Service Optimization and Adjustment Method and Apparatus Based on Parameter Flow Topology

Abstract

This invention discloses a service optimization and adjustment method and apparatus based on parameter flow topology. The method includes: monitoring parameter flow information during the operation of a target service to obtain parameter flow information during the operation of the target service; generating a parameter flow topology diagram 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 diagram to obtain a processing time result set for each parameter flow link; 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; and optimizing and adjusting each parameter flow link according to the weight coefficient when an anomaly is determined in the target service. This invention solves the technical problem in related technologies where optimization and adjustment methods in server systems generally rely on experience or trial and error, resulting in low reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data transmission, and in particular, to a service optimization adjustment method and device based on parameter flow transfer topology. BACKGROUND

[0002] In a complex service system, the flow transfer process 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 becomes a major challenge. Traditional optimization adjustment methods often rely on experience or trial and error, which is inefficient and can easily introduce new problems.

[0003] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0004] The embodiments of the present application provide a service optimization adjustment method and device based on parameter flow transfer topology, to at least solve the technical problem that in related technologies, when a problem occurs in a server system, the optimization adjustment method generally relies on experience or trial and error, and the reliability is low.

[0005] According to an aspect of an embodiment of the present application, a service optimization adjustment method based on parameter flow transfer topology is provided, comprising: monitoring parameter flow transfer information in the running process of a target service to obtain the parameter flow transfer information in the running process of the target service; generating a parameter flow transfer topology graph of the target service according to the parameter flow transfer information; obtaining the processing time of each parameter flow transfer link corresponding to the parameter flow transfer information according to the parameter flow transfer topology graph, to obtain a processing time result set of the each parameter flow transfer link; analyzing and processing the processing time result set and the parameter flow transfer topology graph to obtain a weight coefficient of each node in the parameter flow transfer topology graph, wherein the weight coefficient is used to represent 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 has an abnormality, optimizing and adjusting each parameter flow transfer link according to the weight coefficient.

[0006] Optionally, monitoring the parameter flow transfer information in the running process of a target service to obtain the parameter flow transfer information in the running process of the target service comprises: when it is determined that the target service is running, monitoring the running process of the target service to obtain a monitoring result; recording the flow transfer relationship between each parameter in the target service and different methods according to the monitoring result to obtain the parameter flow transfer information.

[0007] Optionally, the parameter flow conversion topology graph of the target service is generated according to the parameter flow conversion information, including: obtaining the conversion relationship between each parameter and the different methods according to the parameter flow conversion information; and constructing the parameter flow conversion topology graph corresponding to each parameter according to the conversion relationship.

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

[0009] Optionally, the processing time result set is analyzed and processed to obtain a weight coefficient of each node in the parameter flow conversion topology graph, including: inputting the processing time result set and the parameter flow conversion topology graph into a weight coefficient determination model to process the processing time result set and the parameter flow conversion topology graph by using the weight coefficient determination model to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained by machine learning training using a plurality of sets of training data, and each set of the plurality of sets of training data includes: a sample processing time result set and a sample parameter flow conversion topology graph, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow conversion topology graph.

[0010] Optionally, when it is determined that the target service is abnormal, each parameter flow conversion 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, the abnormal node of the target service is located according to the weight coefficient, including: determining the weight coefficient of each node in the parameter flow conversion topology graph; and locating an abnormal node in the parameter flow conversion topology graph according to the size of the weight coefficient of each node.

[0012] Optionally, the abnormal node is optimized and adjusted, including at least one of: adjusting a parameter processing method corresponding to the abnormal node; optimizing a resource allocation mode of the abnormal node; and increasing a cache resource at the abnormal node.

[0013] According to another aspect of the embodiments of the present application, there is also provided a device for adjusting service optimization based on parameter flow conversion topology, comprising: a monitoring module configured to monitor parameter flow conversion information in a running process of a target service to obtain the parameter flow conversion information in the running process of the target service; a generating module configured to generate a parameter flow conversion topology graph of the target service according to the parameter flow conversion information; an obtaining module configured to obtain processing time of each parameter flow conversion link corresponding to the parameter flow conversion information according to the parameter flow conversion topology graph to obtain a processing time result set of the each parameter flow conversion link; an analyzing module configured to analyze and process the processing time result set and the parameter flow conversion topology graph to obtain a weight coefficient of each node in the parameter flow conversion topology graph, wherein the weight coefficient is used to represent an influence degree of each node on other nodes except itself and an importance degree of each node in the target service; and an optimizing module configured to optimize and adjust each parameter flow conversion link according to the weight coefficient when it is determined that the target service is abnormal.

