Business system monitoring method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a service system monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps of obtaining target performance image data of a performance data monitoring interface of a target system in response to an analysis instruction of a system state of the target system; determining at least one performance index of the target performance image data and performance data matched with each performance index based on an image recognition model obtained by pre-training; and determining an abnormal result according to each performance index and the performance data matched with each performance index, and generating a system state analysis report matched with the target system based on the abnormal result. According to the scheme provided by the embodiment of the invention, the business system can be monitored in real time, so that the abnormal root cause of each business in the business system can be quickly and accurately determined.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a monitoring method, device, electronic equipment and storage medium for a business system. Background Art

[0002] When the target business implements the corresponding functions, the data interaction behind it is complex, the number of server nodes and services called is large, and there are complex networking and calling relationships between services. In order to quickly respond to abnormal situations in business processing, it is necessary to monitor the business system where the target business is located in real time.

[0003] At present, the root cause of business anomalies is mainly determined by monitoring and analyzing the log data of each business system. This method requires pre-establishment of monitoring rules and analysis of the acquired logs. Since the business system needs to process multiple businesses at the same time and the number of logs is huge, it is difficult to obtain the root cause of each business anomaly of the business system in real time.

[0004] How to monitor business systems in real time and quickly and accurately determine the root causes of various business anomalies in the business system is a key issue in industry research. Summary of the invention

[0005] The present invention provides a monitoring method, device, electronic device and storage medium for a business system, which can monitor the business system in real time, thereby quickly and accurately determining the root cause of each business anomaly in the business system.

[0006] According to one aspect of the present invention, a method for monitoring a business system is provided, the method comprising:

[0007] In response to an analysis instruction of a system status of a target system, acquiring target performance image data of a performance data monitoring interface of the target system;

[0008] Determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model;

[0009] An abnormal result is determined according to each of the performance indicators and the performance data matching each of the performance indicators, and a system status analysis report matching the target system is generated based on the abnormal result.

[0010] According to another aspect of the present invention, a monitoring device for a business system is provided, the device comprising:

[0011] A target performance image data acquisition module, used for acquiring target performance image data of a performance data monitoring interface of the target system in response to an analysis instruction of a system status of the target system;

[0012] A performance data determination module, used to determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model;

[0013] The abnormal result determination module is used to determine the abnormal result according to each of the performance indicators and the performance data matching each of the performance indicators, and generate a system status analysis report matching the target system based on the abnormal result.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business system monitoring method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the business system monitoring method described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the method for monitoring a business system according to any embodiment of the present invention is implemented.

[0020] The technical solution of the embodiment of the present invention obtains target performance image data of the performance data monitoring interface of the target system by responding to the analysis instruction of the system status of the target system; determines at least one performance indicator of the target performance image data and the performance data matching each of the performance indicators based on a pre-trained image recognition model; determines abnormal results according to each of the performance indicators and the performance data matching each of the performance indicators, and generates a system status analysis report matching the target system based on the abnormal results, so as to monitor the business system in real time, thereby quickly and accurately determining the root cause of each business anomaly in the business system.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 is a flow chart of a method for monitoring a business system provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flowchart of a method for monitoring a business system provided according to Embodiment 2 of the present invention;

[0025] Figure 3 is a schematic diagram of the structure of a monitoring device for a business system provided according to Embodiment 3 of the present invention;

[0026] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the monitoring method of a business system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 1 is a flowchart of a method for monitoring a business system according to Embodiment 1 of the present invention. This embodiment is applicable to the case of real-time monitoring of a business system. The method can be executed by a monitoring device of the business system. The monitoring device of the business system can be implemented in the form of hardware and / or software. The monitoring device of the business system can be configured in an electronic device such as a computer, a server, a cloud server or a tablet computer. Figure 1 As shown, the method includes:

[0031] Step 110 : In response to an analysis instruction of the system status of the target system, target performance image data of a performance data monitoring interface of the target system is acquired.

[0032] Among them, the target system can be any system, for example, a shopping system, a query system or a search engine, etc., which is not limited in this embodiment; the target system can include multiple businesses; illustratively, for a shopping system, it can include an order generation business, an order evaluation business or a payment business, etc., which is not limited in this embodiment.

[0033] In this embodiment, the system status of the target system may be the execution status of a certain business of the target system, for example, whether an abnormality occurs in the order generation business in the shopping system; the system status of the target system may be the execution status of all businesses of the target system, for example, whether an abnormality occurs in the order generation business, order evaluation business, and payment business in the shopping system; the system status of the target system may also be the usage status of the software or hardware of the target system, for example, the memory usage, file system status, or network status of the target system.

