Service optimization adjustment method and device based on parameter circulation topology

Abstract

The invention discloses a service optimization adjustment method and device based on parameter circulation topology. The method comprises the following steps: monitoring parameter circulation information in the operation process of a target service, so as to obtain the parameter circulation information in the operation process of the target service; generating a parameter circulation topological graph of the target service according to the parameter circulation information; obtaining the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topological graph, and obtaining a processing time result set of each parameter flow link; analyzing and processing the processing time result set and the parameter circulation topological graph to obtain a weight coefficient of each node in the parameter circulation topological graph; and when determining that the target service is abnormal, optimizing and adjusting each parameter flow link according to the weight coefficient. According to the method and the device, the technical problems that the optimization and adjustment mode generally depends on experience or trial and error and the reliability is relatively low when a problem occurs in a server system in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of data transmission technology, and in particular to a service optimization adjustment method and device based on parameter flow topology. Background Art

[0002] In complex service systems, the flow of parameters often involves multiple methods and service components. When a service problem occurs, how to quickly locate the source of the problem and effectively adjust and optimize it becomes a major challenge. Traditional optimization and adjustment methods often rely on experience or trial and error, which is inefficient and easily introduces new problems.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiment of the present invention provides a service optimization and adjustment method and device based on parameter flow topology, so as to at least solve the technical problem in the related art that when problems occur in the server system, the optimization and adjustment method generally relies on experience or trial and error and has low reliability.

[0005] According to one aspect of an embodiment of the present invention, a service optimization and adjustment method based on parameter flow topology is provided, comprising: monitoring parameter flow information during the operation of a target service to obtain the parameter flow information of the target service during the operation; generating a parameter flow topology graph of the target service according to the parameter flow information; acquiring the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology graph to obtain a processing time result set of each parameter flow link; analyzing and processing the processing time result set and the parameter flow topology graph to obtain a weight coefficient of each node in the parameter flow topology graph, wherein the weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the degree of importance of each node in the target service; when it is determined that an abnormality occurs in the target service, optimizing and adjusting each parameter flow link according to the weight coefficient.

[0006] Optionally, parameter flow information during the operation of the target service is monitored to obtain the parameter flow information during the operation of the target service, including: when determining that the target service is running, monitoring the operation process of the target service to obtain monitoring results; recording the flow relationship between different methods of each parameter in the target service according to the monitoring results to obtain the parameter flow information.

[0007] Optionally, generating a parameter flow topology diagram of the target service according to the parameter flow information includes: obtaining the flow relationship between each of the parameters between the different methods according to the parameter flow information; and constructing the parameter flow topology diagram corresponding to each of the parameters according to the flow relationship.

[0008] Optionally, the processing time of each parameter flow link corresponding to the parameter flow information is obtained according to the parameter flow topology diagram to obtain a processing time result set of each parameter flow link, including: determining each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram; obtaining the processing time of each parameter flow link; and summarizing the processing time of each parameter flow link to obtain the processing time result set.

[0009] Optionally, the processing time result set is analyzed and processed to obtain the weight coefficient of each node in the parameter flow topology map, including: inputting the processing time result set and the parameter flow topology map into a weight coefficient determination model, so as to use the weight coefficient determination model to process the processing time result set and the parameter flow topology map to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained through machine learning training using multiple groups of training data, and each group of the multiple groups of training data includes: a sample processing time result set and a sample parameter flow topology map, and sample weight coefficients corresponding to the sample processing time result set and the sample parameter flow topology map.

[0010] Optionally, when it is determined that the target service is abnormal, each parameter flow link is optimized and adjusted according to the weight coefficient, including: locating an abnormal node of the target service according to the weight coefficient; and optimizing and adjusting the abnormal node.

[0011] Optionally, locating the abnormal node of the target service according to the weight coefficient includes: determining the weight coefficient of each of the nodes in the parameter flow topology diagram; and locating the abnormal node in the parameter flow topology diagram according to the size of the weight coefficient of each of the nodes.

[0012] Optionally, optimizing and adjusting the abnormal node includes at least one of the following: adjusting a parameter processing method corresponding to the abnormal node; optimizing a resource allocation method for the abnormal node; and increasing cache resources at the abnormal node.

[0013] According to another aspect of an embodiment of the present invention, a service optimization and adjustment device based on parameter flow topology is also provided, including: a monitoring module, which is used to monitor the parameter flow information during the operation of the target service to obtain the parameter flow information of the target service during the operation; a generation module, which is used to generate a parameter flow topology map of the target service according to the parameter flow information; an acquisition module, which is used to obtain the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology map, and obtain a processing time result set of each parameter flow link; an analysis module, which is used to analyze and process the processing time result set and the parameter flow topology map to obtain a weight coefficient of each node in the parameter flow topology map, wherein the weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the importance of each node in the target service; an optimization module, which is used to optimize and adjust each parameter flow link according to the weight coefficient when it is determined that the target service has an abnormality.