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

[0015] Optionally, the generating module comprises: a first obtaining unit configured to obtain the flow conversion relationship between each parameter and the different methods according to the parameter flow conversion information; and a constructing unit configured to construct the parameter flow conversion topology graph corresponding to each parameter according to the flow conversion relationship.

[0016] Optionally, the obtaining module comprises: a determining unit configured to determine each parameter flow conversion link corresponding to the parameter flow conversion information according to the parameter flow conversion topology graph; a second obtaining unit configured to obtain the processing time of each parameter flow conversion link; and a summarizing unit configured to summarize the processing time of each parameter flow conversion link to obtain the processing time result set.

[0017] Optionally, the analyzing module comprises: an input unit configured to input the processing time result set and the parameter flow conversion topology graph into a weight coefficient determination model to process the processing time result set and the parameter flow conversion topology graph by using the weight coefficient determination model to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained by machine learning training using a plurality of sets of training data, each of the plurality of sets of training data comprising: a sample processing time result set and a sample parameter flow conversion topology graph, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow conversion topology graph.

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

[0019] Optionally, the third acquisition unit includes: a determining subunit, used to determine the weight coefficient of each node in the parameter flow topology graph; and a locating subunit, used to locate abnormal nodes in the parameter flow topology graph based on the magnitude of the weight coefficient of each node.

[0020] Optionally, the optimization unit includes at least one of the following: an adjustment subunit for adjusting the parameter processing method corresponding to the abnormal node; an optimization subunit for optimizing the resource allocation method of the abnormal node; and an addition subunit for adding cache resources at the abnormal node.

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

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

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

[0024] In this embodiment of the invention, parameter flow information during the operation of the target service is monitored to obtain parameter flow information during the operation of the target service; 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 for each parameter flow link; 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 represent the degree of influence of each node on other nodes besides 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. The technical solution provided by this invention achieves the goal of recording parameter flow information, constructing a parameter flow topology diagram, obtaining processing time result sets, and analyzing weight coefficients by using a parameter flow monitoring module. Simultaneously, it utilizes machine learning algorithms or expert systems for service optimization and adjustment, and continuously monitors and verifies to ensure efficient and stable operation. This achieves the technical effect of monitoring, analyzing, and optimizing the parameter flow link of the target service, improving service efficiency and reliability. Furthermore, it solves the technical problem in related technologies where optimization and adjustment methods generally rely on experience or trial and error when problems occur in server systems, resulting in low reliability. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

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

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

[0028] Figure 3 This 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 This is a schematic diagram of service optimization and adjustment based on parameter flow topology according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] As described in the background section, in related technologies, when problems occur in server systems, optimization and adjustment methods generally rely on experience or trial and error, resulting in low reliability. To address these shortcomings, embodiments of the present invention provide a service optimization and adjustment method and apparatus based on parameter flow topology, a computer-readable storage medium, a processor, and a computer program product.

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

[0034] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more...Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0035] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the service optimization and adjustment method based on parameter flow topology in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-described networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-described networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station 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 adjustment method based on parameter flow topology is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Figure 2 This is a flowchart of a service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0038] Step S202: 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.

[0039] In this embodiment, monitoring parameter flow information during the operation of the target service can provide a more comprehensive understanding of the dependencies and data flow between services. By analyzing parameter flow information, key information such as the call relationships between services, data transmission paths, and the frequency and scale of parameter transmission can be discovered. Analyzing parameter flow information can also identify bottlenecks between services and the parameter transmission paths where these bottlenecks occur, allowing for targeted optimization and adjustments. For example, frequently transmitted large amounts of data parameters can be compressed or fragmented to reduce network transmission overhead; services with high call frequencies can also be optimized to improve overall service response speed and stability. Furthermore, monitoring parameter flow information can also detect abnormal calls or abnormal data flow between services, enabling timely investigation and repair, thus improving service reliability and security.