[0034] In an optional implementation of this embodiment, after receiving the analysis instruction for the system status of the target system, target performance image data of the performance data monitoring interface of the target system can be further obtained. Optionally, in this embodiment, the target performance image data can be obtained by obtaining the performance data monitoring interface of the target system and processing the performance data monitoring interface.

[0035] Among them, the performance data of the target system may include the utilization rate of each CPU constituting the target system, the memory utilization rate or the network traffic, etc., which is not limited in this embodiment; the performance image data includes the performance data of each performance indicator of the target system; it can be understood that the performance indicators of the target system can be the CPU, memory or network of the target system; the performance data of each performance indicator can be the utilization rate of each CPU, memory utilization rate or the network traffic, etc.

[0036] Optionally, in this embodiment, obtaining performance image data of the performance data monitoring interface of the target system may include: determining the target path of the performance data monitoring interface of the target system based on the system files of the target system, and jumping the current display page of the target system to the performance data monitoring interface; identifying the target area in the performance data monitoring interface, and intercepting the target area to obtain the target performance image data.

[0037] In an optional implementation of the present embodiment, after receiving an instruction to analyze the system status of the target system, the target path of the performance data monitoring interface can be further obtained from the system file of the target system, and the current display page of the target system can be further jumped to the performance data monitoring interface according to the target path; further, the performance data monitoring interface can be identified, the target area can be identified, and the target area can be intercepted in the performance data monitoring interface, and the intercepted target area can be determined as the target performance image data.

[0038] Optionally, in this embodiment, identifying the target area in the performance data monitoring interface may include: inputting the performance data monitoring interface into a pre-trained semantic segmentation model to obtain the target area; the semantic segmentation model is trained by multiple performance data monitoring interfaces and the annotation results of each performance data monitoring interface.

[0039] In an optional implementation of the present embodiment, after locating the performance data monitoring interface according to the target path of the performance data monitoring interface, the performance data monitoring interface can be further input into a pre-trained semantic segmentation model, and the performance data monitoring interface can be semantically segmented by the semantic segmentation model to obtain the target area.

[0040] Optionally, in this embodiment, a large number of performance data monitoring interfaces may be acquired in advance, and iterative training may be performed with the annotation results of each performance data monitoring interface to obtain the semantic segmentation model involved in this embodiment.

[0041] Step 120: Determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on the pre-trained image recognition model.

[0042] In an optional implementation of this embodiment, after the target performance image data is acquired, each performance indicator in the target performance image data and the performance data matching each performance indicator may be further identified.

[0043] Optionally, in this embodiment, after acquiring the target performance image data, the target performance image data can be input into a pre-trained image recognition model, and the target performance image data can be recognized by the image recognition model to output various performance indicators in the target performance image data and performance data matching each performance indicator.

[0044] In an optional implementation of the present embodiment, the image recognition model can be trained based on the following steps: obtaining multiple performance image data, and annotating each performance indicator in each performance image data and the performance data matching each performance indicator to obtain the annotation results of each performance image data; inputting each performance image data and the annotation results of each performance image data into the target deep learning model for iterative training; when the iteration stop condition is met, the image recognition model is obtained.

[0045] Among them, the target deep learning model can be a deep learning model such as YOLO or ResNet, which is not limited in this embodiment; the iteration stop condition can be an accuracy condition, for example, the accuracy reaches a set threshold during training, or an iteration number condition, for example, the number of iterations reaches 10,000 or 100,000 times, etc., which is not limited in this embodiment.

[0046] Optionally, in the present embodiment, multiple performance image data may be obtained, wherein each performance image data may be obtained through different business systems; further, each performance indicator in each performance image data and the performance data matching each performance indicator may be labeled. For example, each performance image data may be labeled using a labeling tool or manually, thereby obtaining a labeling result for each performance image data; further, each performance image data and the labeling result for each performance image data may be input into a target deep learning model for iterative training. When the number of iterations reaches a set threshold, for example, one hundred thousand times, an image recognition model may be output.

[0047] In an optional implementation of the present embodiment, determining at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model may include: inputting the target performance image data into the image recognition model to obtain at least one performance indicator of the target performance image data and performance data matching each of the performance indicators; the performance indicators include: processor, memory or network; the performance data include: processor utilization, memory utilization or network traffic.

[0048] Step 130: determine abnormal results according to each of the performance indicators and the performance data matching each of the performance indicators, and generate a system status analysis report matching the target system based on the abnormal results.