[0014] Optionally, the monitoring module includes: a monitoring unit, used to monitor the operation process of the target service when it is determined that the target service is running, and obtain a monitoring result; a recording unit, used to record the flow relationship between different methods of each parameter in the target service according to the monitoring result, so as to obtain the parameter flow information.

[0015] Optionally, the generation module includes: a first acquisition unit, used to obtain the flow relationship between each parameter between the different methods according to the parameter flow information; and a construction unit, used to construct the parameter flow topology map corresponding to each parameter according to the flow relationship.

[0016] Optionally, the acquisition module includes: a determination unit, used to determine each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram; a second acquisition unit, used to obtain the processing time of each parameter flow link; and a summary unit, used to summarize the processing time of each parameter flow link to obtain the processing time result set.

[0017] Optionally, the analysis module includes: an input unit, used to input the processing time result set and the parameter flow topology map into a weight coefficient determination model, so as to use the weight coefficient determination model to process the processing time result set and the parameter flow topology map to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained through machine learning training using multiple groups of training data, and each group of the multiple groups of training data includes: a sample processing time result set and a sample parameter flow topology map, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow topology map.

[0018] Optionally, the optimization module includes: a third acquisition unit, used to locate the abnormal node of the target service according to the weight coefficient; and an optimization unit, used to optimize and adjust the abnormal node.

[0019] Optionally, the third acquisition unit includes: a determination subunit, used to determine the weight coefficient of each node in the parameter flow topology diagram; and a positioning subunit, used to locate abnormal nodes in the parameter flow topology diagram according to the size of the weight coefficient of each node.

[0020] Optionally, the optimization unit includes at least one of the following: an adjustment subunit, used to adjust the parameter processing method corresponding to the abnormal node; an optimization subunit, used to optimize the resource allocation method of the abnormal node; an increase subunit, used to increase the cache resources at the abnormal node.

[0021] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned service optimization and adjustment methods based on parameter flow topology.

[0022] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein the program, when running, executes any one of the above-mentioned service optimization and adjustment methods based on parameter flow topology.

[0023] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, which, when executed by a processor, perform any one of the above-mentioned service optimization and adjustment methods based on parameter flow topology.

[0024] In an embodiment of the present invention, parameter flow information during the operation of a target service is monitored to obtain the parameter flow information of the target service during the operation; a parameter flow topology diagram of the target service is generated based on the parameter flow information; the processing time of each parameter flow link corresponding to the parameter flow information is obtained based on the parameter flow topology diagram to obtain a processing time result set of each parameter flow link; the processing time result set and the parameter flow topology diagram are analyzed and processed to obtain a weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the degree of importance of each node in the target service; when it is determined that an abnormality occurs in the target service, each parameter flow link is optimized and adjusted according to the weight coefficient. Through the technical solution provided by the present invention, it is achieved that a parameter flow monitoring module is used to record parameter flow information, a parameter flow topology diagram is constructed, a processing time result set is obtained, and a weight coefficient is analyzed. At the same time, a machine learning algorithm or an expert system is used to optimize and adjust the service, and continuous monitoring and verification are carried out to ensure efficient and stable operation, thereby achieving the technical effect of monitoring, analyzing and optimizing the target service parameter flow link, improving the efficiency and reliability of the service, and further solving the technical problem in the related technology that when problems occur in the server system, the optimization and adjustment method generally relies on experience or trial and error, and has low reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a mobile terminal of a service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of a service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention;

[0028] Figure 3 is a flowchart of an optional service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention;

[0029] Figure 4 It is a schematic diagram of service optimization adjustment based on parameter flow topology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] As introduced in the background technology, in the related art, when problems occur in the server system, the optimization and adjustment method generally relies on experience or trial and error, and has low reliability. In view of the above defects, in the embodiments of the present invention, a service optimization and adjustment method and device based on parameter flow topology, a computer-readable storage medium, a processor, and a computer program product are provided.

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0034] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the service optimization adjustment method based on parameter flow topology in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] According to an embodiment of the present invention, a method embodiment of a service optimization and adjustment method based on parameter flow topology is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Figure 2 is a flow chart of a service optimization adjustment method based on parameter flow topology according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0038] Step S202: monitoring parameter flow information during the operation of the target service to obtain parameter flow information during the operation of the target service.