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

[0041] Step S204: Generate the parameter flow topology diagram of the target service based on 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 transmission path of the target service, better understand the dependencies between services and the data transmission logic, and identify potential performance bottlenecks and potential optimization space by analyzing the parameter flow topology diagram. This allows for targeted service optimization and adjustment, identification of potential bottlenecks and delays in the transmission path, timely discovery and resolution of problems, and improvement of service response speed and stability.

[0043] It should be noted that the parameter flow topology diagram mentioned above is a graphical representation of the transmission relationship and flow path between different parameters in a system. It can clearly show the relationship between various parameters in the system, thereby enabling better system design and optimization. Parameter flow topology diagrams are usually displayed in the form of nodes and edges, where nodes represent parameters in the system and edges represent the transmission relationship between parameters. Through this diagram, the flow path of parameters can be traced, the dependencies between parameters can be understood, and potential problems and bottlenecks in the system can be identified.

[0044] Step S206: Obtain 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.

[0045] In this embodiment, by acquiring the processing time of each parameter flow link corresponding to the parameter flow information, the system can optimize service adjustments. By analyzing the processing time of each parameter flow link, bottleneck links in the system can be identified, namely links with long processing times or low efficiency. Based on the processing time result set of the parameter flow links, the resource consumption of each link in the system can be better understood, thereby rationally allocating resources. By optimizing the processing time of the parameter flow links, the service response time can be shortened. By monitoring and optimizing the processing time of the parameter flow links, 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 parameter flow link refers to the path and method by which parameters flow from one stage to another in a system or process.

[0047] Optionally, the above parameter flow chain may include: parameter generation, parameter transmission, parameter processing, parameter storage, and parameter output.

[0048] Step S208: Analyze and process the processing time result set and parameter flow topology graph to obtain the weight coefficient of each node in the parameter flow topology graph. The weight coefficient is used to represent the degree of influence of each node on other nodes except itself and the importance of each node in the target service.

[0049] In this embodiment, analyzing the processing time result set and parameter flow topology diagram can help determine the weight coefficient of each node, thereby better understanding the influence and importance of each node in the entire service process. By analyzing the weight coefficient, key nodes and nodes with significant influence can be identified, allowing for targeted service optimization and adjustments. This can effectively optimize the service process. By analyzing the weight coefficient of each node, we can better understand the importance of each node in the service, and monitor and adjust key nodes in a targeted manner, thereby improving the efficiency and quality of the service.

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

[0051] In this embodiment, when an anomaly is detected in the target service, adjusting the flow links of each parameter according to the weight coefficient can more quickly locate the problem and optimize the performance and stability of the service. By adjusting the weight coefficient, the flow links of the parameters can be dynamically adjusted, enabling the system to adapt to different environments and load conditions more quickly, thereby improving the reliability and efficiency of the system.

[0052] Figure 3 This is a flowchart of an optional service optimization and adjustment method based on parameter flow topology according to an embodiment of the present invention, such as...Figure 3 As shown, the process begins with deploying a parameter flow monitoring module to record parameter flow information during service operation. Next, a parameter flow topology is constructed, generating a topology diagram based on the information recorded by the monitoring module. Then, a processing time result set is obtained, acquiring the processing time of each flow link through service logs and performance monitoring tools. Following this, weight coefficients are analyzed using machine learning algorithms or expert systems based on the processing time result set and the parameter flow topology. Service optimization and adjustments are then implemented, identifying the source and key nodes of problems based on the weight coefficients when issues arise, and taking corresponding optimization measures. Finally, continuous monitoring and verification ensure that the optimized service maintains efficient and stable operation.

[0053] As described above, in this embodiment of the invention, 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; 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 for each parameter flow link; 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 represent the degree of influence of each node on other nodes besides 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. This achieves the technical effect of monitoring, analyzing and optimizing the parameter flow link of the target service by 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, 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. This achieves the technical effect of monitoring, analyzing and optimizing the parameter flow link of the target service, and improves the efficiency and reliability of the service.

[0054] The technical solutions provided by the embodiments of the present invention solve the problem in the related technologies that when problems occur in server systems, the optimization and adjustment methods generally rely on experience or trial and error, resulting in low reliability.

[0055] According to the above embodiments of the present invention, monitoring the parameter flow information during the operation of a target service to obtain the parameter flow information during the operation of the target service includes: monitoring the operation process of the target service when it is determined to be running, and obtaining monitoring results; recording the flow relationship between each parameter in the target service and different methods according to the monitoring results to obtain 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 mentioned above may include Prometheus, Zabbix, and Nagios.

