementation, the visual display of the business system can include: a business topology monitoring display module, a monitoring overview module, an alarm list display module, and a statistical report display module. As Figure 7 shown, the business topology monitoring display module not only contains the association relationship between the hardware / software involved in the business system, but also displays the real-time network traffic between the monitoring devices in the business system, the real-time abnormal situations in the business system, and the basic information of the devices involved in the business system. As Figure 8 shown, the monitoring overview module displays the power-on / off situation of the hardware, the start / stop situation of the software, the operating system occupancy ratio, the network ranking occupancy ratio, the CPU utilization distribution, and the memory utilization distribution, etc. involved in the business system. As Figure 9 shown and Figure 10 shown, the alarm list display module is used to display the alarm information of the hardware or software associated with the device. Through these alarm information, the corresponding AI help can be queried to help users quickly repair business anomalies and ensure the normal and stable operation of the business. The alarm statistics, device / software type distribution statistics, utilization ranking statistics, anomaly statistics, and early warning statistics can be displayed in the statistical report display module.

[0104] In order to obtain the above abnormal statistics, alarm information and other information, and map the above information to the business monitoring topology result for display, in this step, the monitoring data is analyzed based on the business dimension to obtain the monitoring result of the system, and the abnormal statistics and alarm information are obtained according to the monitoring result.

[0105] In order to achieve the accuracy of business system monitoring, in this step, according to different business entity types, business-related monitoring exception rules are customized. According to the business historical monitoring data and business changes, the business exception rules and exception strategies are automatically adjusted to improve the accuracy and adaptability of the monitoring system. Specifically, in this step, the business entity type is first configured. Generally, according to specific business requirements, different business entity types are defined, such as: OA business, ERP business, e-commerce business, etc. Each business entity type corresponds to a specific set of monitoring indicators and rules. Secondly, business monitoring indicators are generated according to the configured business entity type. Since the business monitoring indicators are closely related to the business entity type, the business monitoring indicators can be directly determined according to the business entity type. For example, for the e-commerce business, it focuses on monitoring indicators such as the number of requests per second, service response speed, and current number of connected users of the e-commerce website. Thirdly, business monitoring rules are generated according to the business monitoring indicators and business entity type. Since these rules will define how the system should respond under specific conditions, such as when an indicator exceeds a preset threshold. For example: If the number of connected users and the number of requests per second in the e-commerce system surge, when the number of connected users exceeds the limit value of the business monitoring indicator, the system will trigger an alarm to notify relevant personnel for further analysis and processing to ensure the stable and safe operation of the business system.

[0106] After different business entity types and the corresponding business monitoring indicators and business monitoring rules for each business entity type have been generated, the received hardware and software information is analyzed according to the business entity type and the corresponding business monitoring indicators and business monitoring rules for each business entity type to obtain the monitoring result.

[0107] In this step, the business monitoring rule can be to predict key abnormal indicators in a future period according to business characteristics by selecting a suitable machine learning algorithm, and dynamically adjust the current business system according to the prediction result.

[0108] Specifically, the step of analyzing the monitoring data based on the business dimension to obtain the monitoring result of the business system includes:

[0109] Step S41: Input the monitoring data into the trained prediction model to obtain the predicted values of the operation status data corresponding to different business entity types in different business dimensions.

[0110] Step S42: Input the predicted values and actual values of the operation status data corresponding to different business entity types in different business dimensions into the trained anomaly detection model to obtain the monitoring results output by the anomaly detection model. The anomaly detection model is trained based on the business monitoring metrics and monitoring rules configured for different business entity types in different business dimensions.

[0111] For example: Use a prediction algorithm to predict the number of users in the same time period next month based on the historical data trend of the number of users on an e-commerce website. When the actual user data for next month is obtained, compare the actual user data with the predicted data, and dynamically adjust the parameters of the prediction algorithm according to the comparison results.