[0039] In this embodiment, monitoring the parameter flow information during the operation of the target service can provide a more comprehensive understanding of the dependencies and data flows between services. By analyzing the parameter flow information, key information such as the calling relationship between services, the data transfer path, and the frequency and scale of parameter transfer can be discovered. By analyzing the parameter flow information, the bottlenecks between services and the parameter transfer paths where the bottlenecks are located can be identified, so that targeted optimization and adjustment can be performed. For example, large amounts of data parameters that are frequently transmitted can be compressed or fragmented to reduce network transmission overhead; performance optimization can also be performed on services with higher calling frequencies to improve the response speed and stability of the overall service. In addition, by monitoring the parameter flow information, abnormal calls or abnormal data flows between services can be discovered, and timely investigation and repair can be performed to improve the reliability and security of the service.

[0040] It should be noted that the above-mentioned parameter flow information refers to the process of transferring and processing parameters in the system or software, which usually involves the input, output, transfer, processing and storage of parameters to ensure that the system can correctly obtain and process parameters. By reasonably managing parameter flow information, the reliability and security of the system can be improved.

[0041] Step S204: Generate a parameter flow topology diagram of the target service according to the parameter flow information.

[0042] In this embodiment, generating a parameter flow topology diagram of the target service can help analyze and understand the parameter transfer path of the target service, and better understand the dependencies and data transfer logic between services. By analyzing the parameter flow topology diagram, potential performance bottlenecks and potential optimization space can be found, so that targeted service optimization adjustments can be made. Potential bottlenecks and delays in the transmission path can be identified, and problems can be discovered and resolved in a timely manner, thereby improving the response speed and stability of the service.

[0043] It should be noted that the above-mentioned parameter flow topology diagram refers to a graphical representation of the transfer relationship and flow path between different parameters in a system, which can clearly understand the relationship between various parameters in the system, so as to better design and optimize the system. Usually, the parameter flow topology diagram is displayed in the form of nodes and edges, where nodes represent parameters in the system and edges represent the transfer relationship between parameters. Through this diagram, the flow path of parameters can be traced, the dependency between parameters can be understood, and possible problems and bottlenecks in the system can be found.

[0044] Step S206, obtaining the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram, and obtaining a processing time result set of each parameter flow link.

[0045] In this embodiment, by obtaining the processing time of each parameter flow link corresponding to the parameter flow information, the system can be helped to optimize service adjustments. By analyzing the processing time of each parameter flow link, the bottleneck links in the system can be identified, that is, links with long processing time or low efficiency. According to the processing time result set of the parameter flow link, the resource occupancy of each link in the system can be better understood, so as to reasonably allocate resources. By optimizing the processing time of the parameter flow link, the response time of the service can be shortened. By monitoring and optimizing the processing time of the parameter flow link, the risk of system errors and crashes can be reduced, and the stability and reliability of the system can be improved.

[0046] It should be noted that the above-mentioned parameter flow link refers to the path and method by which parameters flow from one link to another in a system or process.

[0047] Optionally, the parameter transfer link may include: parameter generation, parameter transfer, parameter processing, parameter storage, parameter output and other links.

[0048] Step S208, analyzing and processing the processing time result set and the parameter flow topology diagram to obtain the weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the influence of each node on other nodes except itself and the importance of each node in the target service.

[0049] In this embodiment, analyzing and processing the processing time result set and the parameter flow topology diagram can help determine the weight coefficient of each node, so as to better understand the influence and importance of each node in the entire service process. By analyzing the weight coefficient, key nodes and nodes with greater influence can be identified, so that targeted service optimization adjustments can be made, which can effectively optimize the service process. By analyzing the weight coefficient of each node, the importance of each node in the service can be better understood, and key nodes can be monitored and adjusted in a targeted manner to improve service efficiency and quality.

[0050] Step S210: When it is determined that the target service is abnormal, each parameter flow link is optimized and adjusted according to the weight coefficient.

[0051] In this embodiment, when it is determined that an abnormality occurs in the target service, adjusting each parameter flow link according to the weight coefficient can locate the problem more quickly and optimize the performance and stability of the service. By adjusting the weight coefficient, the parameter flow link can be dynamically adjusted, so that the system can adapt to different environments and load conditions more quickly, thereby improving the reliability and efficiency of the system.

[0052] Figure 3 is a flowchart of an optional service optimization adjustment method based on parameter flow topology according to an embodiment of the present invention. Figure 3 As shown in the figure, first deploy the parameter flow monitoring module to record the parameter flow information during the service operation process; then build the parameter flow topology, and generate the parameter flow topology diagram according to the information recorded by the monitoring module; then obtain the processing time result set, and obtain the processing time of each flow link through service logs, performance monitoring tools, etc.; then analyze the weight coefficient, and use machine learning algorithms or expert systems to analyze the weight coefficient according to the processing time result set and parameter flow topology; the next step is to optimize and adjust the service, locate the source of the problem and key nodes according to the weight coefficient when the problem occurs, and take corresponding optimization measures; finally, continue to monitor and verify to ensure that the optimized service maintains efficient and stable operation.