[0058] It should be noted that the aforementioned monitoring tools can monitor service performance metrics, resource utilization, error logs, and other information in real time, and generate reports or alerts. By monitoring the service's log files, one can understand the service's running status, error information, and other information. Log management tools such as ELK Stack (a data analysis platform composed of three open-source components: Elasticsearch, Logstash, and Kibana) can be used to collect, analyze, and display log information in real time. The aforementioned health checks are periodic health checks performed on the target service to check whether the service is running normally, whether the response time is normal, and whether there are any errors. Monitoring scripts or monitoring systems can be used to periodically check the service's health status. The aforementioned integrated monitoring can be the integration of a monitoring system into the target service to achieve automated monitoring and alerting.

[0059] In this embodiment, by monitoring the service operation process and recording the flow relationship of parameters between different methods, potential performance bottlenecks, resource waste, or other problems in the service can be identified through monitoring and recording parameter flow information. This allows for targeted optimization and adjustment, enabling more effective identification and resolution of problems in the target service, improving service performance and stability. Furthermore, the service architecture design can be optimized based on parameter flow information, enhancing system maintainability and scalability.

[0060] According to the above embodiments of the present invention, generating a parameter flow topology diagram of a target service based on parameter flow information includes: obtaining the flow relationship between each parameter and different methods based on the parameter flow information; and constructing a parameter flow topology diagram corresponding to each parameter based on the flow relationship.

[0061] In this embodiment, by analyzing parameter flow information, the flow relationship between parameters in different methods can be clearly understood, thereby helping to optimize the system's performance and efficiency. By constructing a parameter flow topology diagram, the correlation between parameters can be intuitively displayed, which helps to discover potential performance bottlenecks and optimization space, better understand the system's operating mechanism, identify 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 of parameters between different methods, a parameter flow topology is constructed. For example, when the first parameter is transformed into the second parameter through method A, and the second parameter is then transformed into the third parameter through method B and returned, the resulting parameter flow topology is the first parameter → the second parameter → the third parameter, which is also the parameter flow link.

[0063] According to the above embodiments of the present invention, obtaining the processing time of each parameter flow link corresponding to the parameter flow information based on the parameter flow topology diagram, and obtaining a processing time result set of each parameter flow link, includes: determining each parameter flow link corresponding to the parameter flow information based on the parameter flow topology diagram; obtaining the processing time of each parameter flow link; and summarizing the processing times of each parameter flow link to obtain a processing time result set.

[0064] In this embodiment, determining the parameter flow links corresponding to the parameter flow information based on the parameter flow topology diagram can help the system understand the relationship and flow path between parameters more clearly, thereby enabling targeted optimization and adjustment. Obtaining the processing time of each parameter flow link can help the system identify bottlenecks affecting service performance and find key points that need optimization. By summarizing the processing time of each parameter flow link, a processing time result set can be obtained, which can help the system 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 the 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 session may involve multiple links, each with its own processing time. For example, in the links mentioned above, the time spent by the parameters going through the AB method sequentially is the processing time for that link.

[0067] According to the above embodiments of the present invention, the analysis and processing of the processing time result set to obtain the weight coefficients of each node in the parameter flow topology graph includes: inputting the processing time result set and the parameter flow topology graph into the 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 graph to obtain the weight coefficients of each node. The weight coefficient determination model is obtained by machine learning training using multiple sets of training data. Each set of training data includes: a sample processing time result set and a sample parameter flow topology graph, and sample weight coefficients corresponding to the sample processing time result set and the sample parameter flow topology graph.

[0068] In this embodiment, the weight coefficients are used to determine the weight coefficients of each node by processing the processing time result set and parameter flow topology graph. This helps to identify key nodes and paths, enabling more effective allocation of resources and optimization of processes during service optimization and adjustment. It can also more accurately assess the importance and impact of each node, thereby optimizing service quality, improving efficiency and reducing costs. At the same time, the weight coefficients obtained through machine learning training can be dynamically adjusted according to the actual situation to adapt to the ever-changing environment and needs.

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

[0070] The following example illustrates this embodiment using the influence of node A's changes on other nodes. When node A completes in 5 seconds, the entire service process takes 10 seconds; when node A completes in 10 seconds, the entire service takes 20 seconds, so node A has a larger weight; conversely, when node A completes in 10 seconds, the entire service takes 15 seconds, or even 10 seconds, so its weight is relatively small.