[0049] In an optional implementation of the present embodiment, after determining at least one performance indicator of the target performance image data and the performance data matching each of the performance indicators based on a pre-trained image recognition model, abnormal results can be further determined based on each performance indicator and the performance data matching each performance indicator, and a system status analysis report matching the target system can be determined based on the abnormal results.

[0050] The abnormal result may be abnormal data in each performance data.

[0051] Optionally, in this embodiment, abnormal results are determined according to each of the performance indicators and performance data matching each of the performance indicators, and a system status analysis report matching the target system is generated based on the abnormal results, which may include: determining whether the performance data matching each of the performance indicators exceeds the average value or standard deviation of each of the performance indicators; when it is determined that the target performance data matching the target performance indicator is greater than the average value or standard deviation of the target performance indicator, determining that the target performance data is abnormal; performing statistics on each of the target performance data to determine the machine name, abnormal cause or judgment rule that generates each of the target performance data, and generating the system status analysis report based on the machine name, abnormal cause or judgment rule that generates each of the target performance data.

[0052] The judgment rule may be based on an average value of the performance indicator or based on a standard deviation of the performance indicator, which is not limited in this embodiment.

[0053] In an optional implementation of the present embodiment, after obtaining the target performance data that matches the target performance indicator, the average value or standard deviation that matches the target performance indicator can be further obtained; further, if it is determined that the target performance data is greater than the average value that matches the target performance indicator, it can be determined that the target performance data is abnormal; further, the machine name, abnormal cause, or judgment rule that generates each target performance data can be determined; further, the system status analysis report can be generated based on the machine name, abnormal cause, or judgment rule that generates each target performance data.

[0054] The technical solution of this embodiment obtains target performance image data of the performance data monitoring interface of the target system by responding to the analysis instruction of the system status of the target system; determines at least one performance indicator of the target performance image data and the performance data matching each of the performance indicators based on a pre-trained image recognition model; determines abnormal results according to each of the performance indicators and the performance data matching each of the performance indicators, and generates a system status analysis report matching the target system based on the abnormal results, so as to monitor the business system in real time, thereby quickly and accurately determining the root cause of each business anomaly in the business system.

[0055] Embodiment 2

[0056] Figure 2 1 is a flow chart of a monitoring method for a business system provided according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with each optional solution in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0057] Step 210: In response to the analysis instruction of the system status of the target system, obtain target performance image data of the performance data monitoring interface of the target system.

[0058] Step 220: Determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on the pre-trained image recognition model.

[0059] Step 230: determine abnormal results according to each of the performance indicators and the performance data matching each of the performance indicators, and generate a system status analysis report matching the target system based on the abnormal results.

[0060] Step 240: link the system status analysis report to the display interface of the target system to display the system status analysis report; or, send the system status analysis report to the target user who issued the analysis instruction of the system status of the target system, so that the target user can view the system status analysis report.

[0061] In an optional implementation of this embodiment, after generating a system status analysis report matching the target system, the generated system status analysis report can be further linked to a display interface of the target system so that the display interface of the target system displays the system status analysis report.

[0062] In another optional implementation of this embodiment, after generating a system status analysis report that matches the target system, the system status analysis report can be further sent via email to the target user who issued the system status analysis instruction of the target system, so that the target user can view the system analysis report, determine the root cause of the system anomaly, and repair the system anomaly in a timely manner.

[0063] The solution of this embodiment, after generating a system status analysis report matching the target system, can further link the system status analysis report to the display interface of the target system to display the system status analysis report; or, send the system status analysis report to the target user who issued the analysis instruction of the system status of the target system, so that the target user can view the system status analysis report, so that the target user can quickly determine the root cause of the system abnormality and repair the system abnormality in time.

[0064] In order to better understand the monitoring method of the business system involved in this embodiment, a specific example is used below to illustrate it, which mainly includes:

[0065] 1. The user clicks the analysis button on the product interface to trigger the analysis step;

[0066] 2. When the [Screenshot Device] receives the analysis request, it can capture the image of the system monitoring interface and send it to the [Image Recognizer];

[0067] 3. [Image Recognizer] Recognizes screenshots based on deep learning models and extracts key information (such as CPU usage, memory usage, network traffic, etc.);

[0068] In this embodiment, the image recognizer is obtained through training. The specific training process includes: first, take a screenshot of the page to be taken, and then extract the features and input them into the model, such as machine 1, CPU usage, 85%, memory usage, 95%, prepare 20 screenshots and the data to be captured, and input them into the model (the data to be captured is the feature = output result); then use the [Screenshot] to automatically take a screenshot every minute, and then input it into the model to obtain the result, and manually mark whether it is correct; after the above model is ready, it can be connected to the [Data Analyzer];