[0053] As can be seen from the above, in an embodiment of the present invention, the parameter flow information during the operation of the target service is monitored to obtain the parameter flow information of the target service during the operation; a parameter flow topology diagram of the target service is generated according to the parameter flow information; the processing time of each parameter flow link corresponding to the parameter flow information is obtained according to the parameter flow topology diagram, and a processing time result set of each parameter flow link is obtained; the processing time result set and the parameter flow topology diagram are analyzed and processed to obtain the weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the importance of each node in the target service; when it is determined that the target service is abnormal, each parameter flow link is optimized and adjusted according to the weight coefficient, so as to achieve the purpose of using a parameter flow monitoring module to record parameter flow information, construct a parameter flow topology diagram, obtain a processing time result set, and analyze the weight coefficient. At the same time, a machine learning algorithm or an expert system is used to optimize and adjust the service, and continuous monitoring and verification are performed to ensure efficient and stable operation, thereby achieving the technical effect of monitoring, analyzing and optimizing the parameter flow link of the target service, and improving the efficiency and reliability of the service.

[0054] The technical solution provided by the embodiment of the present invention solves the technical problem in the related art that when a problem occurs in the server system, the optimization and adjustment method generally relies on experience or trial and error and has low reliability.

[0055] According to the above-mentioned embodiment of the present invention, the parameter flow information during the operation of the target service is monitored to obtain the parameter flow information during the operation of the target service, including: when determining that the target service is running, monitoring the operation process of the target service to obtain the monitoring result; recording the flow relationship between different methods of each parameter in the target service according to the monitoring result to obtain the parameter flow information.

[0056] Optionally, the above monitoring methods may include but are not limited to: using monitoring tools, log monitoring, health checks, and integrated monitoring.

[0057] Optionally, the monitoring tools may include: Prometheus, Zabbix, Nagios and other tools.

[0058] It should be noted that the above-mentioned monitoring tools can monitor the performance indicators, resource utilization, error logs and other information of the service in real time, and generate reports or alarms; by monitoring the log files of the service, you can understand the operating status, error information and other information of the service, and you can use ELK Stack (a data analysis platform composed of three open source components: Elasticsearch, Logstash and Kibana) and other log management tools to collect, analyze and display log information in real time; the above-mentioned health check is to regularly check the health of the target service to check whether the service is running normally, whether the response time is normal, whether there are errors, etc., and you can use monitoring scripts or monitoring systems to regularly check the health of the service; the above-mentioned integrated monitoring can be to integrate the monitoring system into the target service to realize automatic monitoring and alarm.

[0059] In this embodiment, the service operation process can be monitored and the flow relationship of parameters between different methods can be recorded. By monitoring and recording the parameter flow information, performance bottlenecks, resource waste or potential problems that may exist in the service can be discovered, and targeted optimization and adjustment can be made. This can more effectively locate and solve problems in the target service, improve the performance and stability of the service, and optimize the service architecture design based on the parameter flow information to improve the maintainability and scalability of the system.

[0060] According to the above embodiment of the present invention, a parameter flow topology diagram of a target service is generated according to parameter flow information, including: obtaining the flow relationship between each parameter in different methods according to the parameter flow information; and constructing a parameter flow topology diagram corresponding to each parameter according to the flow relationship.

[0061] In this embodiment, by analyzing the parameter flow information, the flow relationship between each parameter in different methods can be clearly understood, thereby helping to optimize the performance and efficiency of the system. By constructing a parameter flow topology diagram, the correlation between the parameters can be intuitively displayed, which helps to discover potential performance bottlenecks and optimization space, and can better understand the operating mechanism of the system, find out the key parameters and links that affect service performance, and make targeted adjustments and optimizations.

[0062] like Figure 3 As shown, after recording the flow relationship between parameters in different methods, a parameter flow topology is constructed. For example, when the first parameter is formed into the second parameter through method A, and the second parameter is formed into the third parameter through method B and returned, the parameter flow topology formed is first parameter→second parameter→third parameter, that is, the parameter flow link.

[0063] According to the above-mentioned embodiment of the present invention, the processing time of each parameter flow link corresponding to the parameter flow information is obtained according to the parameter flow topology diagram, and a processing time result set of each parameter flow link is obtained, including: determining each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram; obtaining the processing time of each parameter flow link; and summarizing the processing time of each parameter flow link to obtain a processing time result set.