[0071] Optionally, the above methods can be internal server algorithms or data processing methods.

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

[0073] In this embodiment, locating the abnormal nodes of the target service based on the weight coefficient can help identify the specific nodes causing the service abnormality. By optimizing and adjusting the abnormal nodes, 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 carried out, thereby improving the reliability and stability of the entire service system. Through continuous optimization and adjustment, the performance and user experience of the service can be gradually improved, and the overall operating efficiency and effectiveness of the system can be improved.

[0074] In the above embodiments of the present invention, the abnormal node of the target service is located based on the weight coefficient, including: determining the weight coefficient of each node in the parameter flow topology graph; and locating the abnormal node in the parameter flow topology graph based on the magnitude of the weight coefficient of each node.

[0075] In this embodiment, determining the weight coefficients of each node in the parameter flow topology graph can help the system more accurately assess the impact of each node on system performance. By analyzing the weight coefficients of each node, abnormal nodes can be located, improving system stability and performance, reducing the impact of abnormal nodes on the entire system, and timely locating and handling of abnormal nodes can reduce the system failure rate, improve system throughput and response speed, thereby enhancing the user experience.

[0076] like Figure 3 As shown, when encountering a problem (i.e., the target service malfunctions), it is necessary to optimize and adjust the service. The flow path should be optimized and adjusted according to the weight coefficient. First, locate the source of the problem and key nodes according to the weight coefficient (prioritize checking for anomalies in areas with high weight). Then, take optimization measures for these nodes, such as adjusting the method implementation, optimizing resource allocation, and adding caching. 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 embodiments of the present invention, the optimization and adjustment of abnormal nodes 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; and increasing the cache resources at the abnormal node.

[0079] In this embodiment, adjusting the parameter processing method corresponding to the abnormal node can help the system handle abnormal situations more effectively, improve the stability and reliability of the system, and 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 on the abnormal node and improve the system's response speed and throughput.

[0080] For example, if server resources are limited, resources should be allocated to links with high weight first, while links that can run independently should be allocated fewer resources and run slowly. Links should be connected in parallel to minimize response time. If the allocation scheme is already optimal, measures such as adding caching should be considered.

[0081] The technical solutions provided by the above embodiments of the present invention solve the following problems: 1) When problems occur in complex server systems, traditional optimization and adjustment methods often rely on experience or trial and error, which is inefficient and easily introduces new problems; 2) When problems occur in complex server systems, it is difficult to locate the problem. Simultaneously, it has the following beneficial effects: 1) It can comprehensively track the parameter flow process and construct an accurate parameter flow topology; 2) It can quickly locate the source of the problem and key nodes, improving the accuracy and efficiency of optimization and adjustment; 3) It can perform targeted optimization and adjustment of services based on weight coefficients to ensure optimization effects; 4) It can continuously monitor and verify the performance of the optimized service to ensure that the service maintains efficient and stable operation.

[0082] As can be seen from the above, in this embodiment of the invention, by tracking the parameter flow process, constructing the parameter flow topology, and obtaining the processing time result set and weight coefficients accordingly, the service can be efficiently optimized and adjusted based on the weight coefficients when problems occur, greatly shortening the server repair time.

[0083] That is, the above-mentioned technical solution provided by the embodiments of the present invention takes into account a variety of factors and adopts a server optimization scheme based on parameter flow topology. When the server is abnormal, it helps technicians to locate the problem more quickly, visualizes the influence of invisible parameters flowing into the server, intuitively discovers the influence of parameters flowing out of the server, and optimizes the server resonance.

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

[0085] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0087] According to embodiments of the present invention, a service optimization and adjustment apparatus based on parameter flow topology is also provided for implementing the above-described service optimization and adjustment method based on parameter flow topology. Figure 4 This is a schematic diagram of service optimization and 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 generation module 403, an acquisition module 405, an analysis module 407, and an optimization module 409. The following describes this service optimization and adjustment device based on parameter flow topology.

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

[0089] The generation module 403 is used to generate a parameter flow topology diagram of the target service based on the parameter flow information.

[0090] The acquisition module 405 is used to obtain 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] Analysis module 407 is used to analyze and process the processing time result set and parameter flow topology graph to obtain the weight coefficient of each node in the parameter flow topology graph. The weight coefficient is used to represent the degree of 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 the flow links of each parameter according to the weight coefficient when it is determined that the target service is abnormal.