[0112] Specifically, before the step of analyzing the monitoring data based on the business dimension to obtain the monitoring results of the business system, the following steps are further included:

[0113] According to the corresponding business entity type in the monitoring data, obtain the matching business monitoring metrics and monitoring rules;

[0114] Based on the business monitoring metrics and monitoring rules configured for different business entity types in different business dimensions, and the historical monitoring data corresponding to each business entity type obtained, train the prediction model and the anomaly detection model.

[0115] Taking the OA system as the business entity type below: Set the company's employee size to 100 people (corresponding parameter employees), set the working days to: 8:00 am - 6:00 pm on non-holiday days, and take the number of OA system operations as 30 as an example to illustrate the steps of the adaptive monitoring rules provided in this step in specific applications.

[0116] 1). Generate the monitoring metrics and monitoring rules for the system according to the OA business.

[0117] Ngn ix (Web server) monitoring metric: Number of client connections (Connections active, that is, the number of employees logging in to the client).

[0118] Specific parameter limitations: During working hours: The number of connections cannot be lower than 30% of employees, that is, 30 connections, and cannot be higher than 110% of employees, that is, 110 connections. Anomaly alarms are issued if the number of connections is lower or higher. During non-working hours: The number of connections cannot be higher than 20% of employees. If it is higher than 20, it means the system may be attacked or there are other anomalies, and an alarm is also issued.

[0119] MySQL (Database) Monitoring Metrics: Number of Queries per Second (Command Select per second).

[0120] Specific Parameter Limits: During working hours: The number of queries per second cannot be lower than (employees x 0.3) x 30, where 30 is the number of operations, i.e., 900 queries, and cannot be higher than 110% of employees multiplied by 30, i.e., 3300 queries. If the number of queries is lower or higher than this range, an exception alert will be sent. During non - working hours: The number of queries cannot be higher than 20% of employees multiplied by 30. If it is higher than 600, it indicates that the system may be under attack or there is some other anomaly, and an alert will also be sent.

[0121] Network Port Monitoring Metrics for the Application Server (corresponding to Nginx): Sending Rate and Receiving Rate.

[0122] During working hours: The sending / receiving rate cannot be lower than 3M / second and cannot be higher than 11M / second. If the sending / receiving rate is lower or higher than this range, an exception alert will be sent. During non - working hours: The sending / receiving rate cannot be higher than 2M / M. If it is higher, it indicates that the system may be under attack or there is some other anomaly, and an alert will also be sent.

[0123] The adaptive monitoring rule is: After the system has been running for some time, based on the historical data values, dynamically adjust the monitoring metrics and rules of the OA system. The specific algorithm is: Multiply the average of the highest values of the monitoring items for each day in the past month by 110% as the highest value, and multiply the average of the lowest values of the monitoring items for each day in the past month by 90% as the lowest value, and then dynamically adjust the monitoring metrics.

[0124] 2) Monitor the OA system according to the adaptive monitoring rule and send alerts.

[0125] Combined Figure 11 As shown, first obtain the highest number of client connections during working hours for each day of last month in Nginx of the OA system, and determine whether the highest number of client connections for each day during the current month's working hours reaches the alert upper limit. If it reaches, send an alert and dynamically adjust the upper limit value of the number of Nginx client connections during working hours.

[0126] Then obtain the highest number of client connections during non - working hours for each day of last month in Nginx of the OA system, and determine whether the number of Nginx client connections during the current month's non - working hours reaches the alert upper limit. If it reaches, send an alert and dynamically adjust the upper limit value of the number of Nginx client connections during non - working hours.

[0127] Similarly, obtain the minimum number of client connections during working hours of Nginx in the OA system last month, and determine whether the minimum number of client connections per day during the current month's working hours reaches the alarm upper limit. If it reaches, issue an alarm and dynamically adjust the lower limit value of the number of Nginx client connections during working hours. Also, obtain the minimum number of client connections during non-working hours of Nginx in the OA system last month, and determine whether the number of Nginx client connections during the current month's non-working hours reaches the alarm lower limit. If it reaches, issue an alarm and dynamically adjust the lower limit value of the number of Nginx client connections during non-working hours.