[0064] In this embodiment, determining each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram can help the system to more clearly understand the relationship and flow path between the parameters, so as to perform targeted optimization and adjustment. Obtaining the processing time of each parameter flow link can help the system identify the bottleneck link affecting service performance and find the key points that need to be optimized. By summarizing the processing time of each parameter flow link to obtain a processing time result set, the system can evaluate the overall service performance and provide a basis for optimization and adjustment.

[0065] Specifically, the processing time set is obtained as follows: first, based on the parameter flow topology, the processing time of each flow link is obtained, and then a processing time result set is formed. That is, the processing time of all links is collected together to form a processing time result set. The processing time can be obtained through service logs, performance monitoring tools, etc.

[0066] It should be noted that a single server processing may include multiple links, each of which has its own processing time. For example, for the above link, the time it takes for the parameters to pass through the AB method in sequence is the processing time for processing that link.

[0067] According to the above embodiment of the present invention, the processing time result set is analyzed and processed to obtain the weight coefficient of each node in the parameter flow topology diagram, including: inputting the processing time result set and the parameter flow topology diagram into a weight coefficient determination model, and using the weight coefficient determination model to process the processing time result set and the parameter flow topology diagram to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained by machine learning training using multiple groups of training data, and each group of the multiple groups of training data includes: a sample processing time result set and a sample parameter flow topology diagram, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow topology diagram.

[0068] In this embodiment, the processing time result set and the parameter flow topology diagram are processed through the weight coefficient determination model to obtain the weight coefficient of each node, which can help identify key nodes and paths so as to more effectively allocate resources and optimize processes during service optimization and adjustment. It can more accurately evaluate the importance and influence of each node, thereby optimizing service quality, improving efficiency and reducing costs. At the same time, the weight coefficient obtained through machine learning training can be dynamically adjusted according to actual conditions to adapt to the ever-changing environment and needs.

[0069] The analysis of the weight coefficient is as follows: the processing time result set is analyzed and sorted according to the length of the processing time. The cycle spent on each link is analyzed, and the weight coefficient is formed according to the impact of the change of the parameter node on the change of other nodes. For example: the A1 parameter is converted into the parameter B1 through a certain method (that is, the weight coefficient determination model), and then converted into the parameter C through a certain method; the A2 parameter is converted into the parameter B2 through only one method. Obviously, the weight of A1 is much larger than that of A2. The weight coefficient reflects the importance of the node in the service and the degree of influence on other nodes.

[0070] The following example illustrates the impact of node A changes on other nodes. When node A is completed in 5S, the entire service process is completed in 10S; when node A is completed in 10S, the entire service takes 20S, and node A has a larger weight; conversely, when node A is completed in 10S, the entire service takes 15S; or it is also 10S, then the proportion is relatively small.

[0071] Optionally, the above method may be an algorithm within the server or a data processing method.

[0072] According to the above embodiment of the present invention, when it is determined that the target service is abnormal, each parameter flow link is optimized and adjusted according to the weight coefficient, including: locating the abnormal node of the target service according to the weight coefficient; optimizing and adjusting the abnormal node.

[0073] In this embodiment, locating the abnormal node of the target service according to the weight coefficient can help identify the specific node that causes the service abnormality. By optimizing and adjusting the abnormal node, the performance and stability of the service can be effectively improved, service failures can be reduced, and the impact of failures on the system can be reduced. The root cause of the service abnormality can be quickly located, and targeted optimization and adjustment can be performed, thereby improving the reliability and stability of the entire service system. Through continuous optimization and adjustment, the performance of the service and user experience can be gradually improved, and the overall operating efficiency and effectiveness of the system can be improved.

[0074] In the above embodiment of the present invention, locating the abnormal node of the target service according to the weight coefficient includes: determining the weight coefficient of each node in the parameter flow topology diagram; locating the abnormal node in the parameter flow topology diagram according to the size of the weight coefficient of each node.

[0075] In this embodiment, determining the weight coefficient of each node in the parameter flow topology diagram can help the system more accurately evaluate the impact of each node on the system performance. By analyzing the size of the weight coefficient of each node, abnormal nodes can be located, the stability and performance of the system can be improved, and the impact of abnormal nodes on the entire system can be reduced. By timely locating and processing abnormal nodes, the system failure rate can be reduced, the system throughput and response speed can be improved, thereby improving the user experience.

[0076] like Figure 3 As shown in the figure, when encountering a problem (i.e., the target service has an abnormality), it is necessary to optimize and adjust the service and optimize the flow link according to the weight coefficient. First, locate the source of the problem and the key nodes according to the weight coefficient (prioritizely check whether there are any abnormalities in places with heavy weights); then, take optimization measures for these nodes, such as adjusting the implementation method, optimizing resource allocation, increasing cache, etc.; finally, ensure the optimization effect through continuous monitoring and verification.