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

[0094] As can be seen from the above, in the solution described in the above embodiments of the present invention, firstly, 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 during the operation of the target service; then, the generation module can be used to generate a parameter flow topology diagram of the target service based on 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 based on the parameter flow topology diagram to obtain a 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 diagram to obtain the weight coefficient of each node in the parameter flow topology diagram, wherein, Weighting coefficients are used to represent the degree of influence of each node on other nodes besides itself and the importance of each node in the target service. Finally, when an anomaly is detected in the target service, the optimization module can optimize and adjust the parameter flow links according to the weighting coefficients. This achieves the goal of using a parameter flow monitoring module to record parameter flow information, construct a parameter flow topology diagram, obtain processing time result sets, and analyze weighting coefficients. 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. This achieves the technical effect of monitoring, analyzing, and optimizing the parameter flow links of the target service, improving the efficiency and reliability of the service.

[0095] The technical solutions provided by the embodiments of the present invention solve the problem in the related technologies that when problems occur in server systems, the optimization and adjustment methods generally rely on experience or trial and error, resulting in low reliability.

[0096] In one optional embodiment, the monitoring module includes: a monitoring unit, used to monitor the operation process of the target service when the target service is running, and obtain monitoring results; and a recording unit, used to record the flow relationship of each parameter in the target service between different methods according to the monitoring results, so as to obtain parameter flow information.

[0097] In one optional embodiment, the generation module includes: a first acquisition unit, configured to obtain the flow relationship between each parameter and different methods based on parameter flow information; and a construction unit, configured to construct a parameter flow topology diagram corresponding to each parameter based on the flow relationship.

[0098] In one optional embodiment, the acquisition module includes: a determining unit, configured to determine each parameter flow link corresponding to the parameter flow information based on the parameter flow topology diagram; a second acquisition unit, configured to acquire the processing time of each parameter flow link; and a summarizing unit, configured to summarize the processing time of each parameter flow link to obtain a processing time result set.

[0099] In one optional embodiment, the analysis module includes: an input unit, used to input the processing time result set and the parameter flow topology graph into the 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 graph to obtain the weight coefficient of each node, wherein the weight coefficient determination model is trained by machine learning using multiple sets of training data, and each set of multiple sets of training data includes: a sample processing time result set and a sample parameter flow topology graph, and sample weight coefficients corresponding to the sample processing time result set and the sample parameter flow topology graph.

[0100] In one optional embodiment, the optimization module includes: a third acquisition unit, used to locate abnormal nodes of the target service based on weight coefficients; and an optimization unit, used to optimize and adjust the abnormal nodes.

[0101] In one optional embodiment, the third acquisition unit includes: a determining subunit, used to determine the weight coefficient of each node in the parameter flow topology graph; and a locating subunit, used to locate abnormal nodes in the parameter flow topology graph based on the magnitude of the weight coefficient of each node.

[0102] In one optional embodiment, the optimization unit includes at least one of the following: an adjustment subunit for adjusting the parameter processing method corresponding to the abnormal node; an optimization subunit for optimizing the resource allocation method of the abnormal node; and an addition subunit for adding cache resources at the abnormal node.

[0103] According to another aspect of the present invention, a processor is also provided, which runs a program, wherein the program executes any of the above-described service optimization and adjustment methods based on parameter flow topology. According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, execute any of the above-described service optimization and adjustment methods based on parameter flow topology.

[0104] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described 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 computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: monitoring parameter flow information during the operation of the target service to obtain parameter flow information during the operation of the target service; generating a parameter flow topology diagram 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 diagram 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 diagram to obtain the weight coefficient of each node in the parameter flow topology diagram, wherein the weight coefficient is used to represent the degree of influence of each node on other nodes besides itself and the importance of each node in the target service; and optimizing and adjusting each parameter flow link according to the weight coefficient when it is determined that the target service is abnormal.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: when the target service is running, monitor the running process of the target service and obtain monitoring results; record the flow relationship of each parameter in the target service between different methods 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 code for performing the following steps: obtaining the flow relationship between each parameter in different methods based on the parameter flow information; and constructing a parameter flow topology diagram corresponding to each parameter based on the flow relationship.

[0109] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing 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 times of each parameter flow link to obtain a processing time result set.