[0069] 4. The [Image Recognizer] extracts key information, obtains fields and data, such as machine name, CPU usage, memory usage, etc., and connects and sends them to the [Data Analyzer];

[0070] 5. [Data Analyzer] connects to [Database], pulls data with the same machine name, CPU usage, and memory usage, and makes abnormal judgments on the historical data of the same indicator of the same entity. The judgment rules can be automatic or self-added;

[0071] Exemplarily, in this embodiment, when the index of the screenshot page is higher or lower than the average value or standard deviation, it is judged as abnormal; in this embodiment, if the CPU usage rate is set to exceed 80% (or other values), it is abnormal;

[0072] 6. After the [Data Analyzer] obtains the results, it sends them to the [Report Generator]. The report generator summarizes the pictures and results and displays the abnormal data. For example, a display result can be shown in Table 1:

[0073] Table 1

[0074]

[0075] 7. Page link report generator, directly display the results, and provide monitoring reports via email, instant messaging tools, etc.

[0076] The solution of this embodiment is to take screenshots of the system monitoring interface at regular intervals, perform image recognition on the generated monitoring images, extract key information and compare it with historical data, determine whether the system status is normal, and finally summarize all image recognition results to generate a monitoring report, which can quickly achieve comprehensive monitoring of the system.

[0077] Embodiment 3

[0078] Figure 3 1 is a schematic diagram of the structure of a monitoring device for a business system provided according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a target performance image data acquisition module 310, a performance data determination module 320 and an abnormal result determination module 330.

[0079] The target performance image data acquisition module 310 is used to acquire the target performance image data of the performance data monitoring interface of the target system in response to the analysis instruction of the system status of the target system;

[0080] A performance data determination module 320, configured to determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model;

[0081] The abnormal result determination module 330 is used to determine the abnormal result according to each of the performance indicators and the performance data matching each of the performance indicators, and generate a system status analysis report matching the target system based on the abnormal result.

[0082] The solution of this embodiment is to obtain the target performance image data of the performance data monitoring interface of the target system in response to the analysis instruction of the system status of the target system through the target performance image data acquisition module; determine at least one performance indicator of the target performance image data and the performance data matching each performance indicator based on the pre-trained image recognition model through the performance data determination module; determine the abnormal result according to each performance indicator and the performance data matching each performance indicator through the abnormal result determination module, and generate a system status analysis report matching the target system based on the abnormal result, so as to monitor the business system in real time, so as to quickly and accurately determine the root cause of each business anomaly in the business system.

[0083] In an optional implementation of this embodiment, the target performance image data acquisition module 310 is specifically used to determine the target path of the performance data monitoring interface of the target system according to the system file of the target system, and jump the current display page of the target system to the performance data monitoring interface;

[0084] A target area is identified in the performance data monitoring interface, and the target area is captured to obtain the target performance image data.

[0085] In an optional implementation of this embodiment, the target performance image data acquisition module 310 is further specifically used to input the performance data monitoring interface into a pre-trained semantic segmentation model to obtain the target area;

[0086] The semantic segmentation model is trained by multiple performance data monitoring interfaces and annotation results of each performance data monitoring interface.

[0087] In an optional implementation of this embodiment, the monitoring device of the business system further includes an image recognition model training module, which is used to obtain a plurality of performance image data, and annotate each performance indicator in each performance image data and performance data matching each performance indicator to obtain an annotation result of each performance image data;

[0088] Inputting each of the performance image data and the annotation results of each of the performance image data into a target deep learning model for iterative training;

[0089] When the iteration stopping condition is met, the image recognition model is obtained.

[0090] In an optional implementation of this embodiment, the performance data determination module 320 is specifically configured to input the target performance image data into the image recognition model to obtain at least one performance indicator of the target performance image data and performance data matching each of the performance indicators;

[0091] The performance indicators include: processor, memory or network;

[0092] Performance data includes: processor usage, memory usage, or network traffic.

[0093] In an optional implementation of this embodiment, the abnormal result determination module 330 is specifically used to determine whether the performance data matching each of the performance indicators exceeds the average value or standard deviation of each of the performance indicators;

[0094] When it is determined that the target performance data matching the target performance indicator is greater than the average value or standard deviation of the target performance indicator, it is determined that the target performance data is abnormal;

[0095] Statistics are collected for each of the target performance data to determine the machine name, abnormal cause or judgment rule that generates each of the target performance data, and the system status analysis report is generated based on the machine name, abnormal cause or judgment rule that generates each of the target performance data.