[0077] Optionally, the above problems may include but are not limited to: insufficient bandwidth, network fluctuations, too many devices, and insufficient server performance.

[0078] In the above embodiment of the present invention, optimizing and adjusting the abnormal node includes at least one of the following: adjusting the parameter processing method corresponding to the abnormal node; optimizing the resource allocation method of the abnormal node; increasing the cache resources at the abnormal node.

[0079] In this embodiment, adjusting the corresponding parameter processing method at the abnormal node can help the system handle abnormal situations more effectively and improve the stability and reliability of the system. At the same time, optimizing the resource allocation method of the abnormal node can improve the performance and efficiency of the system and avoid resource waste. Increasing the cache resources at the abnormal node can alleviate the pressure of the abnormal node and improve the response speed and throughput of the system.

[0080] For example, if server resources are limited, resources should be allocated to links with large weights first. Links that can run independently should be allocated fewer resources and run slowly. They can be run in parallel to minimize response time. If the allocation plan is already optimal, consider adding cache and other measures.

[0081] The technical solution provided by the above embodiment of the present invention solves the following problems: 1) When a problem occurs in a complex server system, the traditional optimization and adjustment method often relies on experience or trial and error, which is inefficient and easily introduces new problems; 2) When a problem occurs in a complex server system, it is difficult to locate the problem. At the same time, it has the following beneficial effects: 1) It can comprehensively track the flow process of parameters and build an accurate parameter flow topology; 2) It can quickly locate the source of the problem and key nodes, and improve the accuracy and efficiency of optimization and adjustment; 3) It can optimize and adjust the service in a targeted manner according to the weight coefficient to ensure the optimization effect; 4) It can continuously monitor and verify the performance of the optimized service to ensure that the service remains efficient and stable.

[0082] From the above, it can be seen that in an embodiment of the present invention, by tracking the parameter flow process, a parameter flow topology is constructed, and the processing time result set and weight coefficient are obtained accordingly, so that when a problem occurs, the service can be efficiently optimized and adjusted based on the weight coefficient, thereby greatly shortening the server repair time.

[0083] That is, the above technical solution provided by the embodiment of the present invention comprehensively considers various factors and adopts a server optimization solution based on parameter flow topology. When an abnormality occurs in the server, it helps technicians to locate the problem more quickly, visualize the impact of invisible parameters flowing into the server, intuitively discover the influence of parameters flowing out of the server, and optimize the server resonance amount.

[0084] In addition, it should be noted that the execution entity and the entity collecting information are both servers.

[0085] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0087] According to an embodiment of the present invention, a service optimization adjustment device based on parameter transfer topology is provided for implementing the service optimization adjustment method based on parameter transfer topology. Figure 4 is a schematic diagram of service optimization adjustment based on parameter flow topology according to an embodiment of the present invention, such as Figure 4 As shown, the device includes: a monitoring module 401, a generating module 403, an acquiring module 405, an analyzing module 407 and an optimizing module 409. The service optimization and adjustment device based on parameter flow topology is described below.

[0088] The monitoring module 401 is used to monitor the parameter flow information during the operation of the target service to obtain the parameter flow information during the operation of the target service.

[0089] A generating module 403, used to generate a parameter flow topology diagram of a target service according to the parameter flow information;

[0090] The acquisition module 405 is used to acquire the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram, and obtain the processing time result set of each parameter flow link.

[0091] The analysis module 407 is used to analyze and process the processing time result set and the parameter flow topology diagram to obtain the weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the influence of each node on other nodes except itself and the importance of each node in the target service.

[0092] The optimization module 409 is used to optimize and adjust each parameter flow link according to the weight coefficient when it is determined that the target service is abnormal.

[0093] It should be noted here that the above-mentioned monitoring module 401, generation module 403, acquisition module 405, analysis module 407 and optimization module 409 correspond to steps S202 to S210 in the above-mentioned embodiments, and the five modules are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0094] As can be seen from the above, in the scheme recorded in the above embodiment of the present invention, first, the monitoring module can be used to monitor the parameter flow information during the operation of the target service to obtain the parameter flow information of the target service during the operation; then, the generation module can be used to generate a parameter flow topology map of the target service according to the parameter flow information; secondly, the acquisition module can be used to obtain the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology map, and obtain the processing time result set of each parameter flow link; then, the analysis module can be used to analyze and process the processing time result set and the parameter flow topology map to obtain the weight coefficient of each node in the parameter flow topology map, wherein, The weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the importance of each node in the target service. Finally, when the optimization module determines that the target service has an abnormality, it can optimize and adjust each parameter flow link according to the weight coefficient, thereby achieving the goal of using the parameter flow monitoring module to record parameter flow information, build a parameter flow topology diagram, obtain the processing time result set, and analyze the weight coefficient. At the same time, machine learning algorithms or expert systems are used to optimize and adjust the service, and continuous monitoring and verification are carried out to ensure efficient and stable operation. The technical effect of monitoring, analyzing and optimizing the target service parameter flow link is achieved, and the efficiency and reliability of the service are improved.