[0128] Combined with Figure 12 As shown, in the monitoring method provided in this embodiment, when analyzing the monitoring data based on service monitoring metrics and service exception rules, if a service exception is detected, the exception metrics are displayed on the monitoring overview, and at the same time, the alarm mechanism is triggered, and the exception information is sent via email or text message, etc., so that relevant personnel can analyze the exception and quickly restore the normal operation state of the service.

[0129] The service system monitoring method based on the service dimension provided in this embodiment obtains the correlation relationship between the hardware and software in the service system, constructs a monitoring topology diagram of the service system, analyzes the monitoring data through adaptive monitoring rules to detect service exceptions, triggers alarms and starts response processes at the same time, realizes the overall monitoring and analysis of all hardware and software in the service system, improves the accuracy of monitoring, and at the same time, through visual display of service monitoring results, realizes the rapid and accurate positioning of abnormal services or alarm times, providing technical support for users to solve exception information and quickly restore services.

[0130] On the basis of disclosing the above monitoring method, the present invention also discloses a service system monitoring system based on the service dimension, as Figure 13 shown, including:

[0131] An information acquisition module 100, configured to acquire hardware and software information in the service system, as well as the operation status data of the hardware and software; its function is as described in step S1.

[0132] A topology construction module 200, configured to construct a service monitoring topology result according to the correlation relationship between each hardware and software; its function is as described in step S2.

[0133] A monitoring data generation module 300, configured to map the operation status data of the hardware and software into the service monitoring topology result to form service dimension monitoring data; its function is as described in step S3.

[0134] The monitoring data analysis module 400 is used to analyze the monitoring data based on the business dimension, obtain the monitoring results of the business system, and output and display the monitoring results, and its functions are as described in step S4.

[0135] On the basis of disclosing the above-mentioned monitoring method and monitoring system, the present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores one or more computer-readable programs, and the one or more computer-readable programs can be executed by one or more processors. When the computer-readable program is executed by the processor, the monitoring method of the business system based on the business dimension is implemented.

[0136] The memory may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0137] The processor may be a central processing unit (CPU for short), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present application.

[0138] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, results, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, results, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0139] The above-mentioned embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A business system monitoring method based on business dimension, characterized in that: include: Obtain hardware and software information in the business system, as well as hardware and software operating status data; Build a business monitoring topology structure based on the relationship between various hardware and software; Mapping the operating status data of the hardware and software to the business monitoring topology structure to form business-dimensional monitoring data; The monitoring data is analyzed based on the business dimension to obtain the monitoring results of the business system, and the monitoring results are output and displayed.

2. The business system monitoring method based on business dimension according to claim 1 is characterized in that: The step of obtaining hardware and software information in the business system, as well as the operating status data of the hardware and software includes: Obtain the device information of each hardware in the business system and the software information installed in each hardware device; the device information includes: device name information, IP address information, and subordinate relationship information between devices; The hardware operating status information is obtained based on the physical performance and status of the hardware, and the software operating status information is obtained based on the performance of each application and device resource usage information.

3. The business system monitoring method based on business dimension according to claim 1 is characterized in that: The steps of constructing a service monitoring topology structure according to the association relationship between various hardware and software include: Determine the relationship between the software and the physical machine or virtual machine based on whether the monitored software IP address is the same as the physical machine or virtual machine IP address; According to the detailed information of physical machines and virtual machines scanned by the hyper-convergence protocol, determine the physical machine to which each virtual machine belongs; Obtain the connection relationship between network devices and physical machines or virtual machines based on the network device SNMP protocol; A business monitoring topology structure is constructed based on the relationship between the software and the physical machine or virtual machine, the physical machine to which each virtual machine belongs, and the connection relationship between the network device and the physical machine or virtual machine.