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

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: locating abnormal nodes of the target service based on 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 performing the following steps: determining the weight coefficient of each node in the parameter flow topology graph; and locating abnormal nodes in the parameter flow topology graph based on the magnitude of the weight coefficient of each node.

[0113] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing 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 sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0115] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer 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. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

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

[0118] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0119] If the integrated unit is implemented as 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0120] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for service optimization adjustment based on parameter flow transformation topology, characterized in that, The method comprises the following steps: monitoring parameter flow information in the running process of a target service to obtain the parameter flow information in the running process of the target service; generating a parameter flow topology graph of the target service according to the parameter flow information; obtaining 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 the 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 represent 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, optimizing and adjusting each parameter flow link according to the weight coefficient; wherein, obtaining the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology graph to obtain the processing time result set of the parameter flow link comprises: determining each parameter flow link corresponding to the parameter flow information according to the parameter flow topology graph; obtaining the processing time of each parameter flow link; and collecting the processing time of each parameter flow link to obtain the processing time result set.

2. The method of claim 1, wherein, Monitoring parameter flow information in the running process of a target service to obtain the parameter flow information in the running process of the target service comprises: monitoring the running process of the target service to obtain a monitoring result when it is determined that the target service is running; recording the flow relationship between each parameter in the target service and different methods according to the monitoring result to obtain the parameter flow information.

3. The method of claim 2, wherein, Generating a parameter flow topology graph of the target service according to the parameter flow information comprises: obtaining the flow relationship between each parameter and the different methods according to the parameter flow information; constructing the parameter flow topology graph corresponding to each parameter according to the flow relationship.

4. The method of claim 1, wherein, Analyzing and processing the processing time result set to obtain a weight coefficient of each node in the parameter flow topology graph comprises: inputting the processing time result set and the parameter flow topology graph into a weight coefficient determination model to process the processing time result set and the parameter flow topology graph by using the weight coefficient determination model to obtain the weight coefficient of each node, wherein the weight coefficient determination model is obtained by machine learning training using a plurality of sets of training data, and each set of the plurality of sets of training data comprises: a sample processing time result set and a sample parameter flow topology graph, and a sample weight coefficient corresponding to the sample processing time result set and the sample parameter flow topology graph.

5. The method of claim 1, wherein, When it is determined that the target service is abnormal, optimizing and adjusting each parameter flow link according to the weight coefficient comprises: locating an abnormal node of the target service according to the weight coefficient; optimizing and adjusting the abnormal node.

6. The method of claim 5, wherein, Locating an abnormal node of the target service according to the weight coefficient comprises: determining the weight coefficients of the nodes in the parameter flow topology; locating abnormal nodes in the parameter flow topology according to the weight coefficients of the nodes.

7. The method of claim 5, wherein, optimizing and adjusting the abnormal nodes, including at least one of the following: adjusting the parameter processing method corresponding to the abnormal nodes; optimizing the resource allocation mode of the abnormal nodes; increasing the cache resources at the abnormal nodes.

8. A service optimization and adjustment device based on parameter flow topology, characterized in that, comprising: a monitoring module, configured to monitor parameter flow information in a target service running process to obtain the parameter flow information in the target service running process; a generation module, configured to generate a parameter flow topology of the target service according to the parameter flow information; an acquisition module, configured to acquire processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topology to obtain a processing time result set of the parameter flow link; an analysis module, configured to analyze and process the processing time result set and the parameter flow topology to obtain weight coefficients of nodes in the parameter flow topology, wherein the weight coefficients are used to represent the influence degree of each node on other nodes except itself and the importance degree of each node in the target service; an optimization module, configured to optimize and adjust each parameter flow link according to the weight coefficients when determining that the target service is abnormal. The acquisition module comprises: a determination unit, configured to determine each parameter flow link corresponding to the parameter flow information according to the parameter flow topology; a second acquisition unit, configured to acquire the processing time of each parameter flow link; and a summary unit, configured to summarize the processing time of each parameter flow link to obtain the processing time result set.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program executes the service optimization and adjustment method based on the parameter flow topology of any one of claims 1 to 7.

10. A processor, comprising: The processor is used to run a program, wherein the program runs to execute the service optimization and adjustment method based on the parameter flow topology of any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to execute the service optimization and adjustment method based on the parameter flow topology of any one of claims 1 to 7.

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