[0095] The technical solution provided by the embodiment of the present invention solves the technical problem in the related art that when a problem occurs in the server system, the optimization and adjustment method generally relies on experience or trial and error and has low reliability.

[0096] In an optional embodiment, the monitoring module includes: a monitoring unit, which is used to monitor the operation process of the target service when it is determined that the target service is running, and obtain monitoring results; a recording unit, which is used to record the flow relationship between different methods of various parameters in the target service according to the monitoring results to obtain parameter flow information.

[0097] In an optional embodiment, the generation module includes: a first acquisition unit, used to obtain the flow relationship of each parameter between different methods according to the parameter flow information; and a construction unit, used to construct a parameter flow topology map corresponding to each parameter according to the flow relationship.

[0098] In an optional embodiment, the acquisition module includes: a determination unit, used to determine each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram; a second acquisition unit, used to obtain the processing time of each parameter flow link; and a summary unit, used to summarize the processing time of each parameter flow link to obtain a processing time result set.

[0099] In an optional embodiment, the analysis module includes: an input unit, used to input the processing time result set and the parameter flow topology map into a weight coefficient determination model, so as to use the weight coefficient determination model to process the processing time result set and the parameter flow topology map to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained through machine learning training using multiple sets of training data, and each set of the multiple sets of training data includes: a sample processing time result set and a sample parameter flow topology map, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow topology map.

[0100] In an optional embodiment, the optimization module includes: a third acquisition unit, used for locating abnormal nodes of the target service according to the weight coefficient; and an optimization unit, used for optimizing and adjusting the abnormal nodes.

[0101] In an optional embodiment, the third acquisition unit includes: a determination subunit, used to determine the weight coefficient of each node in the parameter flow topology diagram; a positioning subunit, used to locate abnormal nodes in the parameter flow topology diagram according to the size of the weight coefficient of each node.

[0102] In an optional embodiment, the optimization unit includes at least one of the following: an adjustment subunit, used to adjust the corresponding parameter processing method at the abnormal node; an optimization subunit, used to optimize the resource allocation method of the abnormal node; an increase subunit, used to increase the cache resources at the abnormal node.

[0103] According to another aspect of an embodiment of the present invention, a processor is provided, the processor is used to run a program, wherein when the program is run, any one of the above-mentioned service optimization adjustment methods based on parameter flow topology is executed. According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, and when the computer instructions are executed by the processor, any one of the above-mentioned service optimization adjustment methods based on parameter flow topology is executed.

[0104] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned service optimization and adjustment methods based on parameter flow topology.

[0105] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: monitoring parameter flow information during the operation of the target service to obtain parameter flow information of the target service during the operation; generating a parameter flow topology map of the target service based on the parameter flow information; obtaining the processing time of each parameter flow link corresponding to the parameter flow information based on the parameter flow topology map, and obtaining a processing time result set of each parameter flow link; analyzing and processing the processing time result set and the parameter flow topology map to obtain a weight coefficient of each node in the parameter flow topology map, wherein the weight coefficient is used to indicate the degree of influence of each node on other nodes except itself and the degree of importance of each node in the target service; when it is determined that the target service has an abnormality, optimizing and adjusting each parameter flow link according to the weight coefficient.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when determining that the target service is running, monitoring the running process of the target service to obtain monitoring results; recording the flow relationship between different methods of each parameter in the target service according to the monitoring results to obtain parameter flow information.

[0108] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining the flow relationship between each parameter between different methods according to the parameter flow information; and constructing a parameter flow topology diagram corresponding to each parameter according to the flow relationship.

[0109] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: determining each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram; obtaining the processing time of each parameter flow link; and summarizing the processing time of each parameter flow link to obtain a processing time result set.

[0110] Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for performing the following steps: inputting a processing time result set and a parameter flow topology map into a weight coefficient determination model, and using the weight coefficient determination model to process the processing time result set and the parameter flow topology map to obtain a weight coefficient for each node, wherein the weight coefficient determination model is obtained through machine learning training using multiple sets of training data, and each set of the multiple sets of training data includes: a sample processing time result set and a sample parameter flow topology map, and sample weight coefficients corresponding to the sample processing time result set and the sample parameter flow topology map.