4. The business system monitoring method based on business dimension according to claim 3 is characterized in that: The step of obtaining the connection relationship between the network device and the physical machine or virtual machine according to the network device SNMP protocol includes: Obtain the network MAC address and IP address of each physical machine and each virtual machine through the SNMP protocol; Obtaining MIB table information of all network devices respectively, and determining the first terminal network device according to the MIB table information corresponding to each network device; Determine the device information connected to the first terminal network device according to the port information corresponding to the first terminal network device; Shielding the first end network device, taking the device connected to the first end network device as the second end device, and searching for device information connected to the second end device according to the port information corresponding to the second end network device; Shielding the second end network device, treating the device connected to the second end network device as the third end network device, and searching for device information connected to the third end device according to the port information corresponding to the third end device; Shield the third terminal network device, repeat the above process and use the device connected to the found N-th terminal device as the N+1-th terminal device, and search for the device information connected to the N+1-th terminal device according to the port information corresponding to the N+1-th terminal device, until the network device is queried, and obtain the connection relationship between all network devices and physical machines or virtual machines.

5. The business system monitoring method based on business dimension according to claim 3 or 4 is characterized in that: The steps of constructing a service monitoring topology structure according to the relationship between the software and the physical machine or virtual machine, the physical machine to which each virtual machine belongs, and the connection relationship between the network device and the physical machine or virtual machine include: The connection relationship between network devices and physical machines or virtual machines, the physical machine to which each virtual machine belongs, and the relationship between each software and the physical machine or virtual machine are integrated to build a business monitoring topology structure.

6. The business system monitoring method based on business dimension according to claim 4 is characterized in that: The step of determining the first end network device according to the MIB table information corresponding to each network device includes: Get the iproute table information of each device, and find the MAC address of the next connected device based on the iproute table information of the device; Determine whether the MAC address of the next device to be connected is the same as the MAC addresses of other devices. If the MAC address of the next device to be connected is different from the MAC addresses of other devices, the next device to be connected is an end network device.

7. The business system monitoring method based on business dimension according to claim 1 is characterized in that: The step of analyzing the monitoring data based on the business dimension to obtain the monitoring result of the business system includes: Input the monitoring data into the trained prediction model to obtain the predicted values ​​of the operating status data corresponding to different business entity types in different business dimensions; The predicted values ​​of the running status data corresponding to different business subject types in different business dimensions and the real values ​​of the running status data currently obtained are input into the trained anomaly detection model to obtain the monitoring results output by the anomaly detection model, wherein the anomaly detection model is trained based on the business monitoring indicators and monitoring rules respectively configured for different business subject types in different business dimensions.

8. The business system monitoring method based on business dimension according to claim 7 is characterized in that: Before the step of analyzing the monitoring data based on the business dimension to obtain the monitoring result of the business system, the method further includes: According to the corresponding business subject type in the monitoring data, obtain the matching business monitoring indicators and monitoring rules; The prediction model and anomaly detection model are trained based on the business monitoring indicators and monitoring rules configured for different business subject types in different business dimensions, and the historical monitoring data corresponding to each business subject type obtained.

9. A business system monitoring system based on business dimension, characterized in that: include: The information acquisition module is used to obtain the hardware and software information in the business system, as well as the operating status data of the hardware and software; The topology building module is used to build the business monitoring topology results based on the association between various hardware and software; A monitoring data generation module, used to map the operating status data of hardware and software to the business monitoring topology result to form business-dimensional monitoring data; The monitoring data analysis module is used to analyze the monitoring data based on the business dimension, obtain the monitoring results of the business system, and output and display the monitoring results.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more computer-readable programs, and the one or more computer-readable programs can be executed by one or more processors. When the computer-readable program is executed by the processor, the business system monitoring method based on business dimensions as described in any one of claims 1-8 is implemented.

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