[0096] In an optional implementation of this embodiment, the monitoring device of the business system further includes: a display module, configured to link the system status analysis report to a display interface of the target system to display the system status analysis report;

[0097] Alternatively, the system status analysis report is sent to a target user who issues an analysis instruction for the system status of the target system, so that the target user views the system status analysis report.

[0098] The monitoring device for a business system provided in an embodiment of the present invention can execute the monitoring method for a business system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0099] Embodiment 4

[0100] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0101] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a monitoring method for a business system, the method comprising: in response to an analysis instruction of the system status of the target system, obtaining target performance image data of a performance data monitoring interface of the target system; determining at least one performance indicator of the target performance image data based on a pre-trained image recognition model, and performance data matching each of the performance indicators; determining abnormal results based on each of the performance indicators and the performance data matching each of the performance indicators, and generating a system status analysis report matching the target system based on the abnormal results.

[0104] In some embodiments, the monitoring method of the business system may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the monitoring method of the business system described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the monitoring method of the business system in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0110] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0112] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A monitoring method for a business system, characterized in that: include: In response to an analysis instruction of a system status of a target system, acquiring target performance image data of a performance data monitoring interface of the target system; Determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model; An abnormal result is determined according to each of the performance indicators and the performance data matching each of the performance indicators, and a system status analysis report matching the target system is generated based on the abnormal result.

2. The monitoring method of the business system according to claim 1, characterized in that: The step of obtaining the performance image data of the performance data monitoring interface of the target system includes: Determine the target path of the performance data monitoring interface of the target system according to the system file of the target system, and jump the current display page of the target system to the performance data monitoring interface; A target area is identified in the performance data monitoring interface, and the target area is captured to obtain the target performance image data.

3. The monitoring method of the business system according to claim 2, characterized in that: The identifying the target area in the performance data monitoring interface includes: Inputting the performance data monitoring interface into a pre-trained semantic segmentation model to obtain the target area; The semantic segmentation model is trained by multiple performance data monitoring interfaces and annotation results of each performance data monitoring interface.

4. The monitoring method of a business system according to claim 1, characterized in that: The image recognition model is trained based on the following steps: Acquire multiple performance image data, and annotate each performance indicator in each performance image data and performance data matching each performance indicator to obtain an annotation result of each performance image data; Inputting each of the performance image data and the annotation results of each of the performance image data into a target deep learning model for iterative training; When the iteration stopping condition is met, the image recognition model is obtained.

5. The monitoring method of the business system according to claim 4, characterized in that: The determining of at least one performance indicator of the target performance image data based on the pre-trained image recognition model, and the performance data matching each of the performance indicators, includes: Inputting the target performance image data into the image recognition model to obtain at least one performance indicator of the target performance image data and performance data matching each of the performance indicators; The performance indicators include: processor, memory or network; Performance data includes: processor usage, memory usage, or network traffic.

6. The monitoring method of a business system according to claim 1, characterized in that: The determining of abnormal results according to each of the performance indicators and the performance data matching each of the performance indicators, and generating a system status analysis report matching the target system based on the abnormal results, includes: Determining whether the performance data matching each of the performance indicators exceeds the average value or standard deviation of each of the performance indicators; When it is determined that the target performance data matching the target performance indicator is greater than the average value or standard deviation of the target performance indicator, it is determined that the target performance data is abnormal; Statistics are collected for each of the target performance data to determine the machine name, abnormal cause or judgment rule that generates each of the target performance data, and the system status analysis report is generated based on the machine name, abnormal cause or judgment rule that generates each of the target performance data.

7. The monitoring method of a business system according to claim 1, characterized in that: After generating a system status analysis report matching the target system based on the abnormal result, the method further includes: Linking the system status analysis report to a display interface of the target system to display the system status analysis report; Alternatively, the system status analysis report is sent to a target user who issues an analysis instruction for the system status of the target system, so that the target user views the system status analysis report.

8. A monitoring device for a business system, characterized in that: include: A target performance image data acquisition module, used for acquiring target performance image data of a performance data monitoring interface of the target system in response to an analysis instruction of a system status of the target system; A performance data determination module, used to determine at least one performance indicator of the target performance image data and performance data matching each of the performance indicators based on a pre-trained image recognition model; The abnormal result determination module is used to determine the abnormal result according to each of the performance indicators and the performance data matching each of the performance indicators, and generate a system status analysis report matching the target system based on the abnormal result.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business system monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the business system monitoring method according to any one of claims 1 to 7 when executed.

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