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: locating abnormal nodes of the target service according to the weight coefficients; and optimizing and adjusting the abnormal nodes.

[0112] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: determining the weight coefficient of each node in the parameter flow topology diagram; locating abnormal nodes in the parameter flow topology diagram according to the size of the weight coefficient of each node.

[0113] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: adjusting the parameter processing method corresponding to the abnormal node; optimizing the resource allocation method for the abnormal node; and increasing the cache resources at the abnormal node.

[0114] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0115] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0118] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A service optimization and adjustment method based on parameter flow topology, characterized in that: include: Monitoring the parameter flow information during the operation of the target service to obtain the parameter flow information during the operation of the target service; Generate a parameter flow topology diagram of the target service according to the parameter flow information; Acquire the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram, and obtain a processing time result set of each parameter flow link; Analyze and process the processing time result set and the parameter flow topology diagram to obtain a weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the influence degree of each node on other nodes except itself and the importance degree of each node in the target service; When it is determined that the target service is abnormal, each of the parameter flow links is optimized and adjusted according to the weight coefficient.

2. The service optimization and adjustment method based on parameter flow topology according to claim 1 is characterized in that: Monitoring parameter flow information during the operation of the target service to obtain the parameter flow information during the operation of the target service includes: When determining that the target service is running, monitoring the running process of the target service to obtain a monitoring result; The flow relationship between different methods of each parameter in the target service is recorded according to the monitoring result to obtain the parameter flow information.

3. The service optimization and adjustment method based on parameter flow topology according to claim 2 is characterized in that: Generating a parameter flow topology diagram of the target service according to the parameter flow information includes: Obtaining the flow relationship between the parameters in the different methods according to the parameter flow information; The parameter flow topology diagram corresponding to each of the parameters is constructed according to the flow relationship.

4. The service optimization and adjustment method based on parameter flow topology according to claim 1 is characterized in that: Acquiring the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram, and obtaining a processing time result set of each parameter flow link, including: Determine each of the parameter flow links corresponding to the parameter flow information according to the parameter flow topology diagram; Obtaining the processing time of each parameter transfer link; The processing time of each of the parameter transfer links is aggregated to obtain the processing time result set.

5. The service optimization and adjustment method based on parameter flow topology according to claim 1 is characterized in that: The processing time result set is analyzed and processed to obtain the weight coefficient of each node in the parameter flow topology diagram, including: The processing time result set and the parameter flow topology map are input into a weight coefficient determination model, so as to use the weight coefficient determination model to process the processing time result set and the parameter flow topology map to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained through machine learning training using multiple sets of training data, and each of the multiple sets of training data includes: a sample processing time result set and a sample parameter flow topology map, and sample weight coefficients corresponding to the sample processing time result set and the sample parameter flow topology map.

6. The service optimization and adjustment method based on parameter flow topology according to claim 1 is characterized in that: When it is determined that the target service is abnormal, each of the parameter flow links is optimized and adjusted according to the weight coefficient, including: Locating the abnormal node of the target service according to the weight coefficient; The abnormal nodes are optimized and adjusted.

7. The service optimization and adjustment method based on parameter flow topology according to claim 6 is characterized in that: Locating the abnormal node of the target service according to the weight coefficient includes: Determine the weight coefficient of each of the nodes in the parameter flow topology graph; The abnormal nodes in the parameter flow topology diagram are located according to the size of the weight coefficient of each node.

8. The service optimization and adjustment method based on parameter flow topology according to claim 6 is characterized in that: Optimizing and adjusting the abnormal node includes at least one of the following: Adjusting the parameter processing method corresponding to the abnormal node; Optimizing resource allocation for the abnormal node; Increase the cache resources at the abnormal node.

9. A service optimization and adjustment device based on parameter flow topology, characterized in that: include: A monitoring module, used to monitor the parameter flow information during the operation of the target service to obtain the parameter flow information during the operation of the target service; A generating module, used for generating a parameter flow topology diagram of the target service according to the parameter flow information; An acquisition module, used to acquire the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology diagram, and obtain a processing time result set of each parameter flow link; An analysis module, used to analyze and process the processing time result set and the parameter flow topology diagram to obtain a weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to indicate the influence degree of each node on other nodes except itself and the importance degree of each node in the target service; The optimization module is used to optimize and adjust each of the parameter flow links according to the weight coefficient when it is determined that the target service is abnormal.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the service optimization and adjustment method based on parameter flow topology according to any one of claims 1 to 8.

11. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the service optimization and adjustment method based on parameter flow topology described in any one of claims 1 to 8.

12. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the service optimization and adjustment method based on parameter flow topology described in any one of claims 1 to 8 is performed